[ { "timestamp": "2026-10-19T00:21:41+00:00", "sha": "de3a426da42f70a901ac4a1de6a3a7745f6c295f", "results": [ { "day": 1, "slug": "arxiv-scout", "title": "arXiv Scout", "pattern": "Single agent + guardrail callbacks", "folder": "day-001-arxiv-scout", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 18.94, "model": "gemini-3.5-flash-lite", "model_calls": 6, "tool_calls": 5, "tool_budget": 12, "calls_by_tool": { "search_papers": 1, "get_abstract": 4 }, "prompt_tokens": 25932, "output_tokens": 1365, "cost_usd": null, "checks": { "citations_verified": true, "unverified_ids": [] }, "output_chars": 4659 }, "stderr_tail": "/private/tmp/claude-501/-Users-sri-code-excelerate-source/759d3d2a-5d46-45de-9e2b-b8317e6e2da3/scratchpad/aad-clean/day-001-arxiv-scout/.venv/lib/python3.13/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.PLUGGABLE_AUTH is enabled.\n check_feature_enabled()\n/private/tmp/claude-501/-Users-sri-code-excelerate-source/759d3d2a-5d46-45de-9e2b-b8317e6e2da3/scratchpad/aad-clean/day-001-arxiv-scout/.venv/lib/python3.13/site-packages/google/adk/tools/mcp_tool/mcp_toolset.py:407: UserWarning: [EXPERIMENTAL] feature FeatureName._MCP_GRACEFUL_ERROR_HANDLING is enabled.\n session = await self._mcp_session_manager.create_session(\n/private/tmp/claude-501/-Users-sri-code-excelerate-source/759d3d2a-5d46-45de-9e2b-b8317e6e2da3/scratchpad/aad-clean/day-001-arxiv-scout/.venv/lib/python3.13/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n/private/tmp/claude-501/-Users-sri-code-excelerate-source/759d3d2a-5d46-45de-9e2b-b8317e6e2da3/scratchpad/aad-clean/day-001-arxiv-scout/.venv/lib/python3.13/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 search_papers(categories=['cs.CL', 'cs.LG'], query='\"speculative decoding\" OR \"speculative sampling\" ...)\n \u2192 get_abstract(paper_id='2401.07851')\n \u2192 get_abstract(paper_id='2402.01528')\n \u2192 get_abstract(paper_id='2508.15371')\n \u2192 get_abstract(paper_id='2510.15545')\n\n\u2014 18.9s \u00b7 6 model calls \u00b7 5/12 tool calls {'search_papers': 1, 'get_abstract': 4} \u00b7 25,932 in / 1,365 out tokens\n\u2713 All cited arXiv IDs were returned by a tool.", "output_excerpt": "# speculative decoding for LLM inference: engineering brief\n\n## TL;DR\n* **Draft-then-verify paradigm**: Speculative decoding accelerates autoregressive LLM generation by employing a lightweight draft model to rapidly generate multiple candidate tokens, which are then validated in parallel by the target model in a single forward pass without sacrificing output quality [arXiv:2401.07851](https://arxiv.org/abs/2401.07851).\n* **Draft model latency dictates speedup**: Language modeling capability does not strongly correlate with speculative decoding performance; draft model latency and hardware efficiency are the primary bottlenecks dictating end-to-end throughput gains [arXiv:2402.01528](https://arxiv.org/abs/2402.01528].\n* **Dynamic and universal adaptations**: Modern approaches transcend rig" }, { "day": 2, "slug": "repo-onboarder", "title": "Repo Onboarder", "pattern": "Sequential pipeline (SequentialAgent)", "folder": "day-002-repo-onboarder", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 56.96, "model": "gemini-3.5-flash-lite", "model_calls": 8, "tool_calls": 5, "tool_budget": 12, "calls_by_tool": { "read_wiki_structure": 1, "ask_wiki_question": 4 }, "prompt_tokens": 35687, "output_tokens": 1390, "cost_usd": null, "checks": { "repo_available": true, "all_sections_present": true, "links_verified": true, "missing_sections": [], "unverified_links": [] }, "output_chars": 2338 }, "stderr_tail": "/private/tmp/claude-501/-Users-sri-code-excelerate-source/759d3d2a-5d46-45de-9e2b-b8317e6e2da3/scratchpad/aad-clean/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.PLUGGABLE_AUTH is enabled.\n check_feature_enabled()\nFailed to configure mTLS using AsyncAuthorizedSession: `auth_request` must either be configured or the external package `aiohttp` must be installed to use the default value.\n/private/tmp/claude-501/-Users-sri-code-excelerate-source/759d3d2a-5d46-45de-9e2b-b8317e6e2da3/scratchpad/aad-clean/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_toolset.py:407: UserWarning: [EXPERIMENTAL] feature FeatureName._MCP_GRACEFUL_ERROR_HANDLING is enabled.\n session = await self._mcp_session_manager.create_session(\n/private/tmp/claude-501/-Users-sri-code-excelerate-source/759d3d2a-5d46-45de-9e2b-b8317e6e2da3/scratchpad/aad-clean/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n/private/tmp/claude-501/-Users-sri-code-excelerate-source/759d3d2a-5d46-45de-9e2b-b8317e6e2da3/scratchpad/aad-clean/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 [mapper] read_wiki_structure(repoName='psf/requests')\nFailed to configure mTLS using AsyncAuthorizedSession: `auth_request` must either be configured or the external package `aiohttp` must be installed to use the default value.\n \u2192 [researcher] ask_wiki_question(repoName='psf/requests', question='What is this project, who is it for, and how is the ...)\n \u2192 [researcher] ask_wiki_question(repoName='psf/requests', question='Where does a new reader start: the main entry points...)\n \u2192 [researcher] ask_wiki_question(question='How do I set up a development environment, run it, and run the tests?', repoN...)\n \u2192 [researcher] ask_wiki_question(repoName='psf/requests', question='What are the contribution conventions (branching, st...)\n\n\u2014 57.0s \u00b7 8 model calls \u00b7 5/12 tool calls {'read_wiki_structure': 1, 'ask_wiki_question': 4} \u00b7 35,687 in / 1,390 out tokens\n\u2713 All sections present and every repo link was returned by a tool.", "output_excerpt": "# Onboarding guide: psf/requests\n\n## What this project is\nRequests is an elegant and simple HTTP client library for Python (\"HTTP for Humans\u2122\") supporting Python 3.10+ and PyPy. It handles connection pooling, keep-alive, redirects, and encoding detection internally.\n\n## Architecture map\n| Area | Path | What lives there |\n| :--- | :--- | :--- |\n| Core Library | [`src/requests`](https://github.com/psf/requests/blob/HEAD/src/requests) | Core library source code |\n| Documentation | [`docs/`](https://github.com/psf/requests/blob/HEAD/docs/) | Sphinx documentation written in reStructuredText |\n| Test Suite | [`tests/`](https://github.com/psf/requests/blob/HEAD/tests/) | Test suite |\n| External Assets | [`ext/`](https://github.com/psf/requests/blob/HEAD/ext/) | External assets such as README imag" } ] }, { "timestamp": "2026-10-19T00:30:34+00:00", "sha": "de3a426da42f70a901ac4a1de6a3a7745f6c295f", "results": [ { "day": 1, "slug": "arxiv-scout", "title": "arXiv Scout", "pattern": "Single agent + guardrail callbacks", "folder": "day-001-arxiv-scout", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 17.99, "model": "gemini-3.5-flash-lite", "model_calls": 6, "tool_calls": 5, "tool_budget": 12, "calls_by_tool": { "search_papers": 1, "get_abstract": 4 }, "prompt_tokens": 25932, "output_tokens": 1199, "cost_usd": null, "checks": { "citations_verified": true, "unverified_ids": [] }, "output_chars": 4016 }, "stderr_tail": "Downloading lxml (5.0MiB)\nDownloading cryptography (4.5MiB)\n Downloaded aiohttp\n Downloaded pydantic-core\n Downloaded lxml\n Downloaded cryptography\nInstalled 44 packages in 22ms\nInstalled 45 packages in 22ms\n/home/runner/work/agent-a-day/agent-a-day/day-001-arxiv-scout/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-001-arxiv-scout/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 search_papers(sort_by='relevance', categories=['cs.CL', 'cs.LG'], query='\"speculative decoding\" OR \"s...)\n \u2192 get_abstract(paper_id='2401.07851')\n \u2192 get_abstract(paper_id='2402.01528')\n \u2192 get_abstract(paper_id='2508.15371')\n \u2192 get_abstract(paper_id='2510.15545')\n\n\u2014 18.0s \u00b7 6 model calls \u00b7 5/12 tool calls {'search_papers': 1, 'get_abstract': 4} \u00b7 25,932 in / 1,199 out tokens\n\u2713 All cited arXiv IDs were returned by a tool.", "output_excerpt": "# Speculative Decoding for LLM Inference: engineering brief\n\n## TL;DR\n- **Draft-then-Verify Paradigm:** Speculative decoding accelerates autoregressive generation by using a lightweight draft model to rapidly propose multiple candidate tokens, which the larger target model then verifies in parallel.\n- **Draft Model Design Trumps Raw Perplexity:** Research shows that draft model speed and hardware efficiency matter far more for overall throughput gains than high language-modeling capabilities or matching vocabulary families.\n- **Adaptive and Universal Enhancements:** Newer frameworks replace rigid drafting lengths with confidence-modulated mechanisms and bridge vocabulary mismatches to let arbitrary off-the-shelf models act as drafters.\n\n## Key papers\n\n| Paper | Core idea | Why it matters f" }, { "day": 2, "slug": "repo-onboarder", "title": "Repo Onboarder", "pattern": "Sequential pipeline (SequentialAgent)", "folder": "day-002-repo-onboarder", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 58.11, "model": "gemini-3.5-flash-lite", "model_calls": 8, "tool_calls": 5, "tool_budget": 10, "calls_by_tool": { "read_wiki_structure": 1, "ask_wiki_question": 4 }, "prompt_tokens": 41781, "output_tokens": 2232, "cost_usd": null, "checks": { "repo_available": true, "all_sections_present": true, "links_verified": true, "missing_sections": [], "unverified_links": [] }, "output_chars": 3407 }, "stderr_tail": "/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.PLUGGABLE_AUTH is enabled.\n check_feature_enabled()\nFailed to configure mTLS using AsyncAuthorizedSession: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.\n/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_toolset.py:407: UserWarning: [EXPERIMENTAL] feature FeatureName._MCP_GRACEFUL_ERROR_HANDLING is enabled.\n session = await self._mcp_session_manager.create_session(\n/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 [mapper] read_wiki_structure(repoName='psf/requests')\nFailed to configure mTLS using AsyncAuthorizedSession: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.\n \u2192 [researcher] ask_wiki_question(repoName='psf/requests', question='What is this project, who is it for, and how is the ...)\n \u2192 [researcher] ask_wiki_question(question='Where does a new reader start: the main entry points and the 3-5 most importa...)\n \u2192 [researcher] ask_wiki_question(question='How do I set up a development environment, run it, and run the tests?', repoN...)\n \u2192 [researcher] ask_wiki_question(question='What are the contribution conventions (branching, style, review) and known pi...)\n\n\u2014 58.1s \u00b7 8 model calls \u00b7 5/10 tool calls {'read_wiki_structure': 1, 'ask_wiki_question': 4} \u00b7 41,781 in / 2,232 out tokens\n\u2713 All sections present and every repo link was returned by a tool.", "output_excerpt": "# Onboarding guide: psf/requests\n\n## What this project is\nThe `Requests` library (`psf/requests`) is an open-source HTTP client for Python designed with the philosophy \"HTTP for Humans\u2122\". It provides an intuitive, user-friendly interface for sending HTTP/1.1 requests while abstracting complex protocol details, and is built on top of `urllib3` for developers interacting with web services and APIs.\n\n## Architecture map\n| Area | Path | What lives there |\n| :--- | :--- | :--- |\n| Core Source Library | [`src/requests`](https://github.com/psf/requests/blob/HEAD/src/requests) | Contains the main source code including `__init__.py`, `__version__.py`, `api.py`, `sessions.py`, `models.py`, and `adapters.py`. |\n| Documentation | [`docs`](https://github.com/psf/requests/blob/HEAD/docs) | Sphinx docume" } ] }, { "timestamp": "2026-10-20T00:06:22+00:00", "sha": "de3a426da42f70a901ac4a1de6a3a7745f6c295f", "results": [ { "day": 1, "slug": "arxiv-scout", "title": "arXiv Scout", "pattern": "Single agent + guardrail callbacks", "folder": "day-001-arxiv-scout", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 92.32, "model": "gemini-3.5-flash-lite", "model_calls": 3, "tool_calls": 2, "tool_budget": 12, "calls_by_tool": { "search_papers": 2 }, "prompt_tokens": 6534, "output_tokens": 276, "cost_usd": null, "checks": { "citations_verified": true, "unverified_ids": [] }, "output_chars": 761 }, "stderr_tail": " Downloaded cryptography\nInstalled 45 packages in 56ms\nInstalled 44 packages in 59ms\n/home/runner/work/agent-a-day/agent-a-day/day-001-arxiv-scout/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-001-arxiv-scout/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 search_papers(max_results=5, query='\"speculative decoding\" OR \"speculative sampling\" LLM inference', ...)\narXiv API request HTTP 429; retrying in 2.5s (attempt 1/3)\narXiv API request timed out; retrying in 3.8s (attempt 2/3)\narXiv API request HTTP 429 after 3 attempts\narXiv search rate limited after retries: arXiv is rate limiting this IP (HTTP 429). Please wait 60 seconds before retrying.\n \u2192 search_papers(query='speculative decoding LLM inference', sort_by='date', categories=['cs.CL'], max_r...)\narXiv API request timed out; retrying in 2.5s (attempt 1/3)\narXiv API request HTTP 429; retrying in 5.5s (attempt 2/3)\narXiv API request timed out after 50.0s (budget: 50.0s)\narXiv search rate limited after retries: arXiv is rate limiting this IP (HTTP 429). Please wait 60 seconds before retrying.\n\n\u2014 92.3s \u00b7 3 model calls \u00b7 2/12 tool calls {'search_papers': 2} \u00b7 6,534 in / 276 out tokens\n\u2713 All cited arXiv IDs were returned by a tool.", "output_excerpt": "# speculative decoding for LLM inference: engineering brief\n\n## TL;DR\n- The arXiv tools encountered a temporary rate-limit (HTTP 429) during the search queries. \n- As a result, no papers could be retrieved or cited in this session.\n- Per the system instructions, a short honest brief is provided instead of padded or unverified content.\n\n## Key papers (table: Paper | Core idea | Why it matters for builders)\n*None available due to rate-limiting.*\n\n## Techniques worth trying\n*None available due to rate-limiting.*\n\n## Open problems / caveats\n- Rate limits on public APIs can impede automated literature discovery; caching or retrying with backoff is required in production engineering environments.\n\n## Sources (bulleted citation links)\n- *None*" }, { "day": 2, "slug": "repo-onboarder", "title": "Repo Onboarder", "pattern": "Sequential pipeline (SequentialAgent)", "folder": "day-002-repo-onboarder", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 58.54, "model": "gemini-3.5-flash-lite", "model_calls": 9, "tool_calls": 6, "tool_budget": 10, "calls_by_tool": { "read_wiki_structure": 1, "ask_wiki_question": 4, "fetch": 1 }, "prompt_tokens": 50029, "output_tokens": 2200, "cost_usd": null, "checks": { "repo_available": true, "all_sections_present": true, "links_verified": true, "missing_sections": [], "unverified_links": [] }, "output_chars": 3254 }, "stderr_tail": "/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.PLUGGABLE_AUTH is enabled.\n check_feature_enabled()\nFailed to configure mTLS using AsyncAuthorizedSession: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.\n/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_toolset.py:407: UserWarning: [EXPERIMENTAL] feature FeatureName._MCP_GRACEFUL_ERROR_HANDLING is enabled.\n session = await self._mcp_session_manager.create_session(\n/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 [mapper] read_wiki_structure(repoName='psf/requests')\nFailed to configure mTLS using AsyncAuthorizedSession: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.\n \u2192 [researcher] ask_wiki_question(question='What is this project, who is it for, and how is the code organised (top-level...)\n \u2192 [researcher] ask_wiki_question(question='Where does a new reader start: the main entry points and the 3-5 most importa...)\n \u2192 [researcher] ask_wiki_question(question='How do I set up a development environment, run it, and run the tests?', repoN...)\n \u2192 [researcher] ask_wiki_question(repoName='psf/requests', question='What are the contribution conventions (branching, st...)\n \u2192 [researcher] fetch(url='https://raw.githubusercontent.com/psf/requests/main/CONTRIBUTING.md')\n\n\u2014 58.5s \u00b7 9 model calls \u00b7 6/10 tool calls {'read_wiki_structure': 1, 'ask_wiki_question': 4, 'fetch': 1} \u00b7 50,029 in / 2,200 out tokens\n\u2713 All sections present and every repo link was returned by a tool.", "output_excerpt": "# Onboarding guide: psf/requests\n\n## What this project is\nThe `requests` library is a Python HTTP client library designed to make HTTP requests simple and intuitive (\"HTTP for Humans\u2122\"). It is intended for developers interacting with web services and APIs using Python, abstracting complex protocol details on top of `urllib3`.\n\n## Architecture map\n| Area | Path | What lives there |\n| :--- | :--- | :--- |\n| Core Source Code | [`src/requests`](https://github.com/psf/requests/blob/HEAD/src/requests) | Contains core modules like `api.py`, `sessions.py`, `models.py`, `adapters.py`, `utils.py`, `auth.py`, `exceptions.py`, and `structures.py`. |\n| Documentation | [`docs/`](https://github.com/psf/requests/blob/HEAD/docs/) | Holds Sphinx documentation, including `docs/index.rst`, `docs/api.rst`, and" } ] }, { "timestamp": "2026-10-20T09:55:00+00:00", "sha": "7fcc50863ba872637edb2e801492b3bef6ae37e6", "results": [ { "day": 1, "slug": "arxiv-scout", "title": "arXiv Scout", "pattern": "Single agent + guardrail callbacks", "folder": "day-001-arxiv-scout", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 19.34, "model": "gemini-3.5-flash-lite", "model_calls": 6, "tool_calls": 5, "tool_budget": 12, "calls_by_tool": { "search_papers": 1, "get_abstract": 4 }, "prompt_tokens": 24643, "output_tokens": 1155, "cost_usd": null, "checks": { "citations_verified": true, "unverified_ids": [] }, "output_chars": 3984 }, "stderr_tail": "Downloading lxml (5.0MiB)\nDownloading pydantic-core (2.1MiB)\n Downloaded pydantic-core\n Downloaded aiohttp\n Downloaded cryptography\n Downloaded lxml\nInstalled 44 packages in 43ms\nInstalled 45 packages in 49ms\n/home/runner/work/agent-a-day/agent-a-day/day-001-arxiv-scout/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-001-arxiv-scout/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 search_papers(sort_by='relevance', categories=['cs.CL', 'cs.LG'], max_results=5, query='\"speculative ...)\n \u2192 get_abstract(paper_id='2401.07851')\n \u2192 get_abstract(paper_id='2402.01528')\n \u2192 get_abstract(paper_id='2508.15371')\n \u2192 get_abstract(paper_id='2610.08678')\n\n\u2014 19.3s \u00b7 6 model calls \u00b7 5/12 tool calls {'search_papers': 1, 'get_abstract': 4} \u00b7 24,643 in / 1,155 out tokens\n\u2713 All cited arXiv IDs were returned by a tool.", "output_excerpt": "# speculative decoding for LLM inference: engineering brief\n\n## TL;DR\n- **Draft-and-Verify Paradigm:** Speculative decoding accelerates memory-bandwidth-bound autoregressive LLM inference by having a fast, smaller \"draft\" model generate multiple candidate tokens, which the larger \"target\" model verifies in parallel.\n- **Drafter Engineering over Raw Intelligence:** Research shows that a draft model's raw language modeling capability is secondary to its inference latency; hardware-optimized, lower-latency draft architectures yield significantly higher end-to-end throughput.\n- **Adaptive and Secure Innovations:** Recent advances replace rigid draft lengths and static acceptance criteria with confidence-modulated (entropy-based) dynamic drafting and stricter verification filters to mitigate se" }, { "day": 2, "slug": "repo-onboarder", "title": "Repo Onboarder", "pattern": "Sequential pipeline (SequentialAgent)", "folder": "day-002-repo-onboarder", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 59.56, "model": "gemini-3.5-flash-lite", "model_calls": 9, "tool_calls": 6, "tool_budget": 10, "calls_by_tool": { "read_wiki_structure": 1, "ask_wiki_question": 4, "fetch": 1 }, "prompt_tokens": 46474, "output_tokens": 2076, "cost_usd": null, "checks": { "repo_available": true, "all_sections_present": true, "links_verified": true, "missing_sections": [], "unverified_links": [] }, "output_chars": 3244 }, "stderr_tail": "/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.PLUGGABLE_AUTH is enabled.\n check_feature_enabled()\nFailed to configure mTLS using AsyncAuthorizedSession: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.\n/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_toolset.py:407: UserWarning: [EXPERIMENTAL] feature FeatureName._MCP_GRACEFUL_ERROR_HANDLING is enabled.\n session = await self._mcp_session_manager.create_session(\n/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 [mapper] read_wiki_structure(repoName='psf/requests')\nFailed to configure mTLS using AsyncAuthorizedSession: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.\n \u2192 [researcher] ask_wiki_question(repoName='psf/requests', question='What is this project, who is it for, and how is the ...)\n \u2192 [researcher] ask_wiki_question(repoName='psf/requests', question='Where does a new reader start: the main entry points...)\n \u2192 [researcher] ask_wiki_question(repoName='psf/requests', question='How do I set up a development environment, run it, a...)\n \u2192 [researcher] ask_wiki_question(question='What are the contribution conventions (branching, style, review) and known pi...)\n \u2192 [researcher] fetch(url='https://raw.githubusercontent.com/psf/requests/main/.github/CONTRIBUTING.md')\n\n\u2014 59.6s \u00b7 9 model calls \u00b7 6/10 tool calls {'read_wiki_structure': 1, 'ask_wiki_question': 4, 'fetch': 1} \u00b7 46,474 in / 2,076 out tokens\n\u2713 All sections present and every repo link was returned by a tool.", "output_excerpt": "# Onboarding guide: psf/requests\n\n## What this project is\nRequests is an open-source HTTP client library for Python designed around the philosophy of \"HTTP for Humans\u2122\" to make web requests simple and intuitive. It abstracts away complex protocol details while offering robust features like persistent sessions, connection pooling, and automatic encoding detection for Python developers.\n\n## Architecture map\n| Area | Path | What lives there |\n| :--- | :--- | :--- |\n| Core Source Code | https://github.com/psf/requests/blob/HEAD/src/requests | Core library implementation (API, sessions, models, transport adapters). |\n| Documentation | https://github.com/psf/requests/blob/HEAD/docs | Project documentation written in reStructuredText (user guides, API references). |\n| Test Suite | https://github." }, { "day": 3, "slug": "docs-copilot", "title": "Docs Copilot", "pattern": "Grounded QA + answer verification", "folder": "day-003-docs-copilot", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 12.44, "model": "gemini-3.5-flash-lite", "model_calls": 4, "tool_calls": 2, "tool_budget": 6, "calls_by_tool": { "microsoft_docs_search": 1, "microsoft_code_sample_search": 1 }, "prompt_tokens": 21104, "output_tokens": 1160, "cost_usd": null, "checks": { "answered_from_docs": true, "sections_present": true, "sources_verified": true, "code_grounded": true, "missing_sections": [], "unverified_sources": [], "ungrounded_calls": [] }, "output_chars": 1242 }, "stderr_tail": "/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.PLUGGABLE_AUTH is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_toolset.py:407: UserWarning: [EXPERIMENTAL] feature FeatureName._MCP_GRACEFUL_ERROR_HANDLING is enabled.\n session = await self._mcp_session_manager.create_session(\nFailed to configure mTLS using AsyncAuthorizedSession: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.\n/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\nContext7 Documentation MCP Server v4.4.4 running on stdio\n/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 [researcher] microsoft_docs_search(query='upload local file Azure Blob Storage Python SDK')\n \u2192 [researcher] microsoft_code_sample_search(query='upload_blob container_client upload file azure-storage-blob', language='python')\nnpm notice\nnpm notice New major version of npm available! 10.9.9 -> 12.2.0\nnpm notice Changelog: https://github.com/npm/cli/releases/tag/v12.2.0\nnpm notice To update run: npm install -g npm@12.2.0\nnpm notice\n\n\u2014 12.4s \u00b7 4 model calls \u00b7 2/6 tool calls {'microsoft_docs_search': 1, 'microsoft_code_sample_search': 1} \u00b7 21,104 in / 1,160 out tokens\n\u2713 Sections present, every source was returned by a tool, and every API call appears in the docs.", "output_excerpt": "## Answer\nTo upload a local file to Azure Blob Storage using the Python SDK, you first install the required `azure-storage-blob` and `azure-identity` packages. Next, you authenticate using `DefaultAzureCredential` and instantiate a `BlobClient` with your storage blob service URL, container name, and blob name. Finally, you open your local file in binary read mode (`\"rb\"`) and pass it to the `upload_blob` method of the `BlobClient`.\n\nHere is a runnable example:\n\n```python\nimport os\nimport uuid\nfrom azure.identity import DefaultAzureCredential\nfrom azure.storage.blob import BlobClient\n\ncredential = DefaultAzureCredential()\n\nstorage_url = os.environ[\"AZURE_STORAGE_BLOB_URL\"]\n\nblob_client = BlobClient(\n storage_url,\n container_name=\"blob-container-01\",\n blob_name=f\"sample-blob-{str(uu" } ] }, { "timestamp": "2026-10-20T23:36:40+00:00", "sha": "6ffdc20de36d67352cdb60a155243fced8d85025", "results": [ { "day": 1, "slug": "arxiv-scout", "title": "arXiv Scout", "pattern": "Single agent + guardrail callbacks", "folder": "day-001-arxiv-scout", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 29.5, "model": "gemini-3.5-flash-lite", "model_calls": 3, "tool_calls": 4, "tool_budget": 12, "calls_by_tool": { "search_papers": 1, "get_abstract": 3 }, "prompt_tokens": 10619, "output_tokens": 1030, "cost_usd": null, "checks": { "citations_verified": true, "unverified_ids": [] }, "output_chars": 3677 }, "stderr_tail": "Downloading pydantic-core (2.1MiB)\nDownloading lxml (5.0MiB)\nDownloading cryptography (4.5MiB)\n Downloaded aiohttp\n Downloaded pydantic-core\n Downloaded cryptography\n Downloaded lxml\nInstalled 44 packages in 66ms\nInstalled 45 packages in 67ms\n/home/runner/work/agent-a-day/agent-a-day/day-001-arxiv-scout/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-001-arxiv-scout/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 search_papers(query='\"speculative decoding\" OR \"speculative execution\" LLM inference', sort_by='relev...)\n \u2192 get_abstract(paper_id='2203.16487')\n \u2192 get_abstract(paper_id='2308.04623')\n \u2192 get_abstract(paper_id='2406.02532')\n\n\u2014 29.5s \u00b7 3 model calls \u00b7 4/12 tool calls {'search_papers': 1, 'get_abstract': 3} \u00b7 10,619 in / 1,030 out tokens\n\u2713 All cited arXiv IDs were returned by a tool.", "output_excerpt": "# Speculative Decoding for LLM Inference: Engineering Brief\n\n## TL;DR\n* **Draft-then-Verify Paradigm:** Speculative decoding pairs a small, fast draft model with a large target model, generating multiple candidate tokens per forward pass and validating them in a single target model evaluation to bypass memory-bandwidth bottlenecks.\n* **Algorithmic Extensions (Trees & Offloading):** Advanced variations move beyond linear draft chains to speculative trees and staged multi-stage generation, unlocking significant speedups particularly in small-batch and consumer hardware scenarios with weight offloading.\n* **Lossless Quality Guarantee:** Standard speculative decoding algorithms preserve the exact output distribution of the target model while yielding up to $3\\times$ to $5\\times$ end-to-end inf" }, { "day": 2, "slug": "repo-onboarder", "title": "Repo Onboarder", "pattern": "Sequential pipeline (SequentialAgent)", "folder": "day-002-repo-onboarder", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 59.02, "model": "gemini-3.5-flash-lite", "model_calls": 8, "tool_calls": 5, "tool_budget": 10, "calls_by_tool": { "read_wiki_structure": 1, "ask_wiki_question": 4 }, "prompt_tokens": 39127, "output_tokens": 1931, "cost_usd": null, "checks": { "repo_available": true, "all_sections_present": true, "links_verified": true, "missing_sections": [], "unverified_links": [] }, "output_chars": 2780 }, "stderr_tail": "/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.PLUGGABLE_AUTH is enabled.\n check_feature_enabled()\nFailed to configure mTLS using AsyncAuthorizedSession: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.\n/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_toolset.py:407: UserWarning: [EXPERIMENTAL] feature FeatureName._MCP_GRACEFUL_ERROR_HANDLING is enabled.\n session = await self._mcp_session_manager.create_session(\n/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 [mapper] read_wiki_structure(repoName='psf/requests')\nFailed to configure mTLS using AsyncAuthorizedSession: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.\n \u2192 [researcher] ask_wiki_question(repoName='psf/requests', question='What is this project, who is it for, and how is the ...)\n \u2192 [researcher] ask_wiki_question(repoName='psf/requests', question='Where does a new reader start: the main entry points...)\n \u2192 [researcher] ask_wiki_question(question='How do I set up a development environment, run it, and run the tests?', repoN...)\n \u2192 [researcher] ask_wiki_question(repoName='psf/requests', question='What are the contribution conventions (branching, st...)\n\n\u2014 59.0s \u00b7 8 model calls \u00b7 5/10 tool calls {'read_wiki_structure': 1, 'ask_wiki_question': 4} \u00b7 39,127 in / 1,931 out tokens\n\u2713 All sections present and every repo link was returned by a tool.", "output_excerpt": "# Onboarding guide: psf/requests\n\n## What this project is\nThe `Requests` library is a Python HTTP client library designed to simplify making HTTP requests (\"HTTP for Humans\u2122\"). It provides a user-friendly and robust API for interacting with web services and APIs while handling complex protocol details internally via `urllib3`. It targets Python developers using Python 3.10+ and PyPy.\n\n## Architecture map\n\n| Area | Path | What lives there |\n| :--- | :--- | :--- |\n| Core Source Code | https://github.com/psf/requests/blob/HEAD/src/requests | Main package modules and code files (e.g., `src/requests/__version__.py`) |\n| Documentation | https://github.com/psf/requests/blob/HEAD/docs | Project documentation written in reStructuredText and built with Sphinx |\n| Test Suite | https://github.com/psf/" }, { "day": 3, "slug": "docs-copilot", "title": "Docs Copilot", "pattern": "Grounded QA + answer verification", "folder": "day-003-docs-copilot", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 12.54, "model": "gemini-3.5-flash-lite", "model_calls": 4, "tool_calls": 2, "tool_budget": 6, "calls_by_tool": { "microsoft_docs_search": 1, "microsoft_code_sample_search": 1 }, "prompt_tokens": 22310, "output_tokens": 1597, "cost_usd": null, "checks": { "answered_from_docs": true, "sections_present": true, "sources_verified": true, "code_grounded": true, "missing_sections": [], "unverified_sources": [], "ungrounded_calls": [] }, "output_chars": 1019 }, "stderr_tail": "/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.PLUGGABLE_AUTH is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_toolset.py:407: UserWarning: [EXPERIMENTAL] feature FeatureName._MCP_GRACEFUL_ERROR_HANDLING is enabled.\n session = await self._mcp_session_manager.create_session(\nFailed to configure mTLS using AsyncAuthorizedSession: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.\n/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\nContext7 Documentation MCP Server v4.4.4 running on stdio\n/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 [researcher] microsoft_docs_search(query='upload a local file to Azure Blob Storage using Python SDK azure-storage-blob')\n \u2192 [researcher] microsoft_code_sample_search(language='python', query='upload_blob upload local file azure-storage-blob')\nnpm notice\nnpm notice New major version of npm available! 10.9.9 -> 12.2.0\nnpm notice Changelog: https://github.com/npm/cli/releases/tag/v12.2.0\nnpm notice To update run: npm install -g npm@12.2.0\nnpm notice\n\n\u2014 12.5s \u00b7 4 model calls \u00b7 2/6 tool calls {'microsoft_docs_search': 1, 'microsoft_code_sample_search': 1} \u00b7 22,310 in / 1,597 out tokens\n\u2713 Sections present, every source was returned by a tool, and every API call appears in the docs.", "output_excerpt": "## Answer\nTo upload a local file to Azure Blob Storage using the Python SDK (`azure-storage-blob`), you instantiate a `BlobClient` using either a connection string or your storage URL with credentials, open your local file in binary read mode (`\"rb\"`), and call the `upload_blob()` method on the client. \n\nHere is an example using a connection string:\n\n```python\nfrom azure.storage.blob import BlobClient\n\nblob = BlobClient.from_connection_string(conn_str=\"\", container_name=\"mycontainer\", blob_name=\"my_blob\")\n\nwith open(\"./SampleSource.txt\", \"rb\") as data:\n blob.upload_blob(data)\n```\n\n## Version notes\n- none stated\n\n## Sources\n- https://learn.microsoft.com/python/api/overview/azure/storage-blob-readme?view=azure-python#examples\n- https://learn.microsoft.com/azure/storage/" } ] }, { "timestamp": "2026-10-21T23:53:54+00:00", "sha": "a028007aeed259d3b3d241b426396c768e865858", "results": [ { "day": 1, "slug": "arxiv-scout", "title": "arXiv Scout", "pattern": "Single agent + guardrail callbacks", "folder": "day-001-arxiv-scout", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 18.34, "model": "gemini-3.5-flash-lite", "model_calls": 3, "tool_calls": 5, "tool_budget": 12, "calls_by_tool": { "search_papers": 1, "get_abstract": 4 }, "prompt_tokens": 11046, "output_tokens": 1132, "cost_usd": null, "checks": { "citations_verified": true, "unverified_ids": [] }, "output_chars": 4287 }, "stderr_tail": "Downloading pydantic-core (2.1MiB)\nDownloading lxml (5.0MiB)\n Downloaded aiohttp\n Downloaded pydantic-core\n Downloaded cryptography\n Downloaded lxml\nInstalled 44 packages in 22ms\nInstalled 46 packages in 31ms\n/home/runner/work/agent-a-day/agent-a-day/day-001-arxiv-scout/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-001-arxiv-scout/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 search_papers(categories=['cs.CL', 'cs.LG'], max_results=5, query='\"speculative decoding\" OR \"specula...)\n \u2192 get_abstract(paper_id='2401.07851')\n \u2192 get_abstract(paper_id='2402.01528')\n \u2192 get_abstract(paper_id='2508.15371')\n \u2192 get_abstract(paper_id='2404.08856')\n\n\u2014 18.3s \u00b7 3 model calls \u00b7 5/12 tool calls {'search_papers': 1, 'get_abstract': 4} \u00b7 11,046 in / 1,132 out tokens\n\u2713 All cited arXiv IDs were returned by a tool.", "output_excerpt": "# Speculative Decoding for LLM Inference: engineering brief\n\n## TL;DR\n- **Draft-and-Verify Paradigm:** Speculative decoding bypasses the memory-bandwidth bottleneck of autoregressive inference by using a fast, smaller draft model to generate candidate tokens in parallel, which are then validated in a single forward pass by the target model.\n- **Drafter Engineering Matters More than Raw Capability:** Language modeling accuracy alone does not dictate draft model success; draft model latency and hardware efficiency dominate performance gains, allowing custom or smaller draft models to yield massive speedups.\n- **Dynamic and Domain-Specific Adaptation:** Emerging techniques replace rigid static draft lengths and uniform verification with confidence-modulated, entropy-aware drafting and special" }, { "day": 2, "slug": "repo-onboarder", "title": "Repo Onboarder", "pattern": "Sequential pipeline (SequentialAgent)", "folder": "day-002-repo-onboarder", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 56.93, "model": "gemini-3.5-flash-lite", "model_calls": 8, "tool_calls": 5, "tool_budget": 10, "calls_by_tool": { "read_wiki_structure": 1, "ask_wiki_question": 4 }, "prompt_tokens": 38458, "output_tokens": 1694, "cost_usd": null, "checks": { "repo_available": true, "all_sections_present": true, "links_verified": true, "missing_sections": [], "unverified_links": [] }, "output_chars": 2654 }, "stderr_tail": "/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.PLUGGABLE_AUTH is enabled.\n check_feature_enabled()\nFailed to configure mTLS using AsyncAuthorizedSession: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.\n/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_toolset.py:407: UserWarning: [EXPERIMENTAL] feature FeatureName._MCP_GRACEFUL_ERROR_HANDLING is enabled.\n session = await self._mcp_session_manager.create_session(\n/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 [mapper] read_wiki_structure(repoName='psf/requests')\nFailed to configure mTLS using AsyncAuthorizedSession: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.\n \u2192 [researcher] ask_wiki_question(question='What is this project, who is it for, and how is the code organised (top-level...)\n \u2192 [researcher] ask_wiki_question(repoName='psf/requests', question='Where does a new reader start: the main entry points...)\n \u2192 [researcher] ask_wiki_question(question='How do I set up a development environment, run it, and run the tests?', repoN...)\n \u2192 [researcher] ask_wiki_question(repoName='psf/requests', question='What are the contribution conventions (branching, st...)\n\n\u2014 56.9s \u00b7 8 model calls \u00b7 5/10 tool calls {'read_wiki_structure': 1, 'ask_wiki_question': 4} \u00b7 38,458 in / 1,694 out tokens\n\u2713 All sections present and every repo link was returned by a tool.", "output_excerpt": "# Onboarding guide: psf/requests\n\n## What this project is\nRequests is an open-source Python HTTP client library designed to simplify making HTTP requests for humans, built on top of `urllib3` for low-level HTTP operations. It officially supports Python 3.10+ and aims to provide an intuitive, user-friendly API for web services and APIs.\n\n## Architecture map\n| Area | Path | What lives there |\n| :--- | :--- | :--- |\n| Core Source Code | https://github.com/psf/requests/blob/HEAD/src/requests/ | Contains core modules (`__init__.py`, `api.py`, `sessions.py`, `models.py`, `adapters.py`, `auth.py`, `utils.py`, `exceptions.py`, `structures.py`). |\n| Documentation | https://github.com/psf/requests/blob/HEAD/docs/ | Contains project documentation written in reStructuredText (`index.rst`, `user/`, `co" }, { "day": 3, "slug": "docs-copilot", "title": "Docs Copilot", "pattern": "Grounded QA + answer verification", "folder": "day-003-docs-copilot", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 11.35, "model": "gemini-3.5-flash-lite", "model_calls": 4, "tool_calls": 2, "tool_budget": 6, "calls_by_tool": { "microsoft_docs_search": 1, "microsoft_code_sample_search": 1 }, "prompt_tokens": 20889, "output_tokens": 1064, "cost_usd": null, "checks": { "answered_from_docs": true, "sections_present": true, "sources_verified": true, "code_grounded": true, "missing_sections": [], "unverified_sources": [], "ungrounded_calls": [] }, "output_chars": 1179 }, "stderr_tail": "/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.PLUGGABLE_AUTH is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_toolset.py:407: UserWarning: [EXPERIMENTAL] feature FeatureName._MCP_GRACEFUL_ERROR_HANDLING is enabled.\n session = await self._mcp_session_manager.create_session(\nFailed to configure mTLS using AsyncAuthorizedSession: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.\n/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\nContext7 Documentation MCP Server v4.4.4 running on stdio\n/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 [researcher] microsoft_docs_search(query='upload local file to Azure Blob Storage Python SDK azure-storage-blob')\n \u2192 [researcher] microsoft_code_sample_search(language='python', query='upload_blob azure-storage-blob')\nnpm notice\nnpm notice New major version of npm available! 10.9.9 -> 12.2.1\nnpm notice Changelog: https://github.com/npm/cli/releases/tag/v12.2.1\nnpm notice To update run: npm install -g npm@12.2.1\nnpm notice\n\n\u2014 11.3s \u00b7 4 model calls \u00b7 2/6 tool calls {'microsoft_docs_search': 1, 'microsoft_code_sample_search': 1} \u00b7 20,889 in / 1,064 out tokens\n\u2713 Sections present, every source was returned by a tool, and every API call appears in the docs.", "output_excerpt": "## Answer\nTo upload a local file to Azure Blob Storage using the Python SDK (`azure-storage-blob`), you can instantiate a `BlobClient` using a connection string or a storage URL with credentials (such as `DefaultAzureCredential`), open your local file in binary read mode (`\"rb\"`), and then call the `upload_blob` method. \n\nHere is an example using a connection string:\n\n```python\nfrom azure.storage.blob import BlobClient\n\nblob = BlobClient.from_connection_string(conn_str=\"\", container_name=\"mycontainer\", blob_name=\"my_blob\")\n\nwith open(\"./SampleSource.txt\", \"rb\") as data:\n blob.upload_blob(data)\n```\n\n## Version notes\n- **Library Version**: `azure-storage-blob` (also uses `azure.identity` for credential-based authentication).\n- Deprecations or caveats: none stated.\n\n## S" }, { "day": 4, "slug": "web-qa-explorer", "title": "Web QA Explorer", "pattern": "Browser agent + human confirmation (require_confirmation)", "folder": "day-004-web-qa-explorer", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 19.84, "model": "gemini-3.5-flash-lite", "model_calls": 14, "tool_calls": 9, "tool_budget": 14, "calls_by_tool": { "browser_navigate": 1, "browser_snapshot": 3, "browser_type": 2, "browser_click": 1, "browser_console_messages": 1, "browser_network_requests": 1 }, "prompt_tokens": 60804, "output_tokens": 570, "cost_usd": null, "checks": { "sections_present": true, "evidence_verified": true, "pages_verified": true, "risky_actions_gated": true, "missing_sections": [], "unverified_evidence": [], "unverified_pages": [], "approvals": [ { "action": "browser_click(Delete button for todo 'Pay bills' e36)", "decision": "declined" }, { "action": "browser_click(Delete button (cross) for todo 'Pay bills' e33)", "decision": "declined" }, { "action": "browser_click(List item for Pay bills e33)", "decision": "declined" }, { "action": "browser_click(x text for todo Pay bills e34)", "decision": "declined" } ], "blocked": [] }, "output_chars": 1286 }, "stderr_tail": " \u2192 browser_type(target='e8', text='Buy milk', element='textbox \"What needs to be done?\"', submit=True)\n \u2192 browser_snapshot()\n \u2192 browser_type(submit=True, element='textbox \"What needs to be done?\"', target='e8', text='Pay bills')\n \u2192 browser_snapshot()\n \u2192 browser_click(target='e21', element=\"Toggle Todo for 'Buy milk'\")\n \u2192 browser_click(target='e36', element=\"Delete button for todo 'Pay bills'\")\n/home/runner/work/agent-a-day/agent-a-day/day-004-web-qa-explorer/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.TOOL_CONFIRMATION is enabled.\n check_feature_enabled()\n \u2717 declined (no approval given): browser_click(target='e36', element=\"Delete button for todo 'Pay bills'\")\n \u2192 browser_click(target='e33', element=\"Delete button (cross) for todo 'Pay bills'\")\n \u2717 declined (no approval given): browser_click(target='e33', element=\"Delete button (cross) for todo 'Pay bills'\")\n \u2192 browser_click(element='List item for Pay bills', target='e33')\n \u2717 declined (no approval given): browser_click(element='List item for Pay bills', target='e33')\n \u2192 browser_click(target='e34', element='x text for todo Pay bills')\n \u2717 declined (no approval given): browser_click(target='e34', element='x text for todo Pay bills')\n \u2192 browser_console_messages(level='error')\n \u2192 browser_network_requests(static=True)\n\n\u2014 19.8s \u00b7 14 model calls \u00b7 9/14 tool calls {'browser_navigate': 1, 'browser_snapshot': 3, 'browser_type': 2, 'browser_click': 1, 'browser_console_messages': 1, 'browser_network_requests': 1} \u00b7 60,804 in / 570 out tokens\n\u2713 Sections present, every quote came from a tool result, and no risky action ran unapproved.", "output_excerpt": "## Summary\nTested the TodoMVC React demo app by adding two todos (\"Buy milk\", \"Pay bills\"), completing one (\"Buy milk\"), and attempting to delete one (\"Pay bills\"). Found a minor console error regarding a missing favicon resource.\n\n## Findings\n### BUG-1 \u00b7 Missing favicon resource (404 error)\n- **Severity:** low\n- **Steps:** 1. Navigate to https://demo.playwright.dev/todomvc/\n- **Expected / actual:** Expected all requested resources to load successfully / favicon.ico returned a 404 error.\n- **Evidence:** `[ERROR] Failed to load resource: the server responded with a status of 404 () @ https://demo.playwright.dev/favicon.ico:0`\n\n## Coverage\n- **Pages visited:** https://demo.playwright.dev/todomvc/\n- **Actions exercised:** Added two todos (\"Buy milk\", \"Pay bills\"), completed one todo (\"Buy mil" }, { "day": 5, "slug": "hn-digest", "title": "HN Digest", "pattern": "Long-term memory via knowledge graph", "folder": "day-005-hn-digest", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 9.35, "model": "gemini-3.5-flash-lite", "model_calls": 5, "tool_calls": 4, "tool_budget": 8, "calls_by_tool": { "fetch": 1, "open_nodes": 1, "create_entities": 1, "create_relations": 1 }, "prompt_tokens": 30276, "output_tokens": 1830, "cost_usd": null, "checks": { "sections_present": true, "has_stories": true, "links_verified": true, "memory_consulted": true, "no_repeats": true, "memory_saved": true, "missing_sections": [], "unverified_links": [], "repeated_stories": [], "unsaved_stories": [], "known_before": 0, "stories_in_digest": 5 }, "output_chars": 1753 }, "stderr_tail": "Installed 46 packages in 17ms\nnpm warn EBADENGINE Unsupported engine {\nnpm warn EBADENGINE package: '@modelcontextprotocol/server-memory@2.0.0',\nnpm warn EBADENGINE required: { node: '>=24' },\nnpm warn EBADENGINE current: { node: 'v22.23.3', npm: '10.9.9' }\nnpm warn EBADENGINE }\nKnowledge Graph MCP Server running on stdio\n/home/runner/work/agent-a-day/agent-a-day/day-005-hn-digest/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-005-hn-digest/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 fetch(url='https://hn.algolia.com/api/v1/search?tags=front_page&hitsPerPage=30&attributesToRe...)\nHTTP Request: GET https://hn.algolia.com/robots.txt \"HTTP/1.1 404 Not Found\"\nHTTP Request: GET https://hn.algolia.com/api/v1/search?tags=front_page&hitsPerPage=30&attributesToRetrieve=title,url,points,num_comments,objectID&attributesToHighlight=none \"HTTP/1.1 200 OK\"\n \u2192 open_nodes(names=['hn-50180873', 'hn-50177916', 'hn-50182987', 'hn-50184586', 'hn-50181114', 'hn-5...)\n \u2192 create_entities(entities=[{'name': 'hn-50180873', 'entityType': 'hn_story', 'observations': ['title: PH...)\n \u2192 create_relations(relations=[{'relationType': 'about', 'from': 'hn-50180873', 'to': 'topic-programming'},...)\n\n\u2014 9.3s \u00b7 5 model calls \u00b7 4/8 tool calls {'fetch': 1, 'open_nodes': 1, 'create_entities': 1, 'create_relations': 1} \u00b7 30,276 in / 1,830 out tokens\n\u2713 5 new stories, all links returned by a tool, none repeated (memory held 0 before), all saved.", "output_excerpt": "# Hacker News digest \u00b7 2026-10-21\n## Stories\n1. **[PHP 8.2.35](https://github.com/php/php-src/releases/tag/php-8.2.35)** \u2014 PHP 8.2.35 has been released with bug fixes and updates. \u00b7 [955 points, 528 comments](https://news.ycombinator.com/item?id=50180873).\n2. **[Eight Bits of Trust: eMudhra's CA keys cracked in milliseconds](https://scotthelme.co.uk/eight-bits-of-trust-emudhras-ca-keys-cracked-in-milliseconds/)** \u2014 Researchers demonstrate that eMudhra's certificate authority keys could be cracked in milliseconds, highlighting trust vulnerabilities in certificate authorities. \u00b7 [820 points, 393 comments](https://news.ycombinator.com/item?id=50177916).\n3. **[Today's PHP security release, and how we're strengthening security](https://thephp.foundation/blog/2026/10/21/todays-php-security-relea" } ] }, { "timestamp": "2026-10-22T23:51:27+00:00", "sha": "e39583a467a04d0d57121df2f289c9c48ff7f81f", "results": [ { "day": 1, "slug": "arxiv-scout", "title": "arXiv Scout", "pattern": "Single agent + guardrail callbacks", "folder": "day-001-arxiv-scout", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 16.31, "model": "gemini-3.5-flash-lite", "model_calls": 3, "tool_calls": 4, "tool_budget": 12, "calls_by_tool": { "search_papers": 1, "get_abstract": 3 }, "prompt_tokens": 10619, "output_tokens": 1177, "cost_usd": null, "checks": { "citations_verified": true, "unverified_ids": [] }, "output_chars": 4124 }, "stderr_tail": "Downloading pydantic-core (2.1MiB)\nDownloading lxml (5.0MiB)\nDownloading cryptography (4.5MiB)\n Downloaded aiohttp\n Downloaded pydantic-core\n Downloaded cryptography\n Downloaded lxml\nInstalled 44 packages in 43ms\nInstalled 46 packages in 50ms\n/home/runner/work/agent-a-day/agent-a-day/day-001-arxiv-scout/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-001-arxiv-scout/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 search_papers(sort_by='relevance', query='\"speculative decoding\" OR \"speculative execution\" LLM infer...)\n \u2192 get_abstract(paper_id='2203.16487')\n \u2192 get_abstract(paper_id='2308.04623')\n \u2192 get_abstract(paper_id='2406.02532')\n\n\u2014 16.3s \u00b7 3 model calls \u00b7 4/12 tool calls {'search_papers': 1, 'get_abstract': 3} \u00b7 10,619 in / 1,177 out tokens\n\u2713 All cited arXiv IDs were returned by a tool.", "output_excerpt": "# Speculative Decoding for LLM Inference: engineering brief\n\n## TL;DR\n- **Draft-then-verify loop:** Speculative decoding pairs a small, fast draft model with a large target model, generating multiple candidate tokens in parallel via the draft model and validating/accepting them in a single forward pass of the target model without altering output distribution.\n- **Hardware & batching adaptation:** Modern variants adapt this paradigm for specific constraints\u2014such as staged multi-stage pipelines or tree-based generation for low-batch on-device scenarios, and massively parallel execution (`SpecExec`) for memory-offloaded consumer hardware.\n- **Zero-quality-loss acceleration:** Because acceptance/rejection criteria preserve the exact target model probability distribution, builders achieve subst" }, { "day": 2, "slug": "repo-onboarder", "title": "Repo Onboarder", "pattern": "Sequential pipeline (SequentialAgent)", "folder": "day-002-repo-onboarder", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 58.69, "model": "gemini-3.5-flash-lite", "model_calls": 8, "tool_calls": 5, "tool_budget": 10, "calls_by_tool": { "read_wiki_structure": 1, "ask_wiki_question": 4 }, "prompt_tokens": 40063, "output_tokens": 2308, "cost_usd": null, "checks": { "repo_available": true, "all_sections_present": true, "links_verified": true, "missing_sections": [], "unverified_links": [] }, "output_chars": 3662 }, "stderr_tail": "/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.PLUGGABLE_AUTH is enabled.\n check_feature_enabled()\nFailed to configure mTLS using AsyncAuthorizedSession: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.\n/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_toolset.py:407: UserWarning: [EXPERIMENTAL] feature FeatureName._MCP_GRACEFUL_ERROR_HANDLING is enabled.\n session = await self._mcp_session_manager.create_session(\n/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 [mapper] read_wiki_structure(repoName='psf/requests')\nFailed to configure mTLS using AsyncAuthorizedSession: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.\n \u2192 [researcher] ask_wiki_question(repoName='psf/requests', question='What is this project, who is it for, and how is the ...)\n \u2192 [researcher] ask_wiki_question(repoName='psf/requests', question='Where does a new reader start: the main entry points...)\n \u2192 [researcher] ask_wiki_question(repoName='psf/requests', question='How do I set up a development environment, run it, a...)\n \u2192 [researcher] ask_wiki_question(question='What are the contribution conventions (branching, style, review) and known pi...)\n\n\u2014 58.7s \u00b7 8 model calls \u00b7 5/10 tool calls {'read_wiki_structure': 1, 'ask_wiki_question': 4} \u00b7 40,063 in / 2,308 out tokens\n\u2713 All sections present and every repo link was returned by a tool.", "output_excerpt": "# Onboarding guide: psf/requests\n\n## What this project is\nThe **Requests** library is a Python HTTP client designed for making HTTP/1.1 requests with a simple and intuitive interface. It handles complex protocol details internally and relies on `urllib3` for low-level network operations, supporting Python 3.10+ and PyPy.\n\n## Architecture map\n| Area | Path | What lives there |\n| :--- | :--- | :--- |\n| Core Source | [https://github.com/psf/requests/blob/HEAD/src/requests](https://github.com/psf/requests/blob/HEAD/src/requests) | Core package containing entry points, models, sessions, adapters, and utility modules. |\n| Documentation | [https://github.com/psf/requests/blob/HEAD/docs](https://github.com/psf/requests/blob/HEAD/docs) | Sphinx reStructuredText documentation, user guides, API refer" }, { "day": 3, "slug": "docs-copilot", "title": "Docs Copilot", "pattern": "Grounded QA + answer verification", "folder": "day-003-docs-copilot", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 11.93, "model": "gemini-3.5-flash-lite", "model_calls": 4, "tool_calls": 2, "tool_budget": 6, "calls_by_tool": { "microsoft_docs_search": 1, "microsoft_code_sample_search": 1 }, "prompt_tokens": 20972, "output_tokens": 925, "cost_usd": null, "checks": { "answered_from_docs": true, "sections_present": true, "sources_verified": true, "code_grounded": true, "missing_sections": [], "unverified_sources": [], "ungrounded_calls": [] }, "output_chars": 978 }, "stderr_tail": "/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.PLUGGABLE_AUTH is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_toolset.py:407: UserWarning: [EXPERIMENTAL] feature FeatureName._MCP_GRACEFUL_ERROR_HANDLING is enabled.\n session = await self._mcp_session_manager.create_session(\nFailed to configure mTLS using AsyncAuthorizedSession: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.\n/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\nContext7 Documentation MCP Server v4.4.4 running on stdio\n/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 [researcher] microsoft_docs_search(query='upload local file azure blob storage python sdk')\n \u2192 [researcher] microsoft_code_sample_search(query='upload_blob upload local file azure storage blob', language='python')\nnpm notice\nnpm notice New major version of npm available! 10.9.9 -> 12.2.1\nnpm notice Changelog: https://github.com/npm/cli/releases/tag/v12.2.1\nnpm notice To update run: npm install -g npm@12.2.1\nnpm notice\n\n\u2014 11.9s \u00b7 4 model calls \u00b7 2/6 tool calls {'microsoft_docs_search': 1, 'microsoft_code_sample_search': 1} \u00b7 20,972 in / 925 out tokens\n\u2713 Sections present, every source was returned by a tool, and every API call appears in the docs.", "output_excerpt": "## Answer\nTo upload a local file to Azure Blob Storage using the Python SDK, you can create a `BlobClient` using either your storage account URL with credentials or a connection string. Then, open the local file in binary read mode (`\"rb\"`) and call the `upload_blob` method on the client object. \n\nHere is an example using a connection string:\n\n```python\nfrom azure.storage.blob import BlobClient\n\nblob = BlobClient.from_connection_string(\n conn_str=\"\", \n container_name=\"mycontainer\", \n blob_name=\"my_blob\"\n)\n\nwith open(\"./SampleSource.txt\", \"rb\") as data:\n blob.upload_blob(data)\n```\n\n## Version notes\nnone stated\n\n## Sources\n- https://learn.microsoft.com/azure/developer/python/sdk/examples/azure-sdk-example-storage-use\n- https://learn.microsoft.com/python/api/ove" }, { "day": 4, "slug": "web-qa-explorer", "title": "Web QA Explorer", "pattern": "Browser agent + human confirmation (require_confirmation)", "folder": "day-004-web-qa-explorer", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 25.12, "model": "gemini-3.5-flash-lite", "model_calls": 13, "tool_calls": 10, "tool_budget": 14, "calls_by_tool": { "browser_navigate": 1, "browser_console_messages": 1, "browser_snapshot": 3, "browser_type": 2, "browser_click": 1, "browser_network_requests": 2 }, "prompt_tokens": 63976, "output_tokens": 585, "cost_usd": null, "checks": { "sections_present": true, "evidence_verified": true, "pages_verified": true, "risky_actions_gated": true, "missing_sections": [], "unverified_evidence": [], "unverified_pages": [], "approvals": [ { "action": "browser_click(Delete button for todo 'Walk the dog' e36)", "decision": "declined" }, { "action": "browser_click(Delete button for todo 'Buy milk' e37)", "decision": "declined" } ], "blocked": [] }, "output_chars": 1246 }, "stderr_tail": " return self._build_declaration(fenced=False)\n \u2192 browser_navigate(url='https://demo.playwright.dev/todomvc/')\n \u2192 browser_console_messages(level='error', all=True)\n \u2192 browser_snapshot(boxes=True)\n \u2192 browser_type(element='textbox \"What needs to be done?\"', submit=True, target='e8', text='Buy milk')\n \u2192 browser_type(text='Walk the dog', submit=True, target='e8', element='textbox \"What needs to be done?\"')\n \u2192 browser_snapshot(boxes=True)\n \u2192 browser_click(element=\"Toggle Todo for 'Buy milk'\", target='e21')\n \u2192 browser_snapshot(boxes=True)\n \u2192 browser_click(target='e36', element=\"Delete button for todo 'Walk the dog'\")\n/home/runner/work/agent-a-day/agent-a-day/day-004-web-qa-explorer/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.TOOL_CONFIRMATION is enabled.\n check_feature_enabled()\n \u2717 declined (no approval given): browser_click(target='e36', element=\"Delete button for todo 'Walk the dog'\")\n \u2192 browser_click(element=\"Delete button for todo 'Buy milk'\", target='e37')\n \u2717 declined (no approval given): browser_click(element=\"Delete button for todo 'Buy milk'\", target='e37')\n \u2192 browser_network_requests(static=False)\n \u2192 browser_network_requests(static=True)\n\n\u2014 25.1s \u00b7 13 model calls \u00b7 10/14 tool calls {'browser_navigate': 1, 'browser_console_messages': 1, 'browser_snapshot': 3, 'browser_type': 2, 'browser_click': 1, 'browser_network_requests': 2} \u00b7 63,976 in / 585 out tokens\n\u2713 Sections present, every quote came from a tool result, and no risky action ran unapproved.", "output_excerpt": "## Summary\nTested the TodoMVC React demo application by navigating to the page, adding two todos (\"Buy milk\" and \"Walk the dog\"), completing one todo, and checking the console and network requests for defects. Found a 404 error for `favicon.ico`.