{
  "date": "2026-06-02",
  "stories": [
    {
      "story_id": "gh:1136590548",
      "title": "affaan-m/ECC: The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.",
      "url": "https://github.com/affaan-m/ECC",
      "overall": 8.03,
      "metrics": {
        "signal": 10.0,
        "novelty": 6.2,
        "impact": 8.21,
        "confidence": 7.03,
        "actionability": 6.5,
        "freshness": 10.0
      },
      "badges": {
        "Repo": "https://github.com/affaan-m/ECC"
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "github"
      ],
      "source": "github"
    },
    {
      "story_id": "gh:1201656210",
      "title": "MemPalace/mempalace: The best-benchmarked open-source AI memory system. And it's free.",
      "url": "https://github.com/MemPalace/mempalace",
      "overall": 8.0,
      "metrics": {
        "signal": 10.0,
        "novelty": 6.2,
        "impact": 7.53,
        "confidence": 7.83,
        "actionability": 6.5,
        "freshness": 9.94
      },
      "badges": {
        "Repo": "https://github.com/MemPalace/mempalace",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "github"
      ],
      "source": "github"
    },
    {
      "story_id": "gh:1170821064",
      "title": "paperclipai/paperclip: The open-source app everyone uses to manage agents at work",
      "url": "https://github.com/paperclipai/paperclip",
      "overall": 7.91,
      "metrics": {
        "signal": 10.0,
        "novelty": 6.2,
        "impact": 7.66,
        "confidence": 7.03,
        "actionability": 6.5,
        "freshness": 9.99
      },
      "badges": {
        "Repo": "https://github.com/paperclipai/paperclip",
        "Paper": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "github"
      ],
      "source": "github"
    },
    {
      "story_id": "gh:1197515131",
      "title": "VoltAgent/awesome-design-md: A collection of DESIGN.md files analysis by popular brand design systems. Drop one into your project and let coding agents generate a matching UI.",
      "url": "https://github.com/VoltAgent/awesome-design-md",
      "overall": 7.73,
      "metrics": {
        "signal": 10.0,
        "novelty": 5.1,
        "impact": 7.78,
        "confidence": 7.03,
        "actionability": 6.5,
        "freshness": 10.0
      },
      "badges": {
        "Repo": "https://github.com/VoltAgent/awesome-design-md"
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "github"
      ],
      "source": "github"
    },
    {
      "story_id": "gh:1174820787",
      "title": "karpathy/autoresearch: AI agents running research on single-GPU nanochat training automatically",
      "url": "https://github.com/karpathy/autoresearch",
      "overall": 7.73,
      "metrics": {
        "signal": 10.0,
        "novelty": 5.1,
        "impact": 7.77,
        "confidence": 7.03,
        "actionability": 6.5,
        "freshness": 9.99
      },
      "badges": {
        "Repo": "https://github.com/karpathy/autoresearch"
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "github"
      ],
      "source": "github"
    },
    {
      "story_id": "gh:1158722119",
      "title": "addyosmani/agent-skills: Production-grade engineering skills for AI coding agents.",
      "url": "https://github.com/addyosmani/agent-skills",
      "overall": 7.67,
      "metrics": {
        "signal": 10.0,
        "novelty": 5.1,
        "impact": 7.48,
        "confidence": 7.03,
        "actionability": 6.5,
        "freshness": 9.99
      },
      "badges": {
        "Repo": "https://github.com/addyosmani/agent-skills"
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "github"
      ],
      "source": "github"
    },
    {
      "story_id": "gh:1142983825",
      "title": "multica-ai/andrej-karpathy-skills: A single CLAUDE.md file to improve Claude Code behavior, derived from Andrej Karpathy's observations on LLM coding pitfalls.",
      "url": "https://github.com/multica-ai/andrej-karpathy-skills",
      "overall": 7.61,
      "metrics": {
        "signal": 10.0,
        "novelty": 4.0,
        "impact": 8.11,
        "confidence": 7.03,
        "actionability": 6.5,
        "freshness": 10.0
      },
      "badges": {
        "Repo": "https://github.com/multica-ai/andrej-karpathy-skills"
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "github"
      ],
      "source": "github"
    },
    {
      "story_id": "gh:1139971460",
