{
  "date": "2026-09-18",
  "stories": [
    {
      "story_id": "gh:1148788086",
      "title": "mattpocock/skills: Skills for Real Engineers. Straight from my .agents directory.",
      "url": "https://github.com/mattpocock/skills",
      "overall": 7.86,
      "metrics": {
        "signal": 10.0,
        "novelty": 5.1,
        "impact": 8.35,
        "confidence": 7.03,
        "actionability": 6.5,
        "freshness": 10.0
      },
      "badges": {
        "Repo": "https://github.com/mattpocock/skills"
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "github"
      ],
      "source": "github"
    },
    {
      "story_id": "gh:1147094660",
      "title": "HKUDS/nanobot: Ultra-lightweight, open-source, self-hosted personal AI agent framework in Python with WebUI, tools, memory, MCP, multi-agent workflows, automation, and chat apps",
      "url": "https://github.com/HKUDS/nanobot",
      "overall": 7.86,
      "metrics": {
        "signal": 10.0,
        "novelty": 6.2,
        "impact": 7.48,
        "confidence": 7.03,
        "actionability": 6.5,
        "freshness": 9.93
      },
      "badges": {
        "Repo": "https://github.com/HKUDS/nanobot"
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "github"
      ],
      "source": "github"
    },
    {
      "story_id": "gh:1201173969",
      "title": "JuliusBrussee/caveman: \ud83e\udea8 why use many token when few token do trick. Viral skill + proxy for coding agents that cuts 65% of tokens by talking like a caveman.",
      "url": "https://github.com/JuliusBrussee/caveman",
      "overall": 7.76,
      "metrics": {
        "signal": 10.0,
        "novelty": 5.1,
        "impact": 7.88,
        "confidence": 7.03,
        "actionability": 6.5,
        "freshness": 9.98
      },
      "badges": {
        "Repo": "https://github.com/JuliusBrussee/caveman"
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "github"
      ],
      "source": "github"
    },
    {
      "story_id": "gh:1129940957",
      "title": "headroomlabs-ai/headroom: Compress tool outputs, logs, files, and RAG chunks before they reach the LLM. 20% fewer tokens for coding agents, 60-95% fewer tokens for JSON, same answers. Library, proxy, MCP server.",
      "url": "https://github.com/headroomlabs-ai/headroom",
      "overall": 7.71,
      "metrics": {
        "signal": 10.0,
        "novelty": 5.1,
        "impact": 7.69,
        "confidence": 7.03,
        "actionability": 6.5,
        "freshness": 9.99
      },
      "badges": {
        "Repo": "https://github.com/headroomlabs-ai/headroom"
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "github"
      ],
      "source": "github"
    },
    {
      "story_id": "gh:1140843380",
      "title": "mvanhorn/last30days-skill: AI agent skill that researches any topic across Reddit, X, YouTube, HN, Polymarket, and the web - then synthesizes a grounded summary",
      "url": "https://github.com/mvanhorn/last30days-skill",
      "overall": 7.7,
      "metrics": {
        "signal": 10.0,
        "novelty": 5.1,
        "impact": 7.61,
        "confidence": 7.03,
        "actionability": 6.5,
        "freshness": 9.97
      },
      "badges": {
        "Repo": "https://github.com/mvanhorn/last30days-skill"
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "github"
      ],
      "source": "github"
    },
    {
      "story_id": "gh:1131513930",
      "title": "ZhuLinsen/daily_stock_analysis: LLM \u9a71\u52a8\u7684\u591a\u5e02\u573a\u80a1\u7968\u667a\u80fd\u5206\u6790\u7cfb\u7edf\uff1a\u591a\u6e90\u884c\u60c5\u3001\u5b9e\u65f6\u65b0\u95fb\u3001\u51b3\u7b56\u770b\u677f\u4e0e\u81ea\u52a8\u63a8\u9001\uff0c\u652f\u6301\u96f6\u6210\u672c\u5b9a\u65f6\u8fd0\u884c\u3002  LLM-powered multi-market stock analysis system with multi-source market data, real-time news, decision dashboard, automated notifications, and cost-free scheduled runs.",
      "url": "https://github.com/ZhuLinsen/daily_stock_analysis",
      "overall": 7.7,
      "metrics": {
        "signal": 10.0,
        "novelty": 5.1,
        "impact": 7.64,
        "confidence": 7.03,
        "actionability": 6.5,
        "freshness": 9.92
      },
      "badges": {
        "Repo": "https://github.com/ZhuLinsen/daily_stock_analysis"
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "github"
      ],
      "source": "github"
    },
    {
      "story_id": "gh:1132001614",
      "title": "vercel-labs/agent-browser: Browser automation CLI for AI agents",
      "url": "https://github.com/vercel-labs/agent-browser",
      "overall": 7.65,
      "metrics": {
        "signal": 10.0,
        "novelty": 5.1,
        "impact": 7.42,
        "confidence": 7.03,