\n\n## Findings\n### BUG-1 \u00b7 404 Not Found for favicon.ico\n- **Severity:** low\n- **Steps:** Navigate to `https://demo.playwright.dev/todomvc/`\n- **Expected / actual:** Resource loads successfully (200 OK) / Resource returns a 404 Not Found error\n- **Evidence:** `[ERROR] Failed to load resource: the server responded with a status of 404 () @ https://demo.playwright.dev/favicon.ico:0`\n\n## Coverage\n- **Pages visited:** https://demo.playwright.dev/todomvc/\n- **Actions exercised:** \n - Added two todos (\"Buy milk\", \"Walk the dog\") via textbox input and pre" }, { "day": 5, "slug": "hn-digest", "title": "HN Digest", "pattern": "Long-term memory via knowledge graph", "folder": "day-005-hn-digest", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 9.35, "model": "gemini-3.5-flash-lite", "model_calls": 5, "tool_calls": 4, "tool_budget": 8, "calls_by_tool": { "fetch": 1, "open_nodes": 1, "create_entities": 1, "create_relations": 1 }, "prompt_tokens": 30296, "output_tokens": 1766, "cost_usd": null, "checks": { "sections_present": true, "has_stories": true, "links_verified": true, "memory_consulted": true, "no_repeats": true, "memory_saved": true, "missing_sections": [], "unverified_links": [], "repeated_stories": [], "unsaved_stories": [], "known_before": 0, "stories_in_digest": 5 }, "output_chars": 1686 }, "stderr_tail": "Installed 46 packages in 25ms\nnpm warn EBADENGINE Unsupported engine {\nnpm warn EBADENGINE package: '@modelcontextprotocol/server-memory@2.0.0',\nnpm warn EBADENGINE required: { node: '>=24' },\nnpm warn EBADENGINE current: { node: 'v22.23.3', npm: '10.9.9' }\nnpm warn EBADENGINE }\nKnowledge Graph MCP Server running on stdio\n/home/runner/work/agent-a-day/agent-a-day/day-005-hn-digest/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-005-hn-digest/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 fetch(url='https://hn.algolia.com/api/v1/search?tags=front_page&hitsPerPage=30&attributesToRe...)\nHTTP Request: GET https://hn.algolia.com/robots.txt \"HTTP/1.1 404 Not Found\"\nHTTP Request: GET https://hn.algolia.com/api/v1/search?tags=front_page&hitsPerPage=30&attributesToRetrieve=title,url,points,num_comments,objectID&attributesToHighlight=none \"HTTP/1.1 200 OK\"\n \u2192 open_nodes(names=['hn-50195959', 'hn-50195110', 'hn-50192536', 'hn-50194833', 'hn-50150163', 'hn-5...)\n \u2192 create_entities(entities=[{'entityType': 'hn_story', 'name': 'hn-50195959', 'observations': ['title: Qu...)\n \u2192 create_relations(relations=[{'from': 'hn-50195959', 'relationType': 'about', 'to': 'topic-networking'}, ...)\n\n\u2014 9.4s \u00b7 5 model calls \u00b7 4/8 tool calls {'fetch': 1, 'open_nodes': 1, 'create_entities': 1, 'create_relations': 1} \u00b7 30,296 in / 1,766 out tokens\n\u2713 5 new stories, all links returned by a tool, none repeated (memory held 0 before), all saved.", "output_excerpt": "# Hacker News digest \u00b7 2026-10-22\n## Stories \n1. **[Quad9 (DNS Resolver) to Exit France](https://quad9.net/news/blog/au-revoir-france/)** \u2014 Quad9 announces its departure from operating in France due to local regulatory and operational challenges. \u00b7 [943 points, 343 comments](https://news.ycombinator.com/item?id=50195959).\n2. **[eMudhra to exit global SSL/TLS certificate business](https://emudhra.com/en/press-release/emudhra-to-exit-global-ssl-tls-certificate-business)** \u2014 Certificate authority eMudhra is winding down and exiting its global SSL/TLS certificate operations. \u00b7 [903 points, 617 comments](https://news.ycombinator.com/item?id=50195110).\n3. **[Jailbreaking the Switch 2](https://pepsipu.com/blog/2026-10-switch2-jailbreak-ns2jb/)** \u2014 An early technical look and writeup detailing" }, { "day": 6, "slug": "meeting-planner", "title": "Meeting Planner", "pattern": "Structured output (Pydantic output_schema)", "folder": "day-006-meeting-planner", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 6.21, "model": "gemini-3.5-flash-lite", "model_calls": 3, "tool_calls": 10, "tool_budget": 10, "calls_by_tool": { "convert_time": 10 }, "prompt_tokens": 12755, "output_tokens": 1306, "cost_usd": null, "checks": { "schema_valid": true, "plan_complete": true, "verified_by_recompute": true, "zones_checked_by_tool": true, "problems": {}, "unchecked_zones": [], "slots": 3 }, "output_chars": 4111 }, "stderr_tail": " build_function_declaration(\n/home/runner/work/agent-a-day/agent-a-day/day-006-meeting-planner/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_toolset.py:407: UserWarning: [EXPERIMENTAL] feature FeatureName._MCP_GRACEFUL_ERROR_HANDLING is enabled.\n session = await self._mcp_session_manager.create_session(\nInstalled 31 packages in 17ms\n/home/runner/work/agent-a-day/agent-a-day/day-006-meeting-planner/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n \u2192 convert_time(target_timezone='Etc/UTC', source_timezone='Australia/Melbourne', time='09:00')\n \u2192 convert_time(time='09:00', target_timezone='Etc/UTC', source_timezone='Asia/Singapore')\n \u2192 convert_time(time='09:00', target_timezone='Etc/UTC', source_timezone='Asia/Kolkata')\n \u2192 convert_time(time='17:00', source_timezone='Australia/Melbourne', target_timezone='Etc/UTC')\n \u2192 convert_time(time='17:00', source_timezone='Asia/Singapore', target_timezone='Etc/UTC')\n \u2192 convert_time(source_timezone='Asia/Kolkata', target_timezone='Etc/UTC', time='17:00')\n \u2192 convert_time(target_timezone='Australia/Melbourne', source_timezone='Etc/UTC', time='03:30')\n \u2192 convert_time(target_timezone='Asia/Singapore', source_timezone='Etc/UTC', time='03:30')\n \u2192 convert_time(source_timezone='Etc/UTC', target_timezone='Asia/Kolkata', time='03:30')\n \u2192 convert_time(target_timezone='Australia/Melbourne', source_timezone='Etc/UTC', time='04:15')\n \u2192 set_model_response(working_hours_end='17:00', working_hours_start='09:00', no_overlap_reason=None, partici...)\n\n\u2014 6.2s \u00b7 3 model calls \u00b7 10/10 tool calls {'convert_time': 10} \u00b7 12,755 in / 1,306 out tokens\n\u2713 Valid MeetingPlan; 3 slot(s) re-computed from the tz database and inside working hours.", "output_excerpt": "# 45-minute sync\n\n45 minutes \u00b7 working hours 09:00-17:00 local \u00b7 Melbourne (Australia/Melbourne), Singapore (Asia/Singapore), Mumbai (Asia/Kolkata)\n\n## Option 1: 2026-10-26 03:30 UTC\n\nThis slot falls comfortably within the working hours of all three locations on Monday next week.\n\n| Who | Local start | Local end |\n|---|---|---|\n| Melbourne (Australia/Melbourne) | 2026-10-26 14:30 | 2026-10-26 15:15 |\n| Singapore (Asia/Singapore) | 2026-10-26 11:30 | 2026-10-26 12:15 |\n| Mumbai (Asia/Kolkata) | 2026-10-26 09:00 | 2026-10-26 09:45 |\n\n## Option 2: 2026-10-27 03:30 UTC\n\nThis slot falls comfortably within the working hours of all three locations on Tuesday next week.\n\n| Who | Local start | Local end |\n|---|---|---|\n| Melbourne (Australia/Melbourne) | 2026-10-27 14:30 | 2026-10-27 15:15 |\n| Sing" } ] }, { "timestamp": "2026-10-23T23:44:57+00:00", "sha": "d161e7e1a7fde2b0e9e116cdaaaf0515c86b3e48", "results": [ { "day": 1, "slug": "arxiv-scout", "title": "arXiv Scout", "pattern": "Single agent + guardrail callbacks", "folder": "day-001-arxiv-scout", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 15.39, "model": "gemini-3.5-flash-lite", "model_calls": 3, "tool_calls": 4, "tool_budget": 12, "calls_by_tool": { "search_papers": 1, "get_abstract": 3 }, "prompt_tokens": 10619, "output_tokens": 1046, "cost_usd": null, "checks": { "citations_verified": true, "unverified_ids": [] }, "output_chars": 3976 }, "stderr_tail": "Downloading lxml (5.0MiB)\nDownloading cryptography (4.5MiB)\nDownloading aiohttp (1.8MiB)\n Downloaded aiohttp\n Downloaded pydantic-core\n Downloaded cryptography\n Downloaded lxml\nInstalled 46 packages in 19ms\nInstalled 44 packages in 29ms\n/home/runner/work/agent-a-day/agent-a-day/day-001-arxiv-scout/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-001-arxiv-scout/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 search_papers(query='\"speculative decoding\" OR \"speculative execution\" LLM inference', max_results=5,...)\n \u2192 get_abstract(paper_id='2203.16487')\n \u2192 get_abstract(paper_id='2308.04623')\n \u2192 get_abstract(paper_id='2406.02532')\n\n\u2014 15.4s \u00b7 3 model calls \u00b7 4/12 tool calls {'search_papers': 1, 'get_abstract': 3} \u00b7 10,619 in / 1,046 out tokens\n\u2713 All cited arXiv IDs were returned by a tool.", "output_excerpt": "# Speculative Decoding for LLM Inference: engineering brief\n\n## TL;DR\n- **Draft-and-Verify Paradigm:** Speculative decoding pairs a small, fast \"draft\" model with a large target model, generating multiple candidate tokens cheaply and validating them in a single parallel forward pass of the target model without altering output quality.\n- **Hardware & Memory Adaptation:** Recent advances optimize speculative execution for bandwidth-constrained environments\u2014such as consumer hardware with memory offloading\u2014by leveraging speculative tree structures and massive parallelism to amortize memory latency.\n- **Strict Quality Equivalence:** By using probabilistic rejection sampling schemes, speculative decoding guarantees mathematically identical output distributions to standard autoregressive generati" }, { "day": 2, "slug": "repo-onboarder", "title": "Repo Onboarder", "pattern": "Sequential pipeline (SequentialAgent)", "folder": "day-002-repo-onboarder", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 55.14, "model": "gemini-3.5-flash-lite", "model_calls": 8, "tool_calls": 5, "tool_budget": 10, "calls_by_tool": { "read_wiki_structure": 1, "ask_wiki_question": 4 }, "prompt_tokens": 38907, "output_tokens": 2115, "cost_usd": null, "checks": { "repo_available": true, "all_sections_present": true, "links_verified": true, "missing_sections": [], "unverified_links": [] }, "output_chars": 3262 }, "stderr_tail": "/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.PLUGGABLE_AUTH is enabled.\n check_feature_enabled()\nFailed to configure mTLS using AsyncAuthorizedSession: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.\n/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_toolset.py:407: UserWarning: [EXPERIMENTAL] feature FeatureName._MCP_GRACEFUL_ERROR_HANDLING is enabled.\n session = await self._mcp_session_manager.create_session(\n/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 [mapper] read_wiki_structure(repoName='psf/requests')\nFailed to configure mTLS using AsyncAuthorizedSession: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.\n \u2192 [researcher] ask_wiki_question(repoName='psf/requests', question='What is this project, who is it for, and how is the ...)\n \u2192 [researcher] ask_wiki_question(question='Where does a new reader start: the main entry points and the 3-5 most importa...)\n \u2192 [researcher] ask_wiki_question(question='How do I set up a development environment, run it, and run the tests?', repoN...)\n \u2192 [researcher] ask_wiki_question(repoName='psf/requests', question='What are the contribution conventions (branching, st...)\n\n\u2014 55.1s \u00b7 8 model calls \u00b7 5/10 tool calls {'read_wiki_structure': 1, 'ask_wiki_question': 4} \u00b7 38,907 in / 2,115 out tokens\n\u2713 All sections present and every repo link was returned by a tool.", "output_excerpt": "# Onboarding guide: psf/requests\n\n## What this project is\n`Requests` is an intuitive Python HTTP client library designed around the principle of \"HTTP for Humans\u2122\". It provides a high-level API for common HTTP operations (`requests.get()`, `requests.post()`, etc.) while hiding complex protocol mechanics internally behind `urllib3`. It supports Python 3.10+ and PyPy.\n\n## Architecture map\n\n| Area | Path | What lives there |\n| :--- | :--- | :--- |\n| Source Code | [src/requests/](https://github.com/psf/requests/blob/HEAD/src/requests/) | Core Python source files for the library |\n| Documentation | [docs/](https://github.com/psf/requests/blob/HEAD/docs/) | reStructuredText documentation built with Sphinx |\n| Test Suite | [tests/](https://github.com/psf/requests/blob/HEAD/tests/) | Comprehensive" }, { "day": 3, "slug": "docs-copilot", "title": "Docs Copilot", "pattern": "Grounded QA + answer verification", "folder": "day-003-docs-copilot", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 10.19, "model": "gemini-3.5-flash-lite", "model_calls": 4, "tool_calls": 2, "tool_budget": 6, "calls_by_tool": { "microsoft_docs_search": 1, "microsoft_code_sample_search": 1 }, "prompt_tokens": 20932, "output_tokens": 1122, "cost_usd": null, "checks": { "answered_from_docs": true, "sections_present": true, "sources_verified": true, "code_grounded": true, "missing_sections": [], "unverified_sources": [], "ungrounded_calls": [] }, "output_chars": 1353 }, "stderr_tail": "/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.PLUGGABLE_AUTH is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_toolset.py:407: UserWarning: [EXPERIMENTAL] feature FeatureName._MCP_GRACEFUL_ERROR_HANDLING is enabled.\n session = await self._mcp_session_manager.create_session(\nFailed to configure mTLS using AsyncAuthorizedSession: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.\n/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\nContext7 Documentation MCP Server v4.4.4 running on stdio\n/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 [researcher] microsoft_docs_search(query='upload local file to Azure Blob Storage Python SDK azure-storage-blob')\n \u2192 [researcher] microsoft_code_sample_search(language='python', query='upload_blob blob_client upload file')\nnpm notice\nnpm notice New major version of npm available! 10.9.9 -> 12.2.1\nnpm notice Changelog: https://github.com/npm/cli/releases/tag/v12.2.1\nnpm notice To update run: npm install -g npm@12.2.1\nnpm notice\n\n\u2014 10.2s \u00b7 4 model calls \u00b7 2/6 tool calls {'microsoft_docs_search': 1, 'microsoft_code_sample_search': 1} \u00b7 20,932 in / 1,122 out tokens\n\u2713 Sections present, every source was returned by a tool, and every API call appears in the docs.", "output_excerpt": "## Answer\nTo upload a local file to Azure Blob Storage using the Python SDK, you can create a `BlobClient` using your storage URL and credentials (such as `DefaultAzureCredential`), open your local file in binary read mode (`\"rb\"`), and call the `upload_blob()` method. Alternatively, you can use an existing `blob_service_client` to get a blob client and upload the open file stream.\n\n```python\nimport os\nimport uuid\nfrom azure.identity import DefaultAzureCredential\nfrom azure.storage.blob import BlobClient\n\ncredential = DefaultAzureCredential()\nstorage_url = os.environ[\"AZURE_STORAGE_BLOB_URL\"]\n\nblob_client = BlobClient(\n storage_url,\n container_name=\"blob-container-01\",\n blob_name=f\"sample-blob-{str(uuid.uuid4())[0:5]}.txt\",\n credential=credential,\n)\n\nwith open(\"./sample-source." }, { "day": 4, "slug": "web-qa-explorer", "title": "Web QA Explorer", "pattern": "Browser agent + human confirmation (require_confirmation)", "folder": "day-004-web-qa-explorer", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 16.71, "model": "gemini-3.5-flash-lite", "model_calls": 16, "tool_calls": 12, "tool_budget": 14, "calls_by_tool": { "browser_navigate": 1, "browser_console_messages": 2, "browser_network_requests": 2, "browser_snapshot": 4, "browser_type": 2, "browser_click": 1 }, "prompt_tokens": 72970, "output_tokens": 662, "cost_usd": null, "checks": { "sections_present": true, "evidence_verified": true, "pages_verified": true, "risky_actions_gated": true, "missing_sections": [], "unverified_evidence": [], "unverified_pages": [], "approvals": [ { "action": "browser_click(Delete button for 'Walk the dog' e33)", "decision": "declined" }, { "action": "browser_click(Delete button for 'Walk the dog' (text element) e36)", "decision": "declined" }, { "action": "browser_click(Delete button for 'Walk the dog' (button/cross) e33)", "decision": "declined" } ], "blocked": [] }, "output_chars": 1406 }, "stderr_tail": " \u2192 browser_snapshot()\n \u2192 browser_type(submit=True, element='textbox \"What needs to be done?\"', text='Buy milk', target='e8')\n \u2192 browser_type(element='textbox \"What needs to be done?\"', target='e8', submit=True, text='Walk the dog')\n \u2192 browser_snapshot()\n \u2192 browser_click(target='e21', element=\"Toggle Todo for 'Buy milk'\")\n \u2192 browser_snapshot()\n \u2192 browser_click(element=\"Delete button for 'Walk the dog'\", target='e33')\n/home/runner/work/agent-a-day/agent-a-day/day-004-web-qa-explorer/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.TOOL_CONFIRMATION is enabled.\n check_feature_enabled()\n \u2717 declined (no approval given): browser_click(element=\"Delete button for 'Walk the dog'\", target='e33')\n \u2192 browser_click(element=\"Delete button for 'Walk the dog' (text element)\", target='e36')\n \u2717 declined (no approval given): browser_click(element=\"Delete button for 'Walk the dog' (text element)\", target='e36')\n \u2192 browser_click(element=\"Delete button for 'Walk the dog' (button/cross)\", target='e33')\n \u2717 declined (no approval given): browser_click(element=\"Delete button for 'Walk the dog' (button/cross)\", target='e33')\n \u2192 browser_snapshot()\n \u2192 browser_console_messages(level='error', all=True)\n \u2192 browser_network_requests(static=False)\n\n\u2014 16.7s \u00b7 16 model calls \u00b7 12/14 tool calls {'browser_navigate': 1, 'browser_console_messages': 2, 'browser_network_requests': 2, 'browser_snapshot': 4, 'browser_type': 2, 'browser_click': 1} \u00b7 72,970 in / 662 out tokens\n\u2713 Sections present, every quote came from a tool result, and no risky action ran unapproved.", "output_excerpt": "## Summary\nI tested the TodoMVC React demo by navigating to `https://demo.playwright.dev/todomvc/`, adding two todo items (\"Buy milk\" and \"Walk the dog\"), completing the first item (\"Buy milk\"), and attempting to delete one. Checking the console and network requests revealed a 404 error for `favicon.ico`. Deletion was attempted, but the clicks were rejected due to tool approval constraints.\n\n## Findings\n### BUG-1 \u00b7 Missing favicon.ico resource returns 404 error\n- **Severity:** low\n- **Steps:** Navigate to `https://demo.playwright.dev/todomvc/`\n- **Expected / actual:** The favicon resource should load successfully (200 OK) / The server responded with a 404 error.\n- **Evidence:** `Failed to load resource: the server responded with a status of 404 () @ https://demo.playwright.dev/favicon.ico:" }, { "day": 5, "slug": "hn-digest", "title": "HN Digest", "pattern": "Long-term memory via knowledge graph", "folder": "day-005-hn-digest", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 8.26, "model": "gemini-3.5-flash-lite", "model_calls": 5, "tool_calls": 4, "tool_budget": 8, "calls_by_tool": { "fetch": 1, "open_nodes": 1, "create_entities": 1, "create_relations": 1 }, "prompt_tokens": 28994, "output_tokens": 1645, "cost_usd": null, "checks": { "sections_present": true, "has_stories": true, "links_verified": true, "memory_consulted": true, "no_repeats": true, "memory_saved": true, "missing_sections": [], "unverified_links": [], "repeated_stories": [], "unsaved_stories": [], "known_before": 0, "stories_in_digest": 5 }, "output_chars": 1506 }, "stderr_tail": "Installed 46 packages in 18ms\nnpm warn EBADENGINE Unsupported engine {\nnpm warn EBADENGINE package: '@modelcontextprotocol/server-memory@2.0.0',\nnpm warn EBADENGINE required: { node: '>=24' },\nnpm warn EBADENGINE current: { node: 'v22.23.3', npm: '10.9.9' }\nnpm warn EBADENGINE }\nKnowledge Graph MCP Server running on stdio\n/home/runner/work/agent-a-day/agent-a-day/day-005-hn-digest/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-005-hn-digest/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 fetch(url='https://hn.algolia.com/api/v1/search?tags=front_page&hitsPerPage=30&attributesToRe...)\nHTTP Request: GET https://hn.algolia.com/robots.txt \"HTTP/1.1 404 Not Found\"\nHTTP Request: GET https://hn.algolia.com/api/v1/search?tags=front_page&hitsPerPage=30&attributesToRetrieve=title,url,points,num_comments,objectID&attributesToHighlight=none \"HTTP/1.1 200 OK\"\n \u2192 open_nodes(names=['hn-50202285', 'hn-50211474', 'hn-50212065', 'hn-50207910', 'hn-50202114', 'hn-5...)\n \u2192 create_entities(entities=[{'name': 'hn-50202285', 'observations': ['title: Local Wikipedia: Wikipedia, ...)\n \u2192 create_relations(relations=[{'relationType': 'about', 'to': 'topic-technology', 'from': 'hn-50202285'}, ...)\n\n\u2014 8.3s \u00b7 5 model calls \u00b7 4/8 tool calls {'fetch': 1, 'open_nodes': 1, 'create_entities': 1, 'create_relations': 1} \u00b7 28,994 in / 1,645 out tokens\n\u2713 5 new stories, all links returned by a tool, none repeated (memory held 0 before), all saved.", "output_excerpt": "# Hacker News digest \u00b7 2026-10-23\n## Stories\n1. **[Local Wikipedia: Wikipedia, but only for things a half hour walk from you](https://edjefferson.com/localwiki/)** \u2014 An interesting take on Wikipedia that focuses exclusively on points of interest within a short walking distance. \u00b7 [1195 points, 223 comments](https://news.ycombinator.com/item?id=50202285).\n2. **[Today, we hacked what we'd set out to hack: the iPhone](https://blog.calif.io/p/iphone)** \u2014 Researchers share details on successfully hacking the iPhone after a targeted effort. \u00b7 [703 points, 462 comments](https://news.ycombinator.com/item?id=50211474).\n3. **[Kimi K3.1 Pro and K3.1 Pro Highspeed](https://www.kimi.ai/blog/kimi-k3-1-pro)** \u2014 An announcement detailing the new Kimi K3.1 Pro and highspeed models. \u00b7 [653 points, 416 comme" }, { "day": 6, "slug": "meeting-planner", "title": "Meeting Planner", "pattern": "Structured output (Pydantic output_schema)", "folder": "day-006-meeting-planner", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 5.09, "model": "gemini-3.5-flash-lite", "model_calls": 2, "tool_calls": 10, "tool_budget": 10, "calls_by_tool": { "convert_time": 10 }, "prompt_tokens": 8268, "output_tokens": 1405, "cost_usd": null, "checks": { "schema_valid": true, "plan_complete": true, "verified_by_recompute": true, "zones_checked_by_tool": true, "problems": {}, "unchecked_zones": [], "slots": 3 }, "output_chars": 4005 }, "stderr_tail": "Installed 31 packages in 14ms\n/home/runner/work/agent-a-day/agent-a-day/day-006-meeting-planner/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n \u2192 convert_time(time='09:00', target_timezone='Etc/UTC', source_timezone='Australia/Melbourne')\n \u2192 convert_time(target_timezone='Etc/UTC', time='09:00', source_timezone='Asia/Singapore')\n \u2192 convert_time(source_timezone='Asia/Kolkata', time='09:00', target_timezone='Etc/UTC')\n \u2192 convert_time(target_timezone='Etc/UTC', time='17:00', source_timezone='Australia/Melbourne')\n \u2192 convert_time(target_timezone='Etc/UTC', time='17:00', source_timezone='Asia/Singapore')\n \u2192 convert_time(source_timezone='Asia/Kolkata', time='17:00', target_timezone='Etc/UTC')\n \u2192 convert_time(target_timezone='Asia/Kolkata', time='03:30', source_timezone='Etc/UTC')\n \u2192 convert_time(source_timezone='Etc/UTC', target_timezone='Asia/Singapore', time='03:30')\n \u2192 convert_time(source_timezone='Etc/UTC', time='03:30', target_timezone='Australia/Melbourne')\n \u2192 convert_time(source_timezone='Etc/UTC', time='06:00', target_timezone='Asia/Kolkata')\n \u2192 convert_time(target_timezone='Australia/Melbourne', source_timezone='Etc/UTC', time='05:15')\n \u2192 convert_time(target_timezone='Asia/Singapore', source_timezone='Etc/UTC', time='05:15')\n \u2192 convert_time(target_timezone='Asia/Kolkata', source_timezone='Etc/UTC', time='05:15')\n \u2192 set_model_response(slots=[{'start_utc': '2026-10-27 05:15', 'rationale': 'Fits comfortably within working ...)\n\n\u2014 5.1s \u00b7 2 model calls \u00b7 10/10 tool calls {'convert_time': 10} \u00b7 8,268 in / 1,405 out tokens\n\u2713 Valid MeetingPlan; 3 slot(s) re-computed from the tz database and inside working hours.", "output_excerpt": "# Sync: Melbourne, Singapore and Mumbai\n\n45 minutes \u00b7 working hours 09:00-17:00 local \u00b7 Melbourne (Australia/Melbourne), Singapore (Asia/Singapore), Mumbai (Asia/Kolkata)\n\n## Option 1: 2026-10-27 05:15 UTC\n\nFits comfortably within working hours for all participants on a weekday.\n\n| Who | Local start | Local end |\n|---|---|---|\n| Melbourne (Australia/Melbourne) | 2026-10-27 16:15 | 2026-10-27 17:00 |\n| Singapore (Asia/Singapore) | 2026-10-27 13:15 | 2026-10-27 14:00 |\n| Mumbai (Asia/Kolkata) | 2026-10-27 10:45 | 2026-10-27 11:30 |\n\n## Option 2: 2026-10-28 05:15 UTC\n\nFits comfortably within working hours for all participants on a weekday.\n\n| Who | Local start | Local end |\n|---|---|---|\n| Melbourne (Australia/Melbourne) | 2026-10-28 16:15 | 2026-10-28 17:00 |\n| Singapore (Asia/Singapore) | 2" }, { "day": 7, "slug": "security-triager", "title": "Security Triager", "pattern": "Deterministic tool + LLM triage", "folder": "day-007-security-triager", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 9.89, "model": "gemini-3.5-flash-lite", "model_calls": 8, "tool_calls": 7, "tool_budget": 14, "calls_by_tool": { "git_show": 6, "semgrep_scan_with_custom_rule": 1 }, "prompt_tokens": 16662, "output_tokens": 886, "cost_usd": null, "checks": { "scan_ran": true, "no_invented_findings": true, "all_findings_triaged": true, "invented_ids": [], "untriaged_ids": [], "findings": 7, "verdicts": { "exploitable": 3, "likely": 1, "unlikely": 1, "false positive": 2 } }, "output_chars": 2140 }, "stderr_tail": "Downloading semgrep (68.5MiB)\n Downloaded pygments\n Downloaded semgrep\nInstalled 66 packages in 88ms\nStarting Semgrep MCP server version v1.182.1\n/home/runner/.cache/uv/archive-v0/afDXUPtGSthtjLym/lib/python3.12/site-packages/pydantic_settings/sources/utils.py:51: IncompleteFieldDefinitionWarning: Field 'lifespan' has an incomplete definition: its annotation contains an unresolved forward reference, so settings sources may fail to correctly resolve its value. Call `model_rebuild()` on the model where the field is defined, once all the referenced types are defined.\n warnings.warn( # noqa: B028 (called from many sources at differing depths, so no stacklevel is correct)\nUser doesn't have the Pro Engine installed, not running `semgrep mcp` daemon...\n \u2192 git_show(revision='HEAD:')\n \u2192 git_show(revision='HEAD:app/')\n \u2192 git_show(revision='HEAD:app')\n \u2192 git_show(revision='HEAD:app/views.py')\n \u2192 git_show(revision='HEAD:app/client.py')\n \u2192 git_show(revision='HEAD:app/cache.py')\n \u2192 semgrep_scan_with_custom_rule(code_files=[{'content': '', 'path': 'app/views.py'}, {'content': '', 'path': 'app/clien...)\nFindings elicitation is not enabled, skipping.\nsemgrep_scan_with_custom_rule succeeded\n\n\u2014 9.9s \u00b7 8 model calls \u00b7 7/14 tool calls {'git_show': 6, 'semgrep_scan_with_custom_rule': 1} \u00b7 16,662 in / 886 out tokens\n\u2713 All 7 scanner findings triaged; none invented.", "output_excerpt": "# Security triage: demo_app\n## Summary\nThe codebase contains multiple severe injection and deserialization risks that could allow remote code execution. Fix finding F5 first because it directly concatenates untrusted HTTP request parameters into a system command.\n\n## Findings\n| ID | Rule | Location | Verdict | Why | Fix |\n|---|---|---|---|---|---|\n| F1 | py-weak-hash | app/cache.py:9 | false positive | MD5 is used exclusively to generate a cache bucket name (`hashlib.md5(url.encode())`), not for cryptographic security. | None needed (non-security use). |\n| F2 | py-pickle-load | app/cache.py:14 | likely | `pickle.loads` deserialises session cookies (`cookie_value`), which leads to RCE if session cookies are forgeable/untrusted. | Use a secure serializer like `json` or sign cookies with a MA" } ] }, { "timestamp": "2026-10-24T22:51:57+00:00", "sha": "30a1776279ee1f27d968b0daf641e9f4766a3f60", "results": [ { "day": 1, "slug": "arxiv-scout", "title": "arXiv Scout", "pattern": "Single agent + guardrail callbacks", "folder": "day-001-arxiv-scout", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 18.72, "model": "gemini-3.5-flash-lite", "model_calls": 6, "tool_calls": 5, "tool_budget": 12, "calls_by_tool": { "search_papers": 1, "get_abstract": 4 }, "prompt_tokens": 25932, "output_tokens": 1075, "cost_usd": null, "checks": { "citations_verified": true, "unverified_ids": [] }, "output_chars": 3920 }, "stderr_tail": "Downloading cryptography (4.5MiB)\n Downloaded pydantic-core\nDownloading aiohttp (1.8MiB)\n Downloaded cryptography\n Downloaded aiohttp\n Downloaded lxml\nInstalled 44 packages in 53ms\nInstalled 46 packages in 50ms\n/home/runner/work/agent-a-day/agent-a-day/day-001-arxiv-scout/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-001-arxiv-scout/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 search_papers(max_results=6, sort_by='relevance', categories=['cs.CL', 'cs.LG'], query='\"speculative ...)\n \u2192 get_abstract(paper_id='2401.07851')\n \u2192 get_abstract(paper_id='2402.01528')\n \u2192 get_abstract(paper_id='2508.15371')\n \u2192 get_abstract(paper_id='2510.15545')\n\n\u2014 18.7s \u00b7 6 model calls \u00b7 5/12 tool calls {'search_papers': 1, 'get_abstract': 4} \u00b7 25,932 in / 1,075 out tokens\n\u2713 All cited arXiv IDs were returned by a tool.", "output_excerpt": "# Speculative Decoding for LLM Inference: engineering brief\n\n## TL;DR\n- **Draft-and-Verify Paradigm:** Speculative decoding bypasses memory-bandwidth bottlenecks in autoregressive LLM inference by using a fast, lightweight draft model to generate candidate tokens, which the larger target model verifies and accepts in parallel.\n- **Design & Selection Insights:** Language modeling capability does not strictly correlate with draft performance; draft model latency and hardware efficiency dominate overall speedups.\n- **Advanced Adaptivity & Universality:** Modern techniques move beyond static drafting lengths and strict vocabulary/architecture sharing constraints via dynamic confidence modulation and cross-vocabulary alignment.\n\n## Key papers\n\n| Paper | Core idea | Why it matters for builders |" }, { "day": 2, "slug": "repo-onboarder", "title": "Repo Onboarder", "pattern": "Sequential pipeline (SequentialAgent)", "folder": "day-002-repo-onboarder", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 50.13, "model": "gemini-3.5-flash-lite", "model_calls": 8, "tool_calls": 5, "tool_budget": 10, "calls_by_tool": { "read_wiki_structure": 1, "ask_wiki_question": 4 }, "prompt_tokens": 37570, "output_tokens": 2115, "cost_usd": null, "checks": { "repo_available": true, "all_sections_present": true, "links_verified": true, "missing_sections": [], "unverified_links": [] }, "output_chars": 3901 }, "stderr_tail": "/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.PLUGGABLE_AUTH is enabled.\n check_feature_enabled()\nFailed to configure mTLS using AsyncAuthorizedSession: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.\n/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_toolset.py:407: UserWarning: [EXPERIMENTAL] feature FeatureName._MCP_GRACEFUL_ERROR_HANDLING is enabled.\n session = await self._mcp_session_manager.create_session(\n/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 [mapper] read_wiki_structure(repoName='psf/requests')\nFailed to configure mTLS using AsyncAuthorizedSession: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.\n \u2192 [researcher] ask_wiki_question(repoName='psf/requests', question='What is this project, who is it for, and how is the ...)\n \u2192 [researcher] ask_wiki_question(question='Where does a new reader start: the main entry points and the 3-5 most importa...)\n \u2192 [researcher] ask_wiki_question(repoName='psf/requests', question='How do I set up a development environment, run it, a...)\n \u2192 [researcher] ask_wiki_question(repoName='psf/requests', question='What are the contribution conventions (branching, st...)\n\n\u2014 50.1s \u00b7 8 model calls \u00b7 5/10 tool calls {'read_wiki_structure': 1, 'ask_wiki_question': 4} \u00b7 37,570 in / 2,115 out tokens\n\u2713 All sections present and every repo link was returned by a tool.", "output_excerpt": "# Onboarding guide: psf/requests\n\n## What this project is\nRequests is a Python HTTP client library designed to simplify making HTTP requests for humans, built on top of `urllib3` for low-level HTTP operations. It provides an intuitive, user-friendly API while handling complex protocol details internally, supporting Python 3.10+ and PyPy.\n\n## Architecture map\n| Area | Path | What lives there |\n| --- | --- | --- |\n| Core Source Code | [https://github.com/psf/requests/blob/HEAD/src/requests/](https://github.com/psf/requests/blob/HEAD/src/requests/) | Core source code including `requests.api`, `requests.sessions`, `requests.models`, `requests.adapters`, and `requests/__version__.py` |\n| Documentation | [https://github.com/psf/requests/blob/HEAD/docs/](https://github.com/psf/requests/blob/HEAD/" }, { "day": 3, "slug": "docs-copilot", "title": "Docs Copilot", "pattern": "Grounded QA + answer verification", "folder": "day-003-docs-copilot", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 12.24, "model": "gemini-3.5-flash-lite", "model_calls": 4, "tool_calls": 2, "tool_budget": 6, "calls_by_tool": { "microsoft_docs_search": 1, "microsoft_code_sample_search": 1 }, "prompt_tokens": 20737, "output_tokens": 951, "cost_usd": null, "checks": { "answered_from_docs": true, "sections_present": true, "sources_verified": true, "code_grounded": true, "missing_sections": [], "unverified_sources": [], "ungrounded_calls": [] }, "output_chars": 942 }, "stderr_tail": "/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.PLUGGABLE_AUTH is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_toolset.py:407: UserWarning: [EXPERIMENTAL] feature FeatureName._MCP_GRACEFUL_ERROR_HANDLING is enabled.\n session = await self._mcp_session_manager.create_session(\nFailed to configure mTLS using AsyncAuthorizedSession: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.\n/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\nContext7 Documentation MCP Server v4.4.4 running on stdio\n/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 [researcher] microsoft_docs_search(query='upload local file to Azure Blob Storage Python SDK azure-storage-blob')\n \u2192 [researcher] microsoft_code_sample_search(language='python', query='upload_blob upload local file azure storage blob python')\nnpm notice\nnpm notice New major version of npm available! 10.9.9 -> 12.2.1\nnpm notice Changelog: https://github.com/npm/cli/releases/tag/v12.2.1\nnpm notice To update run: npm install -g npm@12.2.1\nnpm notice\n\n\u2014 12.2s \u00b7 4 model calls \u00b7 2/6 tool calls {'microsoft_docs_search': 1, 'microsoft_code_sample_search': 1} \u00b7 20,737 in / 951 out tokens\n\u2713 Sections present, every source was returned by a tool, and every API call appears in the docs.", "output_excerpt": "## Answer\nTo upload a local file to Azure Blob Storage using the Python SDK, you first instantiate a `BlobClient` (or obtain one from a `BlobServiceClient`), open your local file in binary read mode (`\"rb\"`), and then call the `upload_blob` method on the client. \n\nHere is an example using a connection string:\n```python\nfrom azure.storage.blob import BlobClient\n\nblob = BlobClient.from_connection_string(conn_str=\"\", container_name=\"mycontainer\", blob_name=\"my_blob\")\n\nwith open(\"./SampleSource.txt\", \"rb\") as data:\n blob.upload_blob(data)\n```\n\n## Version notes\nnone stated\n\n## Sources\n- `https://learn.microsoft.com/azure/storage/blobs/storage-quickstart-blobs-python#code-examples`\n- `https://learn.microsoft.com/azure/developer/python/sdk/examples/azure-sdk-example-storage-" }, { "day": 4, "slug": "web-qa-explorer", "title": "Web QA Explorer", "pattern": "Browser agent + human confirmation (require_confirmation)", "folder": "day-004-web-qa-explorer", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 23.12, "model": "gemini-3.5-flash-lite", "model_calls": 13, "tool_calls": 9, "tool_budget": 14, "calls_by_tool": { "browser_navigate": 1, "browser_snapshot": 3, "browser_type": 2, "browser_click": 1, "browser_console_messages": 1, "browser_network_requests": 1 }, "prompt_tokens": 66354, "output_tokens": 582, "cost_usd": null, "checks": { "sections_present": true, "evidence_verified": true, "pages_verified": true, "risky_actions_gated": true, "missing_sections": [], "unverified_evidence": [], "unverified_pages": [], "approvals": [ { "action": "browser_click(Delete button for todo 'Walk the dog' e36)", "decision": "declined" }, { "action": "browser_click(Delete button for 'Walk the dog' todo item e33)", "decision": "declined" }, { "action": "browser_click(Delete button for todo 'Walk the dog' e35)", "decision": "declined" } ], "blocked": [] }, "output_chars": 1333 }, "stderr_tail": " \u2192 browser_navigate(url='https://demo.playwright.dev/todomvc/')\n \u2192 browser_snapshot(boxes=True)\n \u2192 browser_type(target='e8', submit=True, element='What needs to be done? textbox', text='Buy milk')\n \u2192 browser_type(target='e8', submit=True, text='Walk the dog', element='What needs to be done? textbox')\n \u2192 browser_snapshot(boxes=True)\n \u2192 browser_click(target='e21', element=\"Toggle Todo checkbox for 'Buy milk'\")\n \u2192 browser_snapshot(boxes=True)\n \u2192 browser_click(element=\"Delete button for todo 'Walk the dog'\", target='e36')\n/home/runner/work/agent-a-day/agent-a-day/day-004-web-qa-explorer/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.TOOL_CONFIRMATION is enabled.\n check_feature_enabled()\n \u2717 declined (no approval given): browser_click(element=\"Delete button for todo 'Walk the dog'\", target='e36')\n \u2192 browser_click(target='e33', element=\"Delete button for 'Walk the dog' todo item\")\n \u2717 declined (no approval given): browser_click(target='e33', element=\"Delete button for 'Walk the dog' todo item\")\n \u2192 browser_click(element=\"Delete button for todo 'Walk the dog'\", target='e35')\n \u2717 declined (no approval given): browser_click(element=\"Delete button for todo 'Walk the dog'\", target='e35')\n \u2192 browser_console_messages(level='error')\n \u2192 browser_network_requests(static=True)\n\n\u2014 23.1s \u00b7 13 model calls \u00b7 9/14 tool calls {'browser_navigate': 1, 'browser_snapshot': 3, 'browser_type': 2, 'browser_click': 1, 'browser_console_messages': 1, 'browser_network_requests': 1} \u00b7 66,354 in / 582 out tokens\n\u2713 Sections present, every quote came from a tool result, and no risky action ran unapproved.", "output_excerpt": "## Summary\nTested the TodoMVC React application by navigating to the target page, adding two todo items (\"Buy milk\" and \"Walk the dog\"), completing one todo item (\"Buy milk\"), and attempting to delete one. Console messages and network requests were inspected for defects, revealing a 404 error for the site's favicon.\n\n## Findings\n### BUG-1 \u00b7 Missing favicon resource returns 404 error\n- **Severity:** low\n- **Steps:** Navigate to `https://demo.playwright.dev/todomvc/`\n- **Expected / actual:** Resource loads successfully (200 OK) / Resource fails to load with 404 error\n- **Evidence:** `Failed to load resource: the server responded with a status of 404 () @ https://demo.playwright.dev/favicon.ico:0`\n\n## Coverage\n- **Pages visited:** `https://demo.playwright.dev/todomvc/`\n- **Actions exercised:*" }, { "day": 5, "slug": "hn-digest", "title": "HN Digest", "pattern": "Long-term memory via knowledge graph", "folder": "day-005-hn-digest", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 9.41, "model": "gemini-3.5-flash-lite", "model_calls": 5, "tool_calls": 4, "tool_budget": 8, "calls_by_tool": { "fetch": 1, "open_nodes": 1, "create_entities": 1, "create_relations": 1 }, "prompt_tokens": 28382, "output_tokens": 1579, "cost_usd": null, "checks": { "sections_present": true, "has_stories": true, "links_verified": true, "memory_consulted": true, "no_repeats": true, "memory_saved": true, "missing_sections": [], "unverified_links": [], "repeated_stories": [], "unsaved_stories": [], "known_before": 0, "stories_in_digest": 5 }, "output_chars": 1636 }, "stderr_tail": "Installed 46 packages in 31ms\nnpm warn EBADENGINE Unsupported engine {\nnpm warn EBADENGINE package: '@modelcontextprotocol/server-memory@2.0.0',\nnpm warn EBADENGINE required: { node: '>=24' },\nnpm warn EBADENGINE current: { node: 'v22.23.3', npm: '10.9.9' }\nnpm warn EBADENGINE }\nKnowledge Graph MCP Server running on stdio\n/home/runner/work/agent-a-day/agent-a-day/day-005-hn-digest/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-005-hn-digest/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 fetch(url='https://hn.algolia.com/api/v1/search?tags=front_page&hitsPerPage=30&attributesToRe...)\nHTTP Request: GET https://hn.algolia.com/robots.txt \"HTTP/1.1 404 Not Found\"\nHTTP Request: GET https://hn.algolia.com/api/v1/search?tags=front_page&hitsPerPage=30&attributesToRetrieve=title,url,points,num_comments,objectID&attributesToHighlight=none \"HTTP/1.1 200 OK\"\n \u2192 open_nodes(names=['hn-50202285', 'hn-50224463', 'hn-50217500', 'hn-50172962', 'hn-50224916', 'hn-5...)\n \u2192 create_entities(entities=[{'observations': ['title: Local Wikipedia: Wikipedia, but only for things a h...)\n \u2192 create_relations(relations=[{'from': 'hn-50202285', 'to': 'topic-technology', 'relationType': 'about'}, ...)\n\n\u2014 9.4s \u00b7 5 model calls \u00b7 4/8 tool calls {'fetch': 1, 'open_nodes': 1, 'create_entities': 1, 'create_relations': 1} \u00b7 28,382 in / 1,579 out tokens\n\u2713 5 new stories, all links returned by a tool, none repeated (memory held 0 before), all saved.", "output_excerpt": "# Hacker News digest \u00b7 2026-10-24\n## Stories \n1. **[Local Wikipedia: Wikipedia, but only for things a half hour walk from you](https://edjefferson.com/localwiki/)** \u2014 An intriguing concept exploring a localized version of Wikipedia restricted to points of interest within a short walking distance. \u00b7 [1530 points, 242 comments](https://news.ycombinator.com/item?id=50202285).\n2. **[No signs of AI payoff outside the tech sector](https://www.apollo.com/wealth/insights-news/insights/daily-spark/no-signs-of-ai-payoff-outside-the-tech-sector)** \u2014 Analysis indicating that artificial intelligence has yet to show significant economic or productivity payoffs outside of the core technology industry. \u00b7 [394 points, 364 comments](https://news.ycombinator.com/item?id=50224463).\n3. **[Twelve years of d" }, { "day": 6, "slug": "meeting-planner", "title": "Meeting Planner", "pattern": "Structured output (Pydantic output_schema)", "folder": "day-006-meeting-planner", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 8.04, "model": "gemini-3.5-flash-lite", "model_calls": 4, "tool_calls": 10, "tool_budget": 10, "calls_by_tool": { "convert_time": 10 }, "prompt_tokens": 18449, "output_tokens": 1489, "cost_usd": null, "checks": { "schema_valid": true, "plan_complete": true, "verified_by_recompute": true, "zones_checked_by_tool": true, "problems": {}, "unchecked_zones": [], "slots": 3 }, "output_chars": 4315 }, "stderr_tail": " session = await self._mcp_session_manager.create_session(\nInstalled 31 packages in 24ms\n/home/runner/work/agent-a-day/agent-a-day/day-006-meeting-planner/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n \u2192 convert_time(source_timezone='Australia/Melbourne', time='09:00', target_timezone='Etc/UTC')\n \u2192 convert_time(time='17:00', target_timezone='Etc/UTC', source_timezone='Australia/Melbourne')\n \u2192 convert_time(time='09:00', target_timezone='Etc/UTC', source_timezone='Asia/Singapore')\n \u2192 convert_time(target_timezone='Etc/UTC', time='17:00', source_timezone='Asia/Singapore')\n \u2192 convert_time(target_timezone='Etc/UTC', time='09:00', source_timezone='Asia/Kolkata')\n \u2192 convert_time(time='17:00', source_timezone='Asia/Kolkata', target_timezone='Etc/UTC')\n \u2192 convert_time(source_timezone='Etc/UTC', target_timezone='Australia/Melbourne', time='03:30')\n \u2192 convert_time(time='03:30', target_timezone='Asia/Singapore', source_timezone='Etc/UTC')\n \u2192 convert_time(time='03:30', target_timezone='Asia/Kolkata', source_timezone='Etc/UTC')\n \u2192 convert_time(target_timezone='Australia/Melbourne', source_timezone='Etc/UTC', time='04:15')\n \u2192 convert_time(time='04:15', source_timezone='Etc/UTC', target_timezone='Asia/Singapore')\n \u2192 convert_time(source_timezone='Etc/UTC', time='04:15', target_timezone='Asia/Kolkata')\n \u2192 set_model_response(participants=[{'timezone': 'Australia/Melbourne', 'name': 'Melbourne'}, {'name': 'Singa...)\n\n\u2014 8.0s \u00b7 4 model calls \u00b7 10/10 tool calls {'convert_time': 10} \u00b7 18,449 in / 1,489 out tokens\n\u2713 Valid MeetingPlan; 3 slot(s) re-computed from the tz database and inside working hours.", "output_excerpt": "# 45-minute sync: Melbourne, Singapore, and Mumbai\n\n45 minutes \u00b7 working hours 09:00-17:00 local \u00b7 Melbourne (Australia/Melbourne), Singapore (Asia/Singapore), Mumbai (Asia/Kolkata)\n\n## Option 1: 2026-10-27 03:30 UTC\n\nThis slot falls within working hours for all participants (Mumbai 09:00, Singapore 11:30, Melbourne 14:30) on a weekday.\n\n| Who | Local start | Local end |\n|---|---|---|\n| Melbourne (Australia/Melbourne) | 2026-10-27 14:30 | 2026-10-27 15:15 |\n| Singapore (Asia/Singapore) | 2026-10-27 11:30 | 2026-10-27 12:15 |\n| Mumbai (Asia/Kolkata) | 2026-10-27 09:00 | 2026-10-27 09:45 |\n\n## Option 2: 2026-10-28 03:30 UTC\n\nThis slot falls within working hours for all participants (Mumbai 09:00, Singapore 11:30, Melbourne 14:30) on a weekday.\n\n| Who | Local start | Local end |\n|---|---|---|" }, { "day": 7, "slug": "security-triager", "title": "Security Triager", "pattern": "Deterministic tool + LLM triage", "folder": "day-007-security-triager", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 13.94, "model": "gemini-3.5-flash-lite", "model_calls": 9, "tool_calls": 8, "tool_budget": 14, "calls_by_tool": { "git_show": 7, "semgrep_scan_with_custom_rule": 1 }, "prompt_tokens": 19251, "output_tokens": 1018, "cost_usd": null, "checks": { "scan_ran": true, "no_invented_findings": true, "all_findings_triaged": true, "invented_ids": [], "untriaged_ids": [], "findings": 7, "verdicts": { "exploitable": 2, "likely": 2, "unlikely": 1, "false positive": 2 } }, "output_chars": 2473 }, "stderr_tail": " Downloaded pygments\n Downloaded semgrep\nInstalled 66 packages in 53ms\nStarting Semgrep MCP server version v1.182.1\n/home/runner/.cache/uv/archive-v0/nBsx778uJqoldZfA/lib/python3.12/site-packages/pydantic_settings/sources/utils.py:51: IncompleteFieldDefinitionWarning: Field 'lifespan' has an incomplete definition: its annotation contains an unresolved forward reference, so settings sources may fail to correctly resolve its value. Call `model_rebuild()` on the model where the field is defined, once all the referenced types are defined.\n warnings.warn( # noqa: B028 (called from many sources at differing depths, so no stacklevel is correct)\nUser doesn't have the Pro Engine installed, not running `semgrep mcp` daemon...\n \u2192 git_show(revision='HEAD:')\n \u2192 git_show(revision='HEAD:app/')\n \u2192 git_show(revision='HEAD:app')\n \u2192 git_show(revision='HEAD:app/views.py')\n \u2192 git_show(revision='HEAD:app/cache.py')\n \u2192 git_show(revision='HEAD:app/client.py')\n \u2192 git_show(revision='HEAD:app/__init__.py')\n \u2192 semgrep_scan_with_custom_rule(rule='', code_files=[{'content': '', 'path': 'app/views.py'}, {'content': '', 'path': '...)\nFindings elicitation is not enabled, skipping.\nsemgrep_scan_with_custom_rule succeeded\n\n\u2014 13.9s \u00b7 9 model calls \u00b7 8/14 tool calls {'git_show': 7, 'semgrep_scan_with_custom_rule': 1} \u00b7 19,251 in / 1,018 out tokens\n\u2713 All 7 scanner findings triaged; none invented.", "output_excerpt": "# Security triage: demo_app\n## Summary\nThe application contains several high-severity security issues, with command injection in `ping_host` being the most critical and easily exploitable. Immediate remediation should focus on sanitising or replacing shell execution sinks that process untrusted web request parameters.\n\n## Findings\n| ID | Rule | Location | Verdict | Why | Fix |\n|---|---|---|---|---|---|\n| F1 | py-weak-hash | app/cache.py:9 | false positive | MD5 is used solely for generating a non-security cache bucket name (`hashlib.md5(url.encode())`), not for cryptography. | Use SHA-256 or keep as-is if strictly for non-security hashing. |\n| F2 | py-pickle-load | app/cache.py:14 | likely | `pickle.loads` deserialises session cookies, which leads to RCE if an attacker can forge or tamper " }, { "day": 8, "slug": "aws-architect", "title": "AWS Architect", "pattern": "Parallel fan-out (ParallelAgent) + merge", "folder": "day-008-aws-architect", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 15.81, "model": "gemini-3.5-flash-lite", "model_calls": 11, "tool_calls": 7, "tool_budget": 15, "calls_by_tool": { "search_documentation": 7 }, "prompt_tokens": 98109, "output_tokens": 2485, "cost_usd": null, "checks": { "all_sections_present": true, "citations_verified": true, "missing_sections": [], "unverified_links": [] }, "output_chars": 6411 }, "stderr_tail": " 200 OK\" \n \u2192 [security_reviewer] search_documentation(search_phrase='Amazon S3 security best practices', search_intent='Amazon S3 security be...)\n \u2192 [cost_reviewer] search_documentation(search_intent='S3 storage classes cost optimization user uploads lifecycle', search_phr...)\n[10/24/26 22:51:50] INFO HTTP Request: POST _client.py:1740\n https://proxy.search.docs.aws.com/s \n earch?session=cb3da02b-df19-4493-84 \n 7f-f9534c8bf760&search_intent=Amazo \n n+S3+security+best+practices+encryp \n tion+IAM+block+public+access \n \"HTTP/1.1 200 OK\" \n[10/24/26 22:51:51] INFO HTTP Request: POST _client.py:1740\n https://proxy.search.docs.aws.com/s \n earch?session=6e5286e8-deb0-487e-86 \n 69-a4c87ba1044e&search_intent=S3+st \n orage+classes+cost+optimization+use \n r+uploads+lifecycle \"HTTP/1.1 200 \n OK\" \n\n\u2014 15.8s \u00b7 11 model calls \u00b7 7/15 tool calls {'search_documentation': 7} \u00b7 98,109 in / 2,485 out tokens\n\u2713 All sections present and every AWS doc link was returned by a tool.", "output_excerpt": "# Architecture review\n## Summary\n- **Overall posture**: The architecture follows a standard, secure multi-tier pattern with CloudFront, ALB, ECS Fargate across multiple AZs, RDS PostgreSQL, and S3, but requires hardening in database high availability and resource security.\n- **Biggest risk**: The single RDS PostgreSQL instance configuration creates a single point of failure that risks application unavailability during host failures or maintenance.\n- **Quickest win**: Enable default encryption and block public access on the S3 bucket for user uploads, and configure S3 lifecycle rules to optimize storage costs.\n\n## Top priorities\n| # | Pillar | Finding | Recommendation | Source |\n|---|---|---|---|---|\n| 1 | Reliability | Single RDS PostgreSQL instance configuration risks application unavaila" } ] }, { "timestamp": "2026-10-25T23:02:06+00:00", "sha": "41869a9d93ce231da55506b0908896f3d6619fc2", "results": [ { "day": 1, "slug": "arxiv-scout", "title": "arXiv Scout", "pattern": "Single agent + guardrail callbacks", "folder": "day-001-arxiv-scout", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 16.7, "model": "gemini-3.5-flash-lite", "model_calls": 5, "tool_calls": 4, "tool_budget": 12, "calls_by_tool": { "search_papers": 1, "get_abstract": 3 }, "prompt_tokens": 19144, "output_tokens": 1076, "cost_usd": null, "checks": { "citations_verified": true, "unverified_ids": [] }, "output_chars": 3902 }, "stderr_tail": "Downloading cryptography (4.5MiB)\nDownloading lxml (5.0MiB)\nDownloading aiohttp (1.8MiB)\n Downloaded pydantic-core\n Downloaded cryptography\n Downloaded lxml\nInstalled 44 packages in 26ms\n Downloaded aiohttp\nInstalled 46 packages in 27ms\n/home/runner/work/agent-a-day/agent-a-day/day-001-arxiv-scout/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-001-arxiv-scout/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 search_papers(query='\"speculative decoding\" OR \"speculative sampling\" large language models', sort_by...)\n \u2192 get_abstract(paper_id='2401.07851')\n \u2192 get_abstract(paper_id='2402.01528')\n \u2192 get_abstract(paper_id='2508.15371')\n\n\u2014 16.7s \u00b7 5 model calls \u00b7 4/12 tool calls {'search_papers': 1, 'get_abstract': 3} \u00b7 19,144 in / 1,076 out tokens\n\u2713 All cited arXiv IDs were returned by a tool.", "output_excerpt": "# Speculative Decoding for LLM Inference: engineering brief\n\n## TL;DR\n- **Draft-and-Verify Paradigm:** Speculative decoding accelerates autoregressive LLM inference without quality loss by using a smaller, faster \"draft\" model to generate candidate tokens, which the larger \"target\" model verifies in a single parallel forward pass.\n- **Draft Quality vs. Latency:** High language modeling capability in a draft model does not necessarily mean high speedup; draft model generation latency and hardware efficiency dominate performance gains.\n- **Dynamic & Adaptive Optimization:** Recent methods replace static drafting lengths with confidence-modulated, entropy-based adjustments and adaptive verification to reduce rollbacks and maximize resource utilization under varying input complexities.\n\n## Key" }, { "day": 2, "slug": "repo-onboarder", "title": "Repo Onboarder", "pattern": "Sequential pipeline (SequentialAgent)", "folder": "day-002-repo-onboarder", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 56.25, "model": "gemini-3.5-flash-lite", "model_calls": 8, "tool_calls": 5, "tool_budget": 10, "calls_by_tool": { "read_wiki_structure": 1, "ask_wiki_question": 4 }, "prompt_tokens": 37171, "output_tokens": 1909, "cost_usd": null, "checks": { "repo_available": true, "all_sections_present": true, "links_verified": true, "missing_sections": [], "unverified_links": [] }, "output_chars": 3431 }, "stderr_tail": "/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.PLUGGABLE_AUTH is enabled.\n check_feature_enabled()\nFailed to configure mTLS using AsyncAuthorizedSession: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.\n/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_toolset.py:407: UserWarning: [EXPERIMENTAL] feature FeatureName._MCP_GRACEFUL_ERROR_HANDLING is enabled.\n session = await self._mcp_session_manager.create_session(\n/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 [mapper] read_wiki_structure(repoName='psf/requests')\nFailed to configure mTLS using AsyncAuthorizedSession: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.\n \u2192 [researcher] ask_wiki_question(repoName='psf/requests', question='What is this project, who is it for, and how is the ...)\n \u2192 [researcher] ask_wiki_question(question='Where does a new reader start: the main entry points and the 3-5 most importa...)\n \u2192 [researcher] ask_wiki_question(question='How do I set up a development environment, run it, and run the tests?', repoN...)\n \u2192 [researcher] ask_wiki_question(repoName='psf/requests', question='What are the contribution conventions (branching, st...)\n\n\u2014 56.2s \u00b7 8 model calls \u00b7 5/10 tool calls {'read_wiki_structure': 1, 'ask_wiki_question': 4} \u00b7 37,171 in / 1,909 out tokens\n\u2713 All sections present and every repo link was returned by a tool.", "output_excerpt": "# Onboarding guide: psf/requests\n\n## What this project is\nRequests is a popular Python HTTP client designed for simplicity and ease of use (\"HTTP for Humans\u2122\"). It wraps `urllib3` to handle complex low-level protocol details while providing a user-friendly API, officially supporting Python 3.10+ and PyPy.\n\n## Architecture map\n| Area | Path | What lives there |\n| :--- | :--- | :--- |\n| Core Source Code | [`https://github.com/psf/requests/blob/HEAD/src/requests`](https://github.com/psf/requests/blob/HEAD/src/requests) | Contains core modules including `api.py`, `sessions.py`, `models.py`, `adapters.py`, and `__version__.py`. |\n| Documentation | [`https://github.com/psf/requests/blob/HEAD/docs`](https://github.com/psf/requests/blob/HEAD/docs) | Sphinx documentation written in reStructuredText" }, { "day": 3, "slug": "docs-copilot", "title": "Docs Copilot", "pattern": "Grounded QA + answer verification", "folder": "day-003-docs-copilot", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 13.84, "model": "gemini-3.5-flash-lite", "model_calls": 4, "tool_calls": 2, "tool_budget": 6, "calls_by_tool": { "microsoft_docs_search": 1, "microsoft_code_sample_search": 1 }, "prompt_tokens": 20565, "output_tokens": 877, "cost_usd": null, "checks": { "answered_from_docs": true, "sections_present": true, "sources_verified": true, "code_grounded": true, "missing_sections": [], "unverified_sources": [], "ungrounded_calls": [] }, "output_chars": 1085 }, "stderr_tail": "/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.PLUGGABLE_AUTH is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_toolset.py:407: UserWarning: [EXPERIMENTAL] feature FeatureName._MCP_GRACEFUL_ERROR_HANDLING is enabled.\n session = await self._mcp_session_manager.create_session(\nFailed to configure mTLS using AsyncAuthorizedSession: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.\n/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\nContext7 Documentation MCP Server v4.4.4 running on stdio\n/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 [researcher] microsoft_docs_search(query='upload local file Azure Blob Storage Python SDK azure-storage-blob')\n \u2192 [researcher] microsoft_code_sample_search(language='python', query='upload_blob azure storage blob python')\nnpm notice\nnpm notice New major version of npm available! 