      "title": "rtk-ai/rtk: CLI proxy that reduces LLM token consumption by 60-90% on common dev commands. Single Rust binary, zero dependencies",
      "url": "https://github.com/rtk-ai/rtk",
      "overall": 7.49,
      "metrics": {
        "signal": 10.0,
        "novelty": 4.0,
        "impact": 7.57,
        "confidence": 7.03,
        "actionability": 6.5,
        "freshness": 9.97
      },
      "badges": {
        "Repo": "https://github.com/rtk-ai/rtk"
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "github"
      ],
      "source": "github"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2603.19005v2",
      "title": "AgentDS Technical Report: Benchmarking the Future of Human-AI Collaboration in Domain-Specific Data Science",
      "url": "https://arxiv.org/abs/2603.19005",
      "overall": 6.72,
      "metrics": {
        "signal": 9.43,
        "novelty": 6.2,
        "impact": 2.0,
        "confidence": 9.5,
        "actionability": 6.5,
        "freshness": 7.55
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2603.19005",
        "Benchmarks": "https://agentds.org/"
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2603.16572v2",
      "title": "Context Matters: Repository-Aware Security Analysis of the Agent Skill Ecosystem",
      "url": "https://arxiv.org/abs/2603.16572",
      "overall": 6.4,
      "metrics": {
        "signal": 9.43,
        "novelty": 5.1,
        "impact": 2.0,
        "confidence": 8.7,
        "actionability": 6.5,
        "freshness": 7.55
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2603.16572",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2606.02320v1",
      "title": "TVIR: Building Deep Research Agents Towards Text--Visual Interleaved Report Generation",
      "url": "https://arxiv.org/abs/2606.02320",
      "overall": 6.4,
      "metrics": {
        "signal": 9.43,
        "novelty": 5.1,
        "impact": 2.0,
        "confidence": 8.7,
        "actionability": 6.5,
        "freshness": 7.55
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2606.02320",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2511.21140v4",
      "title": "How to Correctly Report LLM-as-a-Judge Evaluations",
      "url": "https://arxiv.org/abs/2511.21140",
      "overall": 6.33,
      "metrics": {
        "signal": 9.43,
        "novelty": 4.0,
        "impact": 2.0,
        "confidence": 9.5,
        "actionability": 6.5,
        "freshness": 7.55
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2511.21140",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2606.00093v1",
      "title": "Agreement Metrics for LLM-as-Judge Evaluation: What to Report and Why",
      "url": "https://arxiv.org/abs/2606.00093",
      "overall": 6.33,
      "metrics": {
        "signal": 9.43,
        "novelty": 4.0,
        "impact": 2.0,
        "confidence": 9.5,
        "actionability": 6.5,
        "freshness": 7.55
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2606.00093",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2606.00440v1",
      "title": "SDR: Set-Distance Rewards for Radiology Report Generation",
      "url": "https://arxiv.org/abs/2606.00440",
      "overall": 6.2,
      "metrics": {
        "signal": 9.43,
        "novelty": 4.0,
        "impact": 2.0,
        "confidence": 8.7,
        "actionability": 6.5,
        "freshness": 7.55
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2606.00440",
        "Benchmarks": "https://anonymous.4open.science/r/Set-Distance-Rewards-CXR-BFDA}{available}."
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2606.02035v1",
      "title": "RL-ACRGNet: Reinforcement Learning-Based Chest Radiology Report Generation Network",
      "url": "https://arxiv.org/abs/2606.02035",
      "overall": 6.2,
      "metrics": {
        "signal": 9.43,
        "novelty": 4.0,
        "impact": 2.0,
        "confidence": 8.7,
        "actionability": 6.5,
        "freshness": 7.55
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2606.02035",
        "Demo": "",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2606.00390v1",
      "title": "Zamba2-VL Technical Report",
      "url": "https://arxiv.org/abs/2606.00390",
      "overall": 6.2,
      "metrics": {
        "signal": 9.43,
        "novelty": 4.0,
        "impact": 2.0,
        "confidence": 8.7,
        "actionability": 6.5,
        "freshness": 7.55
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2606.00390",
        "Benchmarks": "https://huggingface.co/collections/Zyphra/zamba2-vl."