        "actionability": 6.5,
        "freshness": 9.91
      },
      "badges": {
        "Repo": "https://github.com/vercel-labs/agent-browser"
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "github"
      ],
      "source": "github"
    },
    {
      "story_id": "gh:1185389803",
      "title": "MadsLorentzen/ai-job-search: The job search that runs on your machine. AI job application framework built on Claude Code: evaluate postings, tailor CVs, write cover letters, prep interviews. Fork it and own it.",
      "url": "https://github.com/MadsLorentzen/ai-job-search",
      "overall": 7.58,
      "metrics": {
        "signal": 10.0,
        "novelty": 4.0,
        "impact": 7.43,
        "confidence": 7.83,
        "actionability": 6.5,
        "freshness": 9.97
      },
      "badges": {
        "Repo": "https://github.com/MadsLorentzen/ai-job-search",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "github"
      ],
      "source": "github"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2509.25248v2",
      "title": "BuildBench: Benchmarking LLM Agents on Compiling Real-World Open-Source Software",
      "url": "https://arxiv.org/abs/2509.25248",
      "overall": 6.38,
      "metrics": {
        "signal": 9.43,
        "novelty": 7.3,
        "impact": 2.0,
        "confidence": 8.3,
        "actionability": 3.5,
        "freshness": 7.69
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2509.25248",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2609.19775v1",
      "title": "Integrating knowledge from case reports: a medical ontology based multimodal information system with structured summary",
      "url": "https://arxiv.org/abs/2609.19775",
      "overall": 6.21,
      "metrics": {
        "signal": 9.43,
        "novelty": 4.0,
        "impact": 2.0,
        "confidence": 8.7,
        "actionability": 6.5,
        "freshness": 7.69
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2609.19775",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2609.20130v1",
      "title": "AdaRepair-Mem: Adaptive Experience Orchestration for Repository-Level Program Repair",
      "url": "https://arxiv.org/abs/2609.20130",
      "overall": 6.21,
      "metrics": {
        "signal": 9.43,
        "novelty": 4.0,
        "impact": 2.0,
        "confidence": 8.7,
        "actionability": 6.5,
        "freshness": 7.69
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2609.20130",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2609.19865v1",
      "title": "Pretrained Medical Representations for the Practical Screening of Drug Repositioning Candidates",
      "url": "https://arxiv.org/abs/2609.19865",
      "overall": 6.21,
      "metrics": {
        "signal": 9.43,
        "novelty": 4.0,
        "impact": 2.0,
        "confidence": 8.7,
        "actionability": 6.5,
        "freshness": 7.69
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2609.19865",
        "Demo": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2609.20541v1",
      "title": "An Analysis of Training-Free Self-Reported Confidence in Language Models",
      "url": "https://arxiv.org/abs/2609.20541",
      "overall": 6.21,
      "metrics": {
        "signal": 9.43,
        "novelty": 4.0,
        "impact": 2.0,
        "confidence": 8.7,
        "actionability": 6.5,
        "freshness": 7.69
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2609.20541",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2609.19422v1",
      "title": "BurnRiSc: Toward Non-Invasive Burnout Screening in Open Source from Public Repository Signals",
      "url": "https://arxiv.org/abs/2609.19422",
      "overall": 6.21,
      "metrics": {
        "signal": 9.43,
        "novelty": 4.0,
        "impact": 2.0,
        "confidence": 8.7,
        "actionability": 6.5,
        "freshness": 7.69
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2609.19422",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2609.18310v2",
      "title": "SEA-LION-v4.8: A Technical Report",
      "url": "https://arxiv.org/abs/2609.18310",
      "overall": 6.21,
      "metrics": {
        "signal": 9.43,
        "novelty": 4.0,
        "impact": 2.0,
        "confidence": 8.7,
        "actionability": 6.5,
        "freshness": 7.69
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2609.18310"
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2508.06734v3",
      "title": "Evaluating Out-of-Distribution Robustness in Graph-Based Android Malware Classification: A New Principled Benchmark",
      "url": "https://arxiv.org/abs/2508.06734",
      "overall": 6.18,
      "metrics": {
        "signal": 9.43,
        "novelty": 6.2,