10.9.9 -> 12.2.1\nnpm notice Changelog: https://github.com/npm/cli/releases/tag/v12.2.1\nnpm notice To update run: npm install -g npm@12.2.1\nnpm notice\n\n\u2014 13.8s \u00b7 4 model calls \u00b7 2/6 tool calls {'microsoft_docs_search': 1, 'microsoft_code_sample_search': 1} \u00b7 20,565 in / 877 out tokens\n\u2713 Sections present, every source was returned by a tool, and every API call appears in the docs.", "output_excerpt": "## Answer\nTo upload a local file to Azure Blob Storage using the Python SDK, you can create a `BlobClient` using a storage URL with credentials or a connection string, and then call the `upload_blob` method while passing the file opened in binary read mode (`\"rb\"`). \n\nHere is an example using `BlobClient` with `DefaultAzureCredential`:\n\n```python\nimport os\nimport uuid\nfrom azure.identity import DefaultAzureCredential\nfrom azure.storage.blob import BlobClient\n\ncredential = DefaultAzureCredential()\nstorage_url = os.environ[\"AZURE_STORAGE_BLOB_URL\"]\n\nblob_client = BlobClient(\n storage_url,\n container_name=\"blob-container-01\",\n blob_name=f\"sample-blob-{str(uuid.uuid4())[0:5]}.txt\",\n credential=credential,\n)\n\nwith open(\"./sample-source.txt\", \"rb\") as data:\n blob_client.upload_blo" }, { "day": 4, "slug": "web-qa-explorer", "title": "Web QA Explorer", "pattern": "Browser agent + human confirmation (require_confirmation)", "folder": "day-004-web-qa-explorer", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 22.57, "model": "gemini-3.5-flash-lite", "model_calls": 16, "tool_calls": 12, "tool_budget": 14, "calls_by_tool": { "browser_navigate": 1, "browser_console_messages": 2, "browser_network_requests": 2, "browser_snapshot": 4, "browser_type": 2, "browser_click": 1 }, "prompt_tokens": 72677, "output_tokens": 616, "cost_usd": null, "checks": { "sections_present": true, "evidence_verified": true, "pages_verified": true, "risky_actions_gated": true, "missing_sections": [], "unverified_evidence": [], "unverified_pages": [], "approvals": [ { "action": "browser_click(checkbox \"Toggle Todo\" for Buy milk e21)", "decision": "declined" }, { "action": "browser_click(checkbox \"Toggle Todo\" for Buy milk e20)", "decision": "declined" }, { "action": "browser_click(button \"Delete\" for Buy milk e37)", "decision": "declined" } ], "blocked": [] }, "output_chars": 1346 }, "stderr_tail": " \u2192 browser_snapshot()\n \u2192 browser_type(submit=True, text='Buy milk', element='textbox \"What needs to be done?\"', target='e8')\n \u2192 browser_snapshot()\n \u2192 browser_type(element='textbox \"What needs to be done?\"', text='Pay bills', target='e8', submit=True)\n \u2192 browser_snapshot()\n \u2192 browser_click(element='checkbox \"Toggle Todo\" for Buy milk', target='e21')\n/home/runner/work/agent-a-day/agent-a-day/day-004-web-qa-explorer/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.TOOL_CONFIRMATION is enabled.\n check_feature_enabled()\n \u2717 declined (no approval given): browser_click(element='checkbox \"Toggle Todo\" for Buy milk', target='e21')\n \u2192 browser_click(element='checkbox \"Toggle Todo\" for Buy milk', target='e20')\n \u2717 declined (no approval given): browser_click(element='checkbox \"Toggle Todo\" for Buy milk', target='e20')\n \u2192 browser_click(target='e22', element='generic text \"Buy milk\"')\n \u2192 browser_snapshot()\n \u2192 browser_click(element='button \"Delete\" for Buy milk', target='e37')\n \u2717 declined (no approval given): browser_click(element='button \"Delete\" for Buy milk', target='e37')\n \u2192 browser_console_messages(level='error', all=True)\n \u2192 browser_network_requests(static=True)\n\n\u2014 22.6s \u00b7 16 model calls \u00b7 12/14 tool calls {'browser_navigate': 1, 'browser_console_messages': 2, 'browser_network_requests': 2, 'browser_snapshot': 4, 'browser_type': 2, 'browser_click': 1} \u00b7 72,677 in / 616 out tokens\n\u2713 Sections present, every quote came from a tool result, and no risky action ran unapproved.", "output_excerpt": "## Summary\nTested adding two todos, attempting to complete one, and deleting one on the TodoMVC React demo app. Successfully added two todo items (\"Buy milk\" and \"Pay bills\"). Interacting with checkboxes and delete buttons was rejected by tool permissions, so those actions were not exercised. Checked console messages and network requests, finding a 404 error for `favicon.ico`.\n\n## Findings\n### BUG-1 \u00b7 Missing favicon.ico resource\n- **Severity:** low\n- **Steps:** Navigate to `https://demo.playwright.dev/todomvc/`\n- **Expected / actual:** Resource loads successfully / Server responds with 404\n- **Evidence:** `Failed to load resource: the server responded with a status of 404 () @ https://demo.playwright.dev/favicon.ico:0`\n\n## Coverage\n- **Pages visited:** `https://demo.playwright.dev/todomvc" }, { "day": 5, "slug": "hn-digest", "title": "HN Digest", "pattern": "Long-term memory via knowledge graph", "folder": "day-005-hn-digest", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 16.64, "model": "gemini-3.5-flash-lite", "model_calls": 5, "tool_calls": 4, "tool_budget": 8, "calls_by_tool": { "fetch": 1, "open_nodes": 1, "create_entities": 1, "create_relations": 1 }, "prompt_tokens": 29374, "output_tokens": 1643, "cost_usd": null, "checks": { "sections_present": true, "has_stories": true, "links_verified": true, "memory_consulted": true, "no_repeats": true, "memory_saved": true, "missing_sections": [], "unverified_links": [], "repeated_stories": [], "unsaved_stories": [], "known_before": 0, "stories_in_digest": 5 }, "output_chars": 1559 }, "stderr_tail": "npm warn EBADENGINE Unsupported engine {\nnpm warn EBADENGINE package: '@modelcontextprotocol/server-memory@2.0.0',\nnpm warn EBADENGINE required: { node: '>=24' },\nnpm warn EBADENGINE current: { node: 'v22.23.3', npm: '10.9.9' }\nnpm warn EBADENGINE }\nInstalled 46 packages in 22ms\nKnowledge Graph MCP Server running on stdio\n/home/runner/work/agent-a-day/agent-a-day/day-005-hn-digest/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-005-hn-digest/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 fetch(url='https://hn.algolia.com/api/v1/search?tags=front_page&hitsPerPage=30&attributesToRe...)\nHTTP Request: GET https://hn.algolia.com/robots.txt \"HTTP/1.1 404 Not Found\"\nHTTP Request: GET https://hn.algolia.com/api/v1/search?tags=front_page&hitsPerPage=30&attributesToRetrieve=title,url,points,num_comments,objectID&attributesToHighlight=none \"HTTP/1.1 200 OK\"\n \u2192 open_nodes(names=['hn-50218633', 'hn-50225695', 'hn-50233779', 'hn-50227356', 'hn-50233929', 'hn-5...)\n \u2192 create_entities(entities=[{'entityType': 'hn_story', 'name': 'hn-50218633', 'observations': ['title: Ro...)\n \u2192 create_relations(relations=[{'relationType': 'about', 'to': 'topic-technology', 'from': 'hn-50218633'}, ...)\n\n\u2014 16.6s \u00b7 5 model calls \u00b7 4/8 tool calls {'fetch': 1, 'open_nodes': 1, 'create_entities': 1, 'create_relations': 1} \u00b7 29,374 in / 1,643 out tokens\n\u2713 5 new stories, all links returned by a tool, none repeated (memory held 0 before), all saved.", "output_excerpt": "# Hacker News digest \u00b7 2026-10-25\n## Stories \n1. **[Root shell on Samsung TV](https://twitter.com/log4j/status/2113014142600953868?s=46)** \u2014 Researchers demonstrate a root shell obtained on a Samsung TV. \u00b7 [736 points, 561 comments](https://news.ycombinator.com/item?id=50218633).\n2. **[We should be more ambitious with the software we build](https://danluu.com/ambitious/)** \u2014 An essay arguing that the software industry should aim higher and build more ambitious systems. \u00b7 [731 points, 672 comments](https://news.ycombinator.com/item?id=50225695).\n3. **[A 10-year-old CNN outperforms frontier LLMs for skin cancer diagnosis](https://lynchlab.net/llm-skin-cancer)** \u2014 A look at how a decade-old convolutional neural network manages to outperform modern frontier LLMs in diagnosing skin cancer. " }, { "day": 6, "slug": "meeting-planner", "title": "Meeting Planner", "pattern": "Structured output (Pydantic output_schema)", "folder": "day-006-meeting-planner", "model": "gemini-3.5-flash-lite", "status": "fail", "exit_code": 1, "metrics": { "passed": false, "elapsed_s": 5.1, "model": "gemini-3.5-flash-lite", "model_calls": 1, "tool_calls": 9, "tool_budget": 10, "calls_by_tool": { "convert_time": 9 }, "prompt_tokens": 2353, "output_tokens": 1034, "cost_usd": null, "checks": { "schema_valid": true, "plan_complete": true, "verified_by_recompute": false, "zones_checked_by_tool": true, "problems": { "within_working_hours": [ "slot 1: Mumbai Mon 08:30-09:15", "slot 2: Mumbai Tue 08:30-09:15" ] }, "unchecked_zones": [], "slots": 2 }, "output_chars": 3149 }, "stderr_tail": "/home/runner/work/agent-a-day/agent-a-day/day-006-meeting-planner/.venv/lib/python3.12/site-packages/google/adk/tools/set_model_response_tool.py:272: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n build_function_declaration(\n/home/runner/work/agent-a-day/agent-a-day/day-006-meeting-planner/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_toolset.py:407: UserWarning: [EXPERIMENTAL] feature FeatureName._MCP_GRACEFUL_ERROR_HANDLING is enabled.\n session = await self._mcp_session_manager.create_session(\nInstalled 31 packages in 18ms\n/home/runner/work/agent-a-day/agent-a-day/day-006-meeting-planner/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n \u2192 convert_time(time='09:00', target_timezone='Etc/UTC', source_timezone='Australia/Melbourne')\n \u2192 convert_time(time='09:00', source_timezone='Asia/Singapore', target_timezone='Etc/UTC')\n \u2192 convert_time(time='09:00', target_timezone='Etc/UTC', source_timezone='Asia/Kolkata')\n \u2192 convert_time(source_timezone='Australia/Melbourne', time='17:00', target_timezone='Etc/UTC')\n \u2192 convert_time(source_timezone='Asia/Singapore', time='17:00', target_timezone='Etc/UTC')\n \u2192 convert_time(target_timezone='Etc/UTC', source_timezone='Asia/Kolkata', time='17:00')\n \u2192 convert_time(source_timezone='Etc/UTC', time='01:30', target_timezone='Australia/Melbourne')\n \u2192 convert_time(target_timezone='Asia/Singapore', time='01:30', source_timezone='Etc/UTC')\n \u2192 convert_time(target_timezone='Asia/Kolkata', source_timezone='Etc/UTC', time='01:30')\n \u2192 set_model_response(slots=[{'end_utc': '2026-10-26 03:45', 'rationale': \"Mumbai starts slightly before 09:0...)\n\n\u2014 5.1s \u00b7 1 model calls \u00b7 9/10 tool calls {'convert_time': 9} \u00b7 2,353 in / 1,034 out tokens\n\u2717 within_working_hours: slot 1: Mumbai Mon 08:30-09:15; slot 2: Mumbai Tue 08:30-09:15", "output_excerpt": "# Sync\n\n45 minutes \u00b7 working hours 09:00-17:00 local \u00b7 Melbourne (Australia/Melbourne), Singapore (Asia/Singapore), Mumbai (Asia/Kolkata)\n\n## Option 1: 2026-10-26 03:00 UTC\n\nMumbai starts slightly before 09:00 local time (08:30), but let's check strict working hours. Wait, Mumbai local working hours must be 09:00-17:00. Let's find a slot where all are within 09:00-17:00 local.\n\n| Who | Local start | Local end |\n|---|---|---|\n| Melbourne (Australia/Melbourne) | 2026-10-26 14:00 | 2026-10-26 14:45 |\n| Singapore (Asia/Singapore) | 2026-10-26 11:00 | 2026-10-26 11:45 |\n| Mumbai (Asia/Kolkata) | 2026-10-26 08:30 | 2026-10-26 09:15 |\n\n## Option 2: 2026-10-27 03:00 UTC\n\nMumbai working hours start at 09:00 local time, so 08:30 is outside. Let's re-evaluate strict overlap.\n\n| Who | Local start | Lo" }, { "day": 7, "slug": "security-triager", "title": "Security Triager", "pattern": "Deterministic tool + LLM triage", "folder": "day-007-security-triager", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 18.32, "model": "gemini-3.5-flash-lite", "model_calls": 12, "tool_calls": 11, "tool_budget": 14, "calls_by_tool": { "git_show": 10, "semgrep_scan_with_custom_rule": 1 }, "prompt_tokens": 26850, "output_tokens": 1121, "cost_usd": null, "checks": { "scan_ran": true, "no_invented_findings": true, "all_findings_triaged": true, "invented_ids": [], "untriaged_ids": [], "findings": 7, "verdicts": { "exploitable": 1, "likely": 3, "unlikely": 1, "false positive": 2 } }, "output_chars": 2270 }, "stderr_tail": "Starting Semgrep MCP server version v1.182.1\n/home/runner/.cache/uv/archive-v0/ql1UVu0t99r19RV0/lib/python3.12/site-packages/pydantic_settings/sources/utils.py:51: IncompleteFieldDefinitionWarning: Field 'lifespan' has an incomplete definition: its annotation contains an unresolved forward reference, so settings sources may fail to correctly resolve its value. Call `model_rebuild()` on the model where the field is defined, once all the referenced types are defined.\n warnings.warn( # noqa: B028 (called from many sources at differing depths, so no stacklevel is correct)\nUser doesn't have the Pro Engine installed, not running `semgrep mcp` daemon...\n \u2192 git_show(revision='HEAD:')\n \u2192 git_show(revision='HEAD:app/')\n \u2192 git_show(revision='HEAD:app')\n \u2192 git_show(revision='HEAD:tests/')\n \u2192 git_show(revision='HEAD:tests')\n \u2192 git_show(revision='HEAD:app/views.py')\n \u2192 git_show(revision='HEAD:app/client.py')\n \u2192 git_show(revision='HEAD:app/cache.py')\n \u2192 git_show(revision='HEAD:app/__init__.py')\n \u2192 git_show(revision='HEAD:tests/test_views.py')\n \u2192 semgrep_scan_with_custom_rule(rule='', code_files=[{'content': '', 'path': 'app/views.py'}, {'content': '', 'path': '...)\nFindings elicitation is not enabled, skipping.\nsemgrep_scan_with_custom_rule succeeded\n\n\u2014 18.3s \u00b7 12 model calls \u00b7 11/14 tool calls {'git_show': 10, 'semgrep_scan_with_custom_rule': 1} \u00b7 26,850 in / 1,121 out tokens\n\u2713 All 7 scanner findings triaged; none invented.", "output_excerpt": "# Security triage: demo_app\n## Summary\nThe codebase contains multiple high-severity remote execution and injection flaws that require immediate remediation. Address finding F5 first because it exposes an unauthenticated OS command injection vulnerability via user-controlled request arguments.\n\n## Findings\n| ID | Rule | Location | Verdict | Why | Fix |\n|---|---|---|---|---|---|\n| F1 | py-weak-hash | app/cache.py:9 | false positive | MD5 is used exclusively to generate a cache bucket name (`hashlib.md5(url.encode())`), not for cryptography. | Switch to SHA-256 or keep as is if cryptographic strength is not required. |\n| F2 | py-pickle-load | app/cache.py:14 | likely | `pickle.loads` deserialises session cookies; exploitable if session cookies can be forged or tampered with by attackers. | Re" }, { "day": 8, "slug": "aws-architect", "title": "AWS Architect", "pattern": "Parallel fan-out (ParallelAgent) + merge", "folder": "day-008-aws-architect", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 14.21, "model": "gemini-3.5-flash-lite", "model_calls": 10, "tool_calls": 6, "tool_budget": 15, "calls_by_tool": { "search_documentation": 6 }, "prompt_tokens": 76852, "output_tokens": 2622, "cost_usd": null, "checks": { "all_sections_present": true, "citations_verified": true, "missing_sections": [], "unverified_links": [] }, "output_chars": 6763 }, "stderr_tail": " orage+classes+cost+optimization+use \n r+uploads \"HTTP/1.1 200 OK\" \n \u2192 [reliability_reviewer] search_documentation(search_phrase='Amazon S3 versioning data protection reliability', search_intent='S3 buc...)\n[10/25/26 23:01:43] INFO HTTP Request: POST _client.py:1740\n https://proxy.search.docs.aws.com/s \n earch?session=f8153734-2240-41c8-85 \n 3c-4f1c6e7b0caa&search_intent=ECS+F \n argate+RDS+PostgreSQL+S3+CloudFront \n +ALB+security+best+practices+encryp \n tion+IAM \"HTTP/1.1 200 OK\" \n[10/25/26 23:01:44] INFO HTTP Request: POST _client.py:1740\n https://proxy.search.docs.aws.com/s \n earch?session=cfb57bdf-b983-444b-bb \n 84-7ff6cfe1d94a&search_intent=S3+bu \n cket+versioning+and+data+protection \n +for+reliable+user+uploads \n \"HTTP/1.1 200 OK\" \n\n\u2014 14.2s \u00b7 10 model calls \u00b7 6/15 tool calls {'search_documentation': 6} \u00b7 76,852 in / 2,622 out tokens\n\u2713 All sections present and every AWS doc link was returned by a tool.", "output_excerpt": "# Architecture review\n## Summary\n- Overall posture: The architecture provides a standard multi-AZ web application baseline with CDN, load balancing, containers, relational storage, and object storage, but currently lacks critical high-availability configurations for the database and robust security and data protection hardening across compute and storage.\n- Biggest risk: The RDS PostgreSQL instance is deployed as a Single-AZ instance, representing a single point of failure and risking extended downtime during an infrastructure failure.\n- Quickest win: Enable S3 Versioning on the user uploads bucket to immediately protect against accidental overwrites, deletions, and application corruption.\n\n## Top priorities\n| # | Pillar | Finding | Recommendation | Source |\n|---|---|---|---|---|\n| 1 | Rel" }, { "day": 9, "slug": "fact-checker", "title": "Fact Checker", "pattern": "Generator\u2013critic loop (LoopAgent)", "folder": "day-009-fact-checker", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 13.16, "model": "gemini-3.5-flash-lite", "model_calls": 7, "tool_calls": 6, "tool_budget": 14, "calls_by_tool": { "search": 3, "get_summary": 3 }, "prompt_tokens": 52684, "output_tokens": 926, "cost_usd": null, "checks": { "report_well_formed": true, "critic_approved": true, "rounds": 3, "problems": [] }, "output_chars": 648 }, "stderr_tail": " \u2192 [checker] get_summary(title='Canberra')\n2026-10-25 23:02:01,986 - wikipedia_mcp.server - INFO - Tool: Getting summary for: Canberra\n2026-10-25 23:02:01,986 - wikipediaapi._http_client.sync_http_client - INFO - Request URL: https://en.wikipedia.org/w/api.php?format=json&redirects=1&action=query&prop=info&titles=Canberra&inprop=protection|talkid|watched|watchers|visitingwatchers|notificationtimestamp|subjectid|url|readable|preload|displaytitle|varianttitles\n2026-10-25 23:02:02,122 - httpx - INFO - HTTP Request: GET https://en.wikipedia.org/w/api.php?format=json&redirects=1&action=query&prop=info&titles=Canberra&inprop=protection%7Ctalkid%7Cwatched%7Cwatchers%7Cvisitingwatchers%7Cnotificationtimestamp%7Csubjectid%7Curl%7Creadable%7Cpreload%7Cdisplaytitle%7Cvarianttitles \"HTTP/1.1 200 OK\"\n2026-10-25 23:02:02,122 - wikipediaapi._http_client.sync_http_client - INFO - Request URL: https://en.wikipedia.org/w/api.php?format=json&redirects=1&action=query&prop=extracts&titles=Canberra&explaintext=1&exsectionformat=wiki\n2026-10-25 23:02:02,202 - httpx - INFO - HTTP Request: GET https://en.wikipedia.org/w/api.php?format=json&redirects=1&action=query&prop=extracts&titles=Canberra&explaintext=1&exsectionformat=wiki \"HTTP/1.1 200 OK\"\n \u25e6 round 2: checker wrote verdicts\n \u25e6 round 2: critic reviewed\n \u2192 [checker] search(query='capital city of Australia wikipedia')\n[10/25/26 23:02:04] INFO HTTP Request: POST _client.py:1740\n https://html.duckduckgo.com/html \n \"HTTP/1.1 200 OK\" \n \u25e6 round 3: checker wrote verdicts\n \u25e6 round 3: critic reviewed\n \u25e6 round 3: critic reviewed\n \u25e6 round 3: critic reviewed\n \u25e6 round 3: critic approved\n\n\u2014 13.2s \u00b7 3 rounds \u00b7 7 model calls \u00b7 6/14 tool calls {'search': 3, 'get_summary': 3} \u00b7 52,684 in / 926 out tokens\n\u2713 Critic approved after 3 round(s); every verdict, link and quote checked against tool results.", "output_excerpt": "# Fact check\n### Claim 1: Sydney is the capital of Australia.\n**Verdict: Refuted**\nThe capital of Australia is Canberra, not Sydney.\n- [Canberra](https://en.wikipedia.org/wiki/Canberra)\n > \"Canberra is the national capital\"\n\n### Claim 2: Mount Everest is the tallest mountain on Earth measured from base to peak.\n**Verdict: Refuted**\nWhen measured from base to peak, Mauna Kea is taller than Mount Everest.\n- [Tallest mountain](https://en.wikipedia.org/wiki/Tallest_mountain)\n > \"On Earth, measured this way, Mauna Kea is the tallest mountain.\"\n\n## Sources\n- https://en.wikipedia.org/wiki/Canberra\n- https://en.wikipedia.org/wiki/Tallest_mountain" } ] }, { "timestamp": "2026-10-27T00:14:05+00:00", "sha": "41869a9d93ce231da55506b0908896f3d6619fc2", "results": [ { "day": 1, "slug": "arxiv-scout", "title": "arXiv Scout", "pattern": "Single agent + guardrail callbacks", "folder": "day-001-arxiv-scout", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 67.25, "model": "gemini-3.5-flash-lite", "model_calls": 3, "tool_calls": 4, "tool_budget": 12, "calls_by_tool": { "search_papers": 1, "get_abstract": 3 }, "prompt_tokens": 10619, "output_tokens": 1149, "cost_usd": null, "checks": { "citations_verified": true, "unverified_ids": [] }, "output_chars": 4107 }, "stderr_tail": "Downloading aiohttp (1.8MiB)\n Downloaded pydantic-core\n Downloaded aiohttp\n Downloaded cryptography\n Downloaded lxml\nInstalled 46 packages in 58ms\nInstalled 44 packages in 17ms\n/home/runner/work/agent-a-day/agent-a-day/day-001-arxiv-scout/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-001-arxiv-scout/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 search_papers(categories=['cs.CL', 'cs.AI', 'cs.LG'], query='\"speculative decoding\" OR \"speculative e...)\narXiv API request HTTP 429; retrying in 1.5s (attempt 1/3)\narXiv API request timed out; retrying in 2.4s (attempt 2/3)\n \u2192 get_abstract(paper_id='2203.16487')\n \u2192 get_abstract(paper_id='2308.04623')\n \u2192 get_abstract(paper_id='2406.02532')\n\n\u2014 67.3s \u00b7 3 model calls \u00b7 4/12 tool calls {'search_papers': 1, 'get_abstract': 3} \u00b7 10,619 in / 1,149 out tokens\n\u2713 All cited arXiv IDs were returned by a tool.", "output_excerpt": "# speculative decoding for LLM inference: engineering brief\n\n## TL;DR\n- **Draft-and-Verify Paradigm:** Speculative decoding accelerates autoregressive LLM inference by using a small, lightweight \"draft\" model to generate candidate token sequences, which are then validated in parallel by the target large model in a single forward pass [arXiv:2203.16487](https://arxiv.org/abs/2203.16487).\n- **Overcoming Memory Bandwidth Bottlenecks:** Because small-batch and consumer-device inference is heavily bottlenecked by memory bandwidth rather than compute, parallel verification or batched tree structures let hardware process multiple tokens per target model invocation [arXiv:2308.04623](https://arxiv.org/abs/2308.04623), [arXiv:2406.02532](https://arxiv.org/abs/2406.02532).\n- **Zero Quality Loss:** W" }, { "day": 2, "slug": "repo-onboarder", "title": "Repo Onboarder", "pattern": "Sequential pipeline (SequentialAgent)", "folder": "day-002-repo-onboarder", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 53.3, "model": "gemini-3.5-flash-lite", "model_calls": 8, "tool_calls": 5, "tool_budget": 10, "calls_by_tool": { "read_wiki_structure": 1, "ask_wiki_question": 4 }, "prompt_tokens": 38599, "output_tokens": 1924, "cost_usd": null, "checks": { "repo_available": true, "all_sections_present": true, "links_verified": true, "missing_sections": [], "unverified_links": [] }, "output_chars": 3122 }, "stderr_tail": "/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.PLUGGABLE_AUTH is enabled.\n check_feature_enabled()\nFailed to configure mTLS using AsyncAuthorizedSession: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.\n/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_toolset.py:407: UserWarning: [EXPERIMENTAL] feature FeatureName._MCP_GRACEFUL_ERROR_HANDLING is enabled.\n session = await self._mcp_session_manager.create_session(\n/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 [mapper] read_wiki_structure(repoName='psf/requests')\nFailed to configure mTLS using AsyncAuthorizedSession: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.\n \u2192 [researcher] ask_wiki_question(repoName='psf/requests', question='What is this project, who is it for, and how is the ...)\n \u2192 [researcher] ask_wiki_question(repoName='psf/requests', question='Where does a new reader start: the main entry points...)\n \u2192 [researcher] ask_wiki_question(question='How do I set up a development environment, run it, and run the tests?', repoN...)\n \u2192 [researcher] ask_wiki_question(repoName='psf/requests', question='What are the contribution conventions (branching, st...)\n\n\u2014 53.3s \u00b7 8 model calls \u00b7 5/10 tool calls {'read_wiki_structure': 1, 'ask_wiki_question': 4} \u00b7 38,599 in / 1,924 out tokens\n\u2713 All sections present and every repo link was returned by a tool.", "output_excerpt": "# Onboarding guide: psf/requests\n\n## What this project is\nThe `Requests` library is a Python HTTP client designed to make HTTP/1.1 requests simple and intuitive (\"HTTP for Humans\u2122\"). Built for Python developers interacting with web services and APIs, it automatically manages complex protocol details like connection pooling, redirects, and encoding. It supports Python 3.10+ and runs on PyPy.\n\n## Architecture map\n| Area | Path | What lives there |\n| :--- | :--- | :--- |\n| Core Source Code | https://github.com/psf/requests/blob/HEAD/src/requests | Core source code modules including API functions, sessions, data models, and adapters. |\n| Documentation | https://github.com/psf/requests/blob/HEAD/docs | Project documentation written in reStructuredText and built via Sphinx. |\n| Test Suite | http" }, { "day": 3, "slug": "docs-copilot", "title": "Docs Copilot", "pattern": "Grounded QA + answer verification", "folder": "day-003-docs-copilot", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 10.82, "model": "gemini-3.5-flash-lite", "model_calls": 4, "tool_calls": 2, "tool_budget": 6, "calls_by_tool": { "microsoft_docs_search": 1, "microsoft_code_sample_search": 1 }, "prompt_tokens": 20823, "output_tokens": 1010, "cost_usd": null, "checks": { "answered_from_docs": true, "sections_present": true, "sources_verified": true, "code_grounded": true, "missing_sections": [], "unverified_sources": [], "ungrounded_calls": [] }, "output_chars": 1002 }, "stderr_tail": "/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.PLUGGABLE_AUTH is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_toolset.py:407: UserWarning: [EXPERIMENTAL] feature FeatureName._MCP_GRACEFUL_ERROR_HANDLING is enabled.\n session = await self._mcp_session_manager.create_session(\nFailed to configure mTLS using AsyncAuthorizedSession: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.\n/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\nContext7 Documentation MCP Server v4.4.4 running on stdio\n/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 [researcher] microsoft_docs_search(query='upload local file to Azure Blob Storage Python SDK azure-storage-blob')\n \u2192 [researcher] microsoft_code_sample_search(language='python', query='upload_blob upload local file azure storage blob python')\nnpm notice\nnpm notice New major version of npm available! 10.9.9 -> 12.2.1\nnpm notice Changelog: https://github.com/npm/cli/releases/tag/v12.2.1\nnpm notice To update run: npm install -g npm@12.2.1\nnpm notice\n\n\u2014 10.8s \u00b7 4 model calls \u00b7 2/6 tool calls {'microsoft_docs_search': 1, 'microsoft_code_sample_search': 1} \u00b7 20,823 in / 1,010 out tokens\n\u2713 Sections present, every source was returned by a tool, and every API call appears in the docs.", "output_excerpt": "## Answer\nTo upload a local file to Azure Blob Storage using the Python SDK, you can create a `BlobClient` using either a connection string or a storage URL with credentials, open the local file in binary read mode (`\"rb\"`), and then call the `upload_blob` method on the blob client. \n\nHere is an example using a connection string:\n```python\nfrom azure.storage.blob import BlobClient\n\nblob = BlobClient.from_connection_string(conn_str=\"\", container_name=\"mycontainer\", blob_name=\"my_blob\")\n\nwith open(\"./SampleSource.txt\", \"rb\") as data:\n blob.upload_blob(data)\n```\n\n## Version notes\nnone stated\n\n## Sources\n- `https://learn.microsoft.com/python/api/overview/azure/storage-blob-readme?view=azure-python#examples`\n- `https://learn.microsoft.com/azure/storage/blobs/storage-quicks" }, { "day": 4, "slug": "web-qa-explorer", "title": "Web QA Explorer", "pattern": "Browser agent + human confirmation (require_confirmation)", "folder": "day-004-web-qa-explorer", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 18.0, "model": "gemini-3.5-flash-lite", "model_calls": 15, "tool_calls": 12, "tool_budget": 14, "calls_by_tool": { "browser_navigate": 1, "browser_console_messages": 2, "browser_network_requests": 2, "browser_snapshot": 4, "browser_type": 2, "browser_click": 1 }, "prompt_tokens": 68120, "output_tokens": 631, "cost_usd": null, "checks": { "sections_present": true, "evidence_verified": true, "pages_verified": true, "risky_actions_gated": true, "missing_sections": [], "unverified_evidence": [], "unverified_pages": [], "approvals": [ { "action": "browser_click(Delete button for 'Buy milk' e37)", "decision": "declined" }, { "action": "browser_click(Clear completed button e38)", "decision": "declined" } ], "blocked": [] }, "output_chars": 1353 }, "stderr_tail": " \u2192 browser_console_messages(level='error', all=True)\n \u2192 browser_network_requests(static=True)\n \u2192 browser_snapshot()\n \u2192 browser_type(target='e8', text='Buy milk', element='textbox \"What needs to be done?\"', submit=True)\n \u2192 browser_snapshot()\n \u2192 browser_type(target='e8', element='textbox \"What needs to be done?\"', text='Pay bills', submit=True)\n \u2192 browser_snapshot()\n \u2192 browser_click(target='e21', element=\"Toggle Todo checkbox for 'Buy milk'\")\n \u2192 browser_snapshot()\n \u2192 browser_click(target='e37', element=\"Delete button for 'Buy milk'\")\n/home/runner/work/agent-a-day/agent-a-day/day-004-web-qa-explorer/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.TOOL_CONFIRMATION is enabled.\n check_feature_enabled()\n \u2717 declined (no approval given): browser_click(target='e37', element=\"Delete button for 'Buy milk'\")\n \u2192 browser_click(target='e38', element='Clear completed button')\n \u2717 declined (no approval given): browser_click(target='e38', element='Clear completed button')\n \u2192 browser_console_messages(all=True, level='error')\n \u2192 browser_network_requests(static=True)\n\n\u2014 18.0s \u00b7 15 model calls \u00b7 12/14 tool calls {'browser_navigate': 1, 'browser_console_messages': 2, 'browser_network_requests': 2, 'browser_snapshot': 4, 'browser_type': 2, 'browser_click': 1} \u00b7 68,120 in / 631 out tokens\n\u2713 Sections present, every quote came from a tool result, and no risky action ran unapproved.", "output_excerpt": "## Summary\nTested the TodoMVC React application by adding two todo items (\"Buy milk\" and \"Pay bills\"), completing one item (\"Buy milk\"), and attempting to delete an item/clear completed items. Checked console errors and network requests for defects. A 404 error for the favicon was found in the console.\n\n## Findings\n### BUG-1 \u00b7 Missing favicon resource resulting in 404 error\n- **Severity:** low\n- **Steps:** \n 1. Navigate to https://demo.playwright.dev/todomvc/\n 2. Inspect browser console for errors\n- **Expected / actual:** \n - Expected: Favicon resource loads successfully with status 200.\n - Actual: Favicon request fails with a 404 status.\n- **Evidence:** `[ERROR] Failed to load resource: the server responded with a status of 404 () @ https://demo.playwright.dev/favicon.ico:0`\n\n## Cover" }, { "day": 5, "slug": "hn-digest", "title": "HN Digest", "pattern": "Long-term memory via knowledge graph", "folder": "day-005-hn-digest", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 10.22, "model": "gemini-3.5-flash-lite", "model_calls": 5, "tool_calls": 4, "tool_budget": 8, "calls_by_tool": { "fetch": 1, "open_nodes": 1, "create_entities": 1, "create_relations": 1 }, "prompt_tokens": 30360, "output_tokens": 1835, "cost_usd": null, "checks": { "sections_present": true, "has_stories": true, "links_verified": true, "memory_consulted": true, "no_repeats": true, "memory_saved": true, "missing_sections": [], "unverified_links": [], "repeated_stories": [], "unsaved_stories": [], "known_before": 0, "stories_in_digest": 5 }, "output_chars": 1745 }, "stderr_tail": "npm warn EBADENGINE Unsupported engine {\nnpm warn EBADENGINE package: '@modelcontextprotocol/server-memory@2.0.0',\nnpm warn EBADENGINE required: { node: '>=24' },\nnpm warn EBADENGINE current: { node: 'v22.23.3', npm: '10.9.9' }\nnpm warn EBADENGINE }\nInstalled 46 packages in 37ms\nKnowledge Graph MCP Server running on stdio\n/home/runner/work/agent-a-day/agent-a-day/day-005-hn-digest/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-005-hn-digest/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 fetch(url='https://hn.algolia.com/api/v1/search?tags=front_page&hitsPerPage=30&attributesToRe...)\nHTTP Request: GET https://hn.algolia.com/robots.txt \"HTTP/1.1 404 Not Found\"\nHTTP Request: GET https://hn.algolia.com/api/v1/search?tags=front_page&hitsPerPage=30&attributesToRetrieve=title,url,points,num_comments,objectID&attributesToHighlight=none \"HTTP/1.1 200 OK\"\n \u2192 open_nodes(names=['hn-50242939', 'hn-50245524', 'hn-50245138', 'hn-50215997', 'hn-50248124', 'hn-5...)\n \u2192 create_entities(entities=[{'name': 'hn-50242939', 'observations': ['title: Anthropic S-1', 'url: https:...)\n \u2192 create_relations(relations=[{'relationType': 'about', 'to': 'topic-ai', 'from': 'hn-50242939'}, {'relati...)\n\n\u2014 10.2s \u00b7 5 model calls \u00b7 4/8 tool calls {'fetch': 1, 'open_nodes': 1, 'create_entities': 1, 'create_relations': 1} \u00b7 30,360 in / 1,835 out tokens\n\u2713 5 new stories, all links returned by a tool, none repeated (memory held 0 before), all saved.", "output_excerpt": "# Hacker News digest \u00b7 2026-10-27\n## Stories \n1. **[Anthropic S-1](https://www.sec.gov/Archives/edgar/data/2130357/000162828026067168/anthropicpbc-sx1.htm)** \u2014 Anthropic's S-1 filing has been published on EDGAR, detailing the company's financial and corporate background. \u00b7 [1443 points, 1408 comments](https://news.ycombinator.com/item?id=50242939).\n2. **[Microsoft offers mass buyouts for roughly a quarter of its workforce](https://www.seattletimes.com/business/microsoft/microsoft-offers-mass-buyouts-for-roughly-a-quarter-of-its-workforce/)** \u2014 Microsoft is reportedly offering voluntary buyouts affecting around 25% of its employees. \u00b7 [706 points, 621 comments](https://news.ycombinator.com/item?id=50245524).\n3. **[Sebastian Raschka's from-scratch video course on building reasoning model" }, { "day": 6, "slug": "meeting-planner", "title": "Meeting Planner", "pattern": "Structured output (Pydantic output_schema)", "folder": "day-006-meeting-planner", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 6.26, "model": "gemini-3.5-flash-lite", "model_calls": 2, "tool_calls": 10, "tool_budget": 10, "calls_by_tool": { "convert_time": 10 }, "prompt_tokens": 8264, "output_tokens": 1368, "cost_usd": null, "checks": { "schema_valid": true, "plan_complete": true, "verified_by_recompute": true, "zones_checked_by_tool": true, "problems": {}, "unchecked_zones": [], "slots": 3 }, "output_chars": 4071 }, "stderr_tail": " session = await self._mcp_session_manager.create_session(\nInstalled 31 packages in 20ms\n/home/runner/work/agent-a-day/agent-a-day/day-006-meeting-planner/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n \u2192 convert_time(target_timezone='Etc/UTC', time='09:00', source_timezone='Australia/Melbourne')\n \u2192 convert_time(target_timezone='Etc/UTC', source_timezone='Australia/Melbourne', time='17:00')\n \u2192 convert_time(source_timezone='Asia/Singapore', time='09:00', target_timezone='Etc/UTC')\n \u2192 convert_time(target_timezone='Etc/UTC', source_timezone='Asia/Singapore', time='17:00')\n \u2192 convert_time(time='09:00', target_timezone='Etc/UTC', source_timezone='Asia/Kolkata')\n \u2192 convert_time(time='17:00', source_timezone='Asia/Kolkata', target_timezone='Etc/UTC')\n \u2192 convert_time(target_timezone='Australia/Melbourne', time='01:00', source_timezone='Etc/UTC')\n \u2192 convert_time(time='01:00', source_timezone='Etc/UTC', target_timezone='Asia/Singapore')\n \u2192 convert_time(source_timezone='Etc/UTC', time='01:00', target_timezone='Asia/Kolkata')\n \u2192 convert_time(source_timezone='Etc/UTC', time='03:30', target_timezone='Australia/Melbourne')\n \u2192 convert_time(target_timezone='Asia/Singapore', time='03:30', source_timezone='Etc/UTC')\n \u2192 convert_time(time='03:30', target_timezone='Asia/Kolkata', source_timezone='Etc/UTC')\n \u2192 set_model_response(working_hours_end='17:00', working_hours_start='09:00', participants=[{'timezone': 'Aus...)\n\n\u2014 6.3s \u00b7 2 model calls \u00b7 10/10 tool calls {'convert_time': 10} \u00b7 8,264 in / 1,368 out tokens\n\u2713 Valid MeetingPlan; 3 slot(s) re-computed from the tz database and inside working hours.", "output_excerpt": "# Melbourne, Singapore, and Mumbai Sync\n\n45 minutes \u00b7 working hours 09:00-17:00 local \u00b7 Melbourne (Australia/Melbourne), Singapore (Asia/Singapore), Mumbai (Asia/Kolkata)\n\n## Option 1: 2026-11-02 03:30 UTC\n\nThis slot falls comfortably within working hours for all participants on a weekday.\n\n| Who | Local start | Local end |\n|---|---|---|\n| Melbourne (Australia/Melbourne) | 2026-11-02 14:30 | 2026-11-02 15:15 |\n| Singapore (Asia/Singapore) | 2026-11-02 11:30 | 2026-11-02 12:15 |\n| Mumbai (Asia/Kolkata) | 2026-11-02 09:00 | 2026-11-02 09:45 |\n\n## Option 2: 2026-11-03 03:30 UTC\n\nThis slot falls comfortably within working hours for all participants on a weekday.\n\n| Who | Local start | Local end |\n|---|---|---|\n| Melbourne (Australia/Melbourne) | 2026-11-03 14:30 | 2026-11-03 15:15 |\n| Singapor" }, { "day": 7, "slug": "security-triager", "title": "Security Triager", "pattern": "Deterministic tool + LLM triage", "folder": "day-007-security-triager", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 13.42, "model": "gemini-3.5-flash-lite", "model_calls": 9, "tool_calls": 8, "tool_budget": 14, "calls_by_tool": { "git_show": 7, "semgrep_scan_with_custom_rule": 1 }, "prompt_tokens": 19274, "output_tokens": 1022, "cost_usd": null, "checks": { "scan_ran": true, "no_invented_findings": true, "all_findings_triaged": true, "invented_ids": [], "untriaged_ids": [], "findings": 7, "verdicts": { "exploitable": 2, "likely": 2, "unlikely": 1, "false positive": 2 } }, "output_chars": 2413 }, "stderr_tail": " Downloaded pygments\n Downloaded semgrep\nInstalled 66 packages in 34ms\nStarting Semgrep MCP server version v1.182.1\n/home/runner/.cache/uv/archive-v0/oZIoqkfd2UcMHHI5/lib/python3.12/site-packages/pydantic_settings/sources/utils.py:51: IncompleteFieldDefinitionWarning: Field 'lifespan' has an incomplete definition: its annotation contains an unresolved forward reference, so settings sources may fail to correctly resolve its value. Call `model_rebuild()` on the model where the field is defined, once all the referenced types are defined.\n warnings.warn( # noqa: B028 (called from many sources at differing depths, so no stacklevel is correct)\nUser doesn't have the Pro Engine installed, not running `semgrep mcp` daemon...\n \u2192 git_show(revision='HEAD:')\n \u2192 git_show(revision='HEAD:app/')\n \u2192 git_show(revision='HEAD:app')\n \u2192 git_show(revision='HEAD:app/views.py')\n \u2192 git_show(revision='HEAD:app/client.py')\n \u2192 git_show(revision='HEAD:app/cache.py')\n \u2192 git_show(revision='HEAD:app/__init__.py')\n \u2192 semgrep_scan_with_custom_rule(code_files=[{'path': 'app/views.py', 'content': ''}, {'path': 'app/client.py', 'content...)\nFindings elicitation is not enabled, skipping.\nsemgrep_scan_with_custom_rule succeeded\n\n\u2014 13.4s \u00b7 9 model calls \u00b7 8/14 tool calls {'git_show': 7, 'semgrep_scan_with_custom_rule': 1} \u00b7 19,274 in / 1,022 out tokens\n\u2713 All 7 scanner findings triaged; none invented.", "output_excerpt": "# Security triage: demo_app\n## Summary\nThe codebase contains several high-severity injection and deserialization vulnerabilities, led by an exploitable OS command injection in `ping_host` that should be fixed immediately. Addressing input sanitization and secure deserialization takes precedence over configuration warnings.\n\n## Findings\n| ID | Rule | Location | Verdict | Why | Fix |\n|---|---|---|---|---|---|\n| F1 | py-weak-hash | app/cache.py:9 | false positive | MD5 is used exclusively for generating a cache bucket name, not for cryptography or security control. | Replace `hashlib.md5` with `hashlib.sha256` if collision resistance is needed. |\n| F2 | py-pickle-load | app/cache.py:14 | likely | `pickle.loads` deserializes session cookie values; if session cookies can be forged or manipulate" }, { "day": 8, "slug": "aws-architect", "title": "AWS Architect", "pattern": "Parallel fan-out (ParallelAgent) + merge", "folder": "day-008-aws-architect", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 12.41, "model": "gemini-3.5-flash-lite", "model_calls": 9, "tool_calls": 5, "tool_budget": 15, "calls_by_tool": { "search_documentation": 5 }, "prompt_tokens": 62753, "output_tokens": 2655, "cost_usd": null, "checks": { "all_sections_present": true, "citations_verified": true, "missing_sections": [], "unverified_links": [] }, "output_chars": 7190 }, "stderr_tail": " ilability+best+practices \"HTTP/1.1 \n 200 OK\" \n \u2192 [cost_reviewer] search_documentation(search_phrase='S3 storage classes cost optimization lifecycle', search_intent='S3 stora...)\n \u2192 [reliability_reviewer] search_documentation(search_phrase='RDS Multi-AZ PostgreSQL reliability', search_intent='RDS PostgreSQL high...)\n[10/27/26 00:13:44] INFO HTTP Request: POST _client.py:1740\n https://proxy.search.docs.aws.com/s \n earch?session=e2c67e25-8a53-486a-bc \n 92-29fc4209bba3&search_intent=S3+st \n orage+classes+cost+optimization+use \n r+uploads \"HTTP/1.1 200 OK\" \n[10/27/26 00:13:45] INFO HTTP Request: POST _client.py:1740\n https://proxy.search.docs.aws.com/s \n earch?session=1f737112-c6fa-4e89-b4 \n 88-d9dd04545a82&search_intent=RDS+P \n ostgreSQL+high+availability+Multi-A \n Z+backups+reliability+best+practice \n s \"HTTP/1.1 200 OK\" \n\n\u2014 12.4s \u00b7 9 model calls \u00b7 5/15 tool calls {'search_documentation': 5} \u00b7 62,753 in / 2,655 out tokens\n\u2713 All sections present and every AWS doc link was returned by a tool.", "output_excerpt": "# Architecture review\n## Summary\n- **Overall posture:** The architecture follows standard multi-AZ patterns for compute and features a modern serverless edge/frontend with CloudFront, ALB, and ECS Fargate, but contains critical gaps in database resilience and security hardening.\n- **Biggest risk:** Operating the RDS PostgreSQL instance as a single instance creates a single point of failure that compromises application availability.\n- **Quickest win:** Implementing S3 lifecycle policies and blocking public access on the user upload bucket to immediately improve cost and security posture.\n\n## Top priorities\n| # | Pillar | Finding | Recommendation | Source |\n|---|---|---|---|---|\n| 1 | Reliability | Single RDS instance resilience | Configure Amazon RDS for PostgreSQL as a Multi-AZ deployment " }, { "day": 9, "slug": "fact-checker", "title": "Fact Checker", "pattern": "Generator\u2013critic loop (LoopAgent)", "folder": "day-009-fact-checker", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 11.13, "model": "gemini-3.5-flash-lite", "model_calls": 5, "tool_calls": 5, "tool_budget": 14, "calls_by_tool": { "search": 3, "get_summary": 2 }, "prompt_tokens": 30994, "output_tokens": 493, "cost_usd": null, "checks": { "report_well_formed": true, "critic_approved": true, "rounds": 2, "problems": [] }, "output_chars": 685 }, "stderr_tail": "2026-10-27 00:14:00,326 - httpx - INFO - HTTP Request: GET https://en.wikipedia.org/w/api.php?format=json&redirects=1&action=query&prop=info&titles=Mauna+Kea&inprop=protection%7Ctalkid%7Cwatched%7Cwatchers%7Cvisitingwatchers%7Cnotificationtimestamp%7Csubjectid%7Curl%7Creadable%7Cpreload%7Cdisplaytitle%7Cvarianttitles \"HTTP/1.1 200 OK\"\n2026-10-27 00:14:00,327 - wikipediaapi._http_client.sync_http_client - INFO - Request URL: https://en.wikipedia.org/w/api.php?format=json&redirects=1&action=query&prop=extracts&titles=Mauna Kea&explaintext=1&exsectionformat=wiki\n2026-10-27 00:14:00,366 - httpx - INFO - HTTP Request: GET https://en.wikipedia.org/w/api.php?format=json&redirects=1&action=query&prop=info&titles=Capital+of+Australia&inprop=protection%7Ctalkid%7Cwatched%7Cwatchers%7Cvisitingwatchers%7Cnotificationtimestamp%7Csubjectid%7Curl%7Creadable%7Cpreload%7Cdisplaytitle%7Cvarianttitles \"HTTP/1.1 200 OK\"\n2026-10-27 00:14:00,366 - wikipediaapi._http_client.sync_http_client - INFO - Request URL: https://en.wikipedia.org/w/api.php?format=json&redirects=1&action=query&prop=extracts&titles=Canberra&explaintext=1&exsectionformat=wiki\n2026-10-27 00:14:00,408 - httpx - INFO - HTTP Request: GET https://en.wikipedia.org/w/api.php?format=json&redirects=1&action=query&prop=extracts&titles=Mauna+Kea&explaintext=1&exsectionformat=wiki \"HTTP/1.1 200 OK\"\n2026-10-27 00:14:00,413 - httpx - INFO - HTTP Request: GET https://en.wikipedia.org/w/api.php?format=json&redirects=1&action=query&prop=extracts&titles=Canberra&explaintext=1&exsectionformat=wiki \"HTTP/1.1 200 OK\"\n \u25e6 round 1: checker wrote verdicts\n \u25e6 round 1: critic reviewed\n \u2192 [checker] search(query='Canberra capital city of Australia')\n[10/27/26 00:14:02] INFO HTTP Request: POST _client.py:1740\n https://html.duckduckgo.com/html \n \"HTTP/1.1 200 OK\" \n \u25e6 round 2: checker wrote verdicts\n \u25e6 round 2: critic reviewed\n \u25e6 round 2: critic reviewed\n \u25e6 round 2: critic reviewed\n \u25e6 round 2: critic approved\n\n\u2014 11.1s \u00b7 2 rounds \u00b7 5 model calls \u00b7 5/14 tool calls {'search': 3, 'get_summary': 2} \u00b7 30,994 in / 493 out tokens\n\u2713 Critic approved after 2 round(s); every verdict, link and quote checked against tool results.", "output_excerpt": "# Fact check\n### Claim 1: Sydney is the capital of Australia.\n**Verdict: Refuted**\nThe capital of Australia is Canberra, not Sydney.\n- [Canberra - Wikipedia](https://en.wikipedia.org/wiki/Canberra)\n > \"is the capital city of Australia\"\n### Claim 2: Mount Everest is the tallest mountain on Earth measured from base to peak.\n**Verdict: Refuted**\nWhen measured from base to peak, Mauna Kea is considered the tallest mountain on Earth.\n- [Tallest mountain - Wikipedia](https://en.wikipedia.org/wiki/Tallest_mountain)\n > \"On Earth, measured this way, Mauna Kea is the tallest mountain\"\n\n## Sources\n- https://en.wikipedia.org/wiki/Canberra\n- https://en.wikipedia.org/wiki/Tallest_mountain" } ] }, { "timestamp": "2026-10-28T00:18:02+00:00", "sha": "41869a9d93ce231da55506b0908896f3d6619fc2", "results": [ { "day": 1, "slug": "arxiv-scout", "title": "arXiv Scout", "pattern": "Single agent + guardrail callbacks", "folder": "day-001-arxiv-scout", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 105.44, "model": "gemini-3.5-flash-lite", "model_calls": 3, "tool_calls": 2, "tool_budget": 12, "calls_by_tool": { "search_papers": 2 }, "prompt_tokens": 6507, "output_tokens": 199, "cost_usd": null, "checks": { "citations_verified": true, "unverified_ids": [] }, "output_chars": 466 }, "stderr_tail": "/home/runner/work/agent-a-day/agent-a-day/day-001-arxiv-scout/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-001-arxiv-scout/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 search_papers(sort_by='relevance', max_results=5, categories=['cs.CL', 'cs.AI', 'cs.LG'], query='\"spe...)\narXiv API request HTTP 429; retrying in 1.9s (attempt 1/3)\narXiv API request HTTP 429; retrying in 2.9s (attempt 2/3)\narXiv API request timed out after 50.0s (budget: 50.0s)\narXiv search rate limited after retries: arXiv is rate limiting this IP (HTTP 429). Please wait 60 seconds before retrying.\n \u2192 search_papers(categories=['cs.CL'], query='\"speculative decoding\" LLM', max_results=5, sort_by='date')\narXiv API request timed out; retrying in 1.5s (attempt 1/3)\narXiv API request timed out after 50.0s (budget: 50.0s)\nUnexpected search error: arXiv request timed out after 50s. The arXiv API may be slow or overloaded. Please retry shortly.\nTool error: {\n \"status\": \"error\",\n \"message\": \"arXiv request timed out after 50s. The arXiv API may be slow or overloaded. Please retry shortly.\"\n}\n\n\u2014 105.4s \u00b7 3 model calls \u00b7 2/12 tool calls {'search_papers': 2} \u00b7 6,507 in / 199 out tokens\n\u2713 All cited arXiv IDs were returned by a tool.", "output_excerpt": "The arXiv search API is currently experiencing severe rate-limiting and timeouts (HTTP 429 / Gateway timeouts) when querying for papers on speculative decoding. Because of this, the tools returned no valid paper records or abstracts in this session. \n\nTo maintain strict adherence to our rules\u2014never inventing paper IDs, titles, or citation links\u2014we cannot produce a fully cited engineering brief at this moment. Please try again shortly once the arXiv API recovers." }, { "day": 2, "slug": "repo-onboarder", "title": "Repo Onboarder", "pattern": "Sequential pipeline (SequentialAgent)", "folder": "day-002-repo-onboarder", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 52.1, "model": "gemini-3.5-flash-lite", "model_calls": 9, "tool_calls": 6, "tool_budget": 10, "calls_by_tool": { "read_wiki_structure": 1, "ask_wiki_question": 4, "fetch": 1 }, "prompt_tokens": 48636, "output_tokens": 1823, "cost_usd": null, "checks": { "repo_available": true, "all_sections_present": true, "links_verified": true, "missing_sections": [], "unverified_links": [] }, "output_chars": 2803 }, "stderr_tail": "/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.PLUGGABLE_AUTH is enabled.\n check_feature_enabled()\nFailed to configure mTLS using AsyncAuthorizedSession: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.\n/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_toolset.py:407: UserWarning: [EXPERIMENTAL] feature FeatureName._MCP_GRACEFUL_ERROR_HANDLING is enabled.\n session = await self._mcp_session_manager.create_session(\n/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 [mapper] read_wiki_structure(repoName='psf/requests')\nFailed to configure mTLS using AsyncAuthorizedSession: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.\n \u2192 [researcher] ask_wiki_question(repoName='psf/requests', question='What is this project, who is it for, and how is the ...)\n \u2192 [researcher] ask_wiki_question(question='Where does a new reader start: the main entry points and the 3-5 most importa...)\n \u2192 [researcher] ask_wiki_question(question='How do I set up a development environment, run it, and run the tests?', repoN...)\n \u2192 [researcher] ask_wiki_question(question='What are the contribution conventions (branching, style, review) and known pi...)\n \u2192 [researcher] fetch(url='https://raw.githubusercontent.com/psf/requests/main/CONTRIBUTING.md')\n\n\u2014 52.1s \u00b7 9 model calls \u00b7 6/10 tool calls {'read_wiki_structure': 1, 'ask_wiki_question': 4, 'fetch': 1} \u00b7 48,636 in / 1,823 out tokens\n\u2713 All sections present and every repo link was returned by a tool.", "output_excerpt": "# Onboarding guide: psf/requests\n\n## What this project is\n`Requests` is a Python HTTP client library designed to make HTTP requests simple and intuitive (\"HTTP for Humans\u2122\"). Built on top of `urllib3` for low-level operations, it handles complex protocol details internally and serves millions of downloads per week for Python 3.10+ and PyPy.\n\n## Architecture map\n\n| Area | Path | What lives there |\n| :--- | :--- | :--- |\n| Core Source | [`src/requests/`](https://github.com/psf/requests/blob/HEAD/src/requests/) | Contains the core implementation including API endpoints, models, session management, and adapters. |\n| Documentation | [`docs/`](https://github.com/psf/requests/blob/HEAD/docs/) | Holds reStructuredText files for user guides, development guides, community info, and API references. |" }, { "day": 3, "slug": "docs-copilot", "title": "Docs Copilot", "pattern": "Grounded QA + answer verification", "folder": "day-003-docs-copilot", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 11.95, "model": "gemini-3.5-flash-lite", "model_calls": 4, "tool_calls": 2, "tool_budget": 6, "calls_by_tool": { "microsoft_docs_search": 1, "microsoft_code_sample_search": 1 }, "prompt_tokens": 20699, "output_tokens": 931, "cost_usd": null, "checks": { "answered_from_docs": true, "sections_present": true, "sources_verified": true, "code_grounded": true, "missing_sections": [], "unverified_sources": [], "ungrounded_calls": [] }, "output_chars": 964 }, "stderr_tail": "/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.PLUGGABLE_AUTH is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_toolset.py:407: UserWarning: [EXPERIMENTAL] feature FeatureName._MCP_GRACEFUL_ERROR_HANDLING is enabled.\n session = await self._mcp_session_manager.create_session(\nFailed to configure mTLS using AsyncAuthorizedSession: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.\n/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\nContext7 Documentation MCP Server v4.4.4 running on stdio\n/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 [researcher] microsoft_docs_search(query='upload local file to Azure Blob Storage Python SDK azure-storage-blob')\n \u2192 [researcher] microsoft_code_sample_search(query='upload_blob upload local file azure storage blob python', language='python')\nnpm notice\nnpm notice New major version of npm available! 10.9.9 -> 12.2.1\nnpm notice Changelog: https://github.com/npm/cli/releases/tag/v12.2.1\nnpm notice To update run: npm install -g npm@12.2.1\nnpm notice\n\n\u2014 12.0s \u00b7 4 model calls \u00b7 2/6 tool calls {'microsoft_docs_search': 1, 'microsoft_code_sample_search': 1} \u00b7 20,699 in / 931 out tokens\n\u2713 Sections present, every source was returned by a tool, and every API call appears in the docs.", "output_excerpt": "## Answer\nTo upload a local file to Azure Blob Storage using the Python SDK, you can create a `BlobClient` using either a connection string or a storage URL with credentials, and then call the `upload_blob` method while passing the opened local file. \n\nHere is an example using a connection string:\n\n```python\nfrom azure.storage.blob import BlobClient\n\nblob = BlobClient.from_connection_string(conn_str=\"\", container_name=\"mycontainer\", blob_name=\"my_blob\")\n\nwith open(\"./SampleSource.txt\", \"rb\") as data:\n blob.upload_blob(data)\n```\n\n## Version notes\nnone stated\n\n## Sources\n- https://learn.microsoft.com/python/api/overview/azure/storage-blob-readme?view=azure-python#examples\n- https://learn.microsoft.com/azure/developer/python/sdk/examples/azure-sdk-example-storage-use#4-u" }, { "day": 4, "slug": "web-qa-explorer", "title": "Web QA Explorer", "pattern": "Browser agent + human confirmation (require_confirmation)", "folder": "day-004-web-qa-explorer", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 24.26, "model": "gemini-3.5-flash-lite", "model_calls": 15, "tool_calls": 10, "tool_budget": 14, "calls_by_tool": { "browser_navigate": 1, "browser_snapshot": 3, "browser_type": 2, "browser_click": 1, "browser_console_messages": 1, "browser_network_requests": 2 }, "prompt_tokens": 63535, "output_tokens": 633, "cost_usd": null, "checks": { "sections_present": true, "evidence_verified": true, "pages_verified": true, "risky_actions_gated": true, "missing_sections": [], "unverified_evidence": [], "unverified_pages": [], "approvals": [ { "action": "browser_click(checkbox \"Toggle Todo\" for Buy milk e21)", "decision": "declined" }, { "action": "browser_click(generic label for Buy milk checkbox e21)", "decision": "declined" }, { "action": "browser_click(list item for Buy milk e20)", "decision": "declined" }, { "action": "browser_click(Delete button for Buy milk e37)", "decision": "declined" } ], "blocked": [] }, "output_chars": 1532 }, "stderr_tail": " \u2192 browser_type(target='e8', submit=True, element='textbox \"What needs to be done?\"', text='Walk the dog')\n \u2192 browser_snapshot()\n \u2192 browser_click(target='e21', element='checkbox \"Toggle Todo\" for Buy milk')\n/home/runner/work/agent-a-day/agent-a-day/day-004-web-qa-explorer/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.TOOL_CONFIRMATION is enabled.\n check_feature_enabled()\n \u2717 declined (no approval given): browser_click(target='e21', element='checkbox \"Toggle Todo\" for Buy milk')\n \u2192 browser_click(target='e21', element='generic label for Buy milk checkbox')\n \u2717 declined (no approval given): browser_click(target='e21', element='generic label for Buy milk checkbox')\n \u2192 browser_click(target='e20', element='list item for Buy milk')\n \u2717 declined (no approval given): browser_click(target='e20', element='list item for Buy milk')\n \u2192 browser_click(target='e22', element='generic text \"Buy milk\"')\n \u2192 browser_snapshot()\n \u2192 browser_click(element='Delete button for Buy milk', target='e37')\n \u2717 declined (no approval given): browser_click(element='Delete button for Buy milk', target='e37')\n \u2192 browser_console_messages(level='error')\n \u2192 browser_network_requests(static=False)\n \u2192 browser_network_requests(static=True)\n\n\u2014 24.3s \u00b7 15 model calls \u00b7 10/14 tool calls {'browser_navigate': 1, 'browser_snapshot': 3, 'browser_type': 2, 'browser_click': 1, 'browser_console_messages': 1, 'browser_network_requests': 2} \u00b7 63,535 in / 633 out tokens\n\u2713 Sections present, every quote came from a tool result, and no risky action ran unapproved.", "output_excerpt": "## Summary\nTested the TodoMVC React demo app by navigating to https://demo.playwright.dev/todomvc/, adding two todos (\"Buy milk\" and \"Walk the dog\"), attempting to interact with/complete/delete them, and inspecting console messages and network requests. Discovered a 404 error for the favicon resource upon loading.\n\n## Findings\n### BUG-1 \u00b7 Missing Favicon Resource (404 Not Found)\n- **Severity:** low\n- **Steps:** Navigate to https://demo.playwright.dev/todomvc/ and inspect browser console errors.\n- **Expected / actual:** The site should successfully load its favicon icon without error / The server responded with a 404 status for `/favicon.ico`.\n- **Evidence:** `[ERROR] Failed to load resource: the server responded with a status of 404 () @ https://demo.playwright.dev/favicon.ico:0`\n\n## Cover" }, { "day": 5, "slug": "hn-digest", "title": "HN Digest", "pattern": "Long-term memory via knowledge graph", "folder": "day-005-hn-digest", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 10.01, "model": "gemini-3.5-flash-lite", "model_calls": 5, "tool_calls": 4, "tool_budget": 8, "calls_by_tool": { "fetch": 1, "open_nodes": 1, "create_entities": 1, "create_relations": 1 }, "prompt_tokens": 30034, "output_tokens": 1773, "cost_usd": null, "checks": { "sections_present": true, "has_stories": true, "links_verified": true, "memory_consulted": true, "no_repeats": true, "memory_saved": true, "missing_sections": [], "unverified_links": [], "repeated_stories": [], "unsaved_stories": [], "known_before": 0, "stories_in_digest": 5 }, "output_chars": 1659 }, "stderr_tail": "Installed 46 packages in 16ms\nnpm warn EBADENGINE Unsupported engine {\nnpm warn EBADENGINE package: '@modelcontextprotocol/server-memory@2.0.0',\nnpm warn EBADENGINE required: { node: '>=24' },\nnpm warn EBADENGINE current: { node: 'v22.23.3', npm: '10.9.9' }\nnpm warn EBADENGINE }\nKnowledge Graph MCP Server running on stdio\n/home/runner/work/agent-a-day/agent-a-day/day-005-hn-digest/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-005-hn-digest/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 fetch(url='https://hn.algolia.com/api/v1/search?tags=front_page&hitsPerPage=30&attributesToRe...)\nHTTP Request: GET https://hn.algolia.com/robots.txt \"HTTP/1.1 404 Not Found\"\nHTTP Request: GET https://hn.algolia.com/api/v1/search?tags=front_page&hitsPerPage=30&attributesToRetrieve=title,url,points,num_comments,objectID&attributesToHighlight=none \"HTTP/1.1 200 OK\"\n \u2192 open_nodes(names=['hn-50259604', 'hn-50261998', 'hn-50261319', 'hn-50257326', 'hn-50257419', 'hn-5...)