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2606.00860v1",
      "title": "GenPT: Beyond Self-Report for Reliable LLM Psychometrics via Generative Projective Testing",
      "url": "https://arxiv.org/abs/2606.00860",
      "overall": 6.2,
      "metrics": {
        "signal": 9.43,
        "novelty": 4.0,
        "impact": 2.0,
        "confidence": 8.7,
        "actionability": 6.5,
        "freshness": 7.55
      },
      "badges": {
        "Repo": "",
        "Paper": "https://arxiv.org/abs/2606.00860",
        "Benchmarks": "https://github.com/sci-m-wang/GenPT."
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2606.01802v1",
      "title": "MOSS-Audio Technical Report",
      "url": "https://arxiv.org/abs/2606.01802",
      "overall": 6.2,
      "metrics": {
        "signal": 9.43,
        "novelty": 4.0,
        "impact": 2.0,
        "confidence": 8.7,
        "actionability": 6.5,
        "freshness": 7.55
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2606.01802",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2606.02255v1",
      "title": "Who Annotates in NLP? A Large-scale Assessment of Human Annotation Reporting between 2018 and 2025",
      "url": "https://arxiv.org/abs/2606.02255",
      "overall": 6.2,
      "metrics": {
        "signal": 9.43,
        "novelty": 4.0,
        "impact": 2.0,
        "confidence": 8.7,
        "actionability": 6.5,
        "freshness": 7.55
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2606.02255",
        "Demo": "",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2606.00971v1",
      "title": "HypothesisMed: Inference-Time Answer Fusion and Structured Hypothesis-Space Reporting for Biomedical Question Answering",
      "url": "https://arxiv.org/abs/2606.00971",
      "overall": 6.2,
      "metrics": {
        "signal": 9.43,
        "novelty": 4.0,
        "impact": 2.0,
        "confidence": 8.7,
        "actionability": 6.5,
        "freshness": 7.55
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2606.00971",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2602.03619v2",
      "title": "Learning Query-Specific Rubrics from Human Preferences for DeepResearch Report Generation",
      "url": "https://arxiv.org/abs/2602.03619",
      "overall": 6.2,
      "metrics": {
        "signal": 9.43,
        "novelty": 4.0,
        "impact": 2.0,
        "confidence": 8.7,
        "actionability": 6.5,
        "freshness": 7.55
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2602.03619",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2606.01400v1",
      "title": "Consistent and Distinctive: LLM Benchmark Efficiency via Maximum Independent Set Prompt Selection on Similarity Graphs",
      "url": "https://arxiv.org/abs/2606.01400",
      "overall": 6.18,
      "metrics": {
        "signal": 9.43,
        "novelty": 5.1,
        "impact": 2.0,
        "confidence": 8.3,
        "actionability": 5.2,
        "freshness": 7.55
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2606.01400",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2606.01992v1",
      "title": "A Structured Benchmark for Text-Guided Anomaly Detection: When Language Stops Conditioning the Decision",
      "url": "https://arxiv.org/abs/2606.01992",
      "overall": 6.18,
      "metrics": {
        "signal": 9.43,
        "novelty": 5.1,
        "impact": 2.0,
        "confidence": 8.3,
        "actionability": 5.2,
        "freshness": 7.55
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2606.01992",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2606.01046v1",
      "title": "TravelEval: A Comprehensive Benchmarking Framework for Evaluating LLM-Powered Travel Planning Agents",
      "url": "https://arxiv.org/abs/2606.01046",
      "overall": 6.17,
      "metrics": {
        "signal": 9.43,
        "novelty": 6.2,
        "impact": 2.0,
        "confidence": 8.3,
        "actionability": 3.5,
        "freshness": 7.55
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2606.01046",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    }
  ],
  "deep_dives": [
    {
      "story_id": "gh:1136590548",
      "title": "affaan-m/ECC: The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.",
      "url": "https://github.com/affaan-m/ECC",
      "source_domain": "github.com",
      "category_label": "Agent",
      "overall": 8.03,
      "metrics": {