        "impact": 2.0,
        "confidence": 8.3,
        "actionability": 3.5,
        "freshness": 7.69
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2508.06734",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "hn:49754250",
      "title": "AI chatbots becoming experts at changing people's minds. What's their secret?",
      "url": "https://www.science.org/content/article/ai-chatbots-are-becoming-experts-changing-people-s-minds-what-s-their-secret",
      "overall": 6.09,
      "metrics": {
        "signal": 8.54,
        "novelty": 4.0,
        "impact": 5.09,
        "confidence": 6.25,
        "actionability": 3.5,
        "freshness": 9.7
      },
      "badges": {},
      "corroboration_count": 1,
      "corroboration_sources": [
        "hackernews"
      ],
      "source": "hackernews"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2609.19944v1",
      "title": "MaSCoD: A Multi-Agent Framework for Structural-Context-Guided Candidate Causal Graph Generation",
      "url": "https://arxiv.org/abs/2609.19944",
      "overall": 6.06,
      "metrics": {
        "signal": 9.43,
        "novelty": 5.1,
        "impact": 2.0,
        "confidence": 7.5,
        "actionability": 5.2,
        "freshness": 7.69
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2609.19944",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2609.19617v1",
      "title": "DataCanvas-EDU: An Agentic Framework for Instructor-Guided Synthetic Data Generation in Business Analytics Education",
      "url": "https://arxiv.org/abs/2609.19617",
      "overall": 6.06,
      "metrics": {
        "signal": 9.43,
        "novelty": 5.1,
        "impact": 2.0,
        "confidence": 7.5,
        "actionability": 5.2,
        "freshness": 7.69
      },
      "badges": {
        "Repo": "",
        "Paper": "https://arxiv.org/abs/2609.19617",
        "Benchmarks": "https://github.com/BANG23333/datacanvas-edu"
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "hn:49755135",
      "title": "I built Anchor. The open-source ontology layer for AI agents",
      "url": "https://github.com/trybacked/anchor",
      "overall": 6.05,
      "metrics": {
        "signal": 8.36,
        "novelty": 6.2,
        "impact": 2.35,
        "confidence": 7.45,
        "actionability": 3.5,
        "freshness": 9.92
      },
      "badges": {
        "Repo": "https://github.com/trybacked/anchor"
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "hackernews"
      ],
      "source": "hackernews"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2609.20474v1",
      "title": "How Do Agent Harnesses Create Value? Planning Information and Release Control in Stateful LLM Agents",
      "url": "https://arxiv.org/abs/2609.20474",
      "overall": 6.05,
      "metrics": {
        "signal": 9.43,
        "novelty": 6.2,
        "impact": 2.0,
        "confidence": 7.5,
        "actionability": 3.5,
        "freshness": 7.69
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2609.20474"
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "hn:49755152",
      "title": "Instinct Reportedly Seeking $1B at $10B Valuation",
      "url": "https://finance.yahoo.com/technology/ai/articles/instinct-reportedly-seeking-1b-10b-120952107.html",
      "overall": 6.01,
      "metrics": {
        "signal": 8.36,
        "novelty": 4.0,
        "impact": 2.35,
        "confidence": 7.45,
        "actionability": 6.5,
        "freshness": 9.93
      },
      "badges": {},
      "corroboration_count": 1,
      "corroboration_sources": [
        "hackernews"
      ],
      "source": "hackernews"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2603.21925v2",
      "title": "Guideline-grounded retrieval-augmented generation for ophthalmic clinical decision support",
      "url": "https://arxiv.org/abs/2603.21925",
      "overall": 5.99,
      "metrics": {
        "signal": 9.43,
        "novelty": 4.0,
        "impact": 2.0,
        "confidence": 8.3,
        "actionability": 5.2,
        "freshness": 7.69
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2603.21925",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2608.26697v3",
      "title": "Phoneme-guided TTS augmentation for ASR: A unified pipeline and multilingual evaluation",
      "url": "https://arxiv.org/abs/2608.26697",
      "overall": 5.99,
      "metrics": {
        "signal": 9.43,
        "novelty": 4.0,
        "impact": 2.0,
        "confidence": 8.3,
        "actionability": 5.2,
        "freshness": 7.69
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2608.26697",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    }
  ],
  "deep_dives": [
    {