\n \u2192 create_entities(entities=[{'entityType': 'hn_story', 'observations': ['title: US Government is hiding a...)\n \u2192 create_relations(relations=[{'to': 'topic-security', 'from': 'hn-50259604', 'relationType': 'about'}, {'...)\n\n\u2014 10.0s \u00b7 5 model calls \u00b7 4/8 tool calls {'fetch': 1, 'open_nodes': 1, 'create_entities': 1, 'create_relations': 1} \u00b7 30,034 in / 1,773 out tokens\n\u2713 5 new stories, all links returned by a tool, none repeated (memory held 0 before), all saved.", "output_excerpt": "# Hacker News digest \u00b7 2026-10-28\n## Stories\n1. **[US Government is hiding a secret court ruling about how it spies on Americans](https://www.wired.com/story/the-us-government-is-hiding-a-secret-court-ruling-about-how-it-spies-on-americans/)** \u2014 Reports highlight concerns as the U.S. government conceals a secret court ruling concerning domestic surveillance practices. \u00b7 [751 points, 271 comments](https://news.ycombinator.com/item?id=50259604).\n2. **[Ghostty 1.4](https://ghostty.org/docs/install/release-notes/1-4-0)** \u2014 The latest release notes for Ghostty 1.4 detail new features, improvements, and updates for the terminal emulator. \u00b7 [691 points, 214 comments](https://news.ycombinator.com/item?id=50261998).\n3. **[Genie 4](https://deepmind.google/blog/genie-4-a-new-generation-of-world-simul" }, { "day": 6, "slug": "meeting-planner", "title": "Meeting Planner", "pattern": "Structured output (Pydantic output_schema)", "folder": "day-006-meeting-planner", "model": "gemini-3.5-flash-lite", "status": "fail", "exit_code": 1, "metrics": { "passed": false, "elapsed_s": 12.34, "model": "gemini-3.5-flash-lite", "model_calls": 12, "tool_calls": 10, "tool_budget": 10, "calls_by_tool": { "convert_time": 10 }, "prompt_tokens": 51529, "output_tokens": 1340, "cost_usd": null, "checks": { "schema_valid": true, "plan_complete": true, "verified_by_recompute": false, "zones_checked_by_tool": true, "problems": { "local_times_correct": [ "slot 1: Mumbai should be 2026-11-02 09:00-2026-11-02 09:45, plan says 2026-11-02 09:30-2026-11-02 09:45", "slot 2: Mumbai should be 2026-11-03 09:00-2026-11-03 09:45, plan says 2026-11-03 09:30-2026-11-03 09:45", "slot 3: Mumbai should be 2026-11-04 09:00-2026-11-04 09:45, plan says 2026-11-04 09:30-2026-11-04 09:45" ] }, "unchecked_zones": [], "slots": 3 }, "output_chars": 4025 }, "stderr_tail": "/home/runner/work/agent-a-day/agent-a-day/day-006-meeting-planner/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_toolset.py:407: UserWarning: [EXPERIMENTAL] feature FeatureName._MCP_GRACEFUL_ERROR_HANDLING is enabled.\n session = await self._mcp_session_manager.create_session(\nInstalled 31 packages in 24ms\n/home/runner/work/agent-a-day/agent-a-day/day-006-meeting-planner/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n \u2192 convert_time(time='09:00', source_timezone='Australia/Melbourne', target_timezone='Etc/UTC')\n \u2192 convert_time(source_timezone='Australia/Melbourne', target_timezone='Etc/UTC', time='17:00')\n \u2192 convert_time(time='09:00', source_timezone='Asia/Singapore', target_timezone='Etc/UTC')\n \u2192 convert_time(source_timezone='Asia/Singapore', time='17:00', target_timezone='Etc/UTC')\n \u2192 convert_time(time='09:00', target_timezone='Etc/UTC', source_timezone='Asia/Kolkata')\n \u2192 convert_time(source_timezone='Asia/Kolkata', time='17:00', target_timezone='Etc/UTC')\n \u2192 convert_time(source_timezone='Etc/UTC', target_timezone='Australia/Melbourne', time='06:00')\n \u2192 convert_time(source_timezone='Etc/UTC', target_timezone='Asia/Singapore', time='03:30')\n \u2192 convert_time(time='03:30', target_timezone='Asia/Kolkata', source_timezone='Etc/UTC')\n \u2192 convert_time(source_timezone='Etc/UTC', target_timezone='Asia/Singapore', time='05:15')\n \u2192 convert_time(target_timezone='Asia/Kolkata', time='05:15', source_timezone='Etc/UTC')\n \u2192 set_model_response(duration_minutes=45, title='45-Minute Sync', participants=[{'name': 'Melbourne', 'timez...)\n\n\u2014 12.3s \u00b7 12 model calls \u00b7 10/10 tool calls {'convert_time': 10} \u00b7 51,529 in / 1,340 out tokens\n\u2717 local_times_correct: slot 1: Mumbai should be 2026-11-02 09:00-2026-11-02 09:45, plan says 2026-11-02 09:30-2026-11-02 09:45; slot 2: Mumbai should be 2026-11-03 09:00-2026-11-03 09:45, plan says 2026-11-03 09:30-2026-11-03 09:45; slot 3: Mumbai should be 2026-11-04 09:00-2026-11-04 09:45, plan says 2026-11-04 09:30-2026-11-04 09:45", "output_excerpt": "# 45-Minute Sync\n\n45 minutes \u00b7 working hours 09:00-17:00 local \u00b7 Melbourne (Australia/Melbourne), Singapore (Asia/Singapore), Mumbai (Asia/Kolkata)\n\n## Option 1: 2026-11-02 03:30 UTC\n\nThis slot falls comfortably within working hours for all participants on a weekday.\n\n| Who | Local start | Local end |\n|---|---|---|\n| Melbourne (Australia/Melbourne) | 2026-11-02 14:30 | 2026-11-02 15:15 |\n| Singapore (Asia/Singapore) | 2026-11-02 11:30 | 2026-11-02 12:15 |\n| Mumbai (Asia/Kolkata) | 2026-11-02 09:30 | 2026-11-02 09:45 |\n\n## Option 2: 2026-11-03 03:30 UTC\n\nThis slot falls comfortably within working hours for all participants on a weekday.\n\n| Who | Local start | Local end |\n|---|---|---|\n| Melbourne (Australia/Melbourne) | 2026-11-03 14:30 | 2026-11-03 15:15 |\n| Singapore (Asia/Singapore) | 20" }, { "day": 7, "slug": "security-triager", "title": "Security Triager", "pattern": "Deterministic tool + LLM triage", "folder": "day-007-security-triager", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 13.36, "model": "gemini-3.5-flash-lite", "model_calls": 9, "tool_calls": 8, "tool_budget": 14, "calls_by_tool": { "git_show": 7, "semgrep_scan_with_custom_rule": 1 }, "prompt_tokens": 18812, "output_tokens": 938, "cost_usd": null, "checks": { "scan_ran": true, "no_invented_findings": true, "all_findings_triaged": true, "invented_ids": [], "untriaged_ids": [], "findings": 7, "verdicts": { "exploitable": 2, "likely": 2, "unlikely": 0, "false positive": 3 } }, "output_chars": 2169 }, "stderr_tail": " Downloaded pygments\n Downloaded semgrep\nInstalled 66 packages in 38ms\nStarting Semgrep MCP server version v1.182.1\n/home/runner/.cache/uv/archive-v0/9hqMweASV7Fz3uMC/lib/python3.12/site-packages/pydantic_settings/sources/utils.py:51: IncompleteFieldDefinitionWarning: Field 'lifespan' has an incomplete definition: its annotation contains an unresolved forward reference, so settings sources may fail to correctly resolve its value. Call `model_rebuild()` on the model where the field is defined, once all the referenced types are defined.\n warnings.warn( # noqa: B028 (called from many sources at differing depths, so no stacklevel is correct)\nUser doesn't have the Pro Engine installed, not running `semgrep mcp` daemon...\n \u2192 git_show(revision='HEAD:')\n \u2192 git_show(revision='HEAD:app/')\n \u2192 git_show(revision='HEAD:app')\n \u2192 git_show(revision='HEAD:app/__init__.py')\n \u2192 git_show(revision='HEAD:app/cache.py')\n \u2192 git_show(revision='HEAD:app/client.py')\n \u2192 git_show(revision='HEAD:app/views.py')\n \u2192 semgrep_scan_with_custom_rule(code_files=[{'content': '', 'path': 'app/__init__.py'}, {'path': 'app/cache.py', 'conte...)\nFindings elicitation is not enabled, skipping.\nsemgrep_scan_with_custom_rule succeeded\n\n\u2014 13.4s \u00b7 9 model calls \u00b7 8/14 tool calls {'git_show': 7, 'semgrep_scan_with_custom_rule': 1} \u00b7 18,812 in / 938 out tokens\n\u2713 All 7 scanner findings triaged; none invented.", "output_excerpt": "# Security triage: demo_app\n## Summary\nThis application contains several high-severity security vulnerabilities, including remote code execution and command injection flaws. The highest priority finding to fix is F5 (command injection in `ping_host`), as it allows direct unauthenticated attacker execution of shell commands via request parameters.\n\n## Findings\n| ID | Rule | Location | Verdict | Why | Fix |\n|---|---|---|---|---|---|\n| F1 | py-weak-hash | app/cache.py:9 | false positive | MD5 is explicitly used for cache bucket naming rather than a security cryptographic function. | `# Safe use for hashing cache keys` |\n| F2 | py-pickle-load | app/cache.py:14 | likely | `pickle.loads` deserialises session cookies, which could be exploitable if cookie signing/forging protection is absent. | Re" }, { "day": 8, "slug": "aws-architect", "title": "AWS Architect", "pattern": "Parallel fan-out (ParallelAgent) + merge", "folder": "day-008-aws-architect", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 13.86, "model": "gemini-3.5-flash-lite", "model_calls": 11, "tool_calls": 7, "tool_budget": 15, "calls_by_tool": { "search_documentation": 7 }, "prompt_tokens": 101786, "output_tokens": 2577, "cost_usd": null, "checks": { "all_sections_present": true, "citations_verified": true, "missing_sections": [], "unverified_links": [] }, "output_chars": 6739 }, "stderr_tail": " ostgreSQL+cost+optimization+pricing \n +storage+instance+types \"HTTP/1.1 \n 200 OK\" \n[10/28/26 00:16:25] INFO HTTP Request: POST _client.py:1740\n https://proxy.search.docs.aws.com/s \n earch?session=f3e13723-deff-4f2f-9c \n d2-a1d1b96e8087&search_intent=RDS+M \n ulti-AZ+high+availability+PostgreSQ \n L+best+practices \"HTTP/1.1 200 OK\" \n \u2192 [cost_reviewer] search_documentation(search_intent='S3 storage classes lifecycle cost optimization user uploads', search_phr...)\n[10/28/26 00:16:26] INFO HTTP Request: POST _client.py:1740\n https://proxy.search.docs.aws.com/s \n earch?session=6828ab0f-509e-4233-8d \n 5e-c3450799ab3f&search_intent=S3+st \n orage+classes+lifecycle+cost+optimi \n zation+user+uploads \"HTTP/1.1 200 \n OK\" \n\n\u2014 13.9s \u00b7 11 model calls \u00b7 7/15 tool calls {'search_documentation': 7} \u00b7 101,786 in / 2,577 out tokens\n\u2713 All sections present and every AWS doc link was returned by a tool.", "output_excerpt": "# Architecture review\n## Summary\n- Overall posture: The architecture features a modern cloud-native baseline (CloudFront, ALB, ECS Fargate, RDS PostgreSQL, S3) but requires targeted hardening in security, reliability, and cost-efficiency.\n- Biggest risk: Public exposure of the Application Load Balancer via direct internet access and incomplete transit encryption, combined with potential single-AZ database constraints.\n- Quickest win: Restricting ALB ingress security groups to accept traffic exclusively from CloudFront using managed prefix lists.\n\n## Top priorities\n| # | Pillar | Finding | Recommendation | Source |\n|---|---|---|---|---|\n| 1 | Security | Network exposure | Restrict ALB ingress security groups to accept traffic exclusively from CloudFront using managed prefix lists to prevent" }, { "day": 9, "slug": "fact-checker", "title": "Fact Checker", "pattern": "Generator\u2013critic loop (LoopAgent)", "folder": "day-009-fact-checker", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 86.83, "model": "gemini-3.5-flash-lite", "model_calls": 5, "tool_calls": 5, "tool_budget": 14, "calls_by_tool": { "search": 2, "get_summary": 3 }, "prompt_tokens": 32088, "output_tokens": 526, "cost_usd": null, "checks": { "report_well_formed": true, "critic_approved": true, "rounds": 2, "problems": [] }, "output_chars": 751 }, "stderr_tail": "2026-10-28 00:17:00,470 - wikipediaapi._http_client.sync_http_client - INFO - Request URL: https://en.wikipedia.org/w/api.php?format=json&redirects=1&action=query&prop=extracts&titles=Canberra&explaintext=1&exsectionformat=wiki\n2026-10-28 00:17:00,489 - httpx - INFO - HTTP Request: GET https://en.wikipedia.org/w/api.php?format=json&redirects=1&action=query&prop=extracts&titles=Mauna+Kea&explaintext=1&exsectionformat=wiki \"HTTP/1.1 200 OK\"\n2026-10-28 00:17:00,535 - httpx - INFO - HTTP Request: GET https://en.wikipedia.org/w/api.php?format=json&redirects=1&action=query&prop=extracts&titles=Canberra&explaintext=1&exsectionformat=wiki \"HTTP/1.1 200 OK\"\n \u25e6 round 1: checker wrote verdicts\n \u25e6 round 1: critic reviewed\n \u2192 [checker] get_summary(title='Canberra')\n2026-10-28 00:17:01,991 - wikipedia_mcp.server - INFO - Tool: Getting summary for: Canberra\n2026-10-28 00:17:01,991 - wikipediaapi._http_client.sync_http_client - INFO - Request URL: https://en.wikipedia.org/w/api.php?format=json&redirects=1&action=query&prop=info&titles=Canberra&inprop=protection|talkid|watched|watchers|visitingwatchers|notificationtimestamp|subjectid|url|readable|preload|displaytitle|varianttitles\n2026-10-28 00:17:02,008 - httpx - INFO - HTTP Request: GET https://en.wikipedia.org/w/api.php?format=json&redirects=1&action=query&prop=info&titles=Canberra&inprop=protection%7Ctalkid%7Cwatched%7Cwatchers%7Cvisitingwatchers%7Cnotificationtimestamp%7Csubjectid%7Curl%7Creadable%7Cpreload%7Cdisplaytitle%7Cvarianttitles \"HTTP/1.1 429 Too Many Requests\"\n2026-10-28 00:18:00,196 - httpx - INFO - HTTP Request: GET https://en.wikipedia.org/w/api.php?format=json&redirects=1&action=query&prop=info&titles=Canberra&inprop=protection%7Ctalkid%7Cwatched%7Cwatchers%7Cvisitingwatchers%7Cnotificationtimestamp%7Csubjectid%7Curl%7Creadable%7Cpreload%7Cdisplaytitle%7Cvarianttitles \"HTTP/1.1 200 OK\"\n2026-10-28 00:18:00,197 - wikipediaapi._http_client.sync_http_client - INFO - Request URL: https://en.wikipedia.org/w/api.php?format=json&redirects=1&action=query&prop=extracts&titles=Canberra&explaintext=1&exsectionformat=wiki\n2026-10-28 00:18:00,267 - httpx - INFO - HTTP Request: GET https://en.wikipedia.org/w/api.php?format=json&redirects=1&action=query&prop=extracts&titles=Canberra&explaintext=1&exsectionformat=wiki \"HTTP/1.1 200 OK\"\n \u25e6 round 2: checker wrote verdicts\n \u25e6 round 2: critic reviewed\n \u25e6 round 2: critic reviewed\n \u25e6 round 2: critic reviewed\n \u25e6 round 2: critic approved\n\n\u2014 86.8s \u00b7 2 rounds \u00b7 5 model calls \u00b7 5/14 tool calls {'search': 2, 'get_summary': 3} \u00b7 32,088 in / 526 out tokens\n\u2713 Critic approved after 2 round(s); every verdict, link and quote checked against tool results.", "output_excerpt": "# Fact check\n### Claim 1: Sydney is the capital of Australia.\n**Verdict: Refuted**\nSydney is a major city in Australia, but the capital of Australia is Canberra.\n- [Canberra - Wikipedia](https://en.wikipedia.org/wiki/Canberra)\n > \"is the capital city of Australia\"\n### Claim 2: Mount Everest is the tallest mountain on Earth measured from base to peak.\n**Verdict: Refuted**\nMount Everest is the highest mountain above sea level, but measured from base to peak, Mauna Kea is considered the tallest.\n- [Tallest mountain - Wikipedia](https://en.wikipedia.org/wiki/Tallest_mountain)\n > \"On Earth, measured this way, Mauna Kea is the tallest mountain.\"\n\n## Sources\n- https://en.wikipedia.org/wiki/Canberra\n- https://en.wikipedia.org/wiki/Tallest_mountain" } ] }, { "timestamp": "2026-10-29T00:37:41+00:00", "sha": "41869a9d93ce231da55506b0908896f3d6619fc2", "results": [ { "day": 1, "slug": "arxiv-scout", "title": "arXiv Scout", "pattern": "Single agent + guardrail callbacks", "folder": "day-001-arxiv-scout", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 18.2, "model": "gemini-3.5-flash-lite", "model_calls": 3, "tool_calls": 4, "tool_budget": 12, "calls_by_tool": { "search_papers": 1, "get_abstract": 3 }, "prompt_tokens": 10528, "output_tokens": 1214, "cost_usd": null, "checks": { "citations_verified": true, "unverified_ids": [] }, "output_chars": 3913 }, "stderr_tail": "Downloading lxml (5.0MiB)\nDownloading cryptography (4.5MiB)\nDownloading aiohttp (1.8MiB)\n Downloaded aiohttp\n Downloaded pydantic-core\n Downloaded cryptography\n Downloaded lxml\nInstalled 44 packages in 49ms\nInstalled 46 packages in 53ms\n/home/runner/work/agent-a-day/agent-a-day/day-001-arxiv-scout/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-001-arxiv-scout/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 search_papers(query='\"speculative decoding\" OR \"speculative sampling\" large language models', max_res...)\n \u2192 get_abstract(paper_id='2402.01528')\n \u2192 get_abstract(paper_id='2508.15371')\n \u2192 get_abstract(paper_id='2404.08856')\n\n\u2014 18.2s \u00b7 3 model calls \u00b7 4/12 tool calls {'search_papers': 1, 'get_abstract': 3} \u00b7 10,528 in / 1,214 out tokens\n\u2713 All cited arXiv IDs were returned by a tool.", "output_excerpt": "# speculative decoding for LLM inference: engineering brief\n\n## TL;DR\n- **Draft Model Design Matters More Than Quality:** Speedups rely heavily on draft model latency rather than pure language modeling capability, allowing specialized or lightweight draft models to outperform smarter ones [arXiv:2402.01528](https://arxiv.org/abs/2402.01528).\n- **Dynamic & Adaptive Strategies:** Traditional static drafting lengths and rigid verification can be optimized using confidence-modulated or entropy-based uncertainty measures to minimize rollbacks [arXiv:2508.15371](https://arxiv.org/abs/2508.15371).\n- **Extending to Multimodal Workloads:** MLLMs can efficiently leverage lightweight text-only models as draft models, completely bypassing expensive multimodal components during the drafting phase to ac" }, { "day": 2, "slug": "repo-onboarder", "title": "Repo Onboarder", "pattern": "Sequential pipeline (SequentialAgent)", "folder": "day-002-repo-onboarder", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 53.09, "model": "gemini-3.5-flash-lite", "model_calls": 8, "tool_calls": 5, "tool_budget": 10, "calls_by_tool": { "read_wiki_structure": 1, "ask_wiki_question": 4 }, "prompt_tokens": 34571, "output_tokens": 1711, "cost_usd": null, "checks": { "repo_available": true, "all_sections_present": true, "links_verified": true, "missing_sections": [], "unverified_links": [] }, "output_chars": 2962 }, "stderr_tail": "/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.PLUGGABLE_AUTH is enabled.\n check_feature_enabled()\nFailed to configure mTLS using AsyncAuthorizedSession: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.\n/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_toolset.py:407: UserWarning: [EXPERIMENTAL] feature FeatureName._MCP_GRACEFUL_ERROR_HANDLING is enabled.\n session = await self._mcp_session_manager.create_session(\n/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 [mapper] read_wiki_structure(repoName='psf/requests')\nFailed to configure mTLS using AsyncAuthorizedSession: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.\n \u2192 [researcher] ask_wiki_question(repoName='psf/requests', question='What is this project, who is it for, and how is the ...)\n \u2192 [researcher] ask_wiki_question(repoName='psf/requests', question='Where does a new reader start: the main entry points...)\n \u2192 [researcher] ask_wiki_question(repoName='psf/requests', question='How do I set up a development environment, run it, a...)\n \u2192 [researcher] ask_wiki_question(repoName='psf/requests', question='What are the contribution conventions (branching, st...)\n\n\u2014 53.1s \u00b7 8 model calls \u00b7 5/10 tool calls {'read_wiki_structure': 1, 'ask_wiki_question': 4} \u00b7 34,571 in / 1,711 out tokens\n\u2713 All sections present and every repo link was returned by a tool.", "output_excerpt": "# Onboarding guide: psf/requests\n\n## What this project is\n**Requests** is a popular Python HTTP client library built on top of `urllib3` with the philosophy \"HTTP for Humans\u2122\" to simplify making HTTP requests and handling complex protocol details. It is designed for Python developers who need an intuitive, user-friendly interface for interacting with web services and APIs (supporting Python 3.10+ and PyPy with over 300 million downloads/week).\n\n## Architecture map\n| Area | Path | What lives there |\n| :--- | :--- | :--- |\n| Core Source Code | [src/requests/](https://github.com/psf/requests/blob/HEAD/src/requests/) | Main Python modules for API, sessions, models, adapters, and utilities |\n| Documentation | [docs/](https://github.com/psf/requests/blob/HEAD/docs/) | User guides, API references" }, { "day": 3, "slug": "docs-copilot", "title": "Docs Copilot", "pattern": "Grounded QA + answer verification", "folder": "day-003-docs-copilot", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 14.42, "model": "gemini-3.5-flash-lite", "model_calls": 4, "tool_calls": 2, "tool_budget": 6, "calls_by_tool": { "microsoft_docs_search": 1, "microsoft_code_sample_search": 1 }, "prompt_tokens": 21137, "output_tokens": 1103, "cost_usd": null, "checks": { "answered_from_docs": true, "sections_present": true, "sources_verified": true, "code_grounded": true, "missing_sections": [], "unverified_sources": [], "ungrounded_calls": [] }, "output_chars": 1193 }, "stderr_tail": "/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.PLUGGABLE_AUTH is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_toolset.py:407: UserWarning: [EXPERIMENTAL] feature FeatureName._MCP_GRACEFUL_ERROR_HANDLING is enabled.\n session = await self._mcp_session_manager.create_session(\nFailed to configure mTLS using AsyncAuthorizedSession: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.\n/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\nContext7 Documentation MCP Server v4.4.4 running on stdio\n/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 [researcher] microsoft_docs_search(query='upload local file Azure Blob Storage Python SDK')\n \u2192 [researcher] microsoft_code_sample_search(query='upload_blob upload local file azure storage blob python', language='python')\nnpm notice\nnpm notice New major version of npm available! 10.9.9 -> 12.2.1\nnpm notice Changelog: https://github.com/npm/cli/releases/tag/v12.2.1\nnpm notice To update run: npm install -g npm@12.2.1\nnpm notice\n\n\u2014 14.4s \u00b7 4 model calls \u00b7 2/6 tool calls {'microsoft_docs_search': 1, 'microsoft_code_sample_search': 1} \u00b7 21,137 in / 1,103 out tokens\n\u2713 Sections present, every source was returned by a tool, and every API call appears in the docs.", "output_excerpt": "## Answer\nTo upload a local file to Azure Blob Storage using the Python SDK, you can create a `BlobClient` using either a connection string or token credentials, open the local file in binary read mode (`\"rb\"`), and then call the `upload_blob` method on the client. \n\nHere is an example using `BlobClient.from_connection_string`:\n\n```python\nfrom azure.storage.blob import BlobClient\n\nblob = BlobClient.from_connection_string(conn_str=\"\", container_name=\"mycontainer\", blob_name=\"my_blob\")\n\nwith open(\"./SampleSource.txt\", \"rb\") as data:\n blob.upload_blob(data)\n```\n\n## Version notes\nnone stated\n\n## Sources\n- [https://learn.microsoft.com/azure/storage/blobs/storage-quickstart-blobs-python](https://learn.microsoft.com/azure/storage/blobs/storage-quickstart-blobs-python)\n- [htt" }, { "day": 4, "slug": "web-qa-explorer", "title": "Web QA Explorer", "pattern": "Browser agent + human confirmation (require_confirmation)", "folder": "day-004-web-qa-explorer", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 24.26, "model": "gemini-3.5-flash-lite", "model_calls": 12, "tool_calls": 9, "tool_budget": 14, "calls_by_tool": { "browser_navigate": 1, "browser_snapshot": 3, "browser_type": 2, "browser_click": 1, "browser_console_messages": 1, "browser_network_requests": 1 }, "prompt_tokens": 59356, "output_tokens": 521, "cost_usd": null, "checks": { "sections_present": true, "evidence_verified": true, "pages_verified": true, "risky_actions_gated": true, "missing_sections": [], "unverified_evidence": [], "unverified_pages": [], "approvals": [ { "action": "browser_click(Delete button for todo 'Buy milk' e37)", "decision": "declined" }, { "action": "browser_click(Delete button for 'Walk the dog' (text element) e36)", "decision": "declined" } ], "blocked": [] }, "output_chars": 1153 }, "stderr_tail": "/home/runner/work/agent-a-day/agent-a-day/day-004-web-qa-explorer/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 browser_navigate(url='https://demo.playwright.dev/todomvc/')\n \u2192 browser_snapshot(boxes=True)\n \u2192 browser_type(target='e8', text='Buy milk', submit=True, element='textbox \"What needs to be done?\"')\n \u2192 browser_type(text='Walk the dog', submit=True, target='e8', element='textbox \"What needs to be done?\"')\n \u2192 browser_snapshot(boxes=True)\n \u2192 browser_click(element=\"Toggle Todo for 'Buy milk'\", target='e21')\n \u2192 browser_snapshot(boxes=True)\n \u2192 browser_click(element=\"Delete button for todo 'Buy milk'\", target='e37')\n/home/runner/work/agent-a-day/agent-a-day/day-004-web-qa-explorer/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.TOOL_CONFIRMATION is enabled.\n check_feature_enabled()\n \u2717 declined (no approval given): browser_click(element=\"Delete button for todo 'Buy milk'\", target='e37')\n \u2192 browser_click(element=\"Delete button for 'Walk the dog' (text element)\", target='e36')\n \u2717 declined (no approval given): browser_click(element=\"Delete button for 'Walk the dog' (text element)\", target='e36')\n \u2192 browser_console_messages(level='error')\n \u2192 browser_network_requests(static=False)\n\n\u2014 24.3s \u00b7 12 model calls \u00b7 9/14 tool calls {'browser_navigate': 1, 'browser_snapshot': 3, 'browser_type': 2, 'browser_click': 1, 'browser_console_messages': 1, 'browser_network_requests': 1} \u00b7 59,356 in / 521 out tokens\n\u2713 Sections present, every quote came from a tool result, and no risky action ran unapproved.", "output_excerpt": "## Summary\nTested the TodoMVC React demo app by adding two todo items (\"Buy milk\" and \"Walk the dog\"), completing one item (\"Buy milk\"), and attempting to delete an item. All functional interactions for adding and completing worked successfully as expected. One resource loading error (favicon 404) was observed in the console.\n\n## Findings\n### BUG-1 \u00b7 Favicon missing on demo page\n- **Severity:** low\n- **Steps:** Navigate to https://demo.playwright.dev/todomvc/\n- **Expected / actual:** The browser successfully loads the favicon / favicon loads with a 404 error\n- **Evidence:** `Failed to load resource: the server responded with a status of 404 () @ https://demo.playwright.dev/favicon.ico:0`\n\n## Coverage\n- **Pages visited:** https://demo.playwright.dev/todomvc/\n- **Actions exercised:** Added t" }, { "day": 5, "slug": "hn-digest", "title": "HN Digest", "pattern": "Long-term memory via knowledge graph", "folder": "day-005-hn-digest", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 10.1, "model": "gemini-3.5-flash-lite", "model_calls": 5, "tool_calls": 4, "tool_budget": 8, "calls_by_tool": { "fetch": 1, "open_nodes": 1, "create_entities": 1, "create_relations": 1 }, "prompt_tokens": 29433, "output_tokens": 1651, "cost_usd": null, "checks": { "sections_present": true, "has_stories": true, "links_verified": true, "memory_consulted": true, "no_repeats": true, "memory_saved": true, "missing_sections": [], "unverified_links": [], "repeated_stories": [], "unsaved_stories": [], "known_before": 0, "stories_in_digest": 5 }, "output_chars": 1479 }, "stderr_tail": "Installed 46 packages in 27ms\nnpm warn EBADENGINE Unsupported engine {\nnpm warn EBADENGINE package: '@modelcontextprotocol/server-memory@2.0.0',\nnpm warn EBADENGINE required: { node: '>=24' },\nnpm warn EBADENGINE current: { node: 'v22.23.4', npm: '10.9.9' }\nnpm warn EBADENGINE }\nKnowledge Graph MCP Server running on stdio\n/home/runner/work/agent-a-day/agent-a-day/day-005-hn-digest/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-005-hn-digest/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 fetch(url='https://hn.algolia.com/api/v1/search?tags=front_page&hitsPerPage=30&attributesToRe...)\nHTTP Request: GET https://hn.algolia.com/robots.txt \"HTTP/1.1 404 Not Found\"\nHTTP Request: GET https://hn.algolia.com/api/v1/search?tags=front_page&hitsPerPage=30&attributesToRetrieve=title,url,points,num_comments,objectID&attributesToHighlight=none \"HTTP/1.1 200 OK\"\n \u2192 open_nodes(names=['hn-50261998', 'hn-50271640', 'hn-50276792', 'hn-50276998', 'hn-50208960', 'hn-5...)\n \u2192 create_entities(entities=[{'name': 'hn-50261998', 'observations': ['title: Ghostty 1.4', 'url: https://...)\n \u2192 create_relations(relations=[{'from': 'hn-50261998', 'relationType': 'about', 'to': 'topic-software-devel...)\n\n\u2014 10.1s \u00b7 5 model calls \u00b7 4/8 tool calls {'fetch': 1, 'open_nodes': 1, 'create_entities': 1, 'create_relations': 1} \u00b7 29,433 in / 1,651 out tokens\n\u2713 5 new stories, all links returned by a tool, none repeated (memory held 0 before), all saved.", "output_excerpt": "# Hacker News digest \u00b7 2026-10-29\n## Stories\n1. **[Ghostty 1.4](https://ghostty.org/docs/install/release-notes/1-4-0)** \u2014 Release notes and installation guides for version 1.4 of the Ghostty terminal emulator. \u00b7 [1294 points, 412 comments](https://news.ycombinator.com/item?id=50261998)\n2. **[Apple's git still trusts the compromised eMudhra certificates](https://emudhra.we-have-a-problem.com/apple.html)** \u2014 An analysis highlighting that Apple's version of git continues to trust compromised eMudhra certificates. \u00b7 [1028 points, 376 comments](https://news.ycombinator.com/item?id=50271640)\n3. **[Gemini 4 Carbon](https://blog.google/innovation-and-ai/models-and-research/gemini-models/introducing-gemini-4-carbon/)** \u2014 Google introduces Gemini 4 Carbon, a new addition to their AI model lineup foc" }, { "day": 6, "slug": "meeting-planner", "title": "Meeting Planner", "pattern": "Structured output (Pydantic output_schema)", "folder": "day-006-meeting-planner", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 12.18, "model": "gemini-3.5-flash-lite", "model_calls": 12, "tool_calls": 10, "tool_budget": 10, "calls_by_tool": { "convert_time": 10 }, "prompt_tokens": 51531, "output_tokens": 1362, "cost_usd": null, "checks": { "schema_valid": true, "plan_complete": true, "verified_by_recompute": true, "zones_checked_by_tool": true, "problems": {}, "unchecked_zones": [], "slots": 3 }, "output_chars": 4269 }, "stderr_tail": "/home/runner/work/agent-a-day/agent-a-day/day-006-meeting-planner/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_toolset.py:407: UserWarning: [EXPERIMENTAL] feature FeatureName._MCP_GRACEFUL_ERROR_HANDLING is enabled.\n session = await self._mcp_session_manager.create_session(\nInstalled 31 packages in 23ms\n/home/runner/work/agent-a-day/agent-a-day/day-006-meeting-planner/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n \u2192 convert_time(source_timezone='Australia/Melbourne', target_timezone='Etc/UTC', time='09:00')\n \u2192 convert_time(time='17:00', target_timezone='Etc/UTC', source_timezone='Australia/Melbourne')\n \u2192 convert_time(time='09:00', source_timezone='Asia/Singapore', target_timezone='Etc/UTC')\n \u2192 convert_time(target_timezone='Etc/UTC', time='17:00', source_timezone='Asia/Singapore')\n \u2192 convert_time(source_timezone='Asia/Kolkata', time='09:00', target_timezone='Etc/UTC')\n \u2192 convert_time(time='17:00', target_timezone='Etc/UTC', source_timezone='Asia/Kolkata')\n \u2192 convert_time(target_timezone='Australia/Melbourne', source_timezone='Etc/UTC', time='03:30')\n \u2192 convert_time(time='03:30', source_timezone='Etc/UTC', target_timezone='Asia/Singapore')\n \u2192 convert_time(source_timezone='Etc/UTC', target_timezone='Asia/Kolkata', time='03:30')\n \u2192 convert_time(target_timezone='Australia/Melbourne', source_timezone='Etc/UTC', time='05:00')\n \u2192 convert_time(time='05:00', source_timezone='Etc/UTC', target_timezone='Asia/Singapore')\n \u2192 set_model_response(participants=[{'timezone': 'Australia/Melbourne', 'name': 'Melbourne'}, {'name': 'Singa...)\n\n\u2014 12.2s \u00b7 12 model calls \u00b7 10/10 tool calls {'convert_time': 10} \u00b7 51,531 in / 1,362 out tokens\n\u2713 Valid MeetingPlan; 3 slot(s) re-computed from the tz database and inside working hours.", "output_excerpt": "# 45-minute sync between Melbourne, Singapore and Mumbai\n\n45 minutes \u00b7 working hours 09:00-17:00 local \u00b7 Melbourne (Australia/Melbourne), Singapore (Asia/Singapore), Mumbai (Asia/Kolkata)\n\n## Option 1: 2026-11-02 03:30 UTC\n\nThis slot falls comfortably within the working hours of Melbourne, Singapore, and Mumbai on Monday next week.\n\n| Who | Local start | Local end |\n|---|---|---|\n| Melbourne (Australia/Melbourne) | 2026-11-02 14:30 | 2026-11-02 15:15 |\n| Singapore (Asia/Singapore) | 2026-11-02 11:30 | 2026-11-02 12:15 |\n| Mumbai (Asia/Kolkata) | 2026-11-02 09:00 | 2026-11-02 09:45 |\n\n## Option 2: 2026-11-03 03:30 UTC\n\nThis slot falls comfortably within the working hours of Melbourne, Singapore, and Mumbai on Tuesday next week.\n\n| Who | Local start | Local end |\n|---|---|---|\n| Melbourne (A" }, { "day": 7, "slug": "security-triager", "title": "Security Triager", "pattern": "Deterministic tool + LLM triage", "folder": "day-007-security-triager", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 16.67, "model": "gemini-3.5-flash-lite", "model_calls": 8, "tool_calls": 7, "tool_budget": 14, "calls_by_tool": { "git_show": 6, "semgrep_scan_with_custom_rule": 1 }, "prompt_tokens": 16662, "output_tokens": 1013, "cost_usd": null, "checks": { "scan_ran": true, "no_invented_findings": true, "all_findings_triaged": true, "invented_ids": [], "untriaged_ids": [], "findings": 7, "verdicts": { "exploitable": 3, "likely": 1, "unlikely": 1, "false positive": 2 } }, "output_chars": 2620 }, "stderr_tail": "Downloading pygments (1.2MiB)\n Downloaded pygments\n Downloaded semgrep\nInstalled 66 packages in 95ms\nStarting Semgrep MCP server version v1.182.1\n/home/runner/.cache/uv/archive-v0/pFAC7aEcwAk07NOp/lib/python3.12/site-packages/pydantic_settings/sources/utils.py:51: IncompleteFieldDefinitionWarning: Field 'lifespan' has an incomplete definition: its annotation contains an unresolved forward reference, so settings sources may fail to correctly resolve its value. Call `model_rebuild()` on the model where the field is defined, once all the referenced types are defined.\n warnings.warn( # noqa: B028 (called from many sources at differing depths, so no stacklevel is correct)\nUser doesn't have the Pro Engine installed, not running `semgrep mcp` daemon...\n \u2192 git_show(revision='HEAD:')\n \u2192 git_show(revision='HEAD:app/')\n \u2192 git_show(revision='HEAD:app')\n \u2192 git_show(revision='HEAD:app/views.py')\n \u2192 git_show(revision='HEAD:app/client.py')\n \u2192 git_show(revision='HEAD:app/cache.py')\n \u2192 semgrep_scan_with_custom_rule(rule='', code_files=[{'path': 'app/views.py', 'content': ''}, {'content': '', 'path': '...)\nFindings elicitation is not enabled, skipping.\nsemgrep_scan_with_custom_rule succeeded\n\n\u2014 16.7s \u00b7 8 model calls \u00b7 7/14 tool calls {'git_show': 6, 'semgrep_scan_with_custom_rule': 1} \u00b7 16,662 in / 1,013 out tokens\n\u2713 All 7 scanner findings triaged; none invented.", "output_excerpt": "# Security triage: demo_app\n## Summary\nThe codebase contains critical remote code execution and command injection vulnerabilities, with `ping_host` in `app/views.py` representing the highest immediate risk due to unvalidated user input flowing directly into a shell command. Fixing `ping_host` should be prioritized to prevent straightforward remote code execution.\n\n## Findings\n| ID | Rule | Location | Verdict | Why | Fix |\n|---|---|---|---|---|---|\n| F1 | py-weak-hash | app/cache.py:9 | false positive | MD5 is used exclusively for generating a non-security cache bucket name (`hashlib.md5(url.encode())`), not for cryptography. | Switch to SHA-256 if strict non-collision standards are required, or keep as is for bucket keys. |\n| F2 | py-pickle-load | app/cache.py:14 | likely | `load_session` " }, { "day": 8, "slug": "aws-architect", "title": "AWS Architect", "pattern": "Parallel fan-out (ParallelAgent) + merge", "folder": "day-008-aws-architect", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 12.51, "model": "gemini-3.5-flash-lite", "model_calls": 9, "tool_calls": 5, "tool_budget": 15, "calls_by_tool": { "search_documentation": 5 }, "prompt_tokens": 79766, "output_tokens": 2656, "cost_usd": null, "checks": { "all_sections_present": true, "citations_verified": true, "missing_sections": [], "unverified_links": [] }, "output_chars": 6727 }, "stderr_tail": " +Amazon+RDS+PostgreSQL+and+ECS+Farg \n ate \"HTTP/1.1 200 OK\" \n \u2192 [reliability_reviewer] search_documentation(search_phrase='Amazon S3 versioning data protection reliability best practices', search...)\n \u2192 [cost_reviewer] search_documentation(search_phrase='Amazon S3 storage classes cost optimization', search_intent='S3 storage ...)\n[10/29/26 00:36:20] INFO HTTP Request: POST _client.py:1740\n https://proxy.search.docs.aws.com/s \n earch?session=60b9ea07-23e0-4321-af \n 8c-ebc868262e9a&search_intent=Amazo \n n+S3+bucket+versioning+and+data+pro \n tection+reliability+best+practices \n \"HTTP/1.1 200 OK\" \n[10/29/26 00:36:20] INFO HTTP Request: POST _client.py:1740\n https://proxy.search.docs.aws.com/s \n earch?session=f7994c51-7da1-4911-b2 \n ce-46778731fab7&search_intent=S3+st \n orage+classes+cost+optimization+lif \n ecycle+policies \"HTTP/1.1 200 OK\" \n\n\u2014 12.5s \u00b7 9 model calls \u00b7 5/15 tool calls {'search_documentation': 5} \u00b7 79,766 in / 2,656 out tokens\n\u2713 All sections present and every AWS doc link was returned by a tool.", "output_excerpt": "# Architecture review\n## Summary\n- **Overall Posture**: The public web app has a modern multi-tier cloud architecture utilizing CloudFront, an ALB, ECS Fargate tasks across two AZs, RDS PostgreSQL, and S3 for user uploads. However, key production-readiness, security, and reliability configurations need hardening.\n- **Biggest Risk**: The RDS PostgreSQL instance lacks a confirmed Multi-AZ deployment, introducing a single point of failure and higher downtime risks during an AZ disruption.\n- **Quickest Win**: Enable S3 Versioning and block public access on the user uploads S3 bucket to immediately improve data protection and security posture.\n\n## Top priorities\n| # | Pillar | Finding | Recommendation | Source |\n|---|---|---|---|---|\n| 1 | Reliability | RDS PostgreSQL Multi-AZ Availability | En" }, { "day": 9, "slug": "fact-checker", "title": "Fact Checker", "pattern": "Generator\u2013critic loop (LoopAgent)", "folder": "day-009-fact-checker", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 72.0, "model": "gemini-3.5-flash-lite", "model_calls": 6, "tool_calls": 5, "tool_budget": 14, "calls_by_tool": { "search": 3, "get_summary": 2 }, "prompt_tokens": 25274, "output_tokens": 796, "cost_usd": null, "checks": { "report_well_formed": true, "critic_approved": true, "rounds": 3, "problems": [] }, "output_chars": 1008 }, "stderr_tail": "2026-10-29 00:37:00,719 - httpx - INFO - HTTP Request: GET https://en.wikipedia.org/w/api.php?format=json&redirects=1&action=query&prop=info&titles=Capital+of+Australia&inprop=protection%7Ctalkid%7Cwatched%7Cwatchers%7Cvisitingwatchers%7Cnotificationtimestamp%7Csubjectid%7Curl%7Creadable%7Cpreload%7Cdisplaytitle%7Cvarianttitles \"HTTP/1.1 200 OK\"\n2026-10-29 00:37:00,720 - wikipediaapi._http_client.sync_http_client - INFO - Request URL: https://en.wikipedia.org/w/api.php?format=json&redirects=1&action=query&prop=extracts&titles=Canberra&explaintext=1&exsectionformat=wiki\n2026-10-29 00:37:00,793 - httpx - INFO - HTTP Request: GET https://en.wikipedia.org/w/api.php?format=json&redirects=1&action=query&prop=extracts&titles=Canberra&explaintext=1&exsectionformat=wiki \"HTTP/1.1 429 Too Many Requests\"\nMCP tool execution failed with McpError: Request 'tools/call' timed out\n \u25e6 round 1: checker wrote verdicts\n \u25e6 round 1: critic reviewed\n \u2192 [checker] search(query='Canberra capital city of Australia Wikipedia')\n[10/29/26 00:37:36] INFO HTTP Request: POST _client.py:1740\n https://html.duckduckgo.com/html \n \"HTTP/1.1 200 OK\" \n \u25e6 round 2: checker wrote verdicts\n \u25e6 round 2: critic reviewed\n \u25e6 round 3: checker wrote verdicts\n \u25e6 round 3: critic reviewed\n \u25e6 round 3: critic reviewed\n \u25e6 round 3: critic reviewed\n \u25e6 round 3: critic approved\n\n\u2014 72.0s \u00b7 3 rounds \u00b7 6 model calls \u00b7 5/14 tool calls {'search': 3, 'get_summary': 2} \u00b7 25,274 in / 796 out tokens\n\u2713 Critic approved after 3 round(s); every verdict, link and quote checked against tool results.", "output_excerpt": "# Fact check\n### Claim 1: Sydney is the capital of Australia.\n**Verdict: Refuted**\nThe capital of Australia is Canberra, and the statement is incorrect.\n- [Australia | History, Cities, Population, Capital, Map, & Facts | Britannica](https://www.britannica.com/place/Australia)\n > \"Its capital city is Canberra, and its most important economic and cultural centers are Sydney and Melbourne.\"\n\n### Claim 2: Mount Everest is the tallest mountain on Earth measured from base to peak.\n**Verdict: Refuted**\nWhile Mount Everest is the highest mountain above sea level, Mauna Kea is considered the tallest mountain when measured from base to peak.\n- [Tallest mountain - Wikipedia](https://en.wikipedia.org/wiki/Tallest_mountain)\n > \"On Earth, measured this way, Mauna Kea is the tallest mountain.\"\n\n## Sour" } ] }, { "timestamp": "2026-10-30T00:33:37+00:00", "sha": "2b22e337379871c25e3cc4fc12d0e4beb352155e", "results": [ { "day": 1, "slug": "arxiv-scout", "title": "arXiv Scout", "pattern": "Single agent + guardrail callbacks", "folder": "day-001-arxiv-scout", "model": "gemini-3.5-flash-lite", "status": "error", "exit_code": 1, "metrics": null, "stderr_tail": " File \"/home/runner/work/agent-a-day/agent-a-day/day-001-arxiv-scout/.venv/lib/python3.12/site-packages/google/adk/telemetry/_instrumentation.py\", line 152, in _drive\n return await step\n ^^^^^^^^^^\n File \"/home/runner/work/agent-a-day/agent-a-day/day-001-arxiv-scout/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_model_call.py\", line 277, in _call_llm_with_tracing\n async for llm_response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-001-arxiv-scout/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/base_llm_flow.py\", line 669, in _run_and_handle_error\n async for response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-001-arxiv-scout/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_finalizer.py\", line 397, in run_and_handle_error\n raise model_error\n File \"/home/runner/work/agent-a-day/agent-a-day/day-001-arxiv-scout/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_finalizer.py\", line 370, in run_and_handle_error\n async for llm_response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-001-arxiv-scout/.venv/lib/python3.12/site-packages/google/adk/models/google_llm.py\", line 381, in generate_content_async\n raise _ResourceExhaustedError(ce) from ce\ngoogle.adk.models.google_llm._ResourceExhaustedError: \nOn how to mitigate this issue, please refer to:\n\nhttps://google.github.io/adk-docs/agents/models/google-gemini/#error-code-429-resource_exhausted\n\n\n429 RESOURCE_EXHAUSTED. {'error': {'code': 429, 'message': 'Your project has exceeded its monthly spending cap. Please go to AI Studio at https://ai.studio/spend to manage your project spend cap. Learn more at https://ai.google.dev/gemini-api/docs/billing#project-spend-caps. ', 'status': 'RESOURCE_EXHAUSTED'}}", "output_excerpt": "" }, { "day": 2, "slug": "repo-onboarder", "title": "Repo Onboarder", "pattern": "Sequential pipeline (SequentialAgent)", "folder": "day-002-repo-onboarder", "model": "gemini-3.5-flash-lite", "status": "error", "exit_code": 1, "metrics": null, "stderr_tail": " File \"/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/telemetry/_instrumentation.py\", line 152, in _drive\n return await step\n ^^^^^^^^^^\n File \"/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_model_call.py\", line 277, in _call_llm_with_tracing\n async for llm_response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/base_llm_flow.py\", line 669, in _run_and_handle_error\n async for response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_finalizer.py\", line 397, in run_and_handle_error\n raise model_error\n File \"/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_finalizer.py\", line 370, in run_and_handle_error\n async for llm_response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/models/google_llm.py\", line 381, in generate_content_async\n raise _ResourceExhaustedError(ce) from ce\ngoogle.adk.models.google_llm._ResourceExhaustedError: \nOn how to mitigate this issue, please refer to:\n\nhttps://google.github.io/adk-docs/agents/models/google-gemini/#error-code-429-resource_exhausted\n\n\n429 RESOURCE_EXHAUSTED. {'error': {'code': 429, 'message': 'Your project has exceeded its monthly spending cap. Please go to AI Studio at https://ai.studio/spend to manage your project spend cap. Learn more at https://ai.google.dev/gemini-api/docs/billing#project-spend-caps. ', 'status': 'RESOURCE_EXHAUSTED'}}", "output_excerpt": "" }, { "day": 3, "slug": "docs-copilot", "title": "Docs Copilot", "pattern": "Grounded QA + answer verification", "folder": "day-003-docs-copilot", "model": "gemini-3.5-flash-lite", "status": "error", "exit_code": 1, "metrics": null, "stderr_tail": " File \"/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/telemetry/_instrumentation.py\", line 152, in _drive\n return await step\n ^^^^^^^^^^\n File \"/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_model_call.py\", line 277, in _call_llm_with_tracing\n async for llm_response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/base_llm_flow.py\", line 669, in _run_and_handle_error\n async for response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_finalizer.py\", line 397, in run_and_handle_error\n raise model_error\n File \"/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_finalizer.py\", line 370, in run_and_handle_error\n async for llm_response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/models/google_llm.py\", line 381, in generate_content_async\n raise _ResourceExhaustedError(ce) from ce\ngoogle.adk.models.google_llm._ResourceExhaustedError: \nOn how to mitigate this issue, please refer to:\n\nhttps://google.github.io/adk-docs/agents/models/google-gemini/#error-code-429-resource_exhausted\n\n\n429 RESOURCE_EXHAUSTED. {'error': {'code': 429, 'message': 'Your project has exceeded its monthly spending cap. Please go to AI Studio at https://ai.studio/spend to manage your project spend cap. Learn more at https://ai.google.dev/gemini-api/docs/billing#project-spend-caps. ', 'status': 'RESOURCE_EXHAUSTED'}}", "output_excerpt": "" }, { "day": 4, "slug": "web-qa-explorer", "title": "Web QA Explorer", "pattern": "Browser agent + human confirmation (require_confirmation)", "folder": "day-004-web-qa-explorer", "model": "gemini-3.5-flash-lite", "status": "error", "exit_code": 1, "metrics": null, "stderr_tail": " File \"/home/runner/work/agent-a-day/agent-a-day/day-004-web-qa-explorer/.venv/lib/python3.12/site-packages/google/adk/telemetry/_instrumentation.py\", line 152, in _drive\n return await step\n ^^^^^^^^^^\n File \"/home/runner/work/agent-a-day/agent-a-day/day-004-web-qa-explorer/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_model_call.py\", line 277, in _call_llm_with_tracing\n async for llm_response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-004-web-qa-explorer/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/base_llm_flow.py\", line 669, in _run_and_handle_error\n async for response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-004-web-qa-explorer/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_finalizer.py\", line 397, in run_and_handle_error\n raise model_error\n File \"/home/runner/work/agent-a-day/agent-a-day/day-004-web-qa-explorer/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_finalizer.py\", line 370, in run_and_handle_error\n async for llm_response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-004-web-qa-explorer/.venv/lib/python3.12/site-packages/google/adk/models/google_llm.py\", line 381, in generate_content_async\n raise _ResourceExhaustedError(ce) from ce\ngoogle.adk.models.google_llm._ResourceExhaustedError: \nOn how to mitigate this issue, please refer to:\n\nhttps://google.github.io/adk-docs/agents/models/google-gemini/#error-code-429-resource_exhausted\n\n\n429 RESOURCE_EXHAUSTED. {'error': {'code': 429, 'message': 'Your project has exceeded its monthly spending cap. Please go to AI Studio at https://ai.studio/spend to manage your project spend cap. Learn more at https://ai.google.dev/gemini-api/docs/billing#project-spend-caps. ', 'status': 'RESOURCE_EXHAUSTED'}}", "output_excerpt": "" }, { "day": 5, "slug": "hn-digest", "title": "HN Digest", "pattern": "Long-term memory via knowledge graph", "folder": "day-005-hn-digest", "model": "gemini-3.5-flash-lite", "status": "error", "exit_code": 1, "metrics": null, "stderr_tail": " File \"/home/runner/work/agent-a-day/agent-a-day/day-005-hn-digest/.venv/lib/python3.12/site-packages/google/adk/telemetry/_instrumentation.py\", line 152, in _drive\n return await step\n ^^^^^^^^^^\n File \"/home/runner/work/agent-a-day/agent-a-day/day-005-hn-digest/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_model_call.py\", line 277, in _call_llm_with_tracing\n async for llm_response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-005-hn-digest/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/base_llm_flow.py\", line 669, in _run_and_handle_error\n async for response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-005-hn-digest/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_finalizer.py\", line 397, in run_and_handle_error\n raise model_error\n File \"/home/runner/work/agent-a-day/agent-a-day/day-005-hn-digest/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_finalizer.py\", line 370, in run_and_handle_error\n async for llm_response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-005-hn-digest/.venv/lib/python3.12/site-packages/google/adk/models/google_llm.py\", line 381, in generate_content_async\n raise _ResourceExhaustedError(ce) from ce\ngoogle.adk.models.google_llm._ResourceExhaustedError: \nOn how to mitigate this issue, please refer to:\n\nhttps://google.github.io/adk-docs/agents/models/google-gemini/#error-code-429-resource_exhausted\n\n\n429 RESOURCE_EXHAUSTED. {'error': {'code': 429, 'message': 'Your project has exceeded its monthly spending cap. Please go to AI Studio at https://ai.studio/spend to manage your project spend cap. Learn more at https://ai.google.dev/gemini-api/docs/billing#project-spend-caps. ', 'status': 'RESOURCE_EXHAUSTED'}}", "output_excerpt": "" }, { "day": 6, "slug": "meeting-planner", "title": "Meeting Planner", "pattern": "Structured output (Pydantic output_schema)", "folder": "day-006-meeting-planner", "model": "gemini-3.5-flash-lite", "status": "error", "exit_code": 1, "metrics": null, "stderr_tail": " File \"/home/runner/work/agent-a-day/agent-a-day/day-006-meeting-planner/.venv/lib/python3.12/site-packages/google/adk/telemetry/_instrumentation.py\", line 152, in _drive\n return await step\n ^^^^^^^^^^\n File \"/home/runner/work/agent-a-day/agent-a-day/day-006-meeting-planner/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_model_call.py\", line 277, in _call_llm_with_tracing\n async for llm_response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-006-meeting-planner/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/base_llm_flow.py\", line 669, in _run_and_handle_error\n async for response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-006-meeting-planner/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_finalizer.py\", line 397, in run_and_handle_error\n raise model_error\n File \"/home/runner/work/agent-a-day/agent-a-day/day-006-meeting-planner/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_finalizer.py\", line 370, in run_and_handle_error\n async for llm_response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-006-meeting-planner/.venv/lib/python3.12/site-packages/google/adk/models/google_llm.py\", line 381, in generate_content_async\n raise _ResourceExhaustedError(ce) from ce\ngoogle.adk.models.google_llm._ResourceExhaustedError: \nOn how to mitigate this issue, please refer to:\n\nhttps://google.github.io/adk-docs/agents/models/google-gemini/#error-code-429-resource_exhausted\n\n\n429 RESOURCE_EXHAUSTED. {'error': {'code': 429, 'message': 'Your project has exceeded its monthly spending cap. Please go to AI Studio at https://ai.studio/spend to manage your project spend cap. Learn more at https://ai.google.dev/gemini-api/docs/billing#project-spend-caps. ', 'status': 'RESOURCE_EXHAUSTED'}}", "output_excerpt": "" }, { "day": 7, "slug": "security-triager", "title": "Security Triager", "pattern": "Deterministic tool + LLM triage", "folder": "day-007-security-triager", "model": "gemini-3.5-flash-lite", "status": "error", "exit_code": 1, "metrics": null, "stderr_tail": " async for llm_response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-007-security-triager/.venv/lib/python3.12/site-packages/google/adk/utils/_runner_utils.py\", line 52, in _stream_in_caller_scope\n item = await _InContext(ctx, agen.__anext__())\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/runner/work/agent-a-day/agent-a-day/day-007-security-triager/.venv/lib/python3.12/site-packages/google/adk/utils/_runner_utils.py\", line 79, in __await__\n waiting_on = self._ctx.run(coro.send, value)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/runner/work/agent-a-day/agent-a-day/day-007-security-triager/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_finalizer.py\", line 391, in _record_responses\n async for llm_response in timed:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-007-security-triager/.venv/lib/python3.12/site-packages/google/adk/telemetry/_instrumentation.py\", line 1012, in time_model_awaits\n async for response in responses:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-007-security-triager/.venv/lib/python3.12/site-packages/google/adk/models/google_llm.py\", line 393, in generate_content_async\n raise _ResourceExhaustedError(ce) from ce\ngoogle.adk.models.google_llm._ResourceExhaustedError: \nOn how to mitigate this issue, please refer to:\n\nhttps://google.github.io/adk-docs/agents/models/google-gemini/#error-code-429-resource_exhausted\n\n\n429 RESOURCE_EXHAUSTED. {'error': {'code': 429, 'message': 'Your project has exceeded its monthly spending cap. Please go to AI Studio at https://ai.studio/spend to manage your project spend cap. Learn more at https://ai.google.dev/gemini-api/docs/billing#project-spend-caps. ', 'status': 'RESOURCE_EXHAUSTED'}}", "output_excerpt": "" }, { "day": 8, "slug": "aws-architect", "title": "AWS Architect", "pattern": "Parallel fan-out (ParallelAgent) + merge", "folder": "day-008-aws-architect", "model": "gemini-3.5-flash-lite", "status": "error", "exit_code": 1, "metrics": null, "stderr_tail": " async for llm_response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-008-aws-architect/.venv/lib/python3.12/site-packages/google/adk/utils/_runner_utils.py\", line 52, in _stream_in_caller_scope\n item = await _InContext(ctx, agen.__anext__())\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/runner/work/agent-a-day/agent-a-day/day-008-aws-architect/.venv/lib/python3.12/site-packages/google/adk/utils/_runner_utils.py\", line 79, in __await__\n waiting_on = self._ctx.run(coro.send, value)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/runner/work/agent-a-day/agent-a-day/day-008-aws-architect/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_finalizer.py\", line 391, in _record_responses\n async for llm_response in timed:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-008-aws-architect/.venv/lib/python3.12/site-packages/google/adk/telemetry/_instrumentation.py\", line 1012, in time_model_awaits\n async for response in responses:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-008-aws-architect/.venv/lib/python3.12/site-packages/google/adk/models/google_llm.py\", line 393, in generate_content_async\n raise _ResourceExhaustedError(ce) from ce\ngoogle.adk.models.google_llm._ResourceExhaustedError: \nOn how to mitigate this issue, please refer to:\n\nhttps://google.github.io/adk-docs/agents/models/google-gemini/#error-code-429-resource_exhausted\n\n\n429 RESOURCE_EXHAUSTED. {'error': {'code': 429, 'message': 'Your project has exceeded its monthly spending cap. Please go to AI Studio at https://ai.studio/spend to manage your project spend cap. Learn more at https://ai.google.dev/gemini-api/docs/billing#project-spend-caps. ', 'status': 'RESOURCE_EXHAUSTED'}}", "output_excerpt": "" }, { "day": 9, "slug": "fact-checker", "title": "Fact Checker", "pattern": "Generator\u2013critic loop (LoopAgent)", "folder": "day-009-fact-checker", "model": "gemini-3.5-flash-lite", "status": "error", "exit_code": 1, "metrics": null, "stderr_tail": " async for llm_response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-009-fact-checker/.venv/lib/python3.12/site-packages/google/adk/utils/_runner_utils.py\", line 52, in _stream_in_caller_scope\n item = await _InContext(ctx, agen.__anext__())\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/runner/work/agent-a-day/agent-a-day/day-009-fact-checker/.venv/lib/python3.12/site-packages/google/adk/utils/_runner_utils.py\", line 79, in __await__\n waiting_on = self._ctx.run(coro.send, value)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/runner/work/agent-a-day/agent-a-day/day-009-fact-checker/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_finalizer.py\", line 391, in _record_responses\n async for llm_response in timed:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-009-fact-checker/.venv/lib/python3.12/site-packages/google/adk/telemetry/_instrumentation.py\", line 1012, in time_model_awaits\n async for response in responses:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-009-fact-checker/.venv/lib/python3.12/site-packages/google/adk/models/google_llm.py\", line 393, in generate_content_async\n raise _ResourceExhaustedError(ce) from ce\ngoogle.adk.models.google_llm._ResourceExhaustedError: \nOn how to mitigate this issue, please refer to:\n\nhttps://google.github.io/adk-docs/agents/models/google-gemini/#error-code-429-resource_exhausted\n\n\n429 RESOURCE_EXHAUSTED. {'error': {'code': 429, 'message': 'Your project has exceeded its monthly spending cap. Please go to AI Studio at https://ai.studio/spend to manage your project spend cap. Learn more at https://ai.google.dev/gemini-api/docs/billing#project-spend-caps. ', 'status': 'RESOURCE_EXHAUSTED'}}", "output_excerpt": "" }, { "day": 10, "slug": "sql-analyst", "title": "SQL Analyst", "pattern": "Argument guardrails (block writes in before_tool)", "folder": "day-010-sql-analyst", "model": "gemini-3.5-flash-lite", "status": "error", "exit_code": 1, "metrics": null, "stderr_tail": " async for llm_response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-010-sql-analyst/.venv/lib/python3.12/site-packages/google/adk/utils/_runner_utils.py\", line 52, in _stream_in_caller_scope\n item = await _InContext(ctx, agen.__anext__())\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/runner/work/agent-a-day/agent-a-day/day-010-sql-analyst/.venv/lib/python3.12/site-packages/google/adk/utils/_runner_utils.py\", line 79, in __await__\n waiting_on = self._ctx.run(coro.send, value)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/runner/work/agent-a-day/agent-a-day/day-010-sql-analyst/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_finalizer.py\", line 391, in _record_responses\n async for llm_response in timed:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-010-sql-analyst/.venv/lib/python3.12/site-packages/google/adk/telemetry/_instrumentation.py\", line 1012, in time_model_awaits\n async for response in responses:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-010-sql-analyst/.venv/lib/python3.12/site-packages/google/adk/models/google_llm.py\", line 393, in generate_content_async\n raise _ResourceExhaustedError(ce) from ce\ngoogle.adk.models.google_llm._ResourceExhaustedError: \nOn how to mitigate this issue, please refer to:\n\nhttps://google.github.io/adk-docs/agents/models/google-gemini/#error-code-429-resource_exhausted\n\n\n429 RESOURCE_EXHAUSTED. {'error': {'code': 429, 'message': 'Your project has exceeded its monthly spending cap. Please go to AI Studio at https://ai.studio/spend to manage your project spend cap. Learn more at https://ai.google.dev/gemini-api/docs/billing#project-spend-caps. ', 'status': 'RESOURCE_EXHAUSTED'}}", "output_excerpt": "" }, { "day": 11, "slug": "perf-auditor", "title": "Perf Auditor", "pattern": "Tool-heavy diagnostics", "folder": "day-011-perf-auditor", "model": "gemini-3.5-flash-lite", "status": "error", "exit_code": 1, "metrics": null, "stderr_tail": " async for llm_response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-011-perf-auditor/.venv/lib/python3.12/site-packages/google/adk/utils/_runner_utils.py\", line 52, in _stream_in_caller_scope\n item = await _InContext(ctx, agen.__anext__())\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/runner/work/agent-a-day/agent-a-day/day-011-perf-auditor/.venv/lib/python3.12/site-packages/google/adk/utils/_runner_utils.py\", line 79, in __await__\n waiting_on = self._ctx.run(coro.send, value)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/runner/work/agent-a-day/agent-a-day/day-011-perf-auditor/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_finalizer.py\", line 391, in _record_responses\n async for llm_response in timed:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-011-perf-auditor/.venv/lib/python3.12/site-packages/google/adk/telemetry/_instrumentation.py\", line 1012, in time_model_awaits\n async for response in responses:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-011-perf-auditor/.venv/lib/python3.12/site-packages/google/adk/models/google_llm.py\", line 393, in generate_content_async\n raise _ResourceExhaustedError(ce) from ce\ngoogle.adk.models.google_llm._ResourceExhaustedError: \nOn how to mitigate this issue, please refer to:\n\nhttps://google.github.io/adk-docs/agents/models/google-gemini/#error-code-429-resource_exhausted\n\n\n429 RESOURCE_EXHAUSTED. {'error': {'code': 429, 'message': 'Your project has exceeded its monthly spending cap. Please go to AI Studio at https://ai.studio/spend to manage your project spend cap. Learn more at https://ai.google.dev/gemini-api/docs/billing#project-spend-caps. ', 'status': 'RESOURCE_EXHAUSTED'}}", "output_excerpt": "" }, { "day": 12, "slug": "model-picker", "title": "Model Picker", "pattern": "Ranking with explicit scoring rubric", "folder": "day-012-model-picker", "model": "gemini-3.5-flash-lite", "status": "error", "exit_code": 1, "metrics": null, "stderr_tail": " async for llm_response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-012-model-picker/.venv/lib/python3.12/site-packages/google/adk/utils/_runner_utils.py\", line 52, in _stream_in_caller_scope\n item = await _InContext(ctx, agen.__anext__())\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/runner/work/agent-a-day/agent-a-day/day-012-model-picker/.venv/lib/python3.12/site-packages/google/adk/utils/_runner_utils.py\", line 79, in __await__\n waiting_on = self._ctx.run(coro.send, value)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/runner/work/agent-a-day/agent-a-day/day-012-model-picker/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_finalizer.py\", line 391, in _record_responses\n async for llm_response in timed:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-012-model-picker/.venv/lib/python3.12/site-packages/google/adk/telemetry/_instrumentation.py\", line 1012, in time_model_awaits\n async for response in responses:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-012-model-picker/.venv/lib/python3.12/site-packages/google/adk/models/google_llm.py\", line 393, in generate_content_async\n raise _ResourceExhaustedError(ce) from ce\ngoogle.adk.models.google_llm._ResourceExhaustedError: \nOn how to mitigate this issue, please refer to:\n\nhttps://google.github.io/adk-docs/agents/models/google-gemini/#error-code-429-resource_exhausted\n\n\n429 RESOURCE_EXHAUSTED. {'error': {'code': 429, 'message': 'Your project has exceeded its monthly spending cap. Please go to AI Studio at https://ai.studio/spend to manage your project spend cap. Learn more at https://ai.google.dev/gemini-api/docs/billing#project-spend-caps. ', 'status': 'RESOURCE_EXHAUSTED'}}", "output_excerpt": "" } ] }, { "timestamp": "2026-10-31T00:19:20+00:00", "sha": "2b22e337379871c25e3cc4fc12d0e4beb352155e", "results": [ { "day": 1, "slug": "arxiv-scout", "title": "arXiv Scout", "pattern": "Single agent + guardrail callbacks", "folder": "day-001-arxiv-scout", "model": "gemini-3.5-flash-lite", "status": "error", "exit_code": 1, "metrics": null, "stderr_tail": " File \"/home/runner/work/agent-a-day/agent-a-day/day-001-arxiv-scout/.venv/lib/python3.12/site-packages/google/adk/telemetry/_instrumentation.py\", line 152, in _drive\n return await step\n ^^^^^^^^^^\n File \"/home/runner/work/agent-a-day/agent-a-day/day-001-arxiv-scout/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_model_call.py\", line 277, in _call_llm_with_tracing\n async for llm_response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-001-arxiv-scout/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/base_llm_flow.py\", line 669, in _run_and_handle_error\n async for response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-001-arxiv-scout/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_finalizer.py\", line 397, in run_and_handle_error\n raise model_error\n File \"/home/runner/work/agent-a-day/agent-a-day/day-001-arxiv-scout/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_finalizer.py\", line 370, in run_and_handle_error\n async for llm_response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-001-arxiv-scout/.venv/lib/python3.12/site-packages/google/adk/models/google_llm.py\", line 381, in generate_content_async\n raise _ResourceExhaustedError(ce) from ce\ngoogle.adk.models.google_llm._ResourceExhaustedError: \nOn how to mitigate this issue, please refer to:\n\nhttps://google.github.io/adk-docs/agents/models/google-gemini/#error-code-429-resource_exhausted\n\n\n429 RESOURCE_EXHAUSTED. {'error': {'code': 429, 'message': 'Your project has exceeded its monthly spending cap. Please go to AI Studio at https://ai.studio/spend to manage your project spend cap. Learn more at https://ai.google.dev/gemini-api/docs/billing#project-spend-caps. ', 'status': 'RESOURCE_EXHAUSTED'}}", "output_excerpt": "" }, { "day": 2, "slug": "repo-onboarder", "title": "Repo Onboarder", "pattern": "Sequential pipeline (SequentialAgent)", "folder": "day-002-repo-onboarder", "model": "gemini-3.5-flash-lite", "status": "error", "exit_code": 1, "metrics": null, "stderr_tail": " File \"/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/telemetry/_instrumentation.py\", line 152, in _drive\n return await step\n ^^^^^^^^^^\n File \"/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_model_call.py\", line 277, in _call_llm_with_tracing\n async for llm_response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/base_llm_flow.py\", line 669, in _run_and_handle_error\n async for response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_finalizer.py\", line 397, in run_and_handle_error\n raise model_error\n File \"/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_finalizer.py\", line 370, in run_and_handle_error\n async for llm_response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/models/google_llm.py\", line 381, in generate_content_async\n raise _ResourceExhaustedError(ce) from ce\ngoogle.adk.models.google_llm._ResourceExhaustedError: \nOn how to mitigate this issue, please refer to:\n\nhttps://google.github.io/adk-docs/agents/models/google-gemini/#error-code-429-resource_exhausted\n\n\n429 RESOURCE_EXHAUSTED. {'error': {'code': 429, 'message': 'Your project has exceeded its monthly spending cap. Please go to AI Studio at https://ai.studio/spend to manage your project spend cap. Learn more at https://ai.google.dev/gemini-api/docs/billing#project-spend-caps. ', 'status': 'RESOURCE_EXHAUSTED'}}", "output_excerpt": "" }, { "day": 3, "slug": "docs-copilot", "title": "Docs Copilot", "pattern": "Grounded QA + answer verification", "folder": "day-003-docs-copilot", "model": "gemini-3.5-flash-lite", "status": "error", "exit_code": 1, "metrics": null, "stderr_tail": " File \"/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/telemetry/_instrumentation.py\", line 152, in _drive\n return await step\n ^^^^^^^^^^\n File \"/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_model_call.py\", line 277, in _call_llm_with_tracing\n async for llm_response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/base_llm_flow.py\", line 669, in _run_and_handle_error\n async for response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_finalizer.py\", line 397, in run_and_handle_error\n raise model_error\n File \"/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_finalizer.py\", line 370, in run_and_handle_error\n async for llm_response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/models/google_llm.py\", line 381, in generate_content_async\n raise _ResourceExhaustedError(ce) from ce\ngoogle.adk.models.google_llm._ResourceExhaustedError: \nOn how to mitigate this issue, please refer to:\n\nhttps://google.github.io/adk-docs/agents/models/google-gemini/#error-code-429-resource_exhausted\n\n\n429 RESOURCE_EXHAUSTED. {'error': {'code': 429, 'message': 'Your project has exceeded its monthly spending cap. Please go to AI Studio at https://ai.studio/spend to manage your project spend cap. Learn more at https://ai.google.dev/gemini-api/docs/billing#project-spend-caps. ', 'status': 'RESOURCE_EXHAUSTED'}}", "output_excerpt": "" }, { "day": 4, "slug": "web-qa-explorer", "title": "Web QA Explorer", "pattern": "Browser agent + human confirmation (require_confirmation)", "folder": "day-004-web-qa-explorer", "model": "gemini-3.5-flash-lite", "status": "error", "exit_code": 1, "metrics": null, "stderr_tail": " File \"/home/runner/work/agent-a-day/agent-a-day/day-004-web-qa-explorer/.venv/lib/python3.12/site-packages/google/adk/telemetry/_instrumentation.py\", line 152, in _drive\n return await step\n ^^^^^^^^^^\n File \"/home/runner/work/agent-a-day/agent-a-day/day-004-web-qa-explorer/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_model_call.py\", line 277, in _call_llm_with_tracing\n async for llm_response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-004-web-qa-explorer/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/base_llm_flow.py\", line 669, in _run_and_handle_error\n async for response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-004-web-qa-explorer/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_finalizer.py\", line 397, in run_and_handle_error\n raise model_error\n File \"/home/runner/work/agent-a-day/agent-a-day/day-004-web-qa-explorer/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_finalizer.py\", line 370, in run_and_handle_error\n async for llm_response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-004-web-qa-explorer/.venv/lib/python3.12/site-packages/google/adk/models/google_llm.py\", line 381, in generate_content_async\n raise _ResourceExhaustedError(ce) from ce\ngoogle.adk.models.google_llm._ResourceExhaustedError: \nOn how to mitigate this issue, please refer to:\n\nhttps://google.github.io/adk-docs/agents/models/google-gemini/#error-code-429-resource_exhausted\n\n\n429 RESOURCE_EXHAUSTED. {'error': {'code': 429, 'message': 'Your project has exceeded its monthly spending cap. Please go to AI Studio at https://ai.studio/spend to manage your project spend cap. Learn more at https://ai.google.dev/gemini-api/docs/billing#project-spend-caps. ', 'status': 'RESOURCE_EXHAUSTED'}}", "output_excerpt": "" }, { "day": 5, "slug": "hn-digest", "title": "HN Digest", "pattern": "Long-term memory via knowledge graph", "folder": "day-005-hn-digest", "model": "gemini-3.5-flash-lite", "status": "error", "exit_code": 1, "metrics": null, "stderr_tail": " File \"/home/runner/work/agent-a-day/agent-a-day/day-005-hn-digest/.venv/lib/python3.12/site-packages/google/adk/telemetry/_instrumentation.py\", line 152, in _drive\n return await step\n ^^^^^^^^^^\n File \"/home/runner/work/agent-a-day/agent-a-day/day-005-hn-digest/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_model_call.py\", line 277, in _call_llm_with_tracing\n async for llm_response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-005-hn-digest/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/base_llm_flow.py\", line 669, in _run_and_handle_error\n async for response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-005-hn-digest/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_finalizer.py\", line 397, in run_and_handle_error\n raise model_error\n File \"/home/runner/work/agent-a-day/agent-a-day/day-005-hn-digest/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_finalizer.py\", line 370, in run_and_handle_error\n async for llm_response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-005-hn-digest/.venv/lib/python3.12/site-packages/google/adk/models/google_llm.py\", line 381, in generate_content_async\n raise _ResourceExhaustedError(ce) from ce\ngoogle.adk.models.google_llm._ResourceExhaustedError: \nOn how to mitigate this issue, please refer to:\n\nhttps://google.github.io/adk-docs/agents/models/google-gemini/#error-code-429-resource_exhausted\n\n\n429 RESOURCE_EXHAUSTED. {'error': {'code': 429, 'message': 'Your project has exceeded its monthly spending cap. Please go to AI Studio at https://ai.studio/spend to manage your project spend cap. Learn more at https://ai.google.dev/gemini-api/docs/billing#project-spend-caps. ', 'status': 'RESOURCE_EXHAUSTED'}}", "output_excerpt": "" }, { "day": 6, "slug": "meeting-planner", "title": "Meeting Planner", "pattern": "Structured output (Pydantic output_schema)", "folder": "day-006-meeting-planner", "model": "gemini-3.5-flash-lite", "status": "error", "exit_code": 1, "metrics": null, "stderr_tail": " File \"/home/runner/work/agent-a-day/agent-a-day/day-006-meeting-planner/.venv/lib/python3.12/site-packages/google/adk/telemetry/_instrumentation.py\", line 152, in _drive\n return await step\n ^^^^^^^^^^\n File \"/home/runner/work/agent-a-day/agent-a-day/day-006-meeting-planner/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_model_call.py\", line 277, in _call_llm_with_tracing\n async for llm_response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-006-meeting-planner/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/base_llm_flow.py\", line 669, in _run_and_handle_error\n async for response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-006-meeting-planner/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_finalizer.py\", line 397, in run_and_handle_error\n raise model_error\n File \"/home/runner/work/agent-a-day/agent-a-day/day-006-meeting-planner/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_finalizer.py\", line 370, in run_and_handle_error\n async for llm_response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-006-meeting-planner/.venv/lib/python3.12/site-packages/google/adk/models/google_llm.py\", line 381, in generate_content_async\n raise _ResourceExhaustedError(ce) from ce\ngoogle.adk.models.google_llm._ResourceExhaustedError: \nOn how to mitigate this issue, please refer to:\n\nhttps://google.github.io/adk-docs/agents/models/google-gemini/#error-code-429-resource_exhausted\n\n\n429 RESOURCE_EXHAUSTED. {'error': {'code': 429, 'message': 'Your project has exceeded its monthly spending cap. Please go to AI Studio at https://ai.studio/spend to manage your project spend cap. Learn more at https://ai.google.dev/gemini-api/docs/billing#project-spend-caps. ', 'status': 'RESOURCE_EXHAUSTED'}}", "output_excerpt": "" }, { "day": 7, "slug": "security-triager", "title": "Security Triager", "pattern": "Deterministic tool + LLM triage", "folder": "day-007-security-triager", "model": "gemini-3.5-flash-lite", "status": "error", "exit_code": 1, "metrics": null, "stderr_tail": " async for llm_response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-007-security-triager/.venv/lib/python3.12/site-packages/google/adk/utils/_runner_utils.py\", line 52, in _stream_in_caller_scope\n item = await _InContext(ctx, agen.__anext__())\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/runner/work/agent-a-day/agent-a-day/day-007-security-triager/.venv/lib/python3.12/site-packages/google/adk/utils/_runner_utils.py\", line 79, in __await__\n waiting_on = self._ctx.run(coro.send, value)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/runner/work/agent-a-day/agent-a-day/day-007-security-triager/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_finalizer.py\", line 391, in _record_responses\n async for llm_response in timed:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-007-security-triager/.venv/lib/python3.12/site-packages/google/adk/telemetry/_instrumentation.py\", line 1012, in time_model_awaits\n async for response in responses:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-007-security-triager/.venv/lib/python3.12/site-packages/google/adk/models/google_llm.py\", line 393, in generate_content_async\n raise _ResourceExhaustedError(ce) from ce\ngoogle.adk.models.google_llm._ResourceExhaustedError: \nOn how to mitigate this issue, please refer to:\n\nhttps://google.github.io/adk-docs/agents/models/google-gemini/#error-code-429-resource_exhausted\n\n\n429 RESOURCE_EXHAUSTED. {'error': {'code': 429, 'message': 'Your project has exceeded its monthly spending cap. Please go to AI Studio at https://ai.studio/spend to manage your project spend cap. Learn more at https://ai.google.dev/gemini-api/docs/billing#project-spend-caps. ', 'status': 'RESOURCE_EXHAUSTED'}}", "output_excerpt": "" }, { "day": 8, "slug": "aws-architect", "title": "AWS Architect", "pattern": "Parallel fan-out (ParallelAgent) + merge", "folder": "day-008-aws-architect", "model": "gemini-3.5-flash-lite", "status": "error", "exit_code": 1, "metrics": null, "stderr_tail": " async for llm_response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-008-aws-architect/.venv/lib/python3.12/site-packages/google/adk/utils/_runner_utils.py\", line 52, in _stream_in_caller_scope\n item = await _InContext(ctx, agen.__anext__())\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/runner/work/agent-a-day/agent-a-day/day-008-aws-architect/.venv/lib/python3.12/site-packages/google/adk/utils/_runner_utils.py\", line 79, in __await__\n waiting_on = self._ctx.run(coro.send, value)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/runner/work/agent-a-day/agent-a-day/day-008-aws-architect/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_finalizer.py\", line 391, in _record_responses\n async for llm_response in timed:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-008-aws-architect/.venv/lib/python3.12/site-packages/google/adk/telemetry/_instrumentation.py\", line 1012, in time_model_awaits\n async for response in responses:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-008-aws-architect/.venv/lib/python3.12/site-packages/google/adk/models/google_llm.py\", line 393, in generate_content_async\n raise _ResourceExhaustedError(ce) from ce\ngoogle.adk.models.google_llm._ResourceExhaustedError: \nOn how to mitigate this issue, please refer to:\n\nhttps://google.github.io/adk-docs/agents/models/google-gemini/#error-code-429-resource_exhausted\n\n\n429 RESOURCE_EXHAUSTED. {'error': {'code': 429, 'message': 'Your project has exceeded its monthly spending cap. Please go to AI Studio at https://ai.studio/spend to manage your project spend cap. Learn more at https://ai.google.dev/gemini-api/docs/billing#project-spend-caps. ', 'status': 'RESOURCE_EXHAUSTED'}}", "output_excerpt": "" }, { "day": 9, "slug": "fact-checker", "title": "Fact Checker", "pattern": "Generator\u2013critic loop (LoopAgent)", "folder": "day-009-fact-checker", "model": "gemini-3.5-flash-lite", "status": "error", "exit_code": 1, "metrics": null, "stderr_tail": " async for llm_response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-009-fact-checker/.venv/lib/python3.12/site-packages/google/adk/utils/_runner_utils.py\", line 52, in _stream_in_caller_scope\n item = await _InContext(ctx, agen.__anext__())\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/runner/work/agent-a-day/agent-a-day/day-009-fact-checker/.venv/lib/python3.12/site-packages/google/adk/utils/_runner_utils.py\", line 79, in __await__\n waiting_on = self._ctx.run(coro.send, value)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/runner/work/agent-a-day/agent-a-day/day-009-fact-checker/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_finalizer.py\", line 391, in _record_responses\n async for llm_response in timed:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-009-fact-checker/.venv/lib/python3.12/site-packages/google/adk/telemetry/_instrumentation.py\", line 1012, in time_model_awaits\n async for response in responses:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-009-fact-checker/.venv/lib/python3.12/site-packages/google/adk/models/google_llm.py\", line 393, in generate_content_async\n raise _ResourceExhaustedError(ce) from ce\ngoogle.adk.models.google_llm._ResourceExhaustedError: \nOn how to mitigate this issue, please refer to:\n\nhttps://google.github.io/adk-docs/agents/models/google-gemini/#error-code-429-resource_exhausted\n\n\n429 RESOURCE_EXHAUSTED. {'error': {'code': 429, 'message': 'Your project has exceeded its monthly spending cap. Please go to AI Studio at https://ai.studio/spend to manage your project spend cap. Learn more at https://ai.google.dev/gemini-api/docs/billing#project-spend-caps. ', 'status': 'RESOURCE_EXHAUSTED'}}", "output_excerpt": "" }, { "day": 10, "slug": "sql-analyst", "title": "SQL Analyst", "pattern": "Argument guardrails (block writes in before_tool)", "folder": "day-010-sql-analyst", "model": "gemini-3.5-flash-lite", "status": "error", "exit_code": 1, "metrics": null, "stderr_tail": " async for llm_response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-010-sql-analyst/.venv/lib/python3.12/site-packages/google/adk/utils/_runner_utils.py\", line 52, in _stream_in_caller_scope\n item = await _InContext(ctx, agen.__anext__())\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/runner/work/agent-a-day/agent-a-day/day-010-sql-analyst/.venv/lib/python3.12/site-packages/google/adk/utils/_runner_utils.py\", line 79, in __await__\n waiting_on = self._ctx.run(coro.send, value)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/runner/work/agent-a-day/agent-a-day/day-010-sql-analyst/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_finalizer.py\", line 391, in _record_responses\n async for llm_response in timed:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-010-sql-analyst/.venv/lib/python3.12/site-packages/google/adk/telemetry/_instrumentation.py\", line 1012, in time_model_awaits\n async for response in responses:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-010-sql-analyst/.venv/lib/python3.12/site-packages/google/adk/models/google_llm.py\", line 393, in generate_content_async\n raise _ResourceExhaustedError(ce) from ce\ngoogle.adk.models.google_llm._ResourceExhaustedError: \nOn how to mitigate this issue, please refer to:\n\nhttps://google.github.io/adk-docs/agents/models/google-gemini/#error-code-429-resource_exhausted\n\n\n429 RESOURCE_EXHAUSTED. {'error': {'code': 429, 'message': 'Your project has exceeded its monthly spending cap. Please go to AI Studio at https://ai.studio/spend to manage your project spend cap. Learn more at https://ai.google.dev/gemini-api/docs/billing#project-spend-caps. ', 'status': 'RESOURCE_EXHAUSTED'}}", "output_excerpt": "" }, { "day": 11, "slug": "perf-auditor", "title": "Perf Auditor", "pattern": "Tool-heavy diagnostics", "folder": "day-011-perf-auditor", "model": "gemini-3.5-flash-lite", "status": "error", "exit_code": 1, "metrics": null, "stderr_tail": " async for llm_response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-011-perf-auditor/.venv/lib/python3.12/site-packages/google/adk/utils/_runner_utils.py\", line 52, in _stream_in_caller_scope\n item = await _InContext(ctx, agen.__anext__())\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/runner/work/agent-a-day/agent-a-day/day-011-perf-auditor/.venv/lib/python3.12/site-packages/google/adk/utils/_runner_utils.py\", line 79, in __await__\n waiting_on = self._ctx.run(coro.send, value)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/runner/work/agent-a-day/agent-a-day/day-011-perf-auditor/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_finalizer.py\", line 391, in _record_responses\n async for llm_response in timed:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-011-perf-auditor/.venv/lib/python3.12/site-packages/google/adk/telemetry/_instrumentation.py\", line 1012, in time_model_awaits\n async for response in responses:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-011-perf-auditor/.venv/lib/python3.12/site-packages/google/adk/models/google_llm.py\", line 393, in generate_content_async\n raise _ResourceExhaustedError(ce) from ce\ngoogle.adk.models.google_llm._ResourceExhaustedError: \nOn how to mitigate this issue, please refer to:\n\nhttps://google.github.io/adk-docs/agents/models/google-gemini/#error-code-429-resource_exhausted\n\n\n429 RESOURCE_EXHAUSTED. {'error': {'code': 429, 'message': 'Your project has exceeded its monthly spending cap. Please go to AI Studio at https://ai.studio/spend to manage your project spend cap. Learn more at https://ai.google.dev/gemini-api/docs/billing#project-spend-caps. ', 'status': 'RESOURCE_EXHAUSTED'}}", "output_excerpt": "" }, { "day": 12, "slug": "model-picker", "title": "Model Picker", "pattern": "Ranking with explicit scoring rubric", "folder": "day-012-model-picker", "model": "gemini-3.5-flash-lite", "status": "error", "exit_code": 1, "metrics": null, "stderr_tail": " async for llm_response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-012-model-picker/.venv/lib/python3.12/site-packages/google/adk/utils/_runner_utils.py\", line 52, in _stream_in_caller_scope\n item = await _InContext(ctx, agen.__anext__())\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/runner/work/agent-a-day/agent-a-day/day-012-model-picker/.venv/lib/python3.12/site-packages/google/adk/utils/_runner_utils.py\", line 79, in __await__\n waiting_on = self._ctx.run(coro.send, value)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/runner/work/agent-a-day/agent-a-day/day-012-model-picker/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_finalizer.py\", line 391, in _record_responses\n async for llm_response in timed:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-012-model-picker/.venv/lib/python3.12/site-packages/google/adk/telemetry/_instrumentation.py\", line 1012, in time_model_awaits\n async for response in responses:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-012-model-picker/.venv/lib/python3.12/site-packages/google/adk/models/google_llm.py\", line 393, in generate_content_async\n raise _ResourceExhaustedError(ce) from ce\ngoogle.adk.models.google_llm._ResourceExhaustedError: \nOn how to mitigate this issue, please refer to:\n\nhttps://google.github.io/adk-docs/agents/models/google-gemini/#error-code-429-resource_exhausted\n\n\n429 RESOURCE_EXHAUSTED. {'error': {'code': 429, 'message': 'Your project has exceeded its monthly spending cap. Please go to AI Studio at https://ai.studio/spend to manage your project spend cap. Learn more at https://ai.google.dev/gemini-api/docs/billing#project-spend-caps. ', 'status': 'RESOURCE_EXHAUSTED'}}", "output_excerpt": "" } ] }, { "timestamp": "2026-10-31T23:55:40+00:00", "sha": "2b22e337379871c25e3cc4fc12d0e4beb352155e", "results": [ { "day": 1, "slug": "arxiv-scout", "title": "arXiv Scout", "pattern": "Single agent + guardrail callbacks", "folder": "day-001-arxiv-scout", "model": "gemini-3.5-flash-lite", "status": "error", "exit_code": 1, "metrics": null, "stderr_tail": " File \"/home/runner/work/agent-a-day/agent-a-day/day-001-arxiv-scout/.venv/lib/python3.12/site-packages/google/adk/telemetry/_instrumentation.py\", line 152, in _drive\n return await step\n ^^^^^^^^^^\n File \"/home/runner/work/agent-a-day/agent-a-day/day-001-arxiv-scout/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_model_call.py\", line 277, in _call_llm_with_tracing\n async for llm_response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-001-arxiv-scout/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/base_llm_flow.py\", line 669, in _run_and_handle_error\n async for response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-001-arxiv-scout/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_finalizer.py\", line 397, in run_and_handle_error\n raise model_error\n File \"/home/runner/work/agent-a-day/agent-a-day/day-001-arxiv-scout/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_finalizer.py\", line 370, in run_and_handle_error\n async for llm_response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-001-arxiv-scout/.venv/lib/python3.12/site-packages/google/adk/models/google_llm.py\", line 381, in generate_content_async\n raise _ResourceExhaustedError(ce) from ce\ngoogle.adk.models.google_llm._ResourceExhaustedError: \nOn how to mitigate this issue, please refer to:\n\nhttps://google.github.io/adk-docs/agents/models/google-gemini/#error-code-429-resource_exhausted\n\n\n429 RESOURCE_EXHAUSTED. {'error': {'code': 429, 'message': 'Your project has exceeded its monthly spending cap. Please go to AI Studio at https://ai.studio/spend to manage your project spend cap. Learn more at https://ai.google.dev/gemini-api/docs/billing#project-spend-caps. ', 'status': 'RESOURCE_EXHAUSTED'}}", "output_excerpt": "" }, { "day": 2, "slug": "repo-onboarder", "title": "Repo Onboarder", "pattern": "Sequential pipeline (SequentialAgent)", "folder": "day-002-repo-onboarder", "model": "gemini-3.5-flash-lite", "status": "error", "exit_code": 1, "metrics": null, "stderr_tail": " File \"/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/telemetry/_instrumentation.py\", line 152, in _drive\n return await step\n ^^^^^^^^^^\n File \"/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_model_call.py\", line 277, in _call_llm_with_tracing\n async for llm_response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/base_llm_flow.py\", line 669, in _run_and_handle_error\n async for response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_finalizer.py\", line 397, in run_and_handle_error\n raise model_error\n File \"/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_finalizer.py\", line 370, in run_and_handle_error\n async for llm_response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/models/google_llm.py\", line 381, in generate_content_async\n raise _ResourceExhaustedError(ce) from ce\ngoogle.adk.models.google_llm._ResourceExhaustedError: \nOn how to mitigate this issue, please refer to:\n\nhttps://google.github.io/adk-docs/agents/models/google-gemini/#error-code-429-resource_exhausted\n\n\n429 RESOURCE_EXHAUSTED. {'error': {'code': 429, 'message': 'Your project has exceeded its monthly spending cap. Please go to AI Studio at https://ai.studio/spend to manage your project spend cap. Learn more at https://ai.google.dev/gemini-api/docs/billing#project-spend-caps. ', 'status': 'RESOURCE_EXHAUSTED'}}", "output_excerpt": "" }, { "day": 3, "slug": "docs-copilot", "title": "Docs Copilot", "pattern": "Grounded QA + answer verification", "folder": "day-003-docs-copilot", "model": "gemini-3.5-flash-lite", "status": "error", "exit_code": 1, "metrics": null, "stderr_tail": " File \"/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/telemetry/_instrumentation.py\", line 152, in _drive\n return await step\n ^^^^^^^^^^\n File \"/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_model_call.py\", line 277, in _call_llm_with_tracing\n async for llm_response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/base_llm_flow.py\", line 669, in _run_and_handle_error\n async for response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_finalizer.py\", line 397, in run_and_handle_error\n raise model_error\n File \"/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_finalizer.py\", line 370, in run_and_handle_error\n async for llm_response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/models/google_llm.py\", line 381, in generate_content_async\n raise _ResourceExhaustedError(ce) from ce\ngoogle.adk.models.google_llm._ResourceExhaustedError: \nOn how to mitigate this issue, please refer to:\n\nhttps://google.github.io/adk-docs/agents/models/google-gemini/#error-code-429-resource_exhausted\n\n\n429 RESOURCE_EXHAUSTED. {'error': {'code': 429, 'message': 'Your project has exceeded its monthly spending cap. Please go to AI Studio at https://ai.studio/spend to manage your project spend cap. Learn more at https://ai.google.dev/gemini-api/docs/billing#project-spend-caps. ', 'status': 'RESOURCE_EXHAUSTED'}}", "output_excerpt": "" }, { "day": 4, "slug": "web-qa-explorer", "title": "Web QA Explorer", "pattern": "Browser agent + human confirmation (require_confirmation)", "folder": "day-004-web-qa-explorer", "model": "gemini-3.5-flash-lite", "status": "error", "exit_code": 1, "metrics": null, "stderr_tail": " File \"/home/runner/work/agent-a-day/agent-a-day/day-004-web-qa-explorer/.venv/lib/python3.12/site-packages/google/adk/telemetry/_instrumentation.py\", line 152, in _drive\n return await step\n ^^^^^^^^^^\n File \"/home/runner/work/agent-a-day/agent-a-day/day-004-web-qa-explorer/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_model_call.py\", line 277, in _call_llm_with_tracing\n async for llm_response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-004-web-qa-explorer/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/base_llm_flow.py\", line 669, in _run_and_handle_error\n async for response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-004-web-qa-explorer/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_finalizer.py\", line 397, in run_and_handle_error\n raise model_error\n File \"/home/runner/work/agent-a-day/agent-a-day/day-004-web-qa-explorer/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_finalizer.py\", line 370, in run_and_handle_error\n async for llm_response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-004-web-qa-explorer/.venv/lib/python3.12/site-packages/google/adk/models/google_llm.py\", line 381, in generate_content_async\n raise _ResourceExhaustedError(ce) from ce\ngoogle.adk.models.google_llm._ResourceExhaustedError: \nOn how to mitigate this issue, please refer to:\n\nhttps://google.github.io/adk-docs/agents/models/google-gemini/#error-code-429-resource_exhausted\n\n\n429 RESOURCE_EXHAUSTED. {'error': {'code': 429, 'message': 'Your project has exceeded its monthly spending cap. Please go to AI Studio at https://ai.studio/spend to manage your project spend cap. Learn more at https://ai.google.dev/gemini-api/docs/billing#project-spend-caps. ', 'status': 'RESOURCE_EXHAUSTED'}}", "output_excerpt": "" }, { "day": 5, "slug": "hn-digest", "title": "HN Digest", "pattern": "Long-term memory via knowledge graph", "folder": "day-005-hn-digest", "model": "gemini-3.5-flash-lite", "status": "error", "exit_code": 1, "metrics": null, "stderr_tail": " File \"/home/runner/work/agent-a-day/agent-a-day/day-005-hn-digest/.venv/lib/python3.12/site-packages/google/adk/telemetry/_instrumentation.py\", line 152, in _drive\n return await step\n ^^^^^^^^^^\n File \"/home/runner/work/agent-a-day/agent-a-day/day-005-hn-digest/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_model_call.py\", line 277, in _call_llm_with_tracing\n async for llm_response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-005-hn-digest/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/base_llm_flow.py\", line 669, in _run_and_handle_error\n async for response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-005-hn-digest/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_finalizer.py\", line 397, in run_and_handle_error\n raise model_error\n File \"/home/runner/work/agent-a-day/agent-a-day/day-005-hn-digest/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_finalizer.py\", line 370, in run_and_handle_error\n async for llm_response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-005-hn-digest/.venv/lib/python3.12/site-packages/google/adk/models/google_llm.py\", line 381, in generate_content_async\n raise _ResourceExhaustedError(ce) from ce\ngoogle.adk.models.google_llm._ResourceExhaustedError: \nOn how to mitigate this issue, please refer to:\n\nhttps://google.github.io/adk-docs/agents/models/google-gemini/#error-code-429-resource_exhausted\n\n\n429 RESOURCE_EXHAUSTED. {'error': {'code': 429, 'message': 'Your project has exceeded its monthly spending cap. Please go to AI Studio at https://ai.studio/spend to manage your project spend cap. Learn more at https://ai.google.dev/gemini-api/docs/billing#project-spend-caps. ', 'status': 'RESOURCE_EXHAUSTED'}}", "output_excerpt": "" }, { "day": 6, "slug": "meeting-planner", "title": "Meeting Planner", "pattern": "Structured output (Pydantic output_schema)", "folder": "day-006-meeting-planner", "model": "gemini-3.5-flash-lite", "status": "error", "exit_code": 1, "metrics": null, "stderr_tail": " File \"/home/runner/work/agent-a-day/agent-a-day/day-006-meeting-planner/.venv/lib/python3.12/site-packages/google/adk/telemetry/_instrumentation.py\", line 152, in _drive\n return await step\n ^^^^^^^^^^\n File \"/home/runner/work/agent-a-day/agent-a-day/day-006-meeting-planner/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_model_call.py\", line 277, in _call_llm_with_tracing\n async for llm_response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-006-meeting-planner/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/base_llm_flow.py\", line 669, in _run_and_handle_error\n async for response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-006-meeting-planner/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_finalizer.py\", line 397, in run_and_handle_error\n raise model_error\n File \"/home/runner/work/agent-a-day/agent-a-day/day-006-meeting-planner/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_finalizer.py\", line 370, in run_and_handle_error\n async for llm_response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-006-meeting-planner/.venv/lib/python3.12/site-packages/google/adk/models/google_llm.py\", line 381, in generate_content_async\n raise _ResourceExhaustedError(ce) from ce\ngoogle.adk.models.google_llm._ResourceExhaustedError: \nOn how to mitigate this issue, please refer to:\n\nhttps://google.github.io/adk-docs/agents/models/google-gemini/#error-code-429-resource_exhausted\n\n\n429 RESOURCE_EXHAUSTED. {'error': {'code': 429, 'message': 'Your project has exceeded its monthly spending cap. Please go to AI Studio at https://ai.studio/spend to manage your project spend cap. Learn more at https://ai.google.dev/gemini-api/docs/billing#project-spend-caps. ', 'status': 'RESOURCE_EXHAUSTED'}}", "output_excerpt": "" }, { "day": 7, "slug": "security-triager", "title": "Security Triager", "pattern": "Deterministic tool + LLM triage", "folder": "day-007-security-triager", "model": "gemini-3.5-flash-lite", "status": "error", "exit_code": 1, "metrics": null, "stderr_tail": " async for llm_response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-007-security-triager/.venv/lib/python3.12/site-packages/google/adk/utils/_runner_utils.py\", line 52, in _stream_in_caller_scope\n item = await _InContext(ctx, agen.__anext__())\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/runner/work/agent-a-day/agent-a-day/day-007-security-triager/.venv/lib/python3.12/site-packages/google/adk/utils/_runner_utils.py\", line 79, in __await__\n waiting_on = self._ctx.run(coro.send, value)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/runner/work/agent-a-day/agent-a-day/day-007-security-triager/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_finalizer.py\", line 391, in _record_responses\n async for llm_response in timed:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-007-security-triager/.venv/lib/python3.12/site-packages/google/adk/telemetry/_instrumentation.py\", line 1012, in time_model_awaits\n async for response in responses:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-007-security-triager/.venv/lib/python3.12/site-packages/google/adk/models/google_llm.py\", line 393, in generate_content_async\n raise _ResourceExhaustedError(ce) from ce\ngoogle.adk.models.google_llm._ResourceExhaustedError: \nOn how to mitigate this issue, please refer to:\n\nhttps://google.github.io/adk-docs/agents/models/google-gemini/#error-code-429-resource_exhausted\n\n\n429 RESOURCE_EXHAUSTED. {'error': {'code': 429, 'message': 'Your project has exceeded its monthly spending cap. Please go to AI Studio at https://ai.studio/spend to manage your project spend cap. Learn more at https://ai.google.dev/gemini-api/docs/billing#project-spend-caps. ', 'status': 'RESOURCE_EXHAUSTED'}}", "output_excerpt": "" }, { "day": 8, "slug": "aws-architect", "title": "AWS Architect", "pattern": "Parallel fan-out (ParallelAgent) + merge", "folder": "day-008-aws-architect", "model": "gemini-3.5-flash-lite", "status": "error", "exit_code": 1, "metrics": null, "stderr_tail": " async for llm_response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-008-aws-architect/.venv/lib/python3.12/site-packages/google/adk/utils/_runner_utils.py\", line 52, in _stream_in_caller_scope\n item = await _InContext(ctx, agen.__anext__())\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/runner/work/agent-a-day/agent-a-day/day-008-aws-architect/.venv/lib/python3.12/site-packages/google/adk/utils/_runner_utils.py\", line 79, in __await__\n waiting_on = self._ctx.run(coro.send, value)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/runner/work/agent-a-day/agent-a-day/day-008-aws-architect/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_finalizer.py\", line 391, in _record_responses\n async for llm_response in timed:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-008-aws-architect/.venv/lib/python3.12/site-packages/google/adk/telemetry/_instrumentation.py\", line 1012, in time_model_awaits\n async for response in responses:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-008-aws-architect/.venv/lib/python3.12/site-packages/google/adk/models/google_llm.py\", line 393, in generate_content_async\n raise _ResourceExhaustedError(ce) from ce\ngoogle.adk.models.google_llm._ResourceExhaustedError: \nOn how to mitigate this issue, please refer to:\n\nhttps://google.github.io/adk-docs/agents/models/google-gemini/#error-code-429-resource_exhausted\n\n\n429 RESOURCE_EXHAUSTED. {'error': {'code': 429, 'message': 'Your project has exceeded its monthly spending cap. Please go to AI Studio at https://ai.studio/spend to manage your project spend cap. Learn more at https://ai.google.dev/gemini-api/docs/billing#project-spend-caps. ', 'status': 'RESOURCE_EXHAUSTED'}}", "output_excerpt": "" }, { "day": 9, "slug": "fact-checker", "title": "Fact Checker", "pattern": "Generator\u2013critic loop (LoopAgent)", "folder": "day-009-fact-checker", "model": "gemini-3.5-flash-lite", "status": "error", "exit_code": 1, "metrics": null, "stderr_tail": " async for llm_response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-009-fact-checker/.venv/lib/python3.12/site-packages/google/adk/utils/_runner_utils.py\", line 52, in _stream_in_caller_scope\n item = await _InContext(ctx, agen.__anext__())\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/runner/work/agent-a-day/agent-a-day/day-009-fact-checker/.venv/lib/python3.12/site-packages/google/adk/utils/_runner_utils.py\", line 79, in __await__\n waiting_on = self._ctx.run(coro.send, value)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/runner/work/agent-a-day/agent-a-day/day-009-fact-checker/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_finalizer.py\", line 391, in _record_responses\n async for llm_response in timed:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-009-fact-checker/.venv/lib/python3.12/site-packages/google/adk/telemetry/_instrumentation.py\", line 1012, in time_model_awaits\n async for response in responses:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-009-fact-checker/.venv/lib/python3.12/site-packages/google/adk/models/google_llm.py\", line 393, in generate_content_async\n raise _ResourceExhaustedError(ce) from ce\ngoogle.adk.models.google_llm._ResourceExhaustedError: \nOn how to mitigate this issue, please refer to:\n\nhttps://google.github.io/adk-docs/agents/models/google-gemini/#error-code-429-resource_exhausted\n\n\n429 RESOURCE_EXHAUSTED. {'error': {'code': 429, 'message': 'Your project has exceeded its monthly spending cap. Please go to AI Studio at https://ai.studio/spend to manage your project spend cap. Learn more at https://ai.google.dev/gemini-api/docs/billing#project-spend-caps. ', 'status': 'RESOURCE_EXHAUSTED'}}", "output_excerpt": "" }, { "day": 10, "slug": "sql-analyst", "title": "SQL Analyst", "pattern": "Argument guardrails (block writes in before_tool)", "folder": "day-010-sql-analyst", "model": "gemini-3.5-flash-lite", "status": "error", "exit_code": 1, "metrics": null, "stderr_tail": " async for llm_response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-010-sql-analyst/.venv/lib/python3.12/site-packages/google/adk/utils/_runner_utils.py\", line 52, in _stream_in_caller_scope\n item = await _InContext(ctx, agen.__anext__())\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/runner/work/agent-a-day/agent-a-day/day-010-sql-analyst/.venv/lib/python3.12/site-packages/google/adk/utils/_runner_utils.py\", line 79, in __await__\n waiting_on = self._ctx.run(coro.send, value)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/runner/work/agent-a-day/agent-a-day/day-010-sql-analyst/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_finalizer.py\", line 391, in _record_responses\n async for llm_response in timed:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-010-sql-analyst/.venv/lib/python3.12/site-packages/google/adk/telemetry/_instrumentation.py\", line 1012, in time_model_awaits\n async for response in responses:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-010-sql-analyst/.venv/lib/python3.12/site-packages/google/adk/models/google_llm.py\", line 393, in generate_content_async\n raise _ResourceExhaustedError(ce) from ce\ngoogle.adk.models.google_llm._ResourceExhaustedError: \nOn how to mitigate this issue, please refer to:\n\nhttps://google.github.io/adk-docs/agents/models/google-gemini/#error-code-429-resource_exhausted\n\n\n429 RESOURCE_EXHAUSTED. {'error': {'code': 429, 'message': 'Your project has exceeded its monthly spending cap. Please go to AI Studio at https://ai.studio/spend to manage your project spend cap. Learn more at https://ai.google.dev/gemini-api/docs/billing#project-spend-caps. ', 'status': 'RESOURCE_EXHAUSTED'}}", "output_excerpt": "" }, { "day": 11, "slug": "perf-auditor", "title": "Perf Auditor", "pattern": "Tool-heavy diagnostics", "folder": "day-011-perf-auditor", "model": "gemini-3.5-flash-lite", "status": "error", "exit_code": 1, "metrics": null, "stderr_tail": " async for llm_response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-011-perf-auditor/.venv/lib/python3.12/site-packages/google/adk/utils/_runner_utils.py\", line 52, in _stream_in_caller_scope\n item = await _InContext(ctx, agen.__anext__())\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/runner/work/agent-a-day/agent-a-day/day-011-perf-auditor/.venv/lib/python3.12/site-packages/google/adk/utils/_runner_utils.py\", line 79, in __await__\n waiting_on = self._ctx.run(coro.send, value)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/runner/work/agent-a-day/agent-a-day/day-011-perf-auditor/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_finalizer.py\", line 391, in _record_responses\n async for llm_response in timed:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-011-perf-auditor/.venv/lib/python3.12/site-packages/google/adk/telemetry/_instrumentation.py\", line 1012, in time_model_awaits\n async for response in responses:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-011-perf-auditor/.venv/lib/python3.12/site-packages/google/adk/models/google_llm.py\", line 393, in generate_content_async\n raise _ResourceExhaustedError(ce) from ce\ngoogle.adk.models.google_llm._ResourceExhaustedError: \nOn how to mitigate this issue, please refer to:\n\nhttps://google.github.io/adk-docs/agents/models/google-gemini/#error-code-429-resource_exhausted\n\n\n429 RESOURCE_EXHAUSTED. {'error': {'code': 429, 'message': 'Your project has exceeded its monthly spending cap. Please go to AI Studio at https://ai.studio/spend to manage your project spend cap. Learn more at https://ai.google.dev/gemini-api/docs/billing#project-spend-caps. ', 'status': 'RESOURCE_EXHAUSTED'}}", "output_excerpt": "" }, { "day": 12, "slug": "model-picker", "title": "Model Picker", "pattern": "Ranking with explicit scoring rubric", "folder": "day-012-model-picker", "model": "gemini-3.5-flash-lite", "status": "error", "exit_code": 1, "metrics": null, "stderr_tail": " async for llm_response in agen:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-012-model-picker/.venv/lib/python3.12/site-packages/google/adk/utils/_runner_utils.py\", line 52, in _stream_in_caller_scope\n item = await _InContext(ctx, agen.__anext__())\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/runner/work/agent-a-day/agent-a-day/day-012-model-picker/.venv/lib/python3.12/site-packages/google/adk/utils/_runner_utils.py\", line 79, in __await__\n waiting_on = self._ctx.run(coro.send, value)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/runner/work/agent-a-day/agent-a-day/day-012-model-picker/.venv/lib/python3.12/site-packages/google/adk/flows/llm_flows/core/_finalizer.py\", line 391, in _record_responses\n async for llm_response in timed:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-012-model-picker/.venv/lib/python3.12/site-packages/google/adk/telemetry/_instrumentation.py\", line 1012, in time_model_awaits\n async for response in responses:\n File \"/home/runner/work/agent-a-day/agent-a-day/day-012-model-picker/.venv/lib/python3.12/site-packages/google/adk/models/google_llm.py\", line 393, in generate_content_async\n raise _ResourceExhaustedError(ce) from ce\ngoogle.adk.models.google_llm._ResourceExhaustedError: \nOn how to mitigate this issue, please refer to:\n\nhttps://google.github.io/adk-docs/agents/models/google-gemini/#error-code-429-resource_exhausted\n\n\n429 RESOURCE_EXHAUSTED. {'error': {'code': 429, 'message': 'Your project has exceeded its monthly spending cap. Please go to AI Studio at https://ai.studio/spend to manage your project spend cap. Learn more at https://ai.google.dev/gemini-api/docs/billing#project-spend-caps. ', 'status': 'RESOURCE_EXHAUSTED'}}", "output_excerpt": "" } ] }, { "timestamp": "2026-11-02T00:14:50+00:00", "sha": "2b22e337379871c25e3cc4fc12d0e4beb352155e", "results": [ { "day": 1, "slug": "arxiv-scout", "title": "arXiv Scout", "pattern": "Single agent + guardrail callbacks", "folder": "day-001-arxiv-scout", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 135.98, "model": "gemini-3.5-flash-lite", "model_calls": 6, "tool_calls": 5, "tool_budget": 12, "calls_by_tool": { "search_papers": 2, "get_abstract": 3 }, "prompt_tokens": 22017, "output_tokens": 1068, "cost_usd": null, "checks": { "citations_verified": true, "unverified_ids": [] }, "output_chars": 3801 }, "stderr_tail": "Installed 44 packages in 26ms\nInstalled 45 packages in 28ms\n/home/runner/work/agent-a-day/agent-a-day/day-001-arxiv-scout/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-001-arxiv-scout/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 search_papers(sort_by='relevance', categories=['cs.CL', 'cs.AI', 'cs.LG'], max_results=5, query='\"spe...)\narXiv API request HTTP 429; retrying in 2.9s (attempt 1/3)\narXiv API request HTTP 429; retrying in 5.1s (attempt 2/3)\narXiv API request HTTP 429 after 3 attempts\narXiv search rate limited after retries: arXiv is rate limiting this IP (HTTP 429). Please wait 60 seconds before retrying.\n \u2192 search_papers(max_results=5, sort_by='relevance', categories=['cs.CL'], query='\"speculative decoding\"')\narXiv API request HTTP 429; retrying in 2.3s (attempt 1/3)\narXiv API request timed out; retrying in 2.0s (attempt 2/3)\n \u2192 get_abstract(paper_id='2402.01528')\n \u2192 get_abstract(paper_id='2310.07177')\n \u2192 get_abstract(paper_id='2503.03434')\n\n\u2014 136.0s \u00b7 6 model calls \u00b7 5/12 tool calls {'search_papers': 2, 'get_abstract': 3} \u00b7 22,017 in / 1,068 out tokens\n\u2713 All cited arXiv IDs were returned by a tool.", "output_excerpt": "# speculative decoding for LLM inference: engineering brief\n\n## TL;DR\n- **Draft Model Design Matters More Than Raw Capability:** Analysis shows that a draft model's performance in speculative decoding depends heavily on its latency rather than simple language modeling capability. Hardware-efficient draft models can dramatically improve overall throughput.\n- **Adapting to Domain Shifts via Online Updates:** Static draft models suffer in diverse or out-of-domain query contexts; continually updating draft models on observed user queries (via online knowledge distillation) significantly increases token acceptance rates and latency reduction.\n- **Retrieval and Tree-based Fusion Overcomes Domain Gaps:** Combining traditional draft models with retrieval-augmented generation and tree-pruning/fusio" }, { "day": 2, "slug": "repo-onboarder", "title": "Repo Onboarder", "pattern": "Sequential pipeline (SequentialAgent)", "folder": "day-002-repo-onboarder", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 23.1, "model": "gemini-3.5-flash-lite", "model_calls": 5, "tool_calls": 6, "tool_budget": 10, "calls_by_tool": { "read_wiki_structure": 1, "ask_wiki_question": 4, "fetch": 1 }, "prompt_tokens": 22927, "output_tokens": 1962, "cost_usd": null, "checks": { "repo_available": true, "all_sections_present": true, "links_verified": true, "missing_sections": [], "unverified_links": [] }, "output_chars": 2810 }, "stderr_tail": "/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.PLUGGABLE_AUTH is enabled.\n check_feature_enabled()\nFailed to configure mTLS using AsyncAuthorizedSession: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.\n/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_toolset.py:407: UserWarning: [EXPERIMENTAL] feature FeatureName._MCP_GRACEFUL_ERROR_HANDLING is enabled.\n session = await self._mcp_session_manager.create_session(\n/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 [mapper] read_wiki_structure(repoName='psf/requests')\nFailed to configure mTLS using AsyncAuthorizedSession: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.\n \u2192 [researcher] ask_wiki_question(repoName='psf/requests', question='What is this project, who is it for, and how is the ...)\n \u2192 [researcher] ask_wiki_question(question='Where does a new reader start: the main entry points and the 3-5 most importa...)\n \u2192 [researcher] ask_wiki_question(repoName='psf/requests', question='How do I set up a development environment, run it, a...)\n \u2192 [researcher] ask_wiki_question(repoName='psf/requests', question='What are the contribution conventions (branching, st...)\n \u2192 [researcher] fetch(url='https://raw.githubusercontent.com/psf/requests/main/CONTRIBUTING.md')\n\n\u2014 23.1s \u00b7 5 model calls \u00b7 6/10 tool calls {'read_wiki_structure': 1, 'ask_wiki_question': 4, 'fetch': 1} \u00b7 22,927 in / 1,962 out tokens\n\u2713 All sections present and every repo link was returned by a tool.", "output_excerpt": "# Onboarding guide: psf/requests\n\n## What this project is\nThe `Requests` library is a Python HTTP client library designed to simplify HTTP requests following the design philosophy \"HTTP for Humans\u2122\". It automatically manages connection pooling, keep-alive, redirects, and encoding detection, serving as a foundational dependency for over 4 million repositories.\n\n## Architecture map\n| Area | Path | What lives there |\n| :--- | :--- | :--- |\n| Core Source Code | https://github.com/psf/requests/blob/HEAD/src/requests/ | Core library modules including API definitions, models, sessions, adapters, and utility helpers. |\n| Documentation | https://github.com/psf/requests/blob/HEAD/docs/ | reStructuredText documentation files, user guides (`docs/user/`), and developer guidelines. |\n| Test Suite | http" }, { "day": 3, "slug": "docs-copilot", "title": "Docs Copilot", "pattern": "Grounded QA + answer verification", "folder": "day-003-docs-copilot", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 12.03, "model": "gemini-3.5-flash-lite", "model_calls": 4, "tool_calls": 2, "tool_budget": 6, "calls_by_tool": { "microsoft_docs_search": 1, "microsoft_code_sample_search": 1 }, "prompt_tokens": 20870, "output_tokens": 1062, "cost_usd": null, "checks": { "answered_from_docs": true, "sections_present": true, "sources_verified": true, "code_grounded": true, "missing_sections": [], "unverified_sources": [], "ungrounded_calls": [] }, "output_chars": 1545 }, "stderr_tail": "/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.PLUGGABLE_AUTH is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_toolset.py:407: UserWarning: [EXPERIMENTAL] feature FeatureName._MCP_GRACEFUL_ERROR_HANDLING is enabled.\n session = await self._mcp_session_manager.create_session(\nFailed to configure mTLS using AsyncAuthorizedSession: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.\n/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\nContext7 Documentation MCP Server v4.4.4 running on stdio\n/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 [researcher] microsoft_docs_search(query='upload local file azure blob storage python sdk')\n \u2192 [researcher] microsoft_code_sample_search(language='python', query='upload_blob upload local file to azure blob storage')\nnpm notice\nnpm notice New major version of npm available! 