      "story_id": "gh:1147094660",
      "title": "HKUDS/nanobot: Ultra-lightweight, open-source, self-hosted personal AI agent framework in Python with WebUI, tools, memory, MCP, multi-agent workflows, automation, and chat apps",
      "url": "https://github.com/HKUDS/nanobot",
      "source_domain": "github.com",
      "category_label": "Agent",
      "overall": 7.86,
      "metrics": {
        "signal": 10.0,
        "novelty": 6.2,
        "impact": 7.48,
        "confidence": 7.03,
        "actionability": 6.5
      },
      "why_made_cut": "Signal 10.0, Confidence 7.0, and Impact 7.5 combined to rank this in the top set.",
      "badges": [
        "repo"
      ],
      "context": "Ultra-lightweight, open-source, self-hosted personal AI agent framework in Python with WebUI, tools, memory, MCP, multi-agent workflows, automation, and chat apps English | \u7b80\u4f53\u4e2d\u6587 | \u7e41\u9ad4\u4e2d\u6587 | Espa\u00f1ol | Fran\u00e7ais | Bahasa Indonesia | \u65e5\u672c\u8a9e | \ud55c\uad6d\uc5b4 | \u0420\u0443\u0441\u0441\u043a\u0438\u0439 | Ti\u1ebfng Vi...",
      "whats_new": "Important If you want the newest features and experiments, install from source.",
      "key_details": [
        "It runs in a WebUI, terminal, or chat apps and combines tools, long-term memory, MCP integrations, model routing, multi-agent delegation, scheduled automation, and an OpenAI-compatible API in a small, readable core.",
        "| Go to | |---|---| | Install nanobot with no terminal/config background | Start Without Technical Background | | Install quickly and get one CLI reply | Install and Quick Start | | Open the bundled browser UI | WebUI | | Connect Telegram, Discord, WeChat,...",
        "It can: - run in a browser WebUI or terminal - connect to Telegram, Discord, Slack, WeChat, Email, Mattermost, and other chat apps - use tools such as files, shell, web search, web fetch, MCP, cron, image generation, and subagents - keep session history and...",
        "- Chat-native reach: WebUI, API, Telegram, Feishu, Slack, Discord, Teams, email, and Mattermost."
      ],
      "results_evidence": [
        "Overall 7.9/10 with Signal 10.0 and Impact 7.5.",
        "No explicit benchmark number found in extracted text; treat gains as directional pending replication."
      ],
      "limitations_unknowns": [
        "Generalization outside curated tasks is still unclear."
      ],
      "practical_next_steps": [
        "Reproduce one claim with a public baseline and fixed evaluation settings.",
        "Check robustness on out-of-distribution or long-context cases.",
        "Track whether independent teams report matching results."
      ]
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2609.19775v1",
      "title": "Integrating knowledge from case reports: a medical ontology based multimodal information system with structured summary",
      "url": "https://arxiv.org/abs/2609.19775",
      "source_domain": "arxiv.org",
      "category_label": "Cs.Ai",
      "overall": 6.21,
      "metrics": {
        "signal": 9.43,
        "novelty": 4.0,
        "impact": 2.0,
        "confidence": 8.7,
        "actionability": 6.5
      },
      "why_made_cut": "Signal 9.4, Confidence 8.7, and Impact 2.0 combined to rank this in the top set.",
      "badges": [
        "paper"
      ],
      "context": "arXiv:2609.19775v1 Announce Type: new Abstract: Published medical case reports serve as a crucial medical information carrier, documenting discoveries in rare diseases, diagnostic methods, and innovative treatments.",
      "whats_new": "arXiv:2609.19775v1 Announce Type: new Abstract: Published medical case reports serve as a crucial medical information carrier, documenting discoveries in rare diseases, diagnostic methods, and innovative treatments.",
      "key_details": [
        "Despite the wealth of clinical knowledge in millions of case reports in the public medicine literature database (PubMed), accessing relevant information efficiently is hindered by the limitations of traditional keyword-based retrieval tools on unstructured...",
        "To address the above issues, we introduce a comprehensive multimodal information system for case reports integrating structured clinical summaries of patients including medical images and biomedical named entities from 52949 open-access case reports publish...",
        "The multimodal essential information is organized in a well-structured medical ontology.",
        "Also, a powerful interface for searching and browsing case reports is designed to assist junior clinicians in retrieving cases effectively and improving the identification and diagnosis of rare diseases."