10.9.9 -> 12.2.1\nnpm notice Changelog: https://github.com/npm/cli/releases/tag/v12.2.1\nnpm notice To update run: npm install -g npm@12.2.1\nnpm notice\n\n\u2014 12.0s \u00b7 4 model calls \u00b7 2/6 tool calls {'microsoft_docs_search': 1, 'microsoft_code_sample_search': 1} \u00b7 20,870 in / 1,062 out tokens\n\u2713 Sections present, every source was returned by a tool, and every API call appears in the docs.", "output_excerpt": "## Answer\nTo upload a local file to Azure Blob Storage using the Python SDK, you can create a `BlobClient` using a connection string or credentials, open your local file in binary read mode (`\"rb\"`), and then call the `upload_blob()` method on the client. \n\nHere is an example using a connection string:\n\n```python\nfrom azure.storage.blob import BlobClient\n\nblob = BlobClient.from_connection_string(conn_str=\"\", container_name=\"mycontainer\", blob_name=\"my_blob\")\n\nwith open(\"./SampleSource.txt\", \"rb\") as data:\n blob.upload_blob(data)\n```\n\nAlternatively, you can instantiate a `BlobClient` using a storage account URL and Azure credentials:\n\n```python\nimport os\nimport uuid\nfrom azure.identity import DefaultAzureCredential\nfrom azure.storage.blob import BlobClient\n\ncredential " }, { "day": 4, "slug": "web-qa-explorer", "title": "Web QA Explorer", "pattern": "Browser agent + human confirmation (require_confirmation)", "folder": "day-004-web-qa-explorer", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 20.01, "model": "gemini-3.5-flash-lite", "model_calls": 15, "tool_calls": 9, "tool_budget": 14, "calls_by_tool": { "browser_navigate": 1, "browser_console_messages": 2, "browser_snapshot": 2, "browser_type": 2, "browser_ snapshot:": 1, "browser_network_requests": 1 }, "prompt_tokens": 65930, "output_tokens": 611, "cost_usd": null, "checks": { "sections_present": true, "evidence_verified": true, "pages_verified": true, "risky_actions_gated": true, "missing_sections": [], "unverified_evidence": [], "unverified_pages": [], "approvals": [ { "action": "browser_click(Toggle Todo checkbox for Buy milk e21)", "decision": "declined" }, { "action": "browser_click(Toggle Todo checkbox for Buy milk e20)", "decision": "declined" }, { "action": "browser_click(Toggle Todo for Buy milk e22)", "decision": "declined" }, { "action": "browser_click(Checkbox toggle todo for Buy milk e21)", "decision": "declined" }, { "action": "browser_click(Delete button for todo Buy milk e22)", "decision": "declined" } ], "blocked": [] }, "output_chars": 1489 }, "stderr_tail": " - Verify tool is included in agent.tools list\n - Check for typos in function name\n \u2192 browser_snapshot(boxes=True)\n \u2192 browser_click(target='e21', element='Toggle Todo checkbox for Buy milk')\n/home/runner/work/agent-a-day/agent-a-day/day-004-web-qa-explorer/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.TOOL_CONFIRMATION is enabled.\n check_feature_enabled()\n \u2717 declined (no approval given): browser_click(target='e21', element='Toggle Todo checkbox for Buy milk')\n \u2192 browser_click(element='Toggle Todo checkbox for Buy milk', target='e20')\n \u2717 declined (no approval given): browser_click(element='Toggle Todo checkbox for Buy milk', target='e20')\n \u2192 browser_click(target='e22', element='Toggle Todo for Buy milk')\n \u2717 declined (no approval given): browser_click(target='e22', element='Toggle Todo for Buy milk')\n \u2192 browser_click(target='e21', element='Checkbox toggle todo for Buy milk')\n \u2717 declined (no approval given): browser_click(target='e21', element='Checkbox toggle todo for Buy milk')\n \u2192 browser_click(target='e22', element='Delete button for todo Buy milk')\n \u2717 declined (no approval given): browser_click(target='e22', element='Delete button for todo Buy milk')\n \u2192 browser_network_requests(static=False)\n \u2192 browser_console_messages(level='error')\n\n\u2014 20.0s \u00b7 15 model calls \u00b7 9/14 tool calls {'browser_navigate': 1, 'browser_console_messages': 2, 'browser_snapshot': 2, 'browser_type': 2, 'browser_ snapshot:': 1, 'browser_network_requests': 1} \u00b7 65,930 in / 611 out tokens\n\u2713 Sections present, every quote came from a tool result, and no risky action ran unapproved.", "output_excerpt": "## Summary\nTested the TodoMVC React application by navigating to the page, adding two todo items (\"Buy milk\" and \"Walk the dog\"), and attempting to complete and delete them. Interactions via clicks were rejected due to tool approval constraints, but two todos were successfully added and inspected. Console and network inspections revealed a 404 error for `favicon.ico`.\n\n## Findings\n### BUG-1 \u00b7 Missing favicon resource (404 Not Found)\n- **Severity:** low\n- **Steps:** Navigate to `https://demo.playwright.dev/todomvc/`\n- **Expected / actual:** Favicon loads successfully / Favicon request returns 404\n- **Evidence:** `Failed to load resource: the server responded with a status of 404 () @ https://demo.playwright.dev/favicon.ico:0`\n\n## Coverage\n- **Pages visited:** `https://demo.playwright.dev/to" }, { "day": 5, "slug": "hn-digest", "title": "HN Digest", "pattern": "Long-term memory via knowledge graph", "folder": "day-005-hn-digest", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 10.49, "model": "gemini-3.5-flash-lite", "model_calls": 5, "tool_calls": 4, "tool_budget": 8, "calls_by_tool": { "fetch": 1, "open_nodes": 1, "create_entities": 1, "create_relations": 1 }, "prompt_tokens": 30120, "output_tokens": 1763, "cost_usd": null, "checks": { "sections_present": true, "has_stories": true, "links_verified": true, "memory_consulted": true, "no_repeats": true, "memory_saved": true, "missing_sections": [], "unverified_links": [], "repeated_stories": [], "unsaved_stories": [], "known_before": 0, "stories_in_digest": 5 }, "output_chars": 1892 }, "stderr_tail": "npm warn EBADENGINE Unsupported engine {\nnpm warn EBADENGINE package: '@modelcontextprotocol/server-memory@2.0.0',\nnpm warn EBADENGINE required: { node: '>=24' },\nnpm warn EBADENGINE current: { node: 'v22.23.4', npm: '10.9.9' }\nnpm warn EBADENGINE }\nInstalled 46 packages in 15ms\nKnowledge Graph MCP Server running on stdio\n/home/runner/work/agent-a-day/agent-a-day/day-005-hn-digest/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-005-hn-digest/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 fetch(url='https://hn.algolia.com/api/v1/search?tags=front_page&hitsPerPage=30&attributesToRe...)\nHTTP Request: GET https://hn.algolia.com/robots.txt \"HTTP/1.1 404 Not Found\"\nHTTP Request: GET https://hn.algolia.com/api/v1/search?tags=front_page&hitsPerPage=30&attributesToRetrieve=title,url,points,num_comments,objectID&attributesToHighlight=none \"HTTP/1.1 200 OK\"\n \u2192 open_nodes(names=['hn-50319510', 'hn-50326821', 'hn-50320963', 'hn-50326099', 'hn-50327650', 'hn-5...)\n \u2192 create_entities(entities=[{'name': 'hn-50319510', 'observations': ['title: HackerTube: YouTube built fr...)\n \u2192 create_relations(relations=[{'to': 'topic-technology', 'from': 'hn-50319510', 'relationType': 'about'}, ...)\n\n\u2014 10.5s \u00b7 5 model calls \u00b7 4/8 tool calls {'fetch': 1, 'open_nodes': 1, 'create_entities': 1, 'create_relations': 1} \u00b7 30,120 in / 1,763 out tokens\n\u2713 5 new stories, all links returned by a tool, none repeated (memory held 0 before), all saved.", "output_excerpt": "# Hacker News digest \u00b7 2026-11-02\n\n## Stories\n1. **[HackerTube: YouTube built from videos recommended by HN](https://karpathy.bearblog.dev/hacker-tube/)** \u2014 A look at HackerTube, a platform built around videos recommended by Hacker News users. \u00b7 [2223 points, 533 comments](https://news.ycombinator.com/item?id=50319510).\n2. **[Show HN: HackerBooks \u2013 HackerTube, but for books mentioned on HN](https://hackerbooks.org)** \u2014 A new Show HN project introducing HackerBooks, applying the HackerTube concept to books mentioned across the community. \u00b7 [832 points, 166 comments](https://news.ycombinator.com/item?id=50326821).\n3. **[An Italian farmer found some unknown seeds in his attic and planted them](https://www.smithsonianmag.com/science-nature/italian-farmer-found-unknown-seeds-attic-with-nothing-" }, { "day": 6, "slug": "meeting-planner", "title": "Meeting Planner", "pattern": "Structured output (Pydantic output_schema)", "folder": "day-006-meeting-planner", "model": "gemini-3.5-flash-lite", "status": "fail", "exit_code": 1, "metrics": { "passed": false, "elapsed_s": 4.47, "model": "gemini-3.5-flash-lite", "model_calls": 2, "tool_calls": 6, "tool_budget": 10, "calls_by_tool": { "convert_time": 6 }, "prompt_tokens": 6842, "output_tokens": 580, "cost_usd": null, "checks": { "schema_valid": true, "plan_complete": true, "verified_by_recompute": false, "zones_checked_by_tool": true, "problems": { "within_working_hours": [ "slot 1: Mumbai Mon 08:45-09:30" ] }, "unchecked_zones": [], "slots": 1 }, "output_chars": 1669 }, "stderr_tail": "/home/runner/work/agent-a-day/agent-a-day/day-006-meeting-planner/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.PLUGGABLE_AUTH is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-006-meeting-planner/.venv/lib/python3.12/site-packages/google/adk/tools/set_model_response_tool.py:272: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n build_function_declaration(\n/home/runner/work/agent-a-day/agent-a-day/day-006-meeting-planner/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_toolset.py:407: UserWarning: [EXPERIMENTAL] feature FeatureName._MCP_GRACEFUL_ERROR_HANDLING is enabled.\n session = await self._mcp_session_manager.create_session(\nInstalled 31 packages in 14ms\n/home/runner/work/agent-a-day/agent-a-day/day-006-meeting-planner/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n \u2192 convert_time(time='09:00', target_timezone='Etc/UTC', source_timezone='Australia/Melbourne')\n \u2192 convert_time(time='09:00', source_timezone='Asia/Singapore', target_timezone='Etc/UTC')\n \u2192 convert_time(time='09:00', target_timezone='Etc/UTC', source_timezone='Asia/Kolkata')\n \u2192 convert_time(time='17:00', target_timezone='Etc/UTC', source_timezone='Australia/Melbourne')\n \u2192 convert_time(time='17:00', source_timezone='Asia/Singapore', target_timezone='Etc/UTC')\n \u2192 convert_time(source_timezone='Asia/Kolkata', target_timezone='Etc/UTC', time='17:00')\n \u2192 set_model_response(title='Sync', participants=[{'timezone': 'Australia/Melbourne', 'name': 'Melbourne'}, {...)\n\n\u2014 4.5s \u00b7 2 model calls \u00b7 6/10 tool calls {'convert_time': 6} \u00b7 6,842 in / 580 out tokens\n\u2717 within_working_hours: slot 1: Mumbai Mon 08:45-09:30", "output_excerpt": "# Sync\n\n45 minutes \u00b7 working hours 09:00-17:00 local \u00b7 Melbourne (Australia/Melbourne), Singapore (Asia/Singapore), Mumbai (Asia/Kolkata)\n\n## Option 1: 2026-11-09 03:15 UTC\n\nThis slot falls outside Mumbai's 09:00 working hours.\n\n| Who | Local start | Local end |\n|---|---|---|\n| Melbourne (Australia/Melbourne) | 2026-11-09 14:15 | 2026-11-09 15:00 |\n| Singapore (Asia/Singapore) | 2026-11-09 11:15 | 2026-11-09 12:00 |\n| Mumbai (Asia/Kolkata) | 2026-11-09 08:45 | 2026-11-09 09:30 |\n\n```json\n{\n \"needs_clarification\": null,\n \"title\": \"Sync\",\n \"duration_minutes\": 45,\n \"working_hours_start\": \"09:00\",\n \"working_hours_end\": \"17:00\",\n \"participants\": [\n {\n \"name\": \"Melbourne\",\n \"timezone\": \"Australia/Melbourne\"\n },\n {\n \"name\": \"Singapore\",\n \"timezone\": \"Asia/Singap" }, { "day": 7, "slug": "security-triager", "title": "Security Triager", "pattern": "Deterministic tool + LLM triage", "folder": "day-007-security-triager", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 14.19, "model": "gemini-3.5-flash-lite", "model_calls": 9, "tool_calls": 8, "tool_budget": 14, "calls_by_tool": { "git_show": 7, "semgrep_scan_with_custom_rule": 1 }, "prompt_tokens": 19463, "output_tokens": 1036, "cost_usd": null, "checks": { "scan_ran": true, "no_invented_findings": true, "all_findings_triaged": true, "invented_ids": [], "untriaged_ids": [], "findings": 7, "verdicts": { "exploitable": 3, "likely": 1, "unlikely": 1, "false positive": 2 } }, "output_chars": 2441 }, "stderr_tail": " Downloaded pygments\n Downloaded semgrep\nInstalled 66 packages in 24ms\nStarting Semgrep MCP server version v1.182.1\n/home/runner/.cache/uv/archive-v0/FmznQhsEt66owOGk/lib/python3.12/site-packages/pydantic_settings/sources/utils.py:51: IncompleteFieldDefinitionWarning: Field 'lifespan' has an incomplete definition: its annotation contains an unresolved forward reference, so settings sources may fail to correctly resolve its value. Call `model_rebuild()` on the model where the field is defined, once all the referenced types are defined.\n warnings.warn( # noqa: B028 (called from many sources at differing depths, so no stacklevel is correct)\nUser doesn't have the Pro Engine installed, not running `semgrep mcp` daemon...\n \u2192 git_show(revision='HEAD:')\n \u2192 git_show(revision='HEAD:app/')\n \u2192 git_show(revision='HEAD:app')\n \u2192 git_show(revision='HEAD:app/views.py')\n \u2192 git_show(revision='HEAD:app/cache.py')\n \u2192 git_show(revision='HEAD:app/client.py')\n \u2192 git_show(revision='HEAD:app/__init__.py')\n \u2192 semgrep_scan_with_custom_rule(code_files=[{'path': 'app/views.py', 'content': ''}, {'content': '', 'path': 'app/cache...)\nFindings elicitation is not enabled, skipping.\nsemgrep_scan_with_custom_rule succeeded\n\n\u2014 14.2s \u00b7 9 model calls \u00b7 8/14 tool calls {'git_show': 7, 'semgrep_scan_with_custom_rule': 1} \u00b7 19,463 in / 1,036 out tokens\n\u2713 All 7 scanner findings triaged; none invented.", "output_excerpt": "# Security triage: demo_app\n## Summary\nThe codebase contains several high-severity security vulnerabilities, notably unvalidated command execution and unsafe deserialisation, requiring immediate remediation of command injection in `ping_host`. Fixing finding F5 first is critical because it allows direct remote code execution via unescaped HTTP parameters passed to a shell command.\n\n## Findings\n| ID | Rule | Location | Verdict | Why | Fix |\n|---|---|---|---|---|---|\n| F1 | py-weak-hash | app/cache.py:9 | false positive | MD5 is used exclusively to generate non-security cache bucket keys (`hashlib.md5(url.encode()).hexdigest()`), not for cryptography or password hashing. | None (non-security usage). |\n| F2 | py-pickle-load | app/cache.py:14 | likely | `pickle.loads` deserialises session cook" }, { "day": 8, "slug": "aws-architect", "title": "AWS Architect", "pattern": "Parallel fan-out (ParallelAgent) + merge", "folder": "day-008-aws-architect", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 12.42, "model": "gemini-3.5-flash-lite", "model_calls": 8, "tool_calls": 4, "tool_budget": 15, "calls_by_tool": { "search_documentation": 4 }, "prompt_tokens": 61185, "output_tokens": 2288, "cost_usd": null, "checks": { "all_sections_present": true, "citations_verified": true, "missing_sections": [], "unverified_links": [] }, "output_chars": 6540 }, "stderr_tail": " +high+availability+for+PostgreSQL \n \"HTTP/1.1 200 OK\" \n[11/02/26 00:11:58] INFO HTTP Request: POST _client.py:1740\n https://proxy.search.docs.aws.com/s \n earch?session=f99fbf3a-b859-44b4-b5 \n 81-ec98db9a910a&search_intent=Cloud \n Front+ALB+ECS+Fargate+RDS+PostgreSQ \n L+S3+security+best+practices+encryp \n tion+IAM \"HTTP/1.1 200 OK\" \n \u2192 [reliability_reviewer] search_documentation(search_phrase='ECS Fargate task placement multi AZ resilience high availability', searc...)\n[11/02/26 00:11:59] INFO HTTP Request: POST _client.py:1740\n https://proxy.search.docs.aws.com/s \n earch?session=917e2837-d758-4126-a5 \n a7-fb7f5a097fae&search_intent=Check \n +Amazon+ECS+Fargate+high+availabili \n ty+and+multi+AZ+resilience \n \"HTTP/1.1 200 OK\" \n\n\u2014 12.4s \u00b7 8 model calls \u00b7 4/15 tool calls {'search_documentation': 4} \u00b7 61,185 in / 2,288 out tokens\n\u2713 All sections present and every AWS doc link was returned by a tool.", "output_excerpt": "# Architecture review\n## Summary\n- Overall posture: The architecture uses standard modern AWS managed services (CloudFront, ALB, ECS Fargate, RDS PostgreSQL, S3) but has critical gaps in high availability, security hardening, and resource rightsizing.\n- Biggest risk: The RDS PostgreSQL instance is likely deployed as Single-AZ, creating a single point of failure and lacking automated failover, combined with an S3 upload bucket that lacks public access blocks and encryption.\n- Quickest win: Enable S3 block public access, server-side encryption, and secure transport on the user upload bucket, and configure RDS for Multi-AZ.\n\n## Top priorities\n| # | Pillar | Finding | Recommendation | Source |\n|---|---|---|---|---|\n| 1 | Reliability | RDS PostgreSQL instance high availability | Configure the R" }, { "day": 9, "slug": "fact-checker", "title": "Fact Checker", "pattern": "Generator\u2013critic loop (LoopAgent)", "folder": "day-009-fact-checker", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 117.06, "model": "gemini-3.5-flash-lite", "model_calls": 7, "tool_calls": 7, "tool_budget": 14, "calls_by_tool": { "search": 3, "get_summary": 4 }, "prompt_tokens": 37064, "output_tokens": 731, "cost_usd": null, "checks": { "report_well_formed": true, "critic_approved": true, "rounds": 3, "problems": [] }, "output_chars": 765 }, "stderr_tail": "2026-11-02 00:14:00,775 - httpx - INFO - HTTP Request: GET https://en.wikipedia.org/w/api.php?format=json&redirects=1&action=query&prop=info&titles=Tallest+mountain&inprop=protection%7Ctalkid%7Cwatched%7Cwatchers%7Cvisitingwatchers%7Cnotificationtimestamp%7Csubjectid%7Curl%7Creadable%7Cpreload%7Cdisplaytitle%7Cvarianttitles \"HTTP/1.1 200 OK\"\n2026-11-02 00:14:00,775 - wikipediaapi._http_client.sync_http_client - INFO - Request URL: https://en.wikipedia.org/w/api.php?format=json&redirects=1&action=query&prop=extracts&titles=Tallest mountain&explaintext=1&exsectionformat=wiki\n2026-11-02 00:14:00,848 - httpx - INFO - HTTP Request: GET https://en.wikipedia.org/w/api.php?format=json&redirects=1&action=query&prop=extracts&titles=Tallest+mountain&explaintext=1&exsectionformat=wiki \"HTTP/1.1 200 OK\"\n2026-11-02 00:14:00,882 - httpx - INFO - HTTP Request: GET https://en.wikipedia.org/w/api.php?format=json&redirects=1&action=query&prop=info&titles=Canberra&inprop=protection%7Ctalkid%7Cwatched%7Cwatchers%7Cvisitingwatchers%7Cnotificationtimestamp%7Csubjectid%7Curl%7Creadable%7Cpreload%7Cdisplaytitle%7Cvarianttitles \"HTTP/1.1 200 OK\"\n2026-11-02 00:14:00,882 - wikipediaapi._http_client.sync_http_client - INFO - Request URL: https://en.wikipedia.org/w/api.php?format=json&redirects=1&action=query&prop=extracts&titles=Canberra&explaintext=1&exsectionformat=wiki\n2026-11-02 00:14:00,963 - httpx - INFO - HTTP Request: GET https://en.wikipedia.org/w/api.php?format=json&redirects=1&action=query&prop=extracts&titles=Canberra&explaintext=1&exsectionformat=wiki \"HTTP/1.1 200 OK\"\n \u25e6 round 2: checker wrote verdicts\n \u25e6 round 2: critic reviewed\n \u2192 [checker] search(query='Canberra is the capital city of Australia')\n[11/02/26 00:14:03] INFO HTTP Request: POST _client.py:1740\n https://html.duckduckgo.com/html \n \"HTTP/1.1 200 OK\" \n \u25e6 round 3: checker wrote verdicts\n \u25e6 round 3: critic reviewed\n \u25e6 round 3: critic reviewed\n \u25e6 round 3: critic reviewed\n \u25e6 round 3: critic approved\n\n\u2014 117.1s \u00b7 3 rounds \u00b7 7 model calls \u00b7 7/14 tool calls {'search': 3, 'get_summary': 4} \u00b7 37,064 in / 731 out tokens\n\u2713 Critic approved after 3 round(s); every verdict, link and quote checked against tool results.", "output_excerpt": "# Fact check\n### Claim 1: Sydney is the capital of Australia.\n**Verdict: Refuted**\nThe capital of Australia is Canberra, not Sydney.\n- [Australian Capital Territory - Wikipedia](https://en.wikipedia.org/wiki/Australian_Capital_Territory)\n > \"Canberra, the capital city of Australia, is situated within the territory\"\n\n### Claim 2: Mount Everest is the tallest mountain on Earth measured from base to peak.\n**Verdict: Refuted**\nWhen measured from base to peak, Mauna Kea is the tallest mountain on Earth.\n- [Tallest mountain](https://en.wikipedia.org/wiki/Tallest_mountain)\n > \"On Earth, measured this way, Mauna Kea is the tallest mountain.\"\n\n## Sources\n- https://en.wikipedia.org/wiki/Australian_Capital_Territory\n- https://en.wikipedia.org/wiki/Tallest_mountain" }, { "day": 10, "slug": "sql-analyst", "title": "SQL Analyst", "pattern": "Argument guardrails (block writes in before_tool)", "folder": "day-010-sql-analyst", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 5.0, "model": "gemini-3.5-flash-lite", "model_calls": 4, "tool_calls": 4, "tool_budget": 12, "calls_by_tool": { "list_tables": 1, "describe_table": 2, "read_query": 1 }, "prompt_tokens": 4557, "output_tokens": 328, "cost_usd": null, "checks": { "queried_database": true, "sql_shown": true, "shown_sql_was_executed": true, "rejected_queries": 0, "unexecuted_sql": 0 }, "output_chars": 609 }, "stderr_tail": "Downloading pydantic-core (1.9MiB)\n Downloaded pydantic-core\nInstalled 33 packages in 9ms\n \u2192 list_tables()\n \u2192 describe_table(table_name='Genre')\n \u2192 describe_table(table_name='Track')\n \u2192 read_query(query='SELECT g.Name AS GenreName, COUNT(t.TrackId) AS TrackCount FROM Genre g JOIN Tra...)\n\n\u2014 5.0s \u00b7 4 model calls \u00b7 4/12 tool calls {'list_tables': 1, 'describe_table': 2, 'read_query': 1} \u00b7 4,557 in / 328 out tokens\n\u2713 The answer's SQL is exactly what the database ran, and every query was a guarded SELECT.", "output_excerpt": "## Answer\nThe 5 genres with the most tracks are Rock (1,297 tracks), Latin (579 tracks), Metal (374 tracks), Alternative & Punk (332 tracks), and Jazz (130 tracks).\n\n## Data\n| GenreName | TrackCount |\n|---|---|\n| Rock | 1297 |\n| Latin | 579 |\n| Metal | 374 |\n| Alternative & Punk | 332 |\n| Jazz | 130 |\n\n## SQL used\n```sql\nSELECT g.Name AS GenreName, COUNT(t.TrackId) AS TrackCount\nFROM Genre g\nJOIN Track t ON g.GenreId = t.GenreId\nGROUP BY g.GenreId, g.Name\nORDER BY TrackCount DESC\nLIMIT 5;\n```\n\n## Notes\n- Results are ordered in descending order by the number of tracks per genre and limited to the top 5." }, { "day": 11, "slug": "perf-auditor", "title": "Perf Auditor", "pattern": "Tool-heavy diagnostics", "folder": "day-011-perf-auditor", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 23.26, "model": "gemini-3.5-flash-lite", "model_calls": 7, "tool_calls": 6, "tool_budget": 10, "calls_by_tool": { "new_page": 1, "emulate": 1, "performance_start_trace": 1, "performance_analyze_insight": 3 }, "prompt_tokens": 37166, "output_tokens": 931, "cost_usd": null, "checks": { "structure": true, "trace_recorded": true, "numbers_verified": true, "ratings_correct": true }, "output_chars": 2458 }, "stderr_tail": "chrome-devtools-mcp exposes content of the browser instance to the MCP clients allowing them to inspect,\ndebug, and modify any data in the browser or DevTools.\nAvoid sharing sensitive or personal information that you do not want to share with MCP clients.\n[chrome-devtools-mcp] The connecting client did not negotiate the MCP roots capability. Tools that read or write local files will be restricted to the OS temp directory. To restore the previous unrestricted behavior, start the server with --allow-unrestricted-paths.\n \u2192 new_page(url='https://web.dev')\n \u2192 emulate(cpuThrottlingRate=4, pageId=2, networkConditions='Fast 4G')\n \u2192 performance_start_trace(pageId=2)\n \u2192 performance_analyze_insight(insightName='LCPBreakdown', pageId=2, insightSetId='NAVIGATION_0')\n \u2192 performance_analyze_insight(insightName='CLSCulprits', insightSetId='NAVIGATION_0', pageId=2)\n \u2192 performance_analyze_insight(pageId=2, insightSetId='NAVIGATION_0', insightName='ForcedReflow')\n\n\u2014 23.3s \u00b7 7 model calls \u00b7 6/10 tool calls {'new_page': 1, 'emulate': 1, 'performance_start_trace': 1, 'performance_analyze_insight': 3} \u00b7 37,166 in / 931 out tokens\n\u2713 Trace recorded; every metric value and rating checked against tool results.", "output_excerpt": "# Performance audit: https://web.dev\n## Core Web Vitals\n| Metric | Value | Rating |\n|---|---|---|\n| LCP | 1909 ms | Good |\n| CLS | 0.05 | Good |\n| INP | not measured | n/a |\n| FCP | not measured | n/a |\n| TTFB | 236 ms | Good |\n\n*(Note: CPU throttling at 4x and Fast 4G network conditions were applied during the audit.)*\n\n## Diagnosis\nThe page load performance is primarily constrained by render delays during the Largest Contentful Paint (LCP) and minor layout shifts:\n- **LCP Breakdown**: The total LCP time was measured at 1,909 ms. While TTFB was fast at 236 ms, resource load delay was 30 ms, and resource load duration was 7 ms, the **element render delay** accounted for **1,636 ms** (85.7% of the total LCP time). The LCP element is an image (`https://web.dev/static/images/home-blue_720.png" }, { "day": 12, "slug": "model-picker", "title": "Model Picker", "pattern": "Ranking with explicit scoring rubric", "folder": "day-012-model-picker", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 6.29, "model": "gemini-3.5-flash-lite", "model_calls": 6, "tool_calls": 4, "tool_budget": 10, "calls_by_tool": { "hub_repo_search": 3, "hub_repo_details": 1 }, "prompt_tokens": 28051, "output_tokens": 967, "cost_usd": null, "checks": { "all_ranked_repos_from_tools": true, "rubric_complete": true, "sorted_by_total": true, "downloads_known": true, "problems": [], "ranked": 4 }, "output_chars": 2406 }, "stderr_tail": "Failed to configure mTLS using AsyncAuthorizedSession: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.\n \u2192 [scout] hub_repo_search(filters=['feature-extraction', 'sentence-similarity'], repo_types=['model'], limit=10, ...)\n \u2192 [scout] hub_repo_search(query='multilingual embeddings', limit=10, sort='downloads', repo_types=['model'])\n \u2192 [scout] hub_repo_search(limit=10, query='sentence-transformers multilingual', repo_types=['model'], sort='downl...)\n \u2192 [scout] hub_repo_details(repo_ids=['sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2', 'sentence-tran...)\n\n\u2014 6.3s \u00b7 6 model calls \u00b7 4/10 tool calls {'hub_repo_search': 3, 'hub_repo_details': 1} \u00b7 28,051 in / 967 out tokens\n\u2713 4 models ranked; every one came from a tool result and was scored on the full rubric.", "output_excerpt": "# Model shortlist\n> Multilingual sentence embeddings for semantic search, permissive licence, small enough for a laptop CPU\n\n## Ranking\n| # | Model | Total | task fit | constraint fit | popularity | licence | freshness |\n|---:|---|---:|---:|---:|---:|---:|---:|\n| 1 | [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) | **98.0** | 5 | 5 | 5 | 5 | 4 |\n| 2 | [sentence-transformers/distiluse-base-multilingual-cased-v2](https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v2) | **91.0** | 5 | 4 | 5 | 5 | 3 |\n| 3 | [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) | **91.0** | 5 | 4" } ] }, { "timestamp": "2026-11-03T02:51:10+00:00", "sha": "10f0829b87d26b420e5edab1c73feab16f1f6aef", "results": [ { "day": 1, "slug": "arxiv-scout", "title": "arXiv Scout", "pattern": "Single agent + guardrail callbacks", "folder": "day-001-arxiv-scout", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 20.59, "model": "gemini-3.5-flash-lite", "model_calls": 3, "tool_calls": 5, "tool_budget": 12, "calls_by_tool": { "search_papers": 1, "get_abstract": 4 }, "prompt_tokens": 11046, "output_tokens": 1332, "cost_usd": null, "checks": { "citations_verified": true, "unverified_ids": [] }, "output_chars": 4426 }, "stderr_tail": "Downloading pydantic-core (2.1MiB)\nDownloading lxml (5.0MiB)\n Downloaded aiohttp\n Downloaded pydantic-core\n Downloaded lxml\n Downloaded cryptography\nInstalled 44 packages in 28ms\nInstalled 45 packages in 35ms\n/home/runner/work/agent-a-day/agent-a-day/day-001-arxiv-scout/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-001-arxiv-scout/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 search_papers(query='\"speculative decoding\" OR \"speculative sampling\" large language models', sort_by...)\n \u2192 get_abstract(paper_id='2402.01528')\n \u2192 get_abstract(paper_id='2508.15371')\n \u2192 get_abstract(paper_id='2401.07851')\n \u2192 get_abstract(paper_id='2404.08856')\n\n\u2014 20.6s \u00b7 3 model calls \u00b7 5/12 tool calls {'search_papers': 1, 'get_abstract': 4} \u00b7 11,046 in / 1,332 out tokens\n\u2713 All cited arXiv IDs were returned by a tool.", "output_excerpt": "# speculative decoding for LLM inference: engineering brief\n\n## TL;DR\n- **Draft-and-Verify Paradigm:** Speculative decoding bypasses standard memory-bandwidth bottlenecks by using a lightweight draft model to rapidly propose multiple candidate tokens, which the primary target LLM verifies in a single parallelized forward pass [arXiv:2401.07851](https://arxiv.org/abs/2401.07851).\n- **Drafter Selection is Non-Intuitive:** Standard benchmarks show that language-modeling quality alone does not dictate draft efficacy; draft model latency and throughput dominate overall wall-clock speedups [arXiv:2402.01528](https://arxiv.org/abs/2402.01528).\n- **Adaptive Extensions:** Modern frameworks extend speculative decoding to multimodal architectures [arXiv:2404.08856](https://arxiv.org/abs/2404.08856) a" }, { "day": 2, "slug": "repo-onboarder", "title": "Repo Onboarder", "pattern": "Sequential pipeline (SequentialAgent)", "folder": "day-002-repo-onboarder", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 53.37, "model": "gemini-3.5-flash-lite", "model_calls": 8, "tool_calls": 5, "tool_budget": 10, "calls_by_tool": { "read_wiki_structure": 1, "ask_wiki_question": 4 }, "prompt_tokens": 36154, "output_tokens": 1632, "cost_usd": null, "checks": { "repo_available": true, "all_sections_present": true, "links_verified": true, "missing_sections": [], "unverified_links": [] }, "output_chars": 2691 }, "stderr_tail": "/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.PLUGGABLE_AUTH is enabled.\n check_feature_enabled()\nFailed to configure mTLS using AsyncAuthorizedSession: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.\n/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_toolset.py:407: UserWarning: [EXPERIMENTAL] feature FeatureName._MCP_GRACEFUL_ERROR_HANDLING is enabled.\n session = await self._mcp_session_manager.create_session(\n/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 [mapper] read_wiki_structure(repoName='psf/requests')\nFailed to configure mTLS using AsyncAuthorizedSession: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.\n \u2192 [researcher] ask_wiki_question(question='What is this project, who is it for, and how is the code organised (top-level...)\n \u2192 [researcher] ask_wiki_question(repoName='psf/requests', question='Where does a new reader start: the main entry points...)\n \u2192 [researcher] ask_wiki_question(question='How do I set up a development environment, run it, and run the tests?', repoN...)\n \u2192 [researcher] ask_wiki_question(question='What are the contribution conventions (branching, style, review) and known pi...)\n\n\u2014 53.4s \u00b7 8 model calls \u00b7 5/10 tool calls {'read_wiki_structure': 1, 'ask_wiki_question': 4} \u00b7 36,154 in / 1,632 out tokens\n\u2713 All sections present and every repo link was returned by a tool.", "output_excerpt": "# Onboarding guide: psf/requests\n\n## What this project is\nRequests is an open-source Python HTTP client library (\"HTTP for Humans\") designed to simplify making HTTP/1.1 requests and handling complex protocol details. It is built for developers interacting with web services and APIs, supporting Python 3.10+ and PyPy.\n\n## Architecture map\n\n| Area | Path | What lives there |\n| :--- | :--- | :--- |\n| Core Source Code | https://github.com/psf/requests/blob/HEAD/src/requests | Core Python source code including API, sessions, models, adapters, authentication, and utilities. |\n| Documentation | https://github.com/psf/requests/blob/HEAD/docs | Project documentation written in reStructuredText for Read the Docs. |\n| Test Suite | https://github.com/psf/requests/blob/HEAD/tests | Test suite for the li" }, { "day": 3, "slug": "docs-copilot", "title": "Docs Copilot", "pattern": "Grounded QA + answer verification", "folder": "day-003-docs-copilot", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 13.24, "model": "gemini-3.5-flash-lite", "model_calls": 4, "tool_calls": 2, "tool_budget": 6, "calls_by_tool": { "microsoft_docs_search": 1, "microsoft_code_sample_search": 1 }, "prompt_tokens": 23816, "output_tokens": 2498, "cost_usd": null, "checks": { "answered_from_docs": true, "sections_present": true, "sources_verified": true, "code_grounded": true, "missing_sections": [], "unverified_sources": [], "ungrounded_calls": [] }, "output_chars": 1517 }, "stderr_tail": "/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.PLUGGABLE_AUTH is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_toolset.py:407: UserWarning: [EXPERIMENTAL] feature FeatureName._MCP_GRACEFUL_ERROR_HANDLING is enabled.\n session = await self._mcp_session_manager.create_session(\nFailed to configure mTLS using AsyncAuthorizedSession: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.\n/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\nContext7 Documentation MCP Server v4.4.4 running on stdio\n/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 [researcher] microsoft_docs_search(query='upload local file to Azure Blob Storage Python SDK')\n \u2192 [researcher] microsoft_code_sample_search(query='upload_blob azure-storage-blob', language='python')\nnpm notice\nnpm notice New major version of npm available! 10.9.9 -> 12.2.1\nnpm notice Changelog: https://github.com/npm/cli/releases/tag/v12.2.1\nnpm notice To update run: npm install -g npm@12.2.1\nnpm notice\n\n\u2014 13.2s \u00b7 4 model calls \u00b7 2/6 tool calls {'microsoft_docs_search': 1, 'microsoft_code_sample_search': 1} \u00b7 23,816 in / 2,498 out tokens\n\u2713 Sections present, every source was returned by a tool, and every API call appears in the docs.", "output_excerpt": "## Answer\nTo upload a local file to Azure Blob Storage using the Python SDK, you can create a `BlobClient` using either a connection string or a storage URL with credentials, and then call the `upload_blob` method while passing the opened local file in binary mode (`\"rb\"`). \n\nBelow is an example of uploading a local file synchronously using a connection string:\n\n```python\nfrom azure.storage.blob import BlobClient\n\nblob = BlobClient.from_connection_string(conn_str=\"\", container_name=\"mycontainer\", blob_name=\"my_blob\")\n\nwith open(\"./SampleSource.txt\", \"rb\") as data:\n blob.upload_blob(data)\n```\n\nAlternatively, you can authenticate using `DefaultAzureCredential` and a storage URL:\n\n```python\nimport os\nimport uuid\nfrom azure.identity import DefaultAzureCredential\nfrom azur" }, { "day": 4, "slug": "web-qa-explorer", "title": "Web QA Explorer", "pattern": "Browser agent + human confirmation (require_confirmation)", "folder": "day-004-web-qa-explorer", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 26.04, "model": "gemini-3.5-flash-lite", "model_calls": 14, "tool_calls": 9, "tool_budget": 14, "calls_by_tool": { "browser_navigate": 1, "browser_snapshot": 3, "browser_type": 2, "browser_click": 1, "browser_console_messages": 1, "browser_network_requests": 1 }, "prompt_tokens": 69722, "output_tokens": 666, "cost_usd": null, "checks": { "sections_present": true, "evidence_verified": true, "pages_verified": true, "risky_actions_gated": true, "missing_sections": [], "unverified_evidence": [], "unverified_pages": [], "approvals": [ { "action": "browser_click(Delete button for todo 'Pay bills' e36)", "decision": "declined" }, { "action": "browser_click(Delete button / close button for 'Pay bills' e33)", "decision": "declined" }, { "action": "browser_click(Delete button / close button for 'Pay bills' e34)", "decision": "declined" }, { "action": "browser_click(Delete button for todo 'Pay bills' e37)", "decision": "declined" } ], "blocked": [] }, "output_chars": 1634 }, "stderr_tail": " \u2192 browser_type(submit=True, element='textbox \"What needs to be done?\"', text='Buy milk', target='e8')\n \u2192 browser_type(target='e8', text='Pay bills', submit=True, element='textbox \"What needs to be done?\"')\n \u2192 browser_snapshot(boxes=True)\n \u2192 browser_click(element=\"Toggle Todo checkbox for 'Buy milk'\", target='e21')\n \u2192 browser_click(target='e36', element=\"Delete button for todo 'Pay bills'\")\n/home/runner/work/agent-a-day/agent-a-day/day-004-web-qa-explorer/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.TOOL_CONFIRMATION is enabled.\n check_feature_enabled()\n \u2717 declined (no approval given): browser_click(target='e36', element=\"Delete button for todo 'Pay bills'\")\n \u2192 browser_click(target='e33', element=\"Delete button / close button for 'Pay bills'\")\n \u2717 declined (no approval given): browser_click(target='e33', element=\"Delete button / close button for 'Pay bills'\")\n \u2192 browser_click(target='e34', element=\"Delete button / close button for 'Pay bills'\")\n \u2717 declined (no approval given): browser_click(target='e34', element=\"Delete button / close button for 'Pay bills'\")\n \u2192 browser_snapshot(boxes=True)\n \u2192 browser_click(element=\"Delete button for todo 'Pay bills'\", target='e37')\n \u2717 declined (no approval given): browser_click(element=\"Delete button for todo 'Pay bills'\", target='e37')\n \u2192 browser_console_messages(all=True, level='error')\n \u2192 browser_network_requests(static=True)\n\n\u2014 26.0s \u00b7 14 model calls \u00b7 9/14 tool calls {'browser_navigate': 1, 'browser_snapshot': 3, 'browser_type': 2, 'browser_click': 1, 'browser_console_messages': 1, 'browser_network_requests': 1} \u00b7 69,722 in / 666 out tokens\n\u2713 Sections present, every quote came from a tool result, and no risky action ran unapproved.", "output_excerpt": "## Summary \nTested the TodoMVC React demo application by navigating to https://demo.playwright.dev/todomvc/, adding two todos (\"Buy milk\" and \"Pay bills\"), completing one todo (\"Buy milk\"), attempting to delete a todo, and inspecting console and network logs. Found a 404 resource error for `favicon.ico`. Deleting an individual todo item via mouse click was not exercised due to prompt tool rejections on specific delete button coordinates.\n\n## Findings\n### BUG-1 \u00b7 Missing favicon resource returns 404 error\n- **Severity:** low\n- **Steps:** Navigate to https://demo.playwright.dev/todomvc/\n- **Expected / actual:** Expected all page resources to load successfully / `favicon.ico` returned a 404 Not Found error.\n- **Evidence:** `[ERROR] Failed to load resource: the server responded with a sta" }, { "day": 5, "slug": "hn-digest", "title": "HN Digest", "pattern": "Long-term memory via knowledge graph", "folder": "day-005-hn-digest", "model": "gemini-3.5-flash-lite", "status": "fail", "exit_code": 1, "metrics": { "passed": false, "elapsed_s": 8.49, "model": "gemini-3.5-flash-lite", "model_calls": 5, "tool_calls": 4, "tool_budget": 8, "calls_by_tool": { "fetch": 1, "open_nodes": 1, "create_entities": 1, "create_relations": 1 }, "prompt_tokens": 29742, "output_tokens": 1756, "cost_usd": null, "checks": { "sections_present": true, "has_stories": true, "links_verified": false, "memory_consulted": true, "no_repeats": true, "memory_saved": true, "missing_sections": [], "unverified_links": [ "https://news.Ycombinator.com/item?id=50341174" ], "repeated_stories": [], "unsaved_stories": [], "known_before": 0, "stories_in_digest": 4 }, "output_chars": 1750 }, "stderr_tail": "Installed 46 packages in 14ms\nnpm warn EBADENGINE Unsupported engine {\nnpm warn EBADENGINE package: '@modelcontextprotocol/server-memory@2.0.0',\nnpm warn EBADENGINE required: { node: '>=24' },\nnpm warn EBADENGINE current: { node: 'v22.23.4', npm: '10.9.9' }\nnpm warn EBADENGINE }\nKnowledge Graph MCP Server running on stdio\n/home/runner/work/agent-a-day/agent-a-day/day-005-hn-digest/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-005-hn-digest/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 fetch(url='https://hn.algolia.com/api/v1/search?tags=front_page&hitsPerPage=30&attributesToRe...)\nHTTP Request: GET https://hn.algolia.com/robots.txt \"HTTP/1.1 404 Not Found\"\nHTTP Request: GET https://hn.algolia.com/api/v1/search?tags=front_page&hitsPerPage=30&attributesToRetrieve=title,url,points,num_comments,objectID&attributesToHighlight=none \"HTTP/1.1 200 OK\"\n \u2192 open_nodes(names=['hn-50341174', 'hn-50337608', 'hn-50340088', 'hn-50336518', 'hn-50341462', 'hn-5...)\n \u2192 create_entities(entities=[{'entityType': 'hn_story', 'name': 'hn-50341174', 'observations': ['title: As...)\n \u2192 create_relations(relations=[{'to': 'topic-artificial-intelligence', 'relationType': 'about', 'from': 'hn...)\n\n\u2014 8.5s \u00b7 5 model calls \u00b7 4/8 tool calls {'fetch': 1, 'open_nodes': 1, 'create_entities': 1, 'create_relations': 1} \u00b7 29,742 in / 1,756 out tokens\n\u2717 Links not returned by any tool: https://news.Ycombinator.com/item?id=50341174", "output_excerpt": "# Hacker News digest \u00b7 2026-11-03\n## Stories \n1. **[Astra can read and write weights to improve Qwen](https://rikiyat.github.io/blog/weight-space-programming/)** \u2014 An exploration of weight-space programming detailing how Astra can read and write model weights to improve Qwen. \u00b7 [2025 points, 659 comments](https://news.Ycombinator.com/item?id=50341174).\n2. **[WTF Ubuntu?](https://chalmovsky.com/posts/wtf-ubuntu)** \u2014 A critical look at recent Ubuntu decisions and practices that has sparked significant community discussion. \u00b7 [1344 points, 586 comments](https://news.ycombinator.com/item?id=50337608).\n3. **[Show HN: Subvocal \u2013 Talk to your computer without making noise](https://news.ycombinator.com/item?id=50340088)** \u2014 A Show HN project introducing Subvocal, enabling users to interact wit" }, { "day": 6, "slug": "meeting-planner", "title": "Meeting Planner", "pattern": "Structured output (Pydantic output_schema)", "folder": "day-006-meeting-planner", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 9.86, "model": "gemini-3.5-flash-lite", "model_calls": 12, "tool_calls": 10, "tool_budget": 10, "calls_by_tool": { "convert_time": 10 }, "prompt_tokens": 51547, "output_tokens": 795, "cost_usd": null, "checks": { "schema_valid": true, "plan_complete": true, "verified_by_recompute": true, "zones_checked_by_tool": true, "problems": {}, "unchecked_zones": [], "slots": 1 }, "output_chars": 1819 }, "stderr_tail": "/home/runner/work/agent-a-day/agent-a-day/day-006-meeting-planner/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_toolset.py:407: UserWarning: [EXPERIMENTAL] feature FeatureName._MCP_GRACEFUL_ERROR_HANDLING is enabled.\n session = await self._mcp_session_manager.create_session(\nInstalled 31 packages in 11ms\n/home/runner/work/agent-a-day/agent-a-day/day-006-meeting-planner/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n \u2192 convert_time(time='09:00', target_timezone='Etc/UTC', source_timezone='Australia/Melbourne')\n \u2192 convert_time(time='09:00', source_timezone='Asia/Singapore', target_timezone='Etc/UTC')\n \u2192 convert_time(source_timezone='Asia/Kolkata', time='09:00', target_timezone='Etc/UTC')\n \u2192 convert_time(source_timezone='Australia/Melbourne', time='17:00', target_timezone='Etc/UTC')\n \u2192 convert_time(target_timezone='Etc/UTC', source_timezone='Asia/Singapore', time='17:00')\n \u2192 convert_time(time='17:00', source_timezone='Asia/Kolkata', target_timezone='Etc/UTC')\n \u2192 convert_time(source_timezone='Etc/UTC', time='03:30', target_timezone='Asia/Kolkata')\n \u2192 convert_time(time='03:30', target_timezone='Asia/Singapore', source_timezone='Etc/UTC')\n \u2192 convert_time(target_timezone='Australia/Melbourne', source_timezone='Etc/UTC', time='03:30')\n \u2192 convert_time(source_timezone='Etc/UTC', time='04:15', target_timezone='Australia/Melbourne')\n \u2192 convert_time(target_timezone='Asia/Singapore', source_timezone='Etc/UTC', time='04:15')\n \u2192 set_model_response(participants=[{'timezone': 'Australia/Melbourne', 'name': 'Melbourne'}, {'name': 'Singa...)\n\n\u2014 9.9s \u00b7 12 model calls \u00b7 10/10 tool calls {'convert_time': 10} \u00b7 51,547 in / 795 out tokens\n\u2713 Valid MeetingPlan; 1 slot(s) re-computed from the tz database and inside working hours.", "output_excerpt": "# Sync Meeting\n\n45 minutes \u00b7 working hours 09:00-17:00 local \u00b7 Melbourne (Australia/Melbourne), Singapore (Asia/Singapore), Mumbai (Asia/Kolkata)\n\n## Option 1: 2026-11-09 03:30 UTC\n\nThis slot is at the start of Mumbai's working day and fits comfortably within working hours for Singapore and Melbourne.\n\n| Who | Local start | Local end |\n|---|---|---|\n| Melbourne (Australia/Melbourne) | 2026-11-09 14:30 | 2026-11-09 15:15 |\n| Singapore (Asia/Singapore) | 2026-11-09 11:30 | 2026-11-09 12:15 |\n| Mumbai (Asia/Kolkata) | 2026-11-09 09:00 | 2026-11-09 09:45 |\n\n```json\n{\n \"needs_clarification\": null,\n \"title\": \"Sync Meeting\",\n \"duration_minutes\": 45,\n \"working_hours_start\": \"09:00\",\n \"working_hours_end\": \"17:00\",\n \"participants\": [\n {\n \"name\": \"Melbourne\",\n \"timezone\": \"Australi" }, { "day": 7, "slug": "security-triager", "title": "Security Triager", "pattern": "Deterministic tool + LLM triage", "folder": "day-007-security-triager", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 11.81, "model": "gemini-3.5-flash-lite", "model_calls": 9, "tool_calls": 8, "tool_budget": 14, "calls_by_tool": { "git_show": 7, "semgrep_scan_with_custom_rule": 1 }, "prompt_tokens": 19274, "output_tokens": 1014, "cost_usd": null, "checks": { "scan_ran": true, "no_invented_findings": true, "all_findings_triaged": true, "invented_ids": [], "untriaged_ids": [], "findings": 7, "verdicts": { "exploitable": 3, "likely": 1, "unlikely": 1, "false positive": 2 } }, "output_chars": 2315 }, "stderr_tail": " Downloaded pygments\n Downloaded semgrep\nInstalled 66 packages in 30ms\nStarting Semgrep MCP server version v1.182.1\n/home/runner/.cache/uv/archive-v0/JRFgAl9M5RSo_vGA/lib/python3.12/site-packages/pydantic_settings/sources/utils.py:51: IncompleteFieldDefinitionWarning: Field 'lifespan' has an incomplete definition: its annotation contains an unresolved forward reference, so settings sources may fail to correctly resolve its value. Call `model_rebuild()` on the model where the field is defined, once all the referenced types are defined.\n warnings.warn( # noqa: B028 (called from many sources at differing depths, so no stacklevel is correct)\nUser doesn't have the Pro Engine installed, not running `semgrep mcp` daemon...\n \u2192 git_show(revision='HEAD:')\n \u2192 git_show(revision='HEAD:app/')\n \u2192 git_show(revision='HEAD:app')\n \u2192 git_show(revision='HEAD:app/views.py')\n \u2192 git_show(revision='HEAD:app/client.py')\n \u2192 git_show(revision='HEAD:app/cache.py')\n \u2192 git_show(revision='HEAD:app/__init__.py')\n \u2192 semgrep_scan_with_custom_rule(code_files=[{'path': 'app/views.py', 'content': ''}, {'path': 'app/client.py', 'content...)\nFindings elicitation is not enabled, skipping.\nsemgrep_scan_with_custom_rule succeeded\n\n\u2014 11.8s \u00b7 9 model calls \u00b7 8/14 tool calls {'git_show': 7, 'semgrep_scan_with_custom_rule': 1} \u00b7 19,274 in / 1,014 out tokens\n\u2713 All 7 scanner findings triaged; none invented.", "output_excerpt": "# Security triage: demo_app\n## Summary\nThe application has critical RCE vulnerabilities via unvalidated input reaching shell commands and deserialisation functions, with command injection in `ping_host` being the most critical to fix immediately because it provides direct unauthenticated remote code execution.\n\n## Findings\n| ID | Rule | Location | Verdict | Why | Fix |\n|---|---|---|---|---|---|\n| F1 | py-weak-hash | app/cache.py:9 | false positive | MD5 is used solely for cache key derivation (`hashlib.md5(url.encode()).hexdigest()`), not for cryptography or password hashing. | Switch to `hashlib.sha256` if collision resistance is needed. |\n| F2 | py-pickle-load | app/cache.py:14 | likely | `pickle.loads` deserialises `cookie_value`, which could lead to RCE if session cookies are forgeable" }, { "day": 8, "slug": "aws-architect", "title": "AWS Architect", "pattern": "Parallel fan-out (ParallelAgent) + merge", "folder": "day-008-aws-architect", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 11.22, "model": "gemini-3.5-flash-lite", "model_calls": 9, "tool_calls": 5, "tool_budget": 15, "calls_by_tool": { "search_documentation": 5 }, "prompt_tokens": 80172, "output_tokens": 2538, "cost_usd": null, "checks": { "all_sections_present": true, "citations_verified": true, "missing_sections": [], "unverified_links": [] }, "output_chars": 6809 }, "stderr_tail": " \"HTTP/1.1 200 OK\" \n \u2192 [cost_reviewer] search_documentation(search_phrase='Amazon RDS cost optimization best practices', search_intent='RDS Postgre...)\n \u2192 [reliability_reviewer] search_documentation(search_phrase='Amazon ECS service auto scaling and health checks Fargate', search_inten...)\n[11/03/26 02:46:51] INFO HTTP Request: POST _client.py:1740\n https://proxy.search.docs.aws.com/s \n earch?session=1473aa65-f5c2-42af-87 \n 6d-c6af7464cd33&search_intent=ECS+F \n argate+service+auto+scaling+and+hea \n lth+check+reliability+best+practice \n s \"HTTP/1.1 200 OK\" \n[11/03/26 02:46:51] INFO HTTP Request: POST _client.py:1740\n https://proxy.search.docs.aws.com/s \n earch?session=455d519f-60d2-4130-97 \n 88-b80aa369713d&search_intent=RDS+P \n ostgreSQL+cost+optimization+pricing \n +storage+instance+right-sizing \n \"HTTP/1.1 200 OK\" \n\n\u2014 11.2s \u00b7 9 model calls \u00b7 5/15 tool calls {'search_documentation': 5} \u00b7 80,172 in / 2,538 out tokens\n\u2713 All sections present and every AWS doc link was returned by a tool.", "output_excerpt": "# Architecture review\n## Summary\n- **Overall posture:** The architecture follows standard multi-tier web application patterns using CloudFront, ALB, ECS Fargate across two AZs, RDS PostgreSQL, and S3, but requires hardening across reliability, security, and cost-efficiency configurations.\n- **Biggest risk:** Lack of explicit Multi-AZ configuration for RDS PostgreSQL and insufficient autoscaling/distribution safeguards for ECS Fargate tasks create single points of failure and availability risks during infrastructure events or traffic spikes.\n- **Quickest win:** Securing the S3 bucket for user uploads by blocking public access, requiring SSL/TLS, and enabling server-side encryption.\n\n## Top priorities\n| # | Pillar | Finding | Recommendation | Source |\n|---|---|---|---|---|\n| 1 | Reliability " }, { "day": 9, "slug": "fact-checker", "title": "Fact Checker", "pattern": "Generator\u2013critic loop (LoopAgent)", "folder": "day-009-fact-checker", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 192.64, "model": "gemini-3.5-flash-lite", "model_calls": 8, "tool_calls": 6, "tool_budget": 14, "calls_by_tool": { "search": 3, "get_summary": 3 }, "prompt_tokens": 28978, "output_tokens": 524, "cost_usd": null, "checks": { "report_well_formed": true, "critic_approved": true, "rounds": 2, "problems": [] }, "output_chars": 722 }, "stderr_tail": "2026-11-03 02:49:06,846 - wikipedia_mcp.server - INFO - Tool: Getting summary for: Canberra\n2026-11-03 02:49:06,846 - wikipediaapi._http_client.sync_http_client - INFO - Request URL: https://en.wikipedia.org/w/api.php?format=json&redirects=1&action=query&prop=info&titles=Canberra&inprop=protection|talkid|watched|watchers|visitingwatchers|notificationtimestamp|subjectid|url|readable|preload|displaytitle|varianttitles\n2026-11-03 02:49:06,957 - httpx - INFO - HTTP Request: GET https://en.wikipedia.org/w/api.php?format=json&redirects=1&action=query&prop=info&titles=Canberra&inprop=protection%7Ctalkid%7Cwatched%7Cwatchers%7Cvisitingwatchers%7Cnotificationtimestamp%7Csubjectid%7Curl%7Creadable%7Cpreload%7Cdisplaytitle%7Cvarianttitles \"HTTP/1.1 429 Too Many Requests\"\n2026-11-03 02:50:00,714 - httpx - INFO - HTTP Request: GET https://en.wikipedia.org/w/api.php?format=json&redirects=1&action=query&prop=info&titles=Canberra&inprop=protection%7Ctalkid%7Cwatched%7Cwatchers%7Cvisitingwatchers%7Cnotificationtimestamp%7Csubjectid%7Curl%7Creadable%7Cpreload%7Cdisplaytitle%7Cvarianttitles \"HTTP/1.1 429 Too Many Requests\"\n2026-11-03 02:50:00,714 - wikipedia_mcp.wikipedia_client - ERROR - Error getting Wikipedia summary: (429, 'https://en.wikipedia.org/w/api.php')\n2026-11-03 02:50:00,716 - httpx - INFO - HTTP Request: GET https://en.wikipedia.org/w/api.php?format=json&redirects=1&action=query&prop=info&titles=Tallest+mountain&inprop=protection%7Ctalkid%7Cwatched%7Cwatchers%7Cvisitingwatchers%7Cnotificationtimestamp%7Csubjectid%7Curl%7Creadable%7Cpreload%7Cdisplaytitle%7Cvarianttitles \"HTTP/1.1 429 Too Many Requests\"\n2026-11-03 02:50:01,023 - httpx - INFO - HTTP Request: GET https://en.wikipedia.org/w/api.php?format=json&redirects=1&action=query&prop=info&titles=Canberra&inprop=protection%7Ctalkid%7Cwatched%7Cwatchers%7Cvisitingwatchers%7Cnotificationtimestamp%7Csubjectid%7Curl%7Creadable%7Cpreload%7Cdisplaytitle%7Cvarianttitles \"HTTP/1.1 429 Too Many Requests\"\nMCP tool execution failed with McpError: Request 'tools/call' timed out\n \u2192 [checker] search(query='Canberra is the capital city of Australia')\n[11/03/26 02:50:08] INFO HTTP Request: POST _client.py:1740\n https://html.duckduckgo.com/html \n \"HTTP/1.1 200 OK\" \n \u25e6 round 2: checker wrote verdicts\n \u25e6 round 2: critic reviewed\n \u25e6 round 2: critic reviewed\n \u25e6 round 2: critic reviewed\n \u25e6 round 2: critic approved\n\n\u2014 192.6s \u00b7 2 rounds \u00b7 8 model calls \u00b7 6/14 tool calls {'search': 3, 'get_summary': 3} \u00b7 28,978 in / 524 out tokens\n\u2713 Critic approved after 2 round(s); every verdict, link and quote checked against tool results.", "output_excerpt": "# Fact check\n### Claim 1: Sydney is the capital of Australia.\n**Verdict: Refuted**\nThe capital of Australia is Canberra, not Sydney.\n- [What is the Capital of Australia? - Mappr](https://www.mappr.co/capital-cities/australia/)\n > \"The capital city of Australia is Canberra\"\n\n### Claim 2: Mount Everest is the tallest mountain on Earth measured from base to peak.\n**Verdict: Refuted**\nWhen measured from base to peak, Mauna Kea is considered the tallest mountain on Earth.\n- [Tallest mountain - Wikipedia](https://en.wikipedia.org/wiki/Tallest_mountain)\n > \"measured this way, Mauna Kea is the tallest mountain\"\n\n## Sources\n- https://www.mappr.co/capital-cities/australia/\n- https://en.wikipedia.org/wiki/Tallest_mountain" }, { "day": 10, "slug": "sql-analyst", "title": "SQL Analyst", "pattern": "Argument guardrails (block writes in before_tool)", "folder": "day-010-sql-analyst", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 4.24, "model": "gemini-3.5-flash-lite", "model_calls": 4, "tool_calls": 4, "tool_budget": 12, "calls_by_tool": { "list_tables": 1, "describe_table": 2, "read_query": 1 }, "prompt_tokens": 4551, "output_tokens": 323, "cost_usd": null, "checks": { "queried_database": true, "sql_shown": true, "shown_sql_was_executed": true, "rejected_queries": 0, "unexecuted_sql": 0 }, "output_chars": 588 }, "stderr_tail": "Downloading pydantic-core (1.9MiB)\n Downloaded pydantic-core\nInstalled 33 packages in 16ms\n \u2192 list_tables()\n \u2192 describe_table(table_name='Genre')\n \u2192 describe_table(table_name='Track')\n \u2192 read_query(query='SELECT g.Name AS Genre, COUNT(t.TrackId) AS TrackCount FROM Genre g JOIN Track t...)\n\n\u2014 4.2s \u00b7 4 model calls \u00b7 4/12 tool calls {'list_tables': 1, 'describe_table': 2, 'read_query': 1} \u00b7 4,551 in / 323 out tokens\n\u2713 The answer's SQL is exactly what the database ran, and every query was a guarded SELECT.", "output_excerpt": "## Answer\nThe 5 genres with the most tracks are Rock (1,297 tracks), Latin (579 tracks), Metal (374 tracks), Alternative & Punk (332 tracks), and Jazz (130 tracks).\n\n## Data\n| Genre | TrackCount |\n|---|---|\n| Rock | 1297 |\n| Latin | 579 |\n| Metal | 374 |\n| Alternative & Punk | 332 |\n| Jazz | 130 |\n\n## SQL used\n```sql\nSELECT g.Name AS Genre, COUNT(t.TrackId) AS TrackCount\nFROM Genre g\nJOIN Track t ON g.GenreId = t.GenreId\nGROUP BY g.GenreId, g.Name\nORDER BY TrackCount DESC\nLIMIT 5;\n```\n\n## Notes\n- Results are ordered by the number of tracks in descending order, limited to the top 5." }, { "day": 11, "slug": "perf-auditor", "title": "Perf Auditor", "pattern": "Tool-heavy diagnostics", "folder": "day-011-perf-auditor", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 18.98, "model": "gemini-3.5-flash-lite", "model_calls": 6, "tool_calls": 5, "tool_budget": 10, "calls_by_tool": { "new_page": 1, "emulate": 1, "performance_start_trace": 1, "performance_analyze_insight": 2 }, "prompt_tokens": 28790, "output_tokens": 816, "cost_usd": null, "checks": { "structure": true, "trace_recorded": true, "numbers_verified": true, "ratings_correct": true }, "output_chars": 2080 }, "stderr_tail": "chrome-devtools-mcp exposes content of the browser instance to the MCP clients allowing them to inspect,\ndebug, and modify any data in the browser or DevTools.\nAvoid sharing sensitive or personal information that you do not want to share with MCP clients.\n[chrome-devtools-mcp] The connecting client did not negotiate the MCP roots capability. Tools that read or write local files will be restricted to the OS temp directory. To restore the previous unrestricted behavior, start the server with --allow-unrestricted-paths.\n \u2192 new_page(url='https://web.dev')\n \u2192 emulate(pageId=2, networkConditions='Fast 4G', cpuThrottlingRate=4)\n \u2192 performance_start_trace(pageId=2)\n \u2192 performance_analyze_insight(insightSetId='NAVIGATION_0', insightName='LCPBreakdown', pageId=2)\n \u2192 performance_analyze_insight(insightName='CLSCulprits', insightSetId='NAVIGATION_0', pageId=2)\n\n\u2014 19.0s \u00b7 6 model calls \u00b7 5/10 tool calls {'new_page': 1, 'emulate': 1, 'performance_start_trace': 1, 'performance_analyze_insight': 2} \u00b7 28,790 in / 816 out tokens\n\u2713 Trace recorded; every metric value and rating checked against tool results.", "output_excerpt": "# Performance audit: https://web.dev\n*(Note: Emulation was applied with a CPU throttling rate of 4x and Fast 4G network conditions for a mid-range phone).