      ],
      "results_evidence": [
        "arXiv:2609.19775v1 Announce Type: new Abstract: Published medical case reports serve as a crucial medical information carrier, documenting discoveries in rare diseases, diagnostic methods, and innovative treatments.",
        "To address the above issues, we introduce a comprehensive multimodal information system for case reports integrating structured clinical summaries of patients including medical images and biomedical named entities from 52949 open-access case reports publish...",
        "Computer Science > Artificial Intelligence [Submitted on 17 Sep 2026] Title:Integrating knowledge from case reports: a medical ontology based multimodal information system with structured summary View PDF HTML (experimental) Abstract:Published medical case..."
      ],
      "limitations_unknowns": [
        "Despite the wealth of clinical knowledge in millions of case reports in the public medicine literature database (PubMed), accessing relevant information efficiently is hindered by the limitations of traditional keyword-based retrieval tools on unstructured..."
      ],
      "practical_next_steps": [
        "Reproduce one claim with a public baseline and fixed evaluation settings.",
        "Check robustness on out-of-distribution or long-context cases.",
        "Track whether independent teams report matching results."
      ]
    },
    {
      "story_id": "hn:49755152",
      "title": "Instinct Reportedly Seeking $1B at $10B Valuation",
      "url": "https://finance.yahoo.com/technology/ai/articles/instinct-reportedly-seeking-1b-10b-120952107.html",
      "source_domain": "finance.yahoo.com",
      "category_label": "Hn",
      "overall": 6.01,
      "metrics": {
        "signal": 8.36,
        "novelty": 4.0,
        "impact": 2.35,
        "confidence": 7.45,
        "actionability": 6.5
      },
      "why_made_cut": "Signal 8.4, Confidence 7.5, and Impact 2.4 combined to rank this in the top set.",
      "badges": [],
      "context": "Instinct, the invite-only personal AI assistant, has seen its valuation jump fourfold in just three weeks.",
      "whats_new": "Instinct, the invite-only personal AI assistant, has seen its valuation jump fourfold in just three weeks.",
      "key_details": [
        "The company, which hit a $2.5 billion Series B valuation in late August, is now reportedly in talks for a $10 billion valuation.",
        "From its initial seed valuation of roughly $50 million, the startup has reached this $10 billion mark in about six weeks, though it is important to note that this figure is reportedly in talks and the round has not yet closed.",
        "This kind of hyper-growth is becoming the standard operating procedure for the current agentic AI market.",
        "We are witnessing a structural compression of funding timelines that ignores traditional startup growth cycles."
      ],
      "results_evidence": [
        "The company, which hit a $2.5 billion Series B valuation in late August, is now reportedly in talks for a $10 billion valuation.",
        "From its initial seed valuation of roughly $50 million, the startup has reached this $10 billion mark in about six weeks, though it is important to note that this figure is reportedly in talks and the round has not yet closed.",
        "When a company can move from a $50 million valuation to a reported $10 billion in less than two months, the logic driving that capital is clearly decoupling from the standard metrics of production economics."
      ],
      "limitations_unknowns": [
        "The Hidden Cost of Velocity However, there is a hidden cost to this velocity."
      ],
      "practical_next_steps": [
        "Reproduce one claim with a public baseline and fixed evaluation settings.",
        "Check robustness on out-of-distribution or long-context cases.",
        "Track whether independent teams report matching results."
      ]
    }
  ],
  "reality_check": {
    "read_time": "1-2 min",
    "items": [
      {
        "story_id": "gh:1185389803",
        "title": "MadsLorentzen/ai-job-search: The job search that runs on your machine. AI job application framework built on Claude Code: evaluate postings, tailor CVs, write cover letters, prep interviews. Fork it and own it.",
        "url": "https://github.com/MadsLorentzen/ai-job-search",
        "source_domain": "github.com",
        "category_label": "Eval",
        "overall": 7.58,
        "metrics": {
          "signal": 10.0,
          "novelty": 4.0,
          "impact": 7.43,
          "confidence": 7.83,
          "actionability": 6.5
        },
        "badges": [
          "repo"
        ],
        "checklist": {
          "primary_source": "yes",
          "demo": "no",
          "benchmarks_evals": "yes",
          "baselines_ablations": "yes",
          "third_party_corroboration": "no",
          "reproducibility_details": "yes"
        },
        "what_would_change_my_mind": [
          "Independent replication with comparable or better results.",
          "Public benchmark numbers with clear baseline comparisons."