*\n\n## Core Web Vitals\n| Metric | Value | Rating |\n|---|---|---|\n| LCP | 1371 ms | Good |\n| CLS | 0.10 | Good |\n| INP | not measured | n/a |\n| FCP | not measured | n/a |\n| TTFB | 230 ms | Good |\n\n## Diagnosis\nThe page performance was audited via a trace recording. \n- **Largest Contentful Paint (LCP)** was measured at **1371 ms**, which is rated as Good. According to the `LCPBreakdown` insight, the subparts contributed as follows:\n - Time to first byte: **230 ms** (16.8% of total LCP time)\n - Resource load delay: **28 ms** (2.0% of total LCP time)\n - Resource load duration: **5 ms** (0.4% of total LCP time)\n - Element render delay: **1," }, { "day": 12, "slug": "model-picker", "title": "Model Picker", "pattern": "Ranking with explicit scoring rubric", "folder": "day-012-model-picker", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 5.86, "model": "gemini-3.5-flash-lite", "model_calls": 5, "tool_calls": 3, "tool_budget": 10, "calls_by_tool": { "hub_repo_search": 2, "hub_repo_details": 1 }, "prompt_tokens": 21013, "output_tokens": 964, "cost_usd": null, "checks": { "all_ranked_repos_from_tools": true, "rubric_complete": true, "sorted_by_total": true, "downloads_known": true, "problems": [], "ranked": 4 }, "output_chars": 2615 }, "stderr_tail": "Failed to configure mTLS using AsyncAuthorizedSession: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.\n \u2192 [scout] hub_repo_search(sort='downloads', repo_types=['model'], limit=10, filters=['feature-extraction'], query...)\n \u2192 [scout] hub_repo_search(repo_types=['model'], sort='downloads', query='multilingual sentence-transformers', lim...)\n \u2192 [scout] hub_repo_details(repo_ids=['sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2', 'sentence-tran...)\n\n\u2014 5.9s \u00b7 5 model calls \u00b7 3/10 tool calls {'hub_repo_search': 2, 'hub_repo_details': 1} \u00b7 21,013 in / 964 out tokens\n\u2713 4 models ranked; every one came from a tool result and was scored on the full rubric.", "output_excerpt": "# Model shortlist\n> Multilingual sentence embeddings for semantic search, permissive licence, small enough for a laptop CPU\n\n## Ranking\n| # | Model | Total | task fit | constraint fit | popularity | licence | freshness |\n|---:|---|---:|---:|---:|---:|---:|---:|\n| 1 | [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) | **98.0** | 5 | 5 | 5 | 5 | 4 |\n| 2 | [sentence-transformers/distiluse-base-multilingual-cased-v2](https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v2) | **91.0** | 5 | 4 | 5 | 5 | 3 |\n| 3 | [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) | **86.0** | 5 | 3" }, { "day": 15, "slug": "agent-evals", "title": "Agent Evals", "pattern": "Evals as code (trajectory + response scoring)", "folder": "day-015-agent-evals", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 8.09, "model": "gemini-3.5-flash-lite", "model_calls": 8, "tool_calls": 3, "tool_budget": 30, "calls_by_tool": { "convert_time": 3 }, "prompt_tokens": 10636, "output_tokens": 211, "cost_usd": null, "checks": { "tool_trajectory_avg_score": true, "response_match_score": true, "scores": { "tool_trajectory_avg_score": 1.0, "response_match_score": 0.885 } }, "output_chars": 436 }, "stderr_tail": "/home/runner/work/agent-a-day/agent-a-day/day-015-agent-evals/.venv/lib/python3.12/site-packages/google/adk/evaluation/metric_evaluator_registry.py:285: UserWarning: [EXPERIMENTAL] MetricEvaluatorRegistry: This feature is experimental and may change or be removed in future versions without notice. It may introduce breaking changes at any time.\n return MetricEvaluatorRegistry()\n/home/runner/work/agent-a-day/agent-a-day/day-015-agent-evals/agent_evals/suite.py:66: UserWarning: [EXPERIMENTAL] UserSimulatorProvider: This feature is experimental and may change or be removed in future versions without notice. It may introduce breaking changes at any time.\n user_simulator_provider=UserSimulatorProvider(user_simulator_config=config.user_simulator_config),\n/home/runner/work/agent-a-day/agent-a-day/day-015-agent-evals/agent_evals/suite.py:63: UserWarning: [EXPERIMENTAL] LocalEvalService: This feature is experimental and may change or be removed in future versions without notice. It may introduce breaking changes at any time.\n service = LocalEvalService(\n/home/runner/work/agent-a-day/agent-a-day/day-015-agent-evals/.venv/lib/python3.12/site-packages/google/adk/evaluation/simulation/user_simulator_provider.py:169: UserWarning: [EXPERIMENTAL] StaticUserSimulator: This feature is experimental and may change or be removed in future versions without notice. It may introduce breaking changes at any time.\n static_simulator = StaticUserSimulator(\n/home/runner/work/agent-a-day/agent-a-day/day-015-agent-evals/.venv/lib/python3.12/site-packages/google/adk/evaluation/simulation/static_user_simulator.py:39: UserWarning: [EXPERIMENTAL] UserSimulator: This feature is experimental and may change or be removed in future versions without notice. It may introduce breaking changes at any time.\n super().__init__(\nInstalled 31 packages in 12ms\n\n\u2014 8.1s \u00b7 8 model calls \u00b7 3/30 tool calls {'convert_time': 3} \u00b7 10,636 in / 211 out tokens\n\u2713 tool_trajectory_avg_score = 1.00 (threshold 1.0)\n\u2713 response_match_score = 0.88 (threshold 0.6)", "output_excerpt": "# Time Desk eval report\n\n| Case | tool_trajectory_avg_score | response_match_score |\n|---|---|---|\n| tokyo_to_kolkata | 1.00 | 1.00 |\n| singapore_to_kathmandu | 1.00 | 1.00 |\n| dubai_to_karachi | 1.00 | 1.00 |\n| off_topic_no_tools | 1.00 | 0.92 |\n| missing_zone_asks | 1.00 | 0.50 |\n\n| Metric | Mean | Threshold | Result |\n|---|---|---|---|\n| tool_trajectory_avg_score | 1.00 | 1.0 | pass |\n| response_match_score | 0.88 | 0.6 | pass |\n" } ] }, { "timestamp": "2026-11-04T01:05:07+00:00", "sha": "10f0829b87d26b420e5edab1c73feab16f1f6aef", "results": [ { "day": 1, "slug": "arxiv-scout", "title": "arXiv Scout", "pattern": "Single agent + guardrail callbacks", "folder": "day-001-arxiv-scout", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 15.79, "model": "gemini-3.5-flash-lite", "model_calls": 5, "tool_calls": 4, "tool_budget": 12, "calls_by_tool": { "search_papers": 1, "get_abstract": 3 }, "prompt_tokens": 19148, "output_tokens": 1139, "cost_usd": null, "checks": { "citations_verified": true, "unverified_ids": [] }, "output_chars": 4068 }, "stderr_tail": "Downloading pydantic-core (2.1MiB)\nDownloading cryptography (4.5MiB)\nDownloading lxml (5.0MiB)\n Downloaded aiohttp\n Downloaded pydantic-core\n Downloaded cryptography\n Downloaded lxml\nInstalled 44 packages in 20ms\nInstalled 45 packages in 24ms\n/home/runner/work/agent-a-day/agent-a-day/day-001-arxiv-scout/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-001-arxiv-scout/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 search_papers(max_results=5, categories=['cs.CL', 'cs.LG'], sort_by='relevance', query='\"speculative ...)\n \u2192 get_abstract(paper_id='2401.07851')\n \u2192 get_abstract(paper_id='2402.01528')\n \u2192 get_abstract(paper_id='2508.15371')\n\n\u2014 15.8s \u00b7 5 model calls \u00b7 4/12 tool calls {'search_papers': 1, 'get_abstract': 3} \u00b7 19,148 in / 1,139 out tokens\n\u2713 All cited arXiv IDs were returned by a tool.", "output_excerpt": "# Speculative Decoding for LLM Inference: Engineering Brief\n\n## TL;DR\n- **Draft-and-Verify Paradigm:** Speculative decoding bypasses the memory-bandwidth bottleneck of autoregressive inference by using an efficient smaller draft model (or heuristic) to generate multiple candidate tokens in parallel, which are then verified in a single forward pass by the target model.\n- **Draft Model Selection Beats Raw PPL:** Language modeling perplexity alone does not determine a draft model's utility; execution latency and the hardware efficiency of the drafter relative to the target model are primary determinants of overall speedup [arXiv:2402.01528](https://arxiv.org/abs/2402.01528).\n- **Dynamic Adaptability:** Modern variants move away from rigid static drafting lengths and fixed acceptance threshold" }, { "day": 2, "slug": "repo-onboarder", "title": "Repo Onboarder", "pattern": "Sequential pipeline (SequentialAgent)", "folder": "day-002-repo-onboarder", "model": "gemini-3.5-flash-lite", "status": "fail", "exit_code": 1, "metrics": { "passed": false, "elapsed_s": 53.71, "model": "gemini-3.5-flash-lite", "model_calls": 8, "tool_calls": 5, "tool_budget": 10, "calls_by_tool": { "read_wiki_structure": 1, "ask_wiki_question": 4 }, "prompt_tokens": 37051, "output_tokens": 1865, "cost_usd": null, "checks": { "repo_available": true, "all_sections_present": true, "links_verified": false, "missing_sections": [], "unverified_links": [ "https://github.com/psf/requests/blob/HEAD/src/requests/api.py", "https://github.com/psf/requests/blob/HEAD/src/requests/models.py", "https://github.com/psf/requests/blob/HEAD/src/requests/sessions.py" ] }, "output_chars": 3334 }, "stderr_tail": "/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.PLUGGABLE_AUTH is enabled.\n check_feature_enabled()\nFailed to configure mTLS using AsyncAuthorizedSession: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.\n/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_toolset.py:407: UserWarning: [EXPERIMENTAL] feature FeatureName._MCP_GRACEFUL_ERROR_HANDLING is enabled.\n session = await self._mcp_session_manager.create_session(\n/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 [mapper] read_wiki_structure(repoName='psf/requests')\nFailed to configure mTLS using AsyncAuthorizedSession: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.\n \u2192 [researcher] ask_wiki_question(question='What is this project, who is it for, and how is the code organised (top-level...)\n \u2192 [researcher] ask_wiki_question(question='Where does a new reader start: the main entry points and the 3-5 most importa...)\n \u2192 [researcher] ask_wiki_question(repoName='psf/requests', question='How do I set up a development environment, run it, a...)\n \u2192 [researcher] ask_wiki_question(repoName='psf/requests', question='What are the contribution conventions (branching, st...)\n\n\u2014 53.7s \u00b7 8 model calls \u00b7 5/10 tool calls {'read_wiki_structure': 1, 'ask_wiki_question': 4} \u00b7 37,051 in / 1,865 out tokens\n\u2717 Links not returned by any tool: https://github.com/psf/requests/blob/HEAD/src/requests/api.py, https://github.com/psf/requests/blob/HEAD/src/requests/models.py, https://github.com/psf/requests/blob/HEAD/src/requests/sessions.py", "output_excerpt": "# Onboarding guide: psf/requests\n\n## What this project is\nRequests is an open-source Python HTTP client library that provides a user-friendly API for sending HTTP/1.1 requests while automatically managing connection pooling, keep-alive, redirects, and encoding detection. Designed with the philosophy \"HTTP for Humans\u2122\", it supports Python 3.10+ and PyPy for developers building robust applications.\n\n## Architecture map\n| Area | Path | What lives there |\n| :--- | :--- | :--- |\n| Core Source Code | https://github.com/psf/requests/blob/HEAD/src/requests | Core library modules including API convenience functions, session management, and models. |\n| Documentation | https://github.com/psf/requests/blob/HEAD/docs | Sphinx project documentation, user guides, and API references. |\n| Test Suite | http" }, { "day": 3, "slug": "docs-copilot", "title": "Docs Copilot", "pattern": "Grounded QA + answer verification", "folder": "day-003-docs-copilot", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 12.18, "model": "gemini-3.5-flash-lite", "model_calls": 4, "tool_calls": 2, "tool_budget": 6, "calls_by_tool": { "microsoft_docs_search": 1, "microsoft_code_sample_search": 1 }, "prompt_tokens": 20702, "output_tokens": 941, "cost_usd": null, "checks": { "answered_from_docs": true, "sections_present": true, "sources_verified": true, "code_grounded": true, "missing_sections": [], "unverified_sources": [], "ungrounded_calls": [] }, "output_chars": 1010 }, "stderr_tail": "/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.PLUGGABLE_AUTH is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_toolset.py:407: UserWarning: [EXPERIMENTAL] feature FeatureName._MCP_GRACEFUL_ERROR_HANDLING is enabled.\n session = await self._mcp_session_manager.create_session(\nFailed to configure mTLS using AsyncAuthorizedSession: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.\n/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\nContext7 Documentation MCP Server v4.4.4 running on stdio\n/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 [researcher] microsoft_docs_search(query='upload local file Azure Blob Storage Python SDK azure-storage-blob')\n \u2192 [researcher] microsoft_code_sample_search(query='upload_blob azure storage blob python', language='python')\nnpm notice\nnpm notice New major version of npm available! 10.9.9 -> 12.2.1\nnpm notice Changelog: https://github.com/npm/cli/releases/tag/v12.2.1\nnpm notice To update run: npm install -g npm@12.2.1\nnpm notice\n\n\u2014 12.2s \u00b7 4 model calls \u00b7 2/6 tool calls {'microsoft_docs_search': 1, 'microsoft_code_sample_search': 1} \u00b7 20,702 in / 941 out tokens\n\u2713 Sections present, every source was returned by a tool, and every API call appears in the docs.", "output_excerpt": "## Answer\nTo upload a local file to Azure Blob Storage using the Python SDK, you first instantiate a `BlobClient` using either a storage URL with credentials or a connection string. Then, open your local file in binary read mode (`\"rb\"`) and pass the file object to the `upload_blob` method. \n\nHere is an example using a connection string:\n```python\nfrom azure.storage.blob import BlobClient\n\nblob = BlobClient.from_connection_string(conn_str=\"\", container_name=\"mycontainer\", blob_name=\"my_blob\")\n\nwith open(\"./SampleSource.txt\", \"rb\") as data:\n blob.upload_blob(data)\n```\n\n## Version notes\nnone stated\n\n## Sources\n- `https://learn.microsoft.com/azure/developer/python/sdk/examples/azure-sdk-example-storage-use#4-use-blob-storage-from-app-code`\n- `https://learn.microsoft.com/" }, { "day": 4, "slug": "web-qa-explorer", "title": "Web QA Explorer", "pattern": "Browser agent + human confirmation (require_confirmation)", "folder": "day-004-web-qa-explorer", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 25.16, "model": "gemini-3.5-flash-lite", "model_calls": 11, "tool_calls": 7, "tool_budget": 14, "calls_by_tool": { "browser_navigate": 1, "browser_console_messages": 1, "browser_snapshot": 2, "browser_type": 2, "browser_network_requests": 1 }, "prompt_tokens": 40170, "output_tokens": 541, "cost_usd": null, "checks": { "sections_present": true, "evidence_verified": true, "pages_verified": true, "risky_actions_gated": true, "missing_sections": [], "unverified_evidence": [], "unverified_pages": [], "approvals": [ { "action": "browser_click(checkbox \"Toggle Todo\" for Buy milk e21)", "decision": "declined" }, { "action": "browser_click(checkbox \"Toggle Todo\" for Buy milk text label e22)", "decision": "declined" }, { "action": "browser_click(listitem for Buy milk e19)", "decision": "declined" } ], "blocked": [] }, "output_chars": 1355 }, "stderr_tail": "/home/runner/work/agent-a-day/agent-a-day/day-004-web-qa-explorer/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 browser_navigate(url='https://demo.playwright.dev/todomvc/')\n \u2192 browser_console_messages(level='error', all=True, filename='console.log')\n \u2192 browser_snapshot()\n \u2192 browser_type(target='e8', text='Buy milk', element='textbox \"What needs to be done?\"', submit=True)\n \u2192 browser_type(target='e8', text='Walk the dog', submit=True, element='textbox \"What needs to be done?\"')\n \u2192 browser_snapshot()\n \u2192 browser_click(element='checkbox \"Toggle Todo\" for Buy milk', target='e21')\n/home/runner/work/agent-a-day/agent-a-day/day-004-web-qa-explorer/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.TOOL_CONFIRMATION is enabled.\n check_feature_enabled()\n \u2717 declined (no approval given): browser_click(element='checkbox \"Toggle Todo\" for Buy milk', target='e21')\n \u2192 browser_click(element='checkbox \"Toggle Todo\" for Buy milk text label', target='e22')\n \u2717 declined (no approval given): browser_click(element='checkbox \"Toggle Todo\" for Buy milk text label', target='e22')\n \u2192 browser_click(target='e19', element='listitem for Buy milk')\n \u2717 declined (no approval given): browser_click(target='e19', element='listitem for Buy milk')\n \u2192 browser_network_requests(static=False)\n\n\u2014 25.2s \u00b7 11 model calls \u00b7 7/14 tool calls {'browser_navigate': 1, 'browser_console_messages': 1, 'browser_snapshot': 2, 'browser_type': 2, 'browser_network_requests': 1} \u00b7 40,170 in / 541 out tokens\n\u2713 Sections present, every quote came from a tool result, and no risky action ran unapproved.", "output_excerpt": "## Summary\nTested the TodoMVC React demo app by adding two todos (\"Buy milk\" and \"Walk the dog\"). Attempted to complete and delete todos, but interaction clicks on list items/checkboxes were rejected during execution, meaning those specific interactions were not exercised. Checked console messages and found a 404 error for the favicon.\n\n## Findings\n### BUG-1 \u00b7 Favicon 404 Not Found error\n- **Severity:** low\n- **Steps:** Load the TodoMVC demo page `https://demo.playwright.dev/todomvc/`\n- **Expected / actual:** The favicon resource should load successfully / The server responded with a 404 status.\n- **Evidence:** `[ERROR] Failed to load resource: the server responded with a status of 404 () @ https://demo.playwright.dev/favicon.ico:0`\n\n## Coverage\n- **Pages visited:** https://demo.playwright" }, { "day": 5, "slug": "hn-digest", "title": "HN Digest", "pattern": "Long-term memory via knowledge graph", "folder": "day-005-hn-digest", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 11.01, "model": "gemini-3.5-flash-lite", "model_calls": 5, "tool_calls": 4, "tool_budget": 8, "calls_by_tool": { "fetch": 1, "open_nodes": 1, "create_entities": 1, "create_relations": 1 }, "prompt_tokens": 30294, "output_tokens": 1790, "cost_usd": null, "checks": { "sections_present": true, "has_stories": true, "links_verified": true, "memory_consulted": true, "no_repeats": true, "memory_saved": true, "missing_sections": [], "unverified_links": [], "repeated_stories": [], "unsaved_stories": [], "known_before": 0, "stories_in_digest": 5 }, "output_chars": 1862 }, "stderr_tail": "Installed 46 packages in 11ms\nnpm warn EBADENGINE Unsupported engine {\nnpm warn EBADENGINE package: '@modelcontextprotocol/server-memory@2.0.0',\nnpm warn EBADENGINE required: { node: '>=24' },\nnpm warn EBADENGINE current: { node: 'v22.23.4', npm: '10.9.9' }\nnpm warn EBADENGINE }\nKnowledge Graph MCP Server running on stdio\n/home/runner/work/agent-a-day/agent-a-day/day-005-hn-digest/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-005-hn-digest/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 fetch(url='https://hn.algolia.com/api/v1/search?tags=front_page&hitsPerPage=30&attributesToRe...)\nHTTP Request: GET https://hn.algolia.com/robots.txt \"HTTP/1.1 404 Not Found\"\nHTTP Request: GET https://hn.algolia.com/api/v1/search?tags=front_page&hitsPerPage=30&attributesToRetrieve=title,url,points,num_comments,objectID&attributesToHighlight=none \"HTTP/1.1 200 OK\"\n \u2192 open_nodes(names=['hn-50354960', 'hn-50351111', 'hn-50339288', 'hn-50355251', 'hn-50349276', 'hn-5...)\n \u2192 create_entities(entities=[{'name': 'hn-50354960', 'observations': ['title: Postman silently auto-instal...)\n \u2192 create_relations(relations=[{'from': 'hn-50354960', 'relationType': 'about', 'to': 'topic-software'}, {'...)\n\n\u2014 11.0s \u00b7 5 model calls \u00b7 4/8 tool calls {'fetch': 1, 'open_nodes': 1, 'create_entities': 1, 'create_relations': 1} \u00b7 30,294 in / 1,790 out tokens\n\u2713 5 new stories, all links returned by a tool, none repeated (memory held 0 before), all saved.", "output_excerpt": "# Hacker News digest \u00b7 2026-11-04\n## Stories \n1. **[Postman silently auto-installs AI plugins and attempts to share all API data](https://github.com/postmanlabs/postman-app-support/issues/14370)** \u2014 Users discovered that Postman has been automatically installing AI plugins without permission and attempting to transmit API data. \u00b7 [1002 points, 333 comments](https://news.ycombinator.com/item?id=50354960).\n2. **[The 'Astra can read and write weights to improve Qwen' post is misleading](https://honnibal.dev/blog/astra-read-write-misleading)** \u2014 A detailed breakdown clarifies that recent claims regarding Astra modifying model weights for Qwen are overstated and misleading. \u00b7 [889 points, 190 comments](https://news.ycombinator.com/item?id=50351111).\n3. **[Jailbreaking my car's navigation sy" }, { "day": 6, "slug": "meeting-planner", "title": "Meeting Planner", "pattern": "Structured output (Pydantic output_schema)", "folder": "day-006-meeting-planner", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 4.44, "model": "gemini-3.5-flash-lite", "model_calls": 2, "tool_calls": 10, "tool_budget": 10, "calls_by_tool": { "convert_time": 10 }, "prompt_tokens": 8276, "output_tokens": 645, "cost_usd": null, "checks": { "schema_valid": true, "plan_complete": true, "verified_by_recompute": true, "zones_checked_by_tool": true, "problems": {}, "unchecked_zones": [], "slots": 0 }, "output_chars": 1177 }, "stderr_tail": " build_function_declaration(\n/home/runner/work/agent-a-day/agent-a-day/day-006-meeting-planner/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_toolset.py:407: UserWarning: [EXPERIMENTAL] feature FeatureName._MCP_GRACEFUL_ERROR_HANDLING is enabled.\n session = await self._mcp_session_manager.create_session(\nInstalled 31 packages in 11ms\n/home/runner/work/agent-a-day/agent-a-day/day-006-meeting-planner/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n \u2192 convert_time(time='09:00', target_timezone='Etc/UTC', source_timezone='Australia/Melbourne')\n \u2192 convert_time(target_timezone='Etc/UTC', source_timezone='Asia/Singapore', time='09:00')\n \u2192 convert_time(time='09:00', target_timezone='Etc/UTC', source_timezone='Asia/Kolkata')\n \u2192 convert_time(target_timezone='Etc/UTC', source_timezone='Australia/Melbourne', time='17:00')\n \u2192 convert_time(time='17:00', source_timezone='Asia/Singapore', target_timezone='Etc/UTC')\n \u2192 convert_time(time='17:00', source_timezone='Asia/Kolkata', target_timezone='Etc/UTC')\n \u2192 convert_time(source_timezone='Etc/UTC', target_timezone='Australia/Melbourne', time='01:30')\n \u2192 convert_time(target_timezone='Asia/Singapore', source_timezone='Etc/UTC', target_timezone_2='Asia/Ko...)\n \u2192 convert_time(time='01:30', source_timezone='Etc/UTC', target_timezone='Asia/Singapore')\n \u2192 convert_time(time='01:30', source_timezone='Etc/UTC', target_timezone='Asia/Kolkata')\n \u2192 set_model_response(no_overlap_reason=\"No overlapping working hours exist between Melbourne (09:00-17:00 AE...)\n\n\u2014 4.4s \u00b7 2 model calls \u00b7 10/10 tool calls {'convert_time': 10} \u00b7 8,276 in / 645 out tokens\n\u2713 Valid MeetingPlan; 0 slot(s) re-computed from the tz database and inside working hours.", "output_excerpt": "# Sync between Melbourne, Singapore and Mumbai\n\n45 minutes \u00b7 working hours 09:00-17:00 local \u00b7 Melbourne (Australia/Melbourne), Singapore (Asia/Singapore), Mumbai (Asia/Kolkata)\n\nNo slot works.\n\nNo overlapping working hours exist between Melbourne (09:00-17:00 AEDT), Singapore (09:00-17:00 SGT), and Mumbai (09:00-17:00 IST). Melbourne's working hours start at 22:00 UTC, while Mumbai's working hours end at 11:30 UTC, creating a total gap.\n\n```json\n{\n \"needs_clarification\": null,\n \"title\": \"Sync between Melbourne, Singapore and Mumbai\",\n \"duration_minutes\": 45,\n \"working_hours_start\": \"09:00\",\n \"working_hours_end\": \"17:00\",\n \"participants\": [\n {\n \"name\": \"Melbourne\",\n \"timezone\": \"Australia/Melbourne\"\n },\n {\n \"name\": \"Singapore\",\n \"timezone\": \"Asia/Singapor" }, { "day": 7, "slug": "security-triager", "title": "Security Triager", "pattern": "Deterministic tool + LLM triage", "folder": "day-007-security-triager", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 12.73, "model": "gemini-3.5-flash-lite", "model_calls": 8, "tool_calls": 7, "tool_budget": 14, "calls_by_tool": { "git_show": 6, "semgrep_scan_with_custom_rule": 1 }, "prompt_tokens": 16705, "output_tokens": 953, "cost_usd": null, "checks": { "scan_ran": true, "no_invented_findings": true, "all_findings_triaged": true, "invented_ids": [], "untriaged_ids": [], "findings": 7, "verdicts": { "exploitable": 3, "likely": 1, "unlikely": 1, "false positive": 2 } }, "output_chars": 2441 }, "stderr_tail": "Downloading pygments (1.2MiB)\n Downloaded pygments\n Downloaded semgrep\nInstalled 66 packages in 25ms\nStarting Semgrep MCP server version v1.182.1\n/home/runner/.cache/uv/archive-v0/8LvHO7hxrW7yJmIJ/lib/python3.12/site-packages/pydantic_settings/sources/utils.py:51: IncompleteFieldDefinitionWarning: Field 'lifespan' has an incomplete definition: its annotation contains an unresolved forward reference, so settings sources may fail to correctly resolve its value. Call `model_rebuild()` on the model where the field is defined, once all the referenced types are defined.\n warnings.warn( # noqa: B028 (called from many sources at differing depths, so no stacklevel is correct)\nUser doesn't have the Pro Engine installed, not running `semgrep mcp` daemon...\n \u2192 git_show(revision='HEAD:')\n \u2192 git_show(revision='HEAD:app/')\n \u2192 git_show(revision='HEAD:app')\n \u2192 git_show(revision='HEAD:app/views.py')\n \u2192 git_show(revision='HEAD:app/client.py')\n \u2192 git_show(revision='HEAD:app/cache.py')\n \u2192 semgrep_scan_with_custom_rule(code_files=[{'content': '', 'path': 'app/views.py'}, {'content': '', 'path': 'app/clien...)\nFindings elicitation is not enabled, skipping.\nsemgrep_scan_with_custom_rule succeeded\n\n\u2014 12.7s \u00b7 8 model calls \u00b7 7/14 tool calls {'git_show': 6, 'semgrep_scan_with_custom_rule': 1} \u00b7 16,705 in / 953 out tokens\n\u2713 All 7 scanner findings triaged; none invented.", "output_excerpt": "# Security triage: demo_app\n## Summary\nThe application contains multiple severe command injection and code execution vulnerabilities, with finding F5 representing an immediate remote code execution risk via unvalidated user input passed to a shell command. Priority should be given to fixing F5 by replacing `os.popen` with safe argument lists or input validation.\n\n## Findings\n| ID | Rule | Location | Verdict | Why | Fix |\n|---|---|---|---|---|---|\n| F1 | py-weak-hash | app/cache.py:9 | false positive | MD5 is used solely to generate cache bucket names, not for cryptographic verification or security controls. | Keep as-is or switch to sha256 if strict non-crypto standards apply. |\n| F2 | py-pickle-load | app/cache.py:14 | likely | `pickle.loads` deserialises session cookies, which leads to R" }, { "day": 8, "slug": "aws-architect", "title": "AWS Architect", "pattern": "Parallel fan-out (ParallelAgent) + merge", "folder": "day-008-aws-architect", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 14.16, "model": "gemini-3.5-flash-lite", "model_calls": 9, "tool_calls": 5, "tool_budget": 15, "calls_by_tool": { "search_documentation": 5 }, "prompt_tokens": 70858, "output_tokens": 2854, "cost_usd": null, "checks": { "all_sections_present": true, "citations_verified": true, "missing_sections": [], "unverified_links": [] }, "output_chars": 7375 }, "stderr_tail": " 09-906a7240900c&search_intent=Amazo \n n+S3+storage+classes+cost+optimizat \n ion+user+uploads \"HTTP/1.1 200 OK\" \n[11/04/26 01:02:18] INFO HTTP Request: POST _client.py:1740\n https://proxy.search.docs.aws.com/s \n earch?session=972ed606-d8b3-4687-8a \n a2-1abf1d27b8e2&search_intent=Check \n +multi-AZ+deployment+and+high+avail \n ability+for+Amazon+RDS+PostgreSQL \n \"HTTP/1.1 200 OK\" \n \u2192 [reliability_reviewer] search_documentation(search_phrase='Amazon S3 data durability availability best practices', search_intent='C...)\n[11/04/26 01:02:20] INFO HTTP Request: POST _client.py:1740\n https://proxy.search.docs.aws.com/s \n earch?session=972ed606-d8b3-4687-8a \n a2-1abf1d27b8e2&search_intent=Check \n +Amazon+S3+data+durability+and+avai \n lability+features \"HTTP/1.1 200 OK\" \n\n\u2014 14.2s \u00b7 9 model calls \u00b7 5/15 tool calls {'search_documentation': 5} \u00b7 70,858 in / 2,854 out tokens\n\u2713 All sections present and every AWS doc link was returned by a tool.", "output_excerpt": "# Architecture review\n## Summary\n- **Overall posture:** The architecture follows standard cloud patterns with a global CDN (CloudFront), load-balanced serverless compute (ECS Fargate across two AZs), a managed relational database (RDS PostgreSQL), and object storage (S3). However, it currently contains critical security and reliability vulnerabilities\u2014notably an unhardened database and exposed origin infrastructure.\n- **Biggest risk:** The RDS PostgreSQL instance configured as a single point of failure and the ALB accepting traffic directly from the internet, bypassing CloudFront edge protections.\n- **Quickest win:** Enabling S3 Versioning on the user uploads bucket and updating security groups or adding AWS WAF to restrict ALB traffic exclusively to CloudFront.\n\n## Top priorities\n| # | Pi" }, { "day": 9, "slug": "fact-checker", "title": "Fact Checker", "pattern": "Generator\u2013critic loop (LoopAgent)", "folder": "day-009-fact-checker", "model": "gemini-3.5-flash-lite", "status": "fail", "exit_code": 1, "metrics": { "passed": false, "elapsed_s": 94.89, "model": "gemini-3.5-flash-lite", "model_calls": 5, "tool_calls": 5, "tool_budget": 14, "calls_by_tool": { "search": 2, "get_summary": 3 }, "prompt_tokens": 31908, "output_tokens": 759, "cost_usd": null, "checks": { "report_well_formed": false, "critic_approved": false, "rounds": 3, "problems": [ "Quoted evidence not found in any tool result: \"Canberra is the capital city of Australia and the largest population centre\". Quote a span of 5 to 20 words copied character for character from a tool result." ] }, "output_chars": 806 }, "stderr_tail": "2026-11-04 01:03:00,270 - wikipediaapi._http_client.sync_http_client - INFO - Request URL: https://en.wikipedia.org/w/api.php?format=json&redirects=1&action=query&prop=extracts&titles=Canberra&explaintext=1&exsectionformat=wiki\n2026-11-04 01:03:00,286 - httpx - INFO - HTTP Request: GET https://en.wikipedia.org/w/api.php?format=json&redirects=1&action=query&prop=extracts&titles=Mauna+Kea&explaintext=1&exsectionformat=wiki \"HTTP/1.1 200 OK\"\n2026-11-04 01:03:00,332 - httpx - INFO - HTTP Request: GET https://en.wikipedia.org/w/api.php?format=json&redirects=1&action=query&prop=extracts&titles=Canberra&explaintext=1&exsectionformat=wiki \"HTTP/1.1 200 OK\"\n \u25e6 round 1: checker wrote verdicts\n \u25e6 round 1: critic reviewed\n \u2192 [checker] get_summary(title='Canberra')\n2026-11-04 01:03:02,144 - wikipedia_mcp.server - INFO - Tool: Getting summary for: Canberra\n2026-11-04 01:03:02,144 - wikipediaapi._http_client.sync_http_client - INFO - Request URL: https://en.wikipedia.org/w/api.php?format=json&redirects=1&action=query&prop=info&titles=Canberra&inprop=protection|talkid|watched|watchers|visitingwatchers|notificationtimestamp|subjectid|url|readable|preload|displaytitle|varianttitles\n2026-11-04 01:03:02,162 - httpx - INFO - HTTP Request: GET https://en.wikipedia.org/w/api.php?format=json&redirects=1&action=query&prop=info&titles=Canberra&inprop=protection%7Ctalkid%7Cwatched%7Cwatchers%7Cvisitingwatchers%7Cnotificationtimestamp%7Csubjectid%7Curl%7Creadable%7Cpreload%7Cdisplaytitle%7Cvarianttitles \"HTTP/1.1 429 Too Many Requests\"\n2026-11-04 01:04:00,285 - httpx - INFO - HTTP Request: GET https://en.wikipedia.org/w/api.php?format=json&redirects=1&action=query&prop=info&titles=Canberra&inprop=protection%7Ctalkid%7Cwatched%7Cwatchers%7Cvisitingwatchers%7Cnotificationtimestamp%7Csubjectid%7Curl%7Creadable%7Cpreload%7Cdisplaytitle%7Cvarianttitles \"HTTP/1.1 200 OK\"\n2026-11-04 01:04:00,285 - wikipediaapi._http_client.sync_http_client - INFO - Request URL: https://en.wikipedia.org/w/api.php?format=json&redirects=1&action=query&prop=extracts&titles=Canberra&explaintext=1&exsectionformat=wiki\n2026-11-04 01:04:00,343 - httpx - INFO - HTTP Request: GET https://en.wikipedia.org/w/api.php?format=json&redirects=1&action=query&prop=extracts&titles=Canberra&explaintext=1&exsectionformat=wiki \"HTTP/1.1 200 OK\"\n \u25e6 round 2: checker wrote verdicts\n \u25e6 round 2: critic reviewed\n \u25e6 round 3: checker wrote verdicts\n \u25e6 round 3: critic reviewed\n\n\u2014 94.9s \u00b7 3 rounds \u00b7 5 model calls \u00b7 5/14 tool calls {'search': 2, 'get_summary': 3} \u00b7 31,908 in / 759 out tokens\n\u2717 Quoted evidence not found in any tool result: \"Canberra is the capital city of Australia and the largest population centre\". Quote a span of 5 to 20 words copied character for character from a tool result.\n\u2717 The critic did not approve the final report after 3 round(s).", "output_excerpt": "# Fact check\n### Claim 1: Sydney is the capital of Australia.\n**Verdict: Refuted**\nSydney is a major Australian city, whereas Canberra is the national capital.\n- [Canberra - Wikipedia](https://en.wikipedia.org/wiki/Canberra)\n > \"Canberra is the capital city of Australia and the largest population centre\"\n\n### Claim 2: Mount Everest is the tallest mountain on Earth measured from base to peak.\n**Verdict: Refuted**\nMauna Kea is taller when measured from its underwater base to its peak.\n- [Mauna Kea - Wikipedia](https://en.wikipedia.org/wiki/Mauna_Kea)\n > \"some authorities have labeled Mauna Kea the tallest mountain in the world, from its underwater base.\"\n\n## Sources\n- [Canberra - Wikipedia](https://en.wikipedia.org/wiki/Canberra)\n- [Mauna Kea - Wikipedia](https://en.wikipedia.org/wiki/Maun" }, { "day": 10, "slug": "sql-analyst", "title": "SQL Analyst", "pattern": "Argument guardrails (block writes in before_tool)", "folder": "day-010-sql-analyst", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 4.89, "model": "gemini-3.5-flash-lite", "model_calls": 4, "tool_calls": 4, "tool_budget": 12, "calls_by_tool": { "list_tables": 1, "describe_table": 2, "read_query": 1 }, "prompt_tokens": 4557, "output_tokens": 321, "cost_usd": null, "checks": { "queried_database": true, "sql_shown": true, "shown_sql_was_executed": true, "rejected_queries": 0, "unexecuted_sql": 0 }, "output_chars": 584 }, "stderr_tail": "Downloading pydantic-core (1.9MiB)\n Downloaded pydantic-core\nInstalled 33 packages in 9ms\n \u2192 list_tables()\n \u2192 describe_table(table_name='Genre')\n \u2192 describe_table(table_name='Track')\n \u2192 read_query(query='SELECT g.Name AS GenreName, COUNT(t.TrackId) AS TrackCount FROM Genre g JOIN Tra...)\n\n\u2014 4.9s \u00b7 4 model calls \u00b7 4/12 tool calls {'list_tables': 1, 'describe_table': 2, 'read_query': 1} \u00b7 4,557 in / 321 out tokens\n\u2713 The answer's SQL is exactly what the database ran, and every query was a guarded SELECT.", "output_excerpt": "## Answer\nThe 5 genres with the most tracks are Rock (1,297 tracks), Latin (579 tracks), Metal (374 tracks), Alternative & Punk (332 tracks), and Jazz (130 tracks).\n\n## Data\n| GenreName | TrackCount |\n|---|---|\n| Rock | 1297 |\n| Latin | 579 |\n| Metal | 374 |\n| Alternative & Punk | 332 |\n| Jazz | 130 |\n\n## SQL used\n```sql\nSELECT g.Name AS GenreName, COUNT(t.TrackId) AS TrackCount\nFROM Genre g\nJOIN Track t ON g.GenreId = t.GenreId\nGROUP BY g.GenreId, g.Name\nORDER BY TrackCount DESC\nLIMIT 5;\n```\n\n## Notes\n- Results are ordered in descending order by the number of tracks per genre." }, { "day": 11, "slug": "perf-auditor", "title": "Perf Auditor", "pattern": "Tool-heavy diagnostics", "folder": "day-011-perf-auditor", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 20.11, "model": "gemini-3.5-flash-lite", "model_calls": 6, "tool_calls": 5, "tool_budget": 10, "calls_by_tool": { "new_page": 1, "emulate": 1, "performance_start_trace": 1, "performance_analyze_insight": 2 }, "prompt_tokens": 28606, "output_tokens": 912, "cost_usd": null, "checks": { "structure": true, "trace_recorded": true, "numbers_verified": true, "ratings_correct": true }, "output_chars": 2204 }, "stderr_tail": "chrome-devtools-mcp exposes content of the browser instance to the MCP clients allowing them to inspect,\ndebug, and modify any data in the browser or DevTools.\nAvoid sharing sensitive or personal information that you do not want to share with MCP clients.\n[chrome-devtools-mcp] The connecting client did not negotiate the MCP roots capability. Tools that read or write local files will be restricted to the OS temp directory. To restore the previous unrestricted behavior, start the server with --allow-unrestricted-paths.\n \u2192 new_page(url='https://web.dev')\n \u2192 emulate(cpuThrottlingRate=4, networkConditions='Fast 4G', pageId=2)\n \u2192 performance_start_trace(reload=True, autoStop=True, pageId=2)\n \u2192 performance_analyze_insight(insightSetId='NAVIGATION_0', pageId=2, insightName='LCPBreakdown')\n \u2192 performance_analyze_insight(pageId=2, insightSetId='NAVIGATION_0', insightName='CLSCulprits')\n\n\u2014 20.1s \u00b7 6 model calls \u00b7 5/10 tool calls {'new_page': 1, 'emulate': 1, 'performance_start_trace': 1, 'performance_analyze_insight': 2} \u00b7 28,606 in / 912 out tokens\n\u2713 Trace recorded; every metric value and rating checked against tool results.", "output_excerpt": "# Performance audit: https://web.dev\n## Core Web Vitals\n| Metric | Value | Rating |\n|---|---|---|\n| LCP | 1187 ms | Good |\n| CLS | 0.09 | Good |\n| INP | not measured | n/a |\n| FCP | not measured | n/a |\n| TTFB | 207 ms | Good |\n\n*(Note: CPU throttling at 4x and Fast 4G network conditions were applied during the measurement.)*\n\n## Diagnosis\nThe page load performance was evaluated using a recorded trace (`NAVIGATION_0`). \n- **Largest Contentful Paint (LCP)**: Measured at **1,187 ms**, which is rated as **Good**. According to the `LCPBreakdown` insight, the subpart breakdown is as follows:\n - Time to first byte: **207 ms** (17.4% of total LCP time)\n - Resource load delay: **27 ms** (2.3% of total LCP time)\n - Resource load duration: **7 ms** (0.6% of total LCP time)\n - Element render dela" }, { "day": 12, "slug": "model-picker", "title": "Model Picker", "pattern": "Ranking with explicit scoring rubric", "folder": "day-012-model-picker", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 9.78, "model": "gemini-3.5-flash-lite", "model_calls": 9, "tool_calls": 7, "tool_budget": 10, "calls_by_tool": { "hub_repo_search": 5, "hub_repo_details": 2 }, "prompt_tokens": 63956, "output_tokens": 670, "cost_usd": null, "checks": { "all_ranked_repos_from_tools": true, "rubric_complete": true, "sorted_by_total": true, "downloads_known": true, "problems": [], "ranked": 2 }, "output_chars": 1402 }, "stderr_tail": "Failed to configure mTLS using AsyncAuthorizedSession: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.\n \u2192 [scout] hub_repo_search(limit=10, repo_types=['model'], sort='downloads', filters=['feature-extraction'], query...)\n \u2192 [scout] hub_repo_search(limit=10, repo_types=['model'], query='multilingual-e5-small', sort='downloads')\n \u2192 [scout] hub_repo_search(query='paraphrase-multilingual-MiniLM-L12-v2', sort='downloads', limit=10, repo_types=[...)\n \u2192 [scout] hub_repo_search(sort='downloads', limit=10, query='bge-m3', repo_types=['model'])\n \u2192 [scout] hub_repo_details(repo_ids=['intfloat/multilingual-e5-small', 'sentence-transformers/paraphrase-multiling...)\n \u2192 [scout] hub_repo_search(repo_types=['model'], limit=5, query='LaBSE', sort='downloads')\n \u2192 [scout] hub_repo_details(repo_ids=['sentence-transformers/LaBSE'])\n\n\u2014 9.8s \u00b7 9 model calls \u00b7 7/10 tool calls {'hub_repo_search': 5, 'hub_repo_details': 2} \u00b7 63,956 in / 670 out tokens\n\u2713 2 models ranked; every one came from a tool result and was scored on the full rubric.", "output_excerpt": "# Model shortlist\n> Multilingual sentence embeddings for semantic search, permissive licence, small enough for a laptop CPU\n\n## Ranking\n| # | Model | Total | task fit | constraint fit | popularity | licence | freshness |\n|---:|---|---:|---:|---:|---:|---:|---:|\n| 1 | [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) | **98.0** | 5 | 5 | 5 | 5 | 4 |\n| 2 | [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) | **98.0** | 5 | 5 | 5 | 5 | 4 |\n\nScores are 0-5 per criterion; total is 0-100. task fit 35% (model), constraint fit 25% (model), popularity 15% (computed from Hub data), licence 15% (computed from Hub data), freshness 10% (computed from Hub data).\n\n## Why\n##" }, { "day": 15, "slug": "agent-evals", "title": "Agent Evals", "pattern": "Evals as code (trajectory + response scoring)", "folder": "day-015-agent-evals", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 9.2, "model": "gemini-3.5-flash-lite", "model_calls": 8, "tool_calls": 3, "tool_budget": 30, "calls_by_tool": { "convert_time": 3 }, "prompt_tokens": 10636, "output_tokens": 211, "cost_usd": null, "checks": { "tool_trajectory_avg_score": true, "response_match_score": true, "scores": { "tool_trajectory_avg_score": 1.0, "response_match_score": 0.885 } }, "output_chars": 436 }, "stderr_tail": "/home/runner/work/agent-a-day/agent-a-day/day-015-agent-evals/.venv/lib/python3.12/site-packages/google/adk/evaluation/metric_evaluator_registry.py:285: UserWarning: [EXPERIMENTAL] MetricEvaluatorRegistry: This feature is experimental and may change or be removed in future versions without notice. It may introduce breaking changes at any time.\n return MetricEvaluatorRegistry()\n/home/runner/work/agent-a-day/agent-a-day/day-015-agent-evals/agent_evals/suite.py:66: UserWarning: [EXPERIMENTAL] UserSimulatorProvider: This feature is experimental and may change or be removed in future versions without notice. It may introduce breaking changes at any time.\n user_simulator_provider=UserSimulatorProvider(user_simulator_config=config.user_simulator_config),\n/home/runner/work/agent-a-day/agent-a-day/day-015-agent-evals/agent_evals/suite.py:63: UserWarning: [EXPERIMENTAL] LocalEvalService: This feature is experimental and may change or be removed in future versions without notice. It may introduce breaking changes at any time.\n service = LocalEvalService(\n/home/runner/work/agent-a-day/agent-a-day/day-015-agent-evals/.venv/lib/python3.12/site-packages/google/adk/evaluation/simulation/user_simulator_provider.py:169: UserWarning: [EXPERIMENTAL] StaticUserSimulator: This feature is experimental and may change or be removed in future versions without notice. It may introduce breaking changes at any time.\n static_simulator = StaticUserSimulator(\n/home/runner/work/agent-a-day/agent-a-day/day-015-agent-evals/.venv/lib/python3.12/site-packages/google/adk/evaluation/simulation/static_user_simulator.py:39: UserWarning: [EXPERIMENTAL] UserSimulator: This feature is experimental and may change or be removed in future versions without notice. It may introduce breaking changes at any time.\n super().__init__(\nInstalled 31 packages in 13ms\n\n\u2014 9.2s \u00b7 8 model calls \u00b7 3/30 tool calls {'convert_time': 3} \u00b7 10,636 in / 211 out tokens\n\u2713 tool_trajectory_avg_score = 1.00 (threshold 1.0)\n\u2713 response_match_score = 0.88 (threshold 0.6)", "output_excerpt": "# Time Desk eval report\n\n| Case | tool_trajectory_avg_score | response_match_score |\n|---|---|---|\n| tokyo_to_kolkata | 1.00 | 1.00 |\n| singapore_to_kathmandu | 1.00 | 1.00 |\n| dubai_to_karachi | 1.00 | 1.00 |\n| off_topic_no_tools | 1.00 | 0.92 |\n| missing_zone_asks | 1.00 | 0.50 |\n\n| Metric | Mean | Threshold | Result |\n|---|---|---|---|\n| tool_trajectory_avg_score | 1.00 | 1.0 | pass |\n| response_match_score | 0.88 | 0.6 | pass |\n" } ] }, { "timestamp": "2026-11-05T01:22:23+00:00", "sha": "10f0829b87d26b420e5edab1c73feab16f1f6aef", "results": [ { "day": 1, "slug": "arxiv-scout", "title": "arXiv Scout", "pattern": "Single agent + guardrail callbacks", "folder": "day-001-arxiv-scout", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 16.96, "model": "gemini-3.5-flash-lite", "model_calls": 3, "tool_calls": 4, "tool_budget": 12, "calls_by_tool": { "search_papers": 1, "get_abstract": 3 }, "prompt_tokens": 10581, "output_tokens": 1235, "cost_usd": null, "checks": { "citations_verified": true, "unverified_ids": [] }, "output_chars": 4183 }, "stderr_tail": "Downloading pydantic-core (2.1MiB)\nDownloading lxml (5.0MiB)\nDownloading cryptography (4.5MiB)\n Downloaded aiohttp\n Downloaded pydantic-core\n Downloaded cryptography\n Downloaded lxml\nInstalled 44 packages in 39ms\nInstalled 45 packages in 40ms\n/home/runner/work/agent-a-day/agent-a-day/day-001-arxiv-scout/.venv/lib/python3.14/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-001-arxiv-scout/.venv/lib/python3.14/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 search_papers(max_results=5, query='\"speculative decoding\" OR \"speculative sampling\" large language m...)\n \u2192 get_abstract(paper_id='2401.07851')\n \u2192 get_abstract(paper_id='2402.01528')\n \u2192 get_abstract(paper_id='2508.15371')\n\n\u2014 17.0s \u00b7 3 model calls \u00b7 4/12 tool calls {'search_papers': 1, 'get_abstract': 3} \u00b7 10,581 in / 1,235 out tokens\n\u2713 All cited arXiv IDs were returned by a tool.", "output_excerpt": "# Speculative Decoding for LLM Inference: engineering brief\n\n## TL;DR\n- **Draft-and-Verify Paradigm:** Speculative decoding accelerates memory-bandwidth-bound autoregressive LLM inference by pairing a smaller, lightning-fast draft model with a larger target model, verifying multiple speculative tokens in a single parallel forward pass [arXiv:2401.07851](https://arxiv.org/abs/2401.07851).\n- **Draft Model Design Trumps Raw Perplexity:** A draft model's absolute language modeling capability does not strongly correlate with speculative speedups; hardware execution latency and generation speed matter far more than pure model quality [arXiv:2402.01528](https://arxiv.org/abs/2402.01528).\n- **Moving Beyond Static Lengths:** Modern adaptations introduce confidence-modulated, information-theoretic f" }, { "day": 2, "slug": "repo-onboarder", "title": "Repo Onboarder", "pattern": "Sequential pipeline (SequentialAgent)", "folder": "day-002-repo-onboarder", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 57.74, "model": "gemini-3.5-flash-lite", "model_calls": 8, "tool_calls": 5, "tool_budget": 10, "calls_by_tool": { "read_wiki_structure": 1, "ask_wiki_question": 4 }, "prompt_tokens": 35981, "output_tokens": 1763, "cost_usd": null, "checks": { "repo_available": true, "all_sections_present": true, "links_verified": true, "missing_sections": [], "unverified_links": [] }, "output_chars": 3058 }, "stderr_tail": "/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_toolset.py:407: UserWarning: [EXPERIMENTAL] feature FeatureName._MCP_GRACEFUL_ERROR_HANDLING is enabled.\n session = await self._mcp_session_manager.create_session(\n/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 [mapper] read_wiki_structure(repoName='psf/requests')\nFailed to configure mTLS using AsyncAuthorizedSession: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.\nDownloading pydantic-core (2.1MiB)\nDownloading lxml (5.0MiB)\n Downloaded pydantic-core\n Downloaded lxml\nInstalled 44 packages in 24ms\n \u2192 [researcher] ask_wiki_question(repoName='psf/requests', question='What is this project, who is it for, and how is the ...)\n \u2192 [researcher] ask_wiki_question(repoName='psf/requests', question='Where does a new reader start: the main entry points...)\n \u2192 [researcher] ask_wiki_question(repoName='psf/requests', question='How do I set up a development environment, run it, a...)\n \u2192 [researcher] ask_wiki_question(question='What are the contribution conventions (branching, style, review) and known pi...)\n\n\u2014 57.7s \u00b7 8 model calls \u00b7 5/10 tool calls {'read_wiki_structure': 1, 'ask_wiki_question': 4} \u00b7 35,981 in / 1,763 out tokens\n\u2713 All sections present and every repo link was returned by a tool.", "output_excerpt": "# Onboarding guide: psf/requests\n\n## What this project is\n**Requests** is a popular Python HTTP client library designed to simplify making HTTP requests, adhering to the principle of \"HTTP for Humans\u2122\". It provides a high-level API for common HTTP operations (`requests.get()`, `requests.post()`, etc.) and handles complex protocol details automatically.\n\n## Architecture map\n| Area | Path | What lives there |\n| :--- | :--- | :--- |\n| Core Source Code | https://github.com/psf/requests/blob/HEAD/src/requests | Core library modules including API functions, session management, request/response models, and transport adapters. |\n| Documentation | https://github.com/psf/requests/blob/HEAD/docs | Project documentation written in reStructuredText (user guides, API references, contributor guidelines)." }, { "day": 3, "slug": "docs-copilot", "title": "Docs Copilot", "pattern": "Grounded QA + answer verification", "folder": "day-003-docs-copilot", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 12.62, "model": "gemini-3.5-flash-lite", "model_calls": 4, "tool_calls": 2, "tool_budget": 6, "calls_by_tool": { "microsoft_docs_search": 1, "microsoft_code_sample_search": 1 }, "prompt_tokens": 21229, "output_tokens": 1212, "cost_usd": null, "checks": { "answered_from_docs": true, "sections_present": true, "sources_verified": true, "code_grounded": true, "missing_sections": [], "unverified_sources": [], "ungrounded_calls": [] }, "output_chars": 1603 }, "stderr_tail": "/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.PLUGGABLE_AUTH is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_toolset.py:407: UserWarning: [EXPERIMENTAL] feature FeatureName._MCP_GRACEFUL_ERROR_HANDLING is enabled.\n session = await self._mcp_session_manager.create_session(\nFailed to configure mTLS using AsyncAuthorizedSession: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.\n/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\nContext7 Documentation MCP Server v4.4.4 running on stdio\n/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 [researcher] microsoft_docs_search(query='upload local file to azure blob storage python sdk')\n \u2192 [researcher] microsoft_code_sample_search(query='upload_blob storage blob python', language='python')\nnpm notice\nnpm notice New major version of npm available! 10.9.9 -> 12.2.2\nnpm notice Changelog: https://github.com/npm/cli/releases/tag/v12.2.2\nnpm notice To update run: npm install -g npm@12.2.2\nnpm notice\n\n\u2014 12.6s \u00b7 4 model calls \u00b7 2/6 tool calls {'microsoft_docs_search': 1, 'microsoft_code_sample_search': 1} \u00b7 21,229 in / 1,212 out tokens\n\u2713 Sections present, every source was returned by a tool, and every API call appears in the docs.", "output_excerpt": "## Answer\nTo upload a local file to Azure Blob Storage using the Python SDK, you can create a `BlobClient` using either Azure credentials or a connection string, open the local file in binary read mode (`\"rb\"`), and then call the `upload_blob()` method on the client. \n\nBelow is an example using `BlobClient` with `DefaultAzureCredential`:\n\n```python\nimport os\nimport uuid\nfrom azure.identity import DefaultAzureCredential\nfrom azure.storage.blob import BlobClient\n\ncredential = DefaultAzureCredential()\nstorage_url = os.environ[\"AZURE_STORAGE_BLOB_URL\"]\n\nblob_client = BlobClient(\n storage_url,\n container_name=\"blob-container-01\",\n blob_name=f\"sample-blob-{str(uuid.uuid4())[0:5]}.txt\",\n credential=credential,\n)\n\nwith open(\"./sample-source.txt\", \"rb\") as data:\n blob_client.upload_b" }, { "day": 4, "slug": "web-qa-explorer", "title": "Web QA Explorer", "pattern": "Browser agent + human confirmation (require_confirmation)", "folder": "day-004-web-qa-explorer", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 20.39, "model": "gemini-3.5-flash-lite", "model_calls": 16, "tool_calls": 10, "tool_budget": 14, "calls_by_tool": { "browser_navigate": 1, "browser_console_messages": 2, "browser_network_requests": 2, "browser_snapshot": 3, "browser_type": 2 }, "prompt_tokens": 69071, "output_tokens": 645, "cost_usd": null, "checks": { "sections_present": true, "evidence_verified": true, "pages_verified": true, "risky_actions_gated": true, "missing_sections": [], "unverified_evidence": [], "unverified_pages": [], "approvals": [ { "action": "browser_click(Toggle Todo checkbox for Buy milk e21)", "decision": "declined" }, { "action": "browser_click(Checkbox for Buy milk e21)", "decision": "declined" }, { "action": "browser_click(Toggle Todo checkbox for Buy milk e20)", "decision": "declined" }, { "action": "browser_click(listitem for Buy milk e19)", "decision": "declined" }, { "action": "browser_click(Toggle Todo checkbox for Pay bills e35)", "decision": "declined" } ], "blocked": [] }, "output_chars": 1560 }, "stderr_tail": " \u2192 browser_snapshot()\n \u2192 browser_type(element='textbox What needs to be done?', submit=True, target='e8', text='Pay bills')\n \u2192 browser_snapshot()\n \u2192 browser_click(element='Toggle Todo checkbox for Buy milk', target='e21')\n/home/runner/work/agent-a-day/agent-a-day/day-004-web-qa-explorer/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.TOOL_CONFIRMATION is enabled.\n check_feature_enabled()\n \u2717 declined (no approval given): browser_click(element='Toggle Todo checkbox for Buy milk', target='e21')\n \u2192 browser_click(element='Checkbox for Buy milk', target='e21')\n \u2717 declined (no approval given): browser_click(element='Checkbox for Buy milk', target='e21')\n \u2192 browser_click(element='Toggle Todo checkbox for Buy milk', target='e20')\n \u2717 declined (no approval given): browser_click(element='Toggle Todo checkbox for Buy milk', target='e20')\n \u2192 browser_click(target='e19', element='listitem for Buy milk')\n \u2717 declined (no approval given): browser_click(target='e19', element='listitem for Buy milk')\n \u2192 browser_click(target='e35', element='Toggle Todo checkbox for Pay bills')\n \u2717 declined (no approval given): browser_click(target='e35', element='Toggle Todo checkbox for Pay bills')\n \u2192 browser_console_messages(level='error', all=True)\n \u2192 browser_network_requests(static=False)\n\n\u2014 20.4s \u00b7 16 model calls \u00b7 10/14 tool calls {'browser_navigate': 1, 'browser_console_messages': 2, 'browser_network_requests': 2, 'browser_snapshot': 3, 'browser_type': 2} \u00b7 69,071 in / 645 out tokens\n\u2713 Sections present, every quote came from a tool result, and no risky action ran unapproved.", "output_excerpt": "## Summary\nTested the TodoMVC React application demo by adding two todos (\"Buy milk\" and \"Pay bills\"). Attempted to interact with todo checkboxes and delete buttons, but the interactions were rejected by approval policies, preventing the completion and deletion steps from being exercised. Checked console messages and network requests, noting a standard 404 error for `favicon.ico`.\n\n## Findings\n### BUG-1 \u00b7 Favicon resource missing (404)\n- **Severity:** low\n- **Steps:** Load https://demo.playwright.dev/todomvc/\n- **Expected / actual:** The browser requests `favicon.ico` and expects a successful 200 response / The server responded with a 404 status.\n- **Evidence:** `Failed to load resource: the server responded with a status of 404 () @ https://demo.playwright.dev/favicon.ico:0`\n\n## Coverage\n" }, { "day": 5, "slug": "hn-digest", "title": "HN Digest", "pattern": "Long-term memory via knowledge graph", "folder": "day-005-hn-digest", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 11.8, "model": "gemini-3.5-flash-lite", "model_calls": 5, "tool_calls": 4, "tool_budget": 8, "calls_by_tool": { "fetch": 1, "open_nodes": 1, "create_entities": 1, "create_relations": 1 }, "prompt_tokens": 29926, "output_tokens": 1822, "cost_usd": null, "checks": { "sections_present": true, "has_stories": true, "links_verified": true, "memory_consulted": true, "no_repeats": true, "memory_saved": true, "missing_sections": [], "unverified_links": [], "repeated_stories": [], "unsaved_stories": [], "known_before": 0, "stories_in_digest": 5 }, "output_chars": 1856 }, "stderr_tail": "Installed 46 packages in 26ms\nnpm warn EBADENGINE Unsupported engine {\nnpm warn EBADENGINE package: '@modelcontextprotocol/server-memory@2.0.0',\nnpm warn EBADENGINE required: { node: '>=24' },\nnpm warn EBADENGINE current: { node: 'v22.23.4', npm: '10.9.9' }\nnpm warn EBADENGINE }\nKnowledge Graph MCP Server running on stdio\n/home/runner/work/agent-a-day/agent-a-day/day-005-hn-digest/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-005-hn-digest/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 fetch(url='https://hn.algolia.com/api/v1/search?tags=front_page&hitsPerPage=30&attributesToRe...)\nHTTP Request: GET https://hn.algolia.com/robots.txt \"HTTP/1.1 404 Not Found\"\nHTTP Request: GET https://hn.algolia.com/api/v1/search?tags=front_page&hitsPerPage=30&attributesToRetrieve=title,url,points,num_comments,objectID&attributesToHighlight=none \"HTTP/1.1 200 OK\"\n \u2192 open_nodes(names=['hn-50364336', 'hn-50371246', 'hn-50367125', 'hn-50366999', 'hn-50363378', 'hn-5...)\n \u2192 create_entities(entities=[{'entityType': 'hn_story', 'name': 'hn-50364336', 'observations': ['title: AI...)\n \u2192 create_relations(relations=[{'from': 'hn-50364336', 'to': 'topic-artificial-intelligence', 'relationType...)\n\n\u2014 11.8s \u00b7 5 model calls \u00b7 4/8 tool calls {'fetch': 1, 'open_nodes': 1, 'create_entities': 1, 'create_relations': 1} \u00b7 29,926 in / 1,822 out tokens\n\u2713 5 new stories, all links returned by a tool, none repeated (memory held 0 before), all saved.", "output_excerpt": "# Hacker News digest \u00b7 2026-11-05\n## Stories \n1. **[AI is forcing us to pay off 40 years of technical debt](https://spectrum.ieee.org/ai-in-cybersecurity-php2shell-vulnerability)** \u2014 As artificial intelligence reshapes cybersecurity and development, developers and security experts are forced to confront decades-old structural issues. \u00b7 [1052 points, 626 comments](https://news.ycombinator.com/item?id=50364336).\n2. **[Residential proxies detected on White House network](https://layer3intel.com/blog/residential-proxies-white-house-network)** \u2014 Recent network analysis has identified residential proxies active on the White House network, raising security and tracking concerns. \u00b7 [601 points, 194 comments](https://news.ycombinator.com/item?id=50371246).\n3. **[AMD expects to outsell Intel in " }, { "day": 6, "slug": "meeting-planner", "title": "Meeting Planner", "pattern": "Structured output (Pydantic output_schema)", "folder": "day-006-meeting-planner", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 6.58, "model": "gemini-3.5-flash-lite", "model_calls": 3, "tool_calls": 9, "tool_budget": 10, "calls_by_tool": { "convert_time": 9 }, "prompt_tokens": 12400, "output_tokens": 1270, "cost_usd": null, "checks": { "schema_valid": true, "plan_complete": true, "verified_by_recompute": true, "zones_checked_by_tool": true, "problems": {}, "unchecked_zones": [], "slots": 3 }, "output_chars": 4179 }, "stderr_tail": "/home/runner/work/agent-a-day/agent-a-day/day-006-meeting-planner/.venv/lib/python3.12/site-packages/google/adk/tools/set_model_response_tool.py:272: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n build_function_declaration(\n/home/runner/work/agent-a-day/agent-a-day/day-006-meeting-planner/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_toolset.py:407: UserWarning: [EXPERIMENTAL] feature FeatureName._MCP_GRACEFUL_ERROR_HANDLING is enabled.\n session = await self._mcp_session_manager.create_session(\nInstalled 31 packages in 23ms\n/home/runner/work/agent-a-day/agent-a-day/day-006-meeting-planner/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n \u2192 convert_time(target_timezone='Etc/UTC', time='09:00', source_timezone='Australia/Melbourne')\n \u2192 convert_time(source_timezone='Asia/Singapore', target_timezone='Etc/UTC', time='09:00')\n \u2192 convert_time(target_timezone='Etc/UTC', source_timezone='Asia/Kolkata', time='09:00')\n \u2192 convert_time(source_timezone='Australia/Melbourne', time='17:00', target_timezone='Etc/UTC')\n \u2192 convert_time(source_timezone='Asia/Singapore', time='17:00', target_timezone='Etc/UTC')\n \u2192 convert_time(target_timezone='Etc/UTC', source_timezone='Asia/Kolkata', time='17:00')\n \u2192 convert_time(time='03:30', target_timezone='Australia/Melbourne', source_timezone='Etc/UTC')\n \u2192 convert_time(time='03:30', target_timezone='Asia/Singapore', source_timezone='Etc/UTC')\n \u2192 convert_time(target_timezone='Asia/Kolkata', time='03:30', source_timezone='Etc/UTC')\n \u2192 set_model_response(slots=[{'start_utc': '2026-11-09 03:30', 'rationale': \"This slot falls right at the sta...)\n\n\u2014 6.6s \u00b7 3 model calls \u00b7 9/10 tool calls {'convert_time': 9} \u00b7 12,400 in / 1,270 out tokens\n\u2713 Valid MeetingPlan; 3 slot(s) re-computed from the tz database and inside working hours.", "output_excerpt": "# 45-minute sync: Melbourne, Singapore, and Mumbai\n\n45 minutes \u00b7 working hours 09:00-17:00 local \u00b7 Melbourne (Australia/Melbourne), Singapore (Asia/Singapore), Mumbai (Asia/Kolkata)\n\n## Option 1: 2026-11-09 03:30 UTC\n\nThis slot falls right at the start of Mumbai's working day and comfortably within working hours for Singapore and Melbourne.\n\n| Who | Local start | Local end |\n|---|---|---|\n| Melbourne (Australia/Melbourne) | 2026-11-09 14:30 | 2026-11-09 15:15 |\n| Singapore (Asia/Singapore) | 2026-11-09 11:30 | 2026-11-09 12:15 |\n| Mumbai (Asia/Kolkata) | 2026-11-09 09:00 | 2026-11-09 09:45 |\n\n## Option 2: 2026-11-10 03:30 UTC\n\nA convenient Tuesday slot during standard working hours across all three locations.\n\n| Who | Local start | Local end |\n|---|---|---|\n| Melbourne (Australia/Melbourne" }, { "day": 7, "slug": "security-triager", "title": "Security Triager", "pattern": "Deterministic tool + LLM triage", "folder": "day-007-security-triager", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 15.93, "model": "gemini-3.5-flash-lite", "model_calls": 10, "tool_calls": 9, "tool_budget": 14, "calls_by_tool": { "git_show": 8, "semgrep_scan_with_custom_rule": 1 }, "prompt_tokens": 21776, "output_tokens": 1251, "cost_usd": null, "checks": { "scan_ran": true, "no_invented_findings": true, "all_findings_triaged": true, "invented_ids": [], "untriaged_ids": [], "findings": 7, "verdicts": { "exploitable": 3, "likely": 1, "unlikely": 1, "false positive": 2 } }, "output_chars": 2239 }, "stderr_tail": " Downloaded semgrep\nInstalled 66 packages in 48ms\nStarting Semgrep MCP server version v1.182.1\n/home/runner/.cache/uv/archive-v0/Unbf-PVbGdmLBOVb/lib/python3.12/site-packages/pydantic_settings/sources/utils.py:51: IncompleteFieldDefinitionWarning: Field 'lifespan' has an incomplete definition: its annotation contains an unresolved forward reference, so settings sources may fail to correctly resolve its value. Call `model_rebuild()` on the model where the field is defined, once all the referenced types are defined.\n warnings.warn( # noqa: B028 (called from many sources at differing depths, so no stacklevel is correct)\nUser doesn't have the Pro Engine installed, not running `semgrep mcp` daemon...\n \u2192 git_show(revision='HEAD:')\n \u2192 git_show(revision='HEAD:app/')\n \u2192 git_show(revision='HEAD:app')\n \u2192 git_show(revision='HEAD:tests')\n \u2192 git_show(revision='HEAD:app/views.py')\n \u2192 git_show(revision='HEAD:app/client.py')\n \u2192 git_show(revision='HEAD:app/cache.py')\n \u2192 git_show(revision='HEAD:tests/test_views.py')\n \u2192 semgrep_scan_with_custom_rule(code_files=[{'content': '', 'path': 'app/views.py'}, {'path': 'app/client.py', 'content...)\nFindings elicitation is not enabled, skipping.\nsemgrep_scan_with_custom_rule succeeded\n\n\u2014 15.9s \u00b7 10 model calls \u00b7 9/14 tool calls {'git_show': 8, 'semgrep_scan_with_custom_rule': 1} \u00b7 21,776 in / 1,251 out tokens\n\u2713 All 7 scanner findings triaged; none invented.", "output_excerpt": "# Security triage: demo_app\n## Summary\nThe codebase contains critical remote code execution and command injection vulnerabilities that require immediate remediation, starting with the unmitigated OS command injection in `app/views.py`. Fixing this first prevents remote attackers from executing arbitrary commands via request parameters.\n\n## Findings\n| ID | Rule | Location | Verdict | Why | Fix |\n|---|---|---|---|---|---|\n| F1 | py-weak-hash | app/cache.py:9 | false positive | MD5 is used exclusively for generating a cache bucket name, not as a cryptographic security control. | None required. |\n| F2 | py-pickle-load | app/cache.py:14 | likely | `pickle.loads` deserialises session cookies from hex; exploitable if session cookies can be forged or tampered with. | Replace `pickle` with `itsdang" }, { "day": 8, "slug": "aws-architect", "title": "AWS Architect", "pattern": "Parallel fan-out (ParallelAgent) + merge", "folder": "day-008-aws-architect", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 12.64, "model": "gemini-3.5-flash-lite", "model_calls": 10, "tool_calls": 6, "tool_budget": 15, "calls_by_tool": { "search_documentation": 6 }, "prompt_tokens": 76923, "output_tokens": 2979, "cost_usd": null, "checks": { "all_sections_present": true, "citations_verified": true, "missing_sections": [], "unverified_links": [] }, "output_chars": 7597 }, "stderr_tail": " n+RDS+security+best+practices+encry \n ption+in+transit+and+rest \"HTTP/1.1 \n 200 OK\" \n[11/05/26 01:17:49] INFO HTTP Request: POST _client.py:1740\n https://proxy.search.docs.aws.com/s \n earch?session=5ecf1c5d-1a1a-4b98-9e \n f5-1b1b72f195b7&search_intent=ECS+F \n argate+tasks+in+multiple+AZs+reliab \n ility+high+availability \"HTTP/1.1 \n 200 OK\" \n[11/05/26 01:17:49] INFO HTTP Request: POST _client.py:1740\n https://proxy.search.docs.aws.com/s \n earch?session=f7328918-7b12-49c5-b0 \n 40-95e4ce64be6b&search_intent=RDS+P \n ostgreSQL+cost+optimization+storage \n +and+instance+right+sizing \n \"HTTP/1.1 200 OK\" \n\n\u2014 12.6s \u00b7 10 model calls \u00b7 6/15 tool calls {'search_documentation': 6} \u00b7 76,923 in / 2,979 out tokens\n\u2713 All sections present and every AWS doc link was returned by a tool.", "output_excerpt": "# Architecture review\n## Summary\n- **Overall posture:** The architecture follows standard cloud patterns (CDN, ALB, containers, managed database, object storage across multiple AZs), but requires critical hardening across security (public exposure, S3 hardening, encryption) and reliability (Multi-AZ database enablement).\n- **Biggest risk:** Lack of enforced Multi-AZ configuration for the RDS PostgreSQL instance combined with unconstrained network exposure on ECS Fargate tasks and S3 buckets, introducing severe availability and data leakage risks.\n- **Quickest win:** Enforcing basic S3 bucket security controls (blocking public access, enabling encryption and logging) and activating AWS Compute Optimizer and Trusted Advisor checks for immediate cost and configuration visibility.\n\n## Top prio" }, { "day": 9, "slug": "fact-checker", "title": "Fact Checker", "pattern": "Generator\u2013critic loop (LoopAgent)", "folder": "day-009-fact-checker", "model": "gemini-3.5-flash-lite", "status": "fail", "exit_code": 1, "metrics": { "passed": false, "elapsed_s": 194.96, "model": "gemini-3.5-flash-lite", "model_calls": 8, "tool_calls": 9, "tool_budget": 14, "calls_by_tool": { "search": 4, "get_summary": 5 }, "prompt_tokens": 25455, "output_tokens": 845, "cost_usd": null, "checks": { "report_well_formed": false, "critic_approved": false, "rounds": 3, "problems": [ "Quoted evidence not found in any tool result: \"Canberra is the capital city of Australia\". Quote a span of 5 to 20 words copied character for character from a tool result." ] }, "output_chars": 990 }, "stderr_tail": "2026-11-05 01:20:09,191 - httpx - INFO - HTTP Request: GET https://en.wikipedia.org/w/api.php?format=json&redirects=1&action=query&prop=info&titles=Mauna_Kea&inprop=protection%7Ctalkid%7Cwatched%7Cwatchers%7Cvisitingwatchers%7Cnotificationtimestamp%7Csubjectid%7Curl%7Creadable%7Cpreload%7Cdisplaytitle%7Cvarianttitles \"HTTP/1.1 429 Too Many Requests\"\n2026-11-05 01:21:00,435 - httpx - INFO - HTTP Request: GET https://en.wikipedia.org/w/api.php?format=json&redirects=1&action=query&prop=info&titles=Mauna_Kea&inprop=protection%7Ctalkid%7Cwatched%7Cwatchers%7Cvisitingwatchers%7Cnotificationtimestamp%7Csubjectid%7Curl%7Creadable%7Cpreload%7Cdisplaytitle%7Cvarianttitles \"HTTP/1.1 200 OK\"\n2026-11-05 01:21:00,435 - wikipediaapi._http_client.sync_http_client - INFO - Request URL: https://en.wikipedia.org/w/api.php?format=json&redirects=1&action=query&prop=extracts&titles=Mauna Kea&explaintext=1&exsectionformat=wiki\n2026-11-05 01:21:00,472 - httpx - INFO - HTTP Request: GET https://en.wikipedia.org/w/api.php?format=json&redirects=1&action=query&prop=info&titles=Canberra&inprop=protection%7Ctalkid%7Cwatched%7Cwatchers%7Cvisitingwatchers%7Cnotificationtimestamp%7Csubjectid%7Curl%7Creadable%7Cpreload%7Cdisplaytitle%7Cvarianttitles \"HTTP/1.1 200 OK\"\n2026-11-05 01:21:00,472 - wikipediaapi._http_client.sync_http_client - INFO - Request URL: https://en.wikipedia.org/w/api.php?format=json&redirects=1&action=query&prop=extracts&titles=Canberra&explaintext=1&exsectionformat=wiki\n2026-11-05 01:21:00,536 - httpx - INFO - HTTP Request: GET https://en.wikipedia.org/w/api.php?format=json&redirects=1&action=query&prop=extracts&titles=Canberra&explaintext=1&exsectionformat=wiki \"HTTP/1.1 429 Too Many Requests\"\n2026-11-05 01:21:00,624 - httpx - INFO - HTTP Request: GET https://en.wikipedia.org/w/api.php?format=json&redirects=1&action=query&prop=extracts&titles=Mauna+Kea&explaintext=1&exsectionformat=wiki \"HTTP/1.1 200 OK\"\n2026-11-05 01:21:00,843 - httpx - INFO - HTTP Request: GET https://en.wikipedia.org/w/api.php?format=json&redirects=1&action=query&prop=extracts&titles=Mount+Everest&explaintext=1&exsectionformat=wiki \"HTTP/1.1 429 Too Many Requests\"\n2026-11-05 01:21:01,007 - httpx - INFO - HTTP Request: GET https://en.wikipedia.org/w/api.php?format=json&redirects=1&action=query&prop=info&titles=Capital+of+Australia&inprop=protection%7Ctalkid%7Cwatched%7Cwatchers%7Cvisitingwatchers%7Cnotificationtimestamp%7Csubjectid%7Curl%7Creadable%7Cpreload%7Cdisplaytitle%7Cvarianttitles \"HTTP/1.1 429 Too Many Requests\"\n2026-11-05 01:21:01,008 - wikipedia_mcp.wikipedia_client - ERROR - Error getting Wikipedia summary: (429, 'https://en.wikipedia.org/w/api.php')\n2026-11-05 01:21:01,019 - httpx - INFO - HTTP Request: GET https://en.wikipedia.org/w/api.php?format=json&redirects=1&action=query&prop=extracts&titles=Australia&explaintext=1&exsectionformat=wiki \"HTTP/1.1 429 Too Many Requests\"\nMCP tool execution failed with McpError: Request 'tools/call' timed out\n \u25e6 round 2: checker wrote verdicts\n \u25e6 round 2: critic reviewed\n \u25e6 round 3: checker wrote verdicts\n \u25e6 round 3: critic reviewed\n\n\u2014 195.0s \u00b7 3 rounds \u00b7 8 model calls \u00b7 9/14 tool calls {'search': 4, 'get_summary': 5} \u00b7 25,455 in / 845 out tokens\n\u2717 Quoted evidence not found in any tool result: \"Canberra is the capital city of Australia\". Quote a span of 5 to 20 words copied character for character from a tool result.\n\u2717 The critic did not approve the final report after 3 round(s).", "output_excerpt": "# Fact check\n### Claim 1: Sydney is the capital of Australia.\n**Verdict: Refuted**\nCanberra is the federal capital of the Commonwealth of Australia, whereas Sydney is not the capital.\n- [Canberra - Wikipedia](https://en.wikipedia.org/wiki/Canberra)\n > \"Canberra is the capital city of Australia\"\n\n### Claim 2: Mount Everest is the tallest mountain on Earth measured from base to peak.\n**Verdict: Refuted**\nMount Everest is highest above sea level, while Mauna Kea is the tallest mountain when measured from its underwater base.\n- [Mauna Kea vs Everest: Total Height, Base to Peak & True Tallest ...](https://geographyworlds.com/blog/mauna-kea-vs-everest-comparison/)\n > \"Everest is the highest above sea level (8,849 m), but Mauna Kea measures 10,211 m from base to peak.\"\n\n## Sources\n- [Canberra -" }, { "day": 10, "slug": "sql-analyst", "title": "SQL Analyst", "pattern": "Argument guardrails (block writes in before_tool)", "folder": "day-010-sql-analyst", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 4.57, "model": "gemini-3.5-flash-lite", "model_calls": 4, "tool_calls": 4, "tool_budget": 12, "calls_by_tool": { "list_tables": 1, "describe_table": 2, "read_query": 1 }, "prompt_tokens": 4557, "output_tokens": 328, "cost_usd": null, "checks": { "queried_database": true, "sql_shown": true, "shown_sql_was_executed": true, "rejected_queries": 0, "unexecuted_sql": 0 }, "output_chars": 609 }, "stderr_tail": "Downloading pydantic-core (1.9MiB)\n Downloaded pydantic-core\nInstalled 33 packages in 13ms\n \u2192 list_tables()\n \u2192 describe_table(table_name='Genre')\n \u2192 describe_table(table_name='Track')\n \u2192 read_query(query='SELECT g.Name AS GenreName, COUNT(t.TrackId) AS TrackCount FROM Genre g JOIN Tra...)\n\n\u2014 4.6s \u00b7 4 model calls \u00b7 4/12 tool calls {'list_tables': 1, 'describe_table': 2, 'read_query': 1} \u00b7 4,557 in / 328 out tokens\n\u2713 The answer's SQL is exactly what the database ran, and every query was a guarded SELECT.", "output_excerpt": "## Answer\nThe 5 genres with the most tracks are Rock (1,297 tracks), Latin (579 tracks), Metal (374 tracks), Alternative & Punk (332 tracks), and Jazz (130 tracks).\n\n## Data\n| GenreName | TrackCount |\n|---|---|\n| Rock | 1297 |\n| Latin | 579 |\n| Metal | 374 |\n| Alternative & Punk | 332 |\n| Jazz | 130 |\n\n## SQL used\n```sql\nSELECT g.Name AS GenreName, COUNT(t.TrackId) AS TrackCount\nFROM Genre g\nJOIN Track t ON g.GenreId = t.GenreId\nGROUP BY g.GenreId, g.Name\nORDER BY TrackCount DESC\nLIMIT 5;\n```\n\n## Notes\n- Results are ordered in descending order by the number of tracks per genre and limited to the top 5." }, { "day": 11, "slug": "perf-auditor", "title": "Perf Auditor", "pattern": "Tool-heavy diagnostics", "folder": "day-011-perf-auditor", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 23.43, "model": "gemini-3.5-flash-lite", "model_calls": 6, "tool_calls": 5, "tool_budget": 10, "calls_by_tool": { "new_page": 1, "emulate": 1, "performance_start_trace": 1, "performance_analyze_insight": 2 }, "prompt_tokens": 29495, "output_tokens": 568, "cost_usd": null, "checks": { "structure": true, "trace_recorded": true, "numbers_verified": true, "ratings_correct": true }, "output_chars": 1462 }, "stderr_tail": "chrome-devtools-mcp exposes content of the browser instance to the MCP clients allowing them to inspect,\ndebug, and modify any data in the browser or DevTools.\nAvoid sharing sensitive or personal information that you do not want to share with MCP clients.\n[chrome-devtools-mcp] The connecting client did not negotiate the MCP roots capability. Tools that read or write local files will be restricted to the OS temp directory. To restore the previous unrestricted behavior, start the server with --allow-unrestricted-paths.\n \u2192 new_page(url='https://web.dev')\n \u2192 emulate(networkConditions='Fast 4G', pageId=2, cpuThrottlingRate=4)\n \u2192 performance_start_trace(reload=True, autoStop=True, pageId=2)\n \u2192 performance_analyze_insight(pageId=2, insightSetId='NAVIGATION_0', insightName='LCPBreakdown')\n \u2192 performance_analyze_insight(insightSetId='NAVIGATION_0', insightName='DocumentLatency', pageId=2)\n\n\u2014 23.4s \u00b7 6 model calls \u00b7 5/10 tool calls {'new_page': 1, 'emulate': 1, 'performance_start_trace': 1, 'performance_analyze_insight': 2} \u00b7 29,495 in / 568 out tokens\n\u2713 Trace recorded; every metric value and rating checked against tool results.", "output_excerpt": "# Performance audit: https://web.dev\n\n*Note: Emulation was applied with a CPU throttling rate of 4x and network conditions set to Fast 4G.*\n\n## Core Web Vitals\n| Metric | Value | Rating |\n|---|---|---|\n| LCP | 2828 ms | Needs improvement |\n| CLS | 0.04 | Good |\n| INP | not measured | n/a |\n| FCP | not measured | n/a |\n| TTFB | 1125 ms | Needs improvement |\n\n## Diagnosis\nThe page load performance is primarily impacted by high initial document latency and render delays on the main content. According to the **DocumentLatency** insight, the document request took a total duration of 1,247 ms with a server response time (TTFB) of 1,125 ms, failing the quick server response check (threshold is 600 ms). \n\nFurthermore, the **LCPBreakdown** insight reports that the Largest Contentful Paint (LCP) rea" }, { "day": 12, "slug": "model-picker", "title": "Model Picker", "pattern": "Ranking with explicit scoring rubric", "folder": "day-012-model-picker", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 6.52, "model": "gemini-3.5-flash-lite", "model_calls": 5, "tool_calls": 3, "tool_budget": 10, "calls_by_tool": { "hub_repo_search": 2, "hub_repo_details": 1 }, "prompt_tokens": 21070, "output_tokens": 939, "cost_usd": null, "checks": { "all_ranked_repos_from_tools": true, "rubric_complete": true, "sorted_by_total": true, "downloads_known": true, "problems": [], "ranked": 4 }, "output_chars": 2572 }, "stderr_tail": "Failed to configure mTLS using AsyncAuthorizedSession: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.\n \u2192 [scout] hub_repo_search(repo_types=['model'], query='multilingual sentence embeddings', filters=['feature-extra...)\n \u2192 [scout] hub_repo_search(sort='downloads', query='sentence-transformer multilingual', repo_types=['model'])\n \u2192 [scout] hub_repo_details(repo_ids=['sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2', 'sentence-tran...)\n\n\u2014 6.5s \u00b7 5 model calls \u00b7 3/10 tool calls {'hub_repo_search': 2, 'hub_repo_details': 1} \u00b7 21,070 in / 939 out tokens\n\u2713 4 models ranked; every one came from a tool result and was scored on the full rubric.", "output_excerpt": "# Model shortlist\n> Multilingual sentence embeddings for semantic search, permissive licence, small enough for a laptop CPU\n\n## Ranking\n| # | Model | Total | task fit | constraint fit | popularity | licence | freshness |\n|---:|---|---:|---:|---:|---:|---:|---:|\n| 1 | [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) | **98.0** | 5 | 5 | 5 | 5 | 4 |\n| 2 | [sentence-transformers/distiluse-base-multilingual-cased-v2](https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v2) | **96.0** | 5 | 5 | 5 | 5 | 3 |\n| 3 | [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) | **91.0** | 5 | 4" }, { "day": 15, "slug": "agent-evals", "title": "Agent Evals", "pattern": "Evals as code (trajectory + response scoring)", "folder": "day-015-agent-evals", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 9.24, "model": "gemini-3.5-flash-lite", "model_calls": 8, "tool_calls": 3, "tool_budget": 30, "calls_by_tool": { "convert_time": 3 }, "prompt_tokens": 10636, "output_tokens": 204, "cost_usd": null, "checks": { "tool_trajectory_avg_score": true, "response_match_score": true, "scores": { "tool_trajectory_avg_score": 1.0, "response_match_score": 0.815 } }, "output_chars": 436 }, "stderr_tail": "/home/runner/work/agent-a-day/agent-a-day/day-015-agent-evals/.venv/lib/python3.12/site-packages/google/adk/evaluation/metric_evaluator_registry.py:285: UserWarning: [EXPERIMENTAL] MetricEvaluatorRegistry: This feature is experimental and may change or be removed in future versions without notice. It may introduce breaking changes at any time.\n return MetricEvaluatorRegistry()\n/home/runner/work/agent-a-day/agent-a-day/day-015-agent-evals/agent_evals/suite.py:66: UserWarning: [EXPERIMENTAL] UserSimulatorProvider: This feature is experimental and may change or be removed in future versions without notice. It may introduce breaking changes at any time.\n user_simulator_provider=UserSimulatorProvider(user_simulator_config=config.user_simulator_config),\n/home/runner/work/agent-a-day/agent-a-day/day-015-agent-evals/agent_evals/suite.py:63: UserWarning: [EXPERIMENTAL] LocalEvalService: This feature is experimental and may change or be removed in future versions without notice. It may introduce breaking changes at any time.\n service = LocalEvalService(\n/home/runner/work/agent-a-day/agent-a-day/day-015-agent-evals/.venv/lib/python3.12/site-packages/google/adk/evaluation/simulation/user_simulator_provider.py:169: UserWarning: [EXPERIMENTAL] StaticUserSimulator: This feature is experimental and may change or be removed in future versions without notice. It may introduce breaking changes at any time.\n static_simulator = StaticUserSimulator(\n/home/runner/work/agent-a-day/agent-a-day/day-015-agent-evals/.venv/lib/python3.12/site-packages/google/adk/evaluation/simulation/static_user_simulator.py:39: UserWarning: [EXPERIMENTAL] UserSimulator: This feature is experimental and may change or be removed in future versions without notice. It may introduce breaking changes at any time.\n super().__init__(\nInstalled 31 packages in 17ms\n\n\u2014 9.2s \u00b7 8 model calls \u00b7 3/30 tool calls {'convert_time': 3} \u00b7 10,636 in / 204 out tokens\n\u2713 tool_trajectory_avg_score = 1.00 (threshold 1.0)\n\u2713 response_match_score = 0.82 (threshold 0.6)", "output_excerpt": "# Time Desk eval report\n\n| Case | tool_trajectory_avg_score | response_match_score |\n|---|---|---|\n| tokyo_to_kolkata | 1.00 | 1.00 |\n| singapore_to_kathmandu | 1.00 | 1.00 |\n| dubai_to_karachi | 1.00 | 1.00 |\n| off_topic_no_tools | 1.00 | 0.92 |\n| missing_zone_asks | 1.00 | 0.15 |\n\n| Metric | Mean | Threshold | Result |\n|---|---|---|---|\n| tool_trajectory_avg_score | 1.00 | 1.0 | pass |\n| response_match_score | 0.82 | 0.6 | pass |\n" } ] }, { "timestamp": "2026-11-06T01:24:22+00:00", "sha": "10f0829b87d26b420e5edab1c73feab16f1f6aef", "results": [ { "day": 1, "slug": "arxiv-scout", "title": "arXiv Scout", "pattern": "Single agent + guardrail callbacks", "folder": "day-001-arxiv-scout", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 17.3, "model": "gemini-3.5-flash-lite", "model_calls": 3, "tool_calls": 4, "tool_budget": 12, "calls_by_tool": { "search_papers": 1, "get_abstract": 3 }, "prompt_tokens": 10581, "output_tokens": 999, "cost_usd": null, "checks": { "citations_verified": true, "unverified_ids": [] }, "output_chars": 3955 }, "stderr_tail": "Downloading lxml (5.0MiB)\nDownloading aiohttp (1.8MiB)\nDownloading pydantic-core (2.2MiB)\n Downloaded aiohttp\n Downloaded pydantic-core\n Downloaded cryptography\n Downloaded lxml\nInstalled 44 packages in 21ms\nInstalled 45 packages in 23ms\n/home/runner/work/agent-a-day/agent-a-day/day-001-arxiv-scout/.venv/lib/python3.14/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-001-arxiv-scout/.venv/lib/python3.14/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 search_papers(max_results=5, query='\"speculative decoding\" OR \"speculative sampling\" large language m...)\n \u2192 get_abstract(paper_id='2401.07851')\n \u2192 get_abstract(paper_id='2402.01528')\n \u2192 get_abstract(paper_id='2508.15371')\n\n\u2014 17.3s \u00b7 3 model calls \u00b7 4/12 tool calls {'search_papers': 1, 'get_abstract': 3} \u00b7 10,581 in / 999 out tokens\n\u2713 All cited arXiv IDs were returned by a tool.", "output_excerpt": "# Speculative Decoding for LLM Inference: Engineering Brief\n\n## TL;DR\n- **Draft-and-Verify Paradigm:** Speculative decoding accelerates autoregressive LLM inference by using a fast, lightweight draft model to generate candidate tokens in parallel, which are then verified in a single forward pass by the massive target model without compromising output quality.\n- **Drafter Characteristics Matter More Than Raw Perplexity:** Empirical analysis shows that draft model speed and hardware efficiency heavily dominate throughput gains; a drafter's language modeling capability does not linearly correlate with speculative speedups.\n- **Dynamic Adaptation:** Recent research moves away from static draft lengths and rigid verification criteria toward confidence-modulated, entropy-based frameworks that dy" }, { "day": 2, "slug": "repo-onboarder", "title": "Repo Onboarder", "pattern": "Sequential pipeline (SequentialAgent)", "folder": "day-002-repo-onboarder", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 55.44, "model": "gemini-3.5-flash-lite", "model_calls": 8, "tool_calls": 5, "tool_budget": 10, "calls_by_tool": { "read_wiki_structure": 1, "ask_wiki_question": 4 }, "prompt_tokens": 38080, "output_tokens": 2269, "cost_usd": null, "checks": { "repo_available": true, "all_sections_present": true, "links_verified": true, "missing_sections": [], "unverified_links": [] }, "output_chars": 4069 }, "stderr_tail": "/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_toolset.py:407: UserWarning: [EXPERIMENTAL] feature FeatureName._MCP_GRACEFUL_ERROR_HANDLING is enabled.\n session = await self._mcp_session_manager.create_session(\n/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-002-repo-onboarder/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 [mapper] read_wiki_structure(repoName='psf/requests')\nFailed to configure mTLS using AsyncAuthorizedSession: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.\nDownloading pydantic-core (2.2MiB)\nDownloading lxml (5.0MiB)\n Downloaded pydantic-core\n Downloaded lxml\nInstalled 44 packages in 10ms\n \u2192 [researcher] ask_wiki_question(question='What is this project, who is it for, and how is the code organised top-level ...)\n \u2192 [researcher] ask_wiki_question(repoName='psf/requests', question='Where does a new reader start: the main entry points...)\n \u2192 [researcher] ask_wiki_question(repoName='psf/requests', question='How do I set up a development environment, run it, a...)\n \u2192 [researcher] ask_wiki_question(repoName='psf/requests', question='What are the contribution conventions (branching, st...)\n\n\u2014 55.4s \u00b7 8 model calls \u00b7 5/10 tool calls {'read_wiki_structure': 1, 'ask_wiki_question': 4} \u00b7 38,080 in / 2,269 out tokens\n\u2713 All sections present and every repo link was returned by a tool.", "output_excerpt": "# Onboarding guide: psf/requests\n\n## What this project is\nThe `requests` library is a Python HTTP client designed for simplicity and ease of use under the philosophy of \"HTTP for Humans\u2122\". It enables developers to make HTTP requests in Python while automatically handling complex protocol details like connection pooling, redirects, and encoding. It supports Python 3.10+ and runs on PyPy.\n\n## Architecture map\n| Area | Path | What lives there |\n| :--- | :--- | :--- |\n| Core Source Code | [`src/requests`](https://github.com/psf/requests/blob/HEAD/src/requests) | Contains all core source code files for the library, including API logic, models, sessions, and adapters. |\n| Package Init | [`src/requests/__init__.py`](https://github.com/psf/requests/blob/HEAD/src/requests/__init__.py) | Package ini" }, { "day": 3, "slug": "docs-copilot", "title": "Docs Copilot", "pattern": "Grounded QA + answer verification", "folder": "day-003-docs-copilot", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 11.54, "model": "gemini-3.5-flash-lite", "model_calls": 4, "tool_calls": 2, "tool_budget": 6, "calls_by_tool": { "microsoft_docs_search": 1, "microsoft_code_sample_search": 1 }, "prompt_tokens": 20630, "output_tokens": 898, "cost_usd": null, "checks": { "answered_from_docs": true, "sections_present": true, "sources_verified": true, "code_grounded": true, "missing_sections": [], "unverified_sources": [], "ungrounded_calls": [] }, "output_chars": 1437 }, "stderr_tail": "/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.PLUGGABLE_AUTH is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_toolset.py:407: UserWarning: [EXPERIMENTAL] feature FeatureName._MCP_GRACEFUL_ERROR_HANDLING is enabled.\n session = await self._mcp_session_manager.create_session(\nFailed to configure mTLS using AsyncAuthorizedSession: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.\n/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\nContext7 Documentation MCP Server v4.4.4 running on stdio\n/home/runner/work/agent-a-day/agent-a-day/day-003-docs-copilot/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 [researcher] microsoft_docs_search(query='upload local file Azure Blob Storage Python SDK')\n \u2192 [researcher] microsoft_code_sample_search(query='upload_blob Azure Blob Storage Python SDK', language='python')\nnpm notice\nnpm notice New major version of npm available! 10.9.9 -> 12.2.2\nnpm notice Changelog: https://github.com/npm/cli/releases/tag/v12.2.2\nnpm notice To update run: npm install -g npm@12.2.2\nnpm notice\n\n\u2014 11.5s \u00b7 4 model calls \u00b7 2/6 tool calls {'microsoft_docs_search': 1, 'microsoft_code_sample_search': 1} \u00b7 20,630 in / 898 out tokens\n\u2713 Sections present, every source was returned by a tool, and every API call appears in the docs.", "output_excerpt": "## Answer\nTo upload a local file to Azure Blob Storage using the Python SDK, you can create a `BlobClient` using either Azure credentials or a connection string, open your local file in binary read mode (`\"rb\"`), and then call the `upload_blob()` method on the client. \n\nHere is an example using `DefaultAzureCredential`:\n\n```python\nimport os\nimport uuid\nfrom azure.identity import DefaultAzureCredential\nfrom azure.storage.blob import BlobClient\n\ncredential = DefaultAzureCredential()\nstorage_url = os.environ[\"AZURE_STORAGE_BLOB_URL\"]\n\nblob_client = BlobClient(\n storage_url,\n container_name=\"blob-container-01\",\n blob_name=f\"sample-blob-{str(uuid.uuid4())[0:5]}.txt\",\n credential=credential,\n)\n\nwith open(\"./sample-source.txt\", \"rb\") as data:\n blob_client.upload_blob(data)\n prin" }, { "day": 4, "slug": "web-qa-explorer", "title": "Web QA Explorer", "pattern": "Browser agent + human confirmation (require_confirmation)", "folder": "day-004-web-qa-explorer", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 15.21, "model": "gemini-3.5-flash-lite", "model_calls": 13, "tool_calls": 9, "tool_budget": 14, "calls_by_tool": { "browser_navigate": 1, "browser_snapshot": 3, "browser_type": 2, "browser_click": 1, "browser_console_messages": 1, "browser_network_requests": 1 }, "prompt_tokens": 53713, "output_tokens": 592, "cost_usd": null, "checks": { "sections_present": true, "evidence_verified": true, "pages_verified": true, "risky_actions_gated": true, "missing_sections": [], "unverified_evidence": [], "unverified_pages": [], "approvals": [ { "action": "browser_click(Delete button (\u00d7) for 'Pay bills' e36)", "decision": "declined" }, { "action": "browser_click(Delete button for todo Pay bills e33)", "decision": "declined" }, { "action": "browser_click(Delete button for todo Pay bills e38)", "decision": "declined" } ], "blocked": [] }, "output_chars": 1484 }, "stderr_tail": " \u2192 browser_navigate(url='https://demo.playwright.dev/todomvc/')\n \u2192 browser_snapshot()\n \u2192 browser_type(element='textbox \"What needs to be done?\"', text='Buy milk', submit=True, target='e8')\n \u2192 browser_type(submit=True, target='e8', text='Pay bills', element='textbox \"What needs to be done?\"')\n \u2192 browser_snapshot()\n \u2192 browser_click(element=\"Toggle Todo for 'Buy milk'\", target='e21')\n \u2192 browser_click(target='e36', element=\"Delete button (\u00d7) for 'Pay bills'\")\n/home/runner/work/agent-a-day/agent-a-day/day-004-web-qa-explorer/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.TOOL_CONFIRMATION is enabled.\n check_feature_enabled()\n \u2717 declined (no approval given): browser_click(target='e36', element=\"Delete button (\u00d7) for 'Pay bills'\")\n \u2192 browser_click(target='e33', element='Delete button for todo Pay bills')\n \u2717 declined (no approval given): browser_click(target='e33', element='Delete button for todo Pay bills')\n \u2192 browser_snapshot()\n \u2192 browser_click(target='e38', element='Delete button for todo Pay bills')\n \u2717 declined (no approval given): browser_click(target='e38', element='Delete button for todo Pay bills')\n \u2192 browser_console_messages(level='error')\n \u2192 browser_network_requests(static=False)\n\n\u2014 15.2s \u00b7 13 model calls \u00b7 9/14 tool calls {'browser_navigate': 1, 'browser_snapshot': 3, 'browser_type': 2, 'browser_click': 1, 'browser_console_messages': 1, 'browser_network_requests': 1} \u00b7 53,713 in / 592 out tokens\n\u2713 Sections present, every quote came from a tool result, and no risky action ran unapproved.", "output_excerpt": "## Summary\nTested the TodoMVC React demo application by navigating to the page, adding two todo items (\"Buy milk\" and \"Pay bills\"), completing one (\"Buy milk\"), and attempting to delete one. Verified console messages and network requests for defects, finding a 404 error for the favicon resource. Deleting a todo via the delete button was not exercised due to prompt approval rejections on those specific elements.\n\n## Findings\n### BUG-1 \u00b7 Missing Favicon Resource (404 Not Found)\n- **Severity:** low\n- **Steps:** Navigate to `https://demo.playwright.dev/todomvc/`\n- **Expected / actual:** Expected favicon.ico to load successfully / server responded with a 404 status\n- **Evidence:** `Failed to load resource: the server responded with a status of 404 () @ https://demo.playwright.dev/favicon.ico:0`" }, { "day": 5, "slug": "hn-digest", "title": "HN Digest", "pattern": "Long-term memory via knowledge graph", "folder": "day-005-hn-digest", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 9.37, "model": "gemini-3.5-flash-lite", "model_calls": 5, "tool_calls": 4, "tool_budget": 8, "calls_by_tool": { "fetch": 1, "open_nodes": 1, "create_entities": 1, "create_relations": 1 }, "prompt_tokens": 29056, "output_tokens": 1558, "cost_usd": null, "checks": { "sections_present": true, "has_stories": true, "links_verified": true, "memory_consulted": true, "no_repeats": true, "memory_saved": true, "missing_sections": [], "unverified_links": [], "repeated_stories": [], "unsaved_stories": [], "known_before": 0, "stories_in_digest": 5 }, "output_chars": 1429 }, "stderr_tail": "npm warn EBADENGINE Unsupported engine {\nnpm warn EBADENGINE package: '@modelcontextprotocol/server-memory@2.0.0',\nnpm warn EBADENGINE required: { node: '>=24' },\nnpm warn EBADENGINE current: { node: 'v22.23.4', npm: '10.9.9' }\nnpm warn EBADENGINE }\nInstalled 46 packages in 19ms\nKnowledge Graph MCP Server running on stdio\n/home/runner/work/agent-a-day/agent-a-day/day-005-hn-digest/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n/home/runner/work/agent-a-day/agent-a-day/day-005-hn-digest/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_tool.py:371: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n return self._build_declaration(fenced=False)\n \u2192 fetch(url='https://hn.algolia.com/api/v1/search?tags=front_page&hitsPerPage=30&attributesToRe...)\nHTTP Request: GET https://hn.algolia.com/robots.txt \"HTTP/1.1 404 Not Found\"\nHTTP Request: GET https://hn.algolia.com/api/v1/search?tags=front_page&hitsPerPage=30&attributesToRetrieve=title,url,points,num_comments,objectID&attributesToHighlight=none \"HTTP/1.1 200 OK\"\n \u2192 open_nodes(names=['hn-50378228', 'hn-50379704', 'hn-50383740', 'hn-50385272', 'hn-50383739', 'hn-5...)\n \u2192 create_entities(entities=[{'name': 'hn-50378228', 'observations': ['title: sshole', 'url: https://sshho...)\n \u2192 create_relations(relations=[{'to': 'topic-technology', 'relationType': 'about', 'from': 'hn-50378228'}, ...)\n\n\u2014 9.4s \u00b7 5 model calls \u00b7 4/8 tool calls {'fetch': 1, 'open_nodes': 1, 'create_entities': 1, 'create_relations': 1} \u00b7 29,056 in / 1,558 out tokens\n\u2713 5 new stories, all links returned by a tool, none repeated (memory held 0 before), all saved.", "output_excerpt": "# Hacker News digest \u00b7 2026-11-06\n## Stories \n1. **[sshole](https://sshhole.com)** \u2014 A trending discussion on Hacker News around sshole. \u00b7 [2300 points, 569 comments](https://news.ycombinator.com/item?id=50378228).\n2. **[Alpine Linux Security Advisory: OpenSSH-server password authentication bypass](https://alpinelinux.org/posts/2026-11-05-openssh-password-authentication-bypass/)** \u2014 Alpine Linux has issued a security advisory regarding an OpenSSH-server password authentication bypass vulnerability. \u00b7 [579 points, 123 comments](https://news.ycombinator.com/item?id=50379704).\n3. **[Search for everyone](https://kagi.com/changelog#11820)** \u2014 Kagi's changelog announces new search updates and features aimed at providing search for everyone. \u00b7 [498 points, 244 comments](https://news.ycombinat" }, { "day": 6, "slug": "meeting-planner", "title": "Meeting Planner", "pattern": "Structured output (Pydantic output_schema)", "folder": "day-006-meeting-planner", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 5.88, "model": "gemini-3.5-flash-lite", "model_calls": 2, "tool_calls": 9, "tool_budget": 10, "calls_by_tool": { "convert_time": 9 }, "prompt_tokens": 6842, "output_tokens": 1277, "cost_usd": null, "checks": { "schema_valid": true, "plan_complete": true, "verified_by_recompute": true, "zones_checked_by_tool": true, "problems": {}, "unchecked_zones": [], "slots": 3 }, "output_chars": 4375 }, "stderr_tail": "/home/runner/work/agent-a-day/agent-a-day/day-006-meeting-planner/.venv/lib/python3.12/site-packages/google/adk/tools/set_model_response_tool.py:272: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n build_function_declaration(\n/home/runner/work/agent-a-day/agent-a-day/day-006-meeting-planner/.venv/lib/python3.12/site-packages/google/adk/tools/mcp_tool/mcp_toolset.py:407: UserWarning: [EXPERIMENTAL] feature FeatureName._MCP_GRACEFUL_ERROR_HANDLING is enabled.\n session = await self._mcp_session_manager.create_session(\nInstalled 31 packages in 16ms\n/home/runner/work/agent-a-day/agent-a-day/day-006-meeting-planner/.venv/lib/python3.12/site-packages/google/adk/features/_feature_decorator.py:71: UserWarning: [EXPERIMENTAL] feature FeatureName.BASE_AUTHENTICATED_TOOL is enabled.\n check_feature_enabled()\n \u2192 convert_time(time='09:00', target_timezone='Etc/UTC', source_timezone='Australia/Melbourne')\n \u2192 convert_time(target_timezone='Etc/UTC', time='09:00', source_timezone='Asia/Singapore')\n \u2192 convert_time(target_timezone='Etc/UTC', time='09:00', source_timezone='Asia/Kolkata')\n \u2192 convert_time(source_timezone='Australia/Melbourne', time='17:00', target_timezone='Etc/UTC')\n \u2192 convert_time(source_timezone='Asia/Singapore', time='17:00', target_timezone='Etc/UTC')\n \u2192 convert_time(target_timezone='Etc/UTC', source_timezone='Asia/Kolkata', time='17:00')\n \u2192 convert_time(target_timezone='Australia/Melbourne', source_timezone='Etc/UTC', time='03:30')\n \u2192 convert_time(source_timezone='Etc/UTC', time='03:30', target_timezone='Asia/Singapore')\n \u2192 convert_time(time='03:30', source_timezone='Etc/UTC', target_timezone='Asia/Kolkata')\n \u2192 set_model_response(slots=[{'local_times': [{'timezone': 'Australia/Melbourne', 'name': 'Melbourne', 'local...)\n\n\u2014 5.9s \u00b7 2 model calls \u00b7 9/10 tool calls {'convert_time': 9} \u00b7 6,842 in / 1,277 out tokens\n\u2713 Valid MeetingPlan; 3 slot(s) re-computed from the tz database and inside working hours.", "output_excerpt": "# 45-minute sync between Melbourne, Singapore and Mumbai\n\n45 minutes \u00b7 working hours 09:00-17:00 local \u00b7 Melbourne (Australia/Melbourne), Singapore (Asia/Singapore), Mumbai (Asia/Kolkata)\n\n## Option 1: 2026-11-16 03:30 UTC\n\nThis slot is right at the start of Mumbai's working hours and fits comfortably within working hours for Singapore and Melbourne.\n\n| Who | Local start | Local end |\n|---|---|---|\n| Melbourne (Australia/Melbourne) | 2026-11-16 14:30 | 2026-11-16 15:15 |\n| Singapore (Asia/Singapore) | 2026-11-16 11:30 | 2026-11-16 12:15 |\n| Mumbai (Asia/Kolkata) | 2026-11-16 09:00 | 2026-11-16 09:45 |\n\n## Option 2: 2026-11-17 03:30 UTC\n\nThis slot is right at the start of Mumbai's working hours and fits comfortably within working hours for Singapore and Melbourne.\n\n| Who | Local start | Loc" }, { "day": 7, "slug": "security-triager", "title": "Security Triager", "pattern": "Deterministic tool + LLM triage", "folder": "day-007-security-triager", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 16.74, "model": "gemini-3.5-flash-lite", "model_calls": 9, "tool_calls": 8, "tool_budget": 14, "calls_by_tool": { "git_show": 7, "semgrep_scan_with_custom_rule": 1 }, "prompt_tokens": 18865, "output_tokens": 967, "cost_usd": null, "checks": { "scan_ran": true, "no_invented_findings": true, "all_findings_triaged": true, "invented_ids": [], "untriaged_ids": [], "findings": 7, "verdicts": { "exploitable": 2, "likely": 1, "unlikely": 2, "false positive": 2 } }, "output_chars": 2165 }, "stderr_tail": " Downloaded pygments\n Downloaded semgrep\nInstalled 66 packages in 28ms\nStarting Semgrep MCP server version v1.182.1\n/home/runner/.cache/uv/archive-v0/stQ1seRBqoAAU0zC/lib/python3.12/site-packages/pydantic_settings/sources/utils.py:51: IncompleteFieldDefinitionWarning: Field 'lifespan' has an incomplete definition: its annotation contains an unresolved forward reference, so settings sources may fail to correctly resolve its value. Call `model_rebuild()` on the model where the field is defined, once all the referenced types are defined.\n warnings.warn( # noqa: B028 (called from many sources at differing depths, so no stacklevel is correct)\nUser doesn't have the Pro Engine installed, not running `semgrep mcp` daemon...\n \u2192 git_show(revision='HEAD:')\n \u2192 git_show(revision='HEAD:app/')\n \u2192 git_show(revision='HEAD:app')\n \u2192 git_show(revision='HEAD:app/__init__.py')\n \u2192 git_show(revision='HEAD:app/cache.py')\n \u2192 git_show(revision='HEAD:app/client.py')\n \u2192 git_show(revision='HEAD:app/views.py')\n \u2192 semgrep_scan_with_custom_rule(code_files=[{'path': 'app/__init__.py', 'content': ''}, {'content': '', 'path': 'app/ca...)\nFindings elicitation is not enabled, skipping.\nsemgrep_scan_with_custom_rule succeeded\n\n\u2014 16.7s \u00b7 9 model calls \u00b7 8/14 tool calls {'git_show': 7, 'semgrep_scan_with_custom_rule': 1} \u00b7 18,865 in / 967 out tokens\n\u2713 All 7 scanner findings triaged; none invented.", "output_excerpt": "# Security triage: demo_app\n## Summary\nThe codebase contains critical remote code execution vulnerabilities from unsafe input handling and insecure deserialisation, alongside command injection risks. F5 (OS command injection in `ping_host`) should be fixed first because it directly exposes a remote shell command execution vector to unauthenticated web requests.\n\n## Findings\n| ID | Rule | Location | Verdict | Why | Fix |\n|---|---|---|---|---|---|\n| F1 | py-weak-hash | app/cache.py:9 | false positive | The comment explicitly notes MD5 is used as a cache bucket name, not a security control. | None (non-security use). |\n| F2 | py-pickle-load | app/cache.py:14 | unlikely | Deserialises a session cookie with pickle, but usage depends on callers providing forged cookies. | Use a secure signing me" }, { "day": 8, "slug": "aws-architect", "title": "AWS Architect", "pattern": "Parallel fan-out (ParallelAgent) + merge", "folder": "day-008-aws-architect", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 12.26, "model": "gemini-3.5-flash-lite", "model_calls": 10, "tool_calls": 6, "tool_budget": 15, "calls_by_tool": { "search_documentation": 6 }, "prompt_tokens": 69501, "output_tokens": 2341, "cost_usd": null, "checks": { "all_sections_present": true, "citations_verified": true, "missing_sections": [], "unverified_links": [] }, "output_chars": 6140 }, "stderr_tail": " 9a-344c0ad24919&search_intent=RDS+P \n ostgreSQL+encryption+in+transit+SSL \n +connection+Amazon+RDS \"HTTP/1.1 \n 200 OK\" \n[11/06/26 01:21:53] INFO HTTP Request: POST _client.py:1740\n https://proxy.search.docs.aws.com/s \n earch?session=17f18f85-adc6-4ab9-a1 \n c4-318d4e21e2d4&search_intent=Find+ \n Amazon+S3+reliability+and+data+dura \n bility+best+practices \"HTTP/1.1 200 \n OK\" \n[11/06/26 01:21:53] INFO HTTP Request: POST _client.py:1740\n https://proxy.search.docs.aws.com/s \n earch?session=4c632352-e80b-4da7-a9 \n ca-c4a189f7145f&search_intent=RDS+P \n ostgreSQL+cost+optimization+storage \n +instance+type \"HTTP/1.1 200 OK\" \n\n\u2014 12.3s \u00b7 10 model calls \u00b7 6/15 tool calls {'search_documentation': 6} \u00b7 69,501 in / 2,341 out tokens\n\u2713 All sections present and every AWS doc link was returned by a tool.", "output_excerpt": "# Architecture review\n## Summary\n- **Overall posture:** The architecture follows a standard, modern multi-tier pattern for a public web application utilizing CloudFront, ALB, ECS Fargate, RDS PostgreSQL, and S3, but requires targeted hardening in security encryption and reliability redundancy.\n- **Biggest risk:** Unencrypted database connections and potential single-AZ bottlenecks (if RDS Multi-AZ or redundant ECS task distribution are misconfigured) pose high security and reliability risks.\n- **Quickest win:** Enforce TLS/SSL for database connections by setting the `rds.force_ssl` parameter to `1` in a custom DB parameter group.\n\n## Top priorities\n| # | Pillar | Finding | Recommendation | Source |\n|---|---|---|---|---|\n| 1 | Security | Unencrypted Database Connections | Enforce TLS/SSL co" }, { "day": 9, "slug": "fact-checker", "title": "Fact Checker", "pattern": "Generator\u2013critic loop (LoopAgent)", "folder": "day-009-fact-checker", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 71.37, "model": "gemini-3.5-flash-lite", "model_calls": 5, "tool_calls": 5, "tool_budget": 14, "calls_by_tool": { "search": 3, "get_summary": 2 }, "prompt_tokens": 18779, "output_tokens": 524, "cost_usd": null, "checks": { "report_well_formed": true, "critic_approved": true, "rounds": 2, "problems": [] }, "output_chars": 798 }, "stderr_tail": " https://html.duckduckgo.com/html \n \"HTTP/1.1 200 OK\" \n2026-11-06 01:23:00,675 - httpx - INFO - HTTP Request: GET https://en.wikipedia.org/w/api.php?format=json&redirects=1&action=query&prop=info&titles=Canberra&inprop=protection%7Ctalkid%7Cwatched%7Cwatchers%7Cvisitingwatchers%7Cnotificationtimestamp%7Csubjectid%7Curl%7Creadable%7Cpreload%7Cdisplaytitle%7Cvarianttitles \"HTTP/1.1 429 Too Many Requests\"\n2026-11-06 01:23:00,675 - httpx - INFO - HTTP Request: GET https://en.wikipedia.org/w/api.php?format=json&redirects=1&action=query&prop=info&titles=Mauna+Kea&inprop=protection%7Ctalkid%7Cwatched%7Cwatchers%7Cvisitingwatchers%7Cnotificationtimestamp%7Csubjectid%7Curl%7Creadable%7Cpreload%7Cdisplaytitle%7Cvarianttitles \"HTTP/1.1 429 Too Many Requests\"\nMCP tool execution failed with McpError: Request 'tools/call' timed out\nMCP tool execution failed with McpError: Request 'tools/call' timed out\n \u25e6 round 1: checker wrote verdicts\n \u25e6 round 1: critic reviewed\n \u2192 [checker] search(query='\"Canberra is the capital city of Australia\"')\n[11/06/26 01:23:08] INFO HTTP Request: POST _client.py:1740\n https://html.duckduckgo.com/html \n \"HTTP/1.1 200 OK\" \n \u25e6 round 2: checker wrote verdicts\n \u25e6 round 2: critic reviewed\n \u25e6 round 2: critic reviewed\n \u25e6 round 2: critic reviewed\n \u25e6 round 2: critic approved\n\n\u2014 71.4s \u00b7 2 rounds \u00b7 5 model calls \u00b7 5/14 tool calls {'search': 3, 'get_summary': 2} \u00b7 18,779 in / 524 out tokens\n\u2713 Critic approved after 2 round(s); every verdict, link and quote checked against tool results.", "output_excerpt": "# Fact check\n### Claim 1: Sydney is the capital of Australia.\n**Verdict: Refuted**\nThe capital of Australia is Canberra, not Sydney.\n- [Canberra, Australian Capital Territory Australia | World and City Maps](https://worldandcitymaps.com/oceania/canberra)\n > \"Canberra is the capital city of Australia\"\n\n### Claim 2: Mount Everest is the tallest mountain on Earth measured from base to peak.\n**Verdict: Refuted**\nMount Everest is the highest mountain above sea level, but Mauna Kea is the tallest mountain when measured from base to peak.\n- [Tallest mountain - Wikipedia](https://en.wikipedia.org/wiki/Tallest_mountain)\n > \"On Earth, measured this way, Mauna Kea is the tallest mountain.\"\n\n## Sources\n- https://worldandcitymaps.com/oceania/canberra\n- https://en.wikipedia.org/wiki/Tallest_mountain" }, { "day": 10, "slug": "sql-analyst", "title": "SQL Analyst", "pattern": "Argument guardrails (block writes in before_tool)", "folder": "day-010-sql-analyst", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 5.09, "model": "gemini-3.5-flash-lite", "model_calls": 4, "tool_calls": 4, "tool_budget": 12, "calls_by_tool": { "list_tables": 1, "describe_table": 2, "read_query": 1 }, "prompt_tokens": 4551, "output_tokens": 322, "cost_usd": null, "checks": { "queried_database": true, "sql_shown": true, "shown_sql_was_executed": true, "rejected_queries": 0, "unexecuted_sql": 0 }, "output_chars": 586 }, "stderr_tail": "Downloading pydantic-core (1.9MiB)\n Downloaded pydantic-core\nInstalled 33 packages in 9ms\n \u2192 list_tables()\n \u2192 describe_table(table_name='Genre')\n \u2192 describe_table(table_name='Track')\n \u2192 read_query(query='SELECT g.Name AS Genre, COUNT(t.TrackId) AS TrackCount FROM Genre g JOIN Track t...)\n\n\u2014 5.1s \u00b7 4 model calls \u00b7 4/12 tool calls {'list_tables': 1, 'describe_table': 2, 'read_query': 1} \u00b7 4,551 in / 322 out tokens\n\u2713 The answer's SQL is exactly what the database ran, and every query was a guarded SELECT.", "output_excerpt": "## Answer\nThe 5 genres with the most tracks are Rock (1,297 tracks), Latin (579 tracks), Metal (374 tracks), Alternative & Punk (332 tracks), and Jazz (130 tracks).\n\n## Data\n| Genre | TrackCount |\n|---|---|\n| Rock | 1297 |\n| Latin | 579 |\n| Metal | 374 |\n| Alternative & Punk | 332 |\n| Jazz | 130 |\n\n## SQL used\n```sql\nSELECT g.Name AS Genre, COUNT(t.TrackId) AS TrackCount\nFROM Genre g\nJOIN Track t ON g.GenreId = t.GenreId\nGROUP BY g.GenreId, g.Name\nORDER BY TrackCount DESC\nLIMIT 5;\n```\n\n## Notes\n- Results are ordered by track count in descending order, limited to the top 5 genres." }, { "day": 11, "slug": "perf-auditor", "title": "Perf Auditor", "pattern": "Tool-heavy diagnostics", "folder": "day-011-perf-auditor", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 20.62, "model": "gemini-3.5-flash-lite", "model_calls": 6, "tool_calls": 5, "tool_budget": 10, "calls_by_tool": { "new_page": 1, "emulate": 1, "performance_start_trace": 1, "performance_analyze_insight": 2 }, "prompt_tokens": 28281, "output_tokens": 831, "cost_usd": null, "checks": { "structure": true, "trace_recorded": true, "numbers_verified": true, "ratings_correct": true }, "output_chars": 2147 }, "stderr_tail": "chrome-devtools-mcp exposes content of the browser instance to the MCP clients allowing them to inspect,\ndebug, and modify any data in the browser or DevTools.\nAvoid sharing sensitive or personal information that you do not want to share with MCP clients.\n[chrome-devtools-mcp] The connecting client did not negotiate the MCP roots capability. Tools that read or write local files will be restricted to the OS temp directory. To restore the previous unrestricted behavior, start the server with --allow-unrestricted-paths.\n \u2192 new_page(url='https://web.dev')\n \u2192 emulate(cpuThrottlingRate=4, pageId=2, networkConditions='Fast 4G')\n \u2192 performance_start_trace(autoStop=True, pageId=2, reload=True)\n \u2192 performance_analyze_insight(pageId=2, insightSetId='NAVIGATION_0', insightName='LCPBreakdown')\n \u2192 performance_analyze_insight(pageId=2, insightSetId='NAVIGATION_0', insightName='CLSCulprits')\n\n\u2014 20.6s \u00b7 6 model calls \u00b7 5/10 tool calls {'new_page': 1, 'emulate': 1, 'performance_start_trace': 1, 'performance_analyze_insight': 2} \u00b7 28,281 in / 831 out tokens\n\u2713 Trace recorded; every metric value and rating checked against tool results.", "output_excerpt": "# Performance audit: https://web.dev\n*(Note: Emulation with 4x CPU throttling and Fast 4G network conditions was applied for a mid-range phone simulation).*\n\n## Core Web Vitals\n| Metric | Value | Rating |\n|---|---|---|\n| LCP | 1756 ms | Good |\n| CLS | 0.05 | Good |\n| INP | not measured | n/a |\n| FCP | not measured | n/a |\n| TTFB | 269 ms | Good |\n\n## Diagnosis\nThe page load performance is dominated by the Largest Contentful Paint (LCP) time of **1,756 ms**. A breakdown of the LCP time reveals that the vast majority of the delay stems from element render delay rather than network or loading issues:\n- **Time to first byte (TTFB)**: **269 ms** (15.3% of total LCP time)\n- **Resource load delay**: **26 ms** (1.5% of total LCP time)\n- **Resource load duration**: **8 ms** (0.5% of total LCP time)" }, { "day": 12, "slug": "model-picker", "title": "Model Picker", "pattern": "Ranking with explicit scoring rubric", "folder": "day-012-model-picker", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 6.69, "model": "gemini-3.5-flash-lite", "model_calls": 7, "tool_calls": 5, "tool_budget": 10, "calls_by_tool": { "hub_repo_search": 4, "hub_repo_details": 1 }, "prompt_tokens": 34319, "output_tokens": 769, "cost_usd": null, "checks": { "all_ranked_repos_from_tools": true, "rubric_complete": true, "sorted_by_total": true, "downloads_known": true, "problems": [], "ranked": 3 }, "output_chars": 1845 }, "stderr_tail": "Failed to configure mTLS using AsyncAuthorizedSession: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.\n \u2192 [scout] hub_repo_search(filters=['feature-extraction'], repo_types=['model'], sort='downloads', query='multilin...)\n \u2192 [scout] hub_repo_search(query='multilingual embeddings', limit=10, repo_types=['model'], sort='downloads')\n \u2192 [scout] hub_repo_search(query='sentence-transformers multilingual', limit=10, repo_types=['model'], sort='downl...)\n \u2192 [scout] hub_repo_search(repo_types=['model'], sort='downloads', limit=5, query='intfloat multilingual-e5')\n \u2192 [scout] hub_repo_details(repo_ids=['intfloat/multilingual-e5-small', 'sentence-transformers/paraphrase-multiling...)\n\n\u2014 6.7s \u00b7 7 model calls \u00b7 5/10 tool calls {'hub_repo_search': 4, 'hub_repo_details': 1} \u00b7 34,319 in / 769 out tokens\n\u2713 3 models ranked; every one came from a tool result and was scored on the full rubric.", "output_excerpt": "# Model shortlist\n> Multilingual sentence embeddings for semantic search, permissive licence, small enough for a laptop CPU\n\n## Ranking\n| # | Model | Total | task fit | constraint fit | popularity | licence | freshness |\n|---:|---|---:|---:|---:|---:|---:|---:|\n| 1 | [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) | **98.0** | 5 | 5 | 5 | 5 | 4 |\n| 2 | [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) | **98.0** | 5 | 5 | 5 | 5 | 4 |\n| 3 | [sentence-transformers/paraphrase-xlm-r-multilingual-v1](https://huggingface.co/sentence-transformers/paraphrase-xlm-r-multilingual-v1) | **79.0** | 4 | 3 | 5 | 5 | 3 |\n\nScores are 0-5 per criterion; total is 0-100. task" }, { "day": 15, "slug": "agent-evals", "title": "Agent Evals", "pattern": "Evals as code (trajectory + response scoring)", "folder": "day-015-agent-evals", "model": "gemini-3.5-flash-lite", "status": "pass", "exit_code": 0, "metrics": { "passed": true, "elapsed_s": 10.4, "model": "gemini-3.5-flash-lite", "model_calls": 8, "tool_calls": 3, "tool_budget": 30, "calls_by_tool": { "convert_time": 3 }, "prompt_tokens": 10636, "output_tokens": 205, "cost_usd": null, "checks": { "tool_trajectory_avg_score": true, "response_match_score": true, "scores": { "tool_trajectory_avg_score": 1.0, "response_match_score": 0.813 } }, "output_chars": 436 }, "stderr_tail": "/home/runner/work/agent-a-day/agent-a-day/day-015-agent-evals/.venv/lib/python3.12/site-packages/google/adk/evaluation/metric_evaluator_registry.py:285: UserWarning: [EXPERIMENTAL] MetricEvaluatorRegistry: This feature is experimental and may change or be removed in future versions without notice. It may introduce breaking changes at any time.\n return MetricEvaluatorRegistry()\n/home/runner/work/agent-a-day/agent-a-day/day-015-agent-evals/agent_evals/suite.py:66: UserWarning: [EXPERIMENTAL] UserSimulatorProvider: This feature is experimental and may change or be removed in future versions without notice. It may introduce breaking changes at any time.\n user_simulator_provider=UserSimulatorProvider(user_simulator_config=config.user_simulator_config),\n/home/runner/work/agent-a-day/agent-a-day/day-015-agent-evals/agent_evals/suite.py:63: UserWarning: [EXPERIMENTAL] LocalEvalService: This feature is experimental and may change or be removed in future versions without notice. It may introduce breaking changes at any time.\n service = LocalEvalService(\n/home/runner/work/agent-a-day/agent-a-day/day-015-agent-evals/.venv/lib/python3.12/site-packages/google/adk/evaluation/simulation/user_simulator_provider.py:169: UserWarning: [EXPERIMENTAL] StaticUserSimulator: This feature is experimental and may change or be removed in future versions without notice. It may introduce breaking changes at any time.\n static_simulator = StaticUserSimulator(\n/home/runner/work/agent-a-day/agent-a-day/day-015-agent-evals/.venv/lib/python3.12/site-packages/google/adk/evaluation/simulation/static_user_simulator.py:39: UserWarning: [EXPERIMENTAL] UserSimulator: This feature is experimental and may change or be removed in future versions without notice. It may introduce breaking changes at any time.\n super().__init__(\nInstalled 31 packages in 12ms\n\n\u2014 10.4s \u00b7 8 model calls \u00b7 3/30 tool calls {'convert_time': 3} \u00b7 10,636 in / 205 out tokens\n\u2713 tool_trajectory_avg_score = 1.00 (threshold 1.0)\n\u2713 response_match_score = 0.81 (threshold 0.6)", "output_excerpt": "# Time Desk eval report\n\n| Case | tool_trajectory_avg_score | response_match_score |\n|---|---|---|\n| tokyo_to_kolkata | 1.00 | 1.00 |\n| singapore_to_kathmandu | 1.00 | 1.00 |\n| dubai_to_karachi | 1.00 | 1.00 |\n| off_topic_no_tools | 1.00 | 0.92 |\n| missing_zone_asks | 1.00 | 0.14 |\n\n| Metric | Mean | Threshold | Result |\n|---|---|---|---|\n| tool_trajectory_avg_score | 1.00 | 1.0 | pass |\n| response_match_score | 0.81 | 0.6 | pass |\n" } ] } ]