        ],
        "likely_failure_mode": "Performance may collapse outside curated demos or narrow tasks."
      },
      {
        "story_id": "gh:1148788086",
        "title": "mattpocock/skills: Skills for Real Engineers. Straight from my .agents directory.",
        "url": "https://github.com/mattpocock/skills",
        "source_domain": "github.com",
        "category_label": "Agent",
        "overall": 7.86,
        "metrics": {
          "signal": 10.0,
          "novelty": 5.1,
          "impact": 8.35,
          "confidence": 7.03,
          "actionability": 6.5
        },
        "badges": [
          "repo"
        ],
        "checklist": {
          "primary_source": "yes",
          "demo": "no",
          "benchmarks_evals": "no",
          "baselines_ablations": "no",
          "third_party_corroboration": "no",
          "reproducibility_details": "yes"
        },
        "what_would_change_my_mind": [
          "Independent replication with comparable or better results.",
          "Public benchmark numbers with clear baseline comparisons."
        ],
        "likely_failure_mode": "Performance may collapse outside curated demos or narrow tasks."
      },
      {
        "story_id": "arxiv:oai:arXiv.org:2609.19775v1",
        "title": "Integrating knowledge from case reports: a medical ontology based multimodal information system with structured summary",
        "url": "https://arxiv.org/abs/2609.19775",
        "source_domain": "arxiv.org",
        "category_label": "Cs.Ai",
        "overall": 6.21,
        "metrics": {
          "signal": 9.43,
          "novelty": 4.0,
          "impact": 2.0,
          "confidence": 8.7,
          "actionability": 6.5
        },
        "badges": [
          "paper"
        ],
        "checklist": {
          "primary_source": "yes",
          "demo": "no",
          "benchmarks_evals": "yes",
          "baselines_ablations": "no",
          "third_party_corroboration": "no",
          "reproducibility_details": "yes"
        },
        "what_would_change_my_mind": [
          "Independent replication with comparable or better results.",
          "Public benchmark numbers with clear baseline comparisons."
        ],
        "likely_failure_mode": "Performance may collapse outside curated demos or narrow tasks."
      },
      {
        "story_id": "arxiv:oai:arXiv.org:2609.20130v1",
        "title": "AdaRepair-Mem: Adaptive Experience Orchestration for Repository-Level Program Repair",
        "url": "https://arxiv.org/abs/2609.20130",
        "source_domain": "arxiv.org",
        "category_label": "Cs.Ai",
        "overall": 6.21,
        "metrics": {
          "signal": 9.43,
          "novelty": 4.0,
          "impact": 2.0,
          "confidence": 8.7,
          "actionability": 6.5
        },
        "badges": [
          "paper"
        ],
        "checklist": {
          "primary_source": "yes",
          "demo": "no",
          "benchmarks_evals": "yes",
          "baselines_ablations": "no",
          "third_party_corroboration": "no",
          "reproducibility_details": "yes"
        },
        "what_would_change_my_mind": [
          "Independent replication with comparable or better results.",
          "Public benchmark numbers with clear baseline comparisons."
        ],
        "likely_failure_mode": "Performance may collapse outside curated demos or narrow tasks."
      }
    ]
  },
  "lab_notes": {
    "tool_repo_of_the_day": {
      "title": "mattpocock/skills: Skills for Real Engineers. Straight from my .agents directory.",
      "url": "https://github.com/mattpocock/skills",
      "source_domain": "github.com"
    },
    "prompt_workflow_of_the_day": "summarize claim -> evidence -> risk in three passes before acting",
    "tiny_snippet": "uv run python -m msd.run --scheduled"
  },
  "forecast_watchlist": {
    "read_time": "1-2 min",
    "watch_prefix": "Watch:",
    "topics": [
      "cs.ai",
      "cs.lg",
      "rss",
      "cs.cl",
      "python",
      "benchmark",
      "eval",
      "repo"
    ],
    "subscribe": {
      "label": "Subscribe for Daily Emails",
      "url": "mailto:morning-singularity-digest@localhost?subject=Subscribe%20for%20Daily%20Emails"
    }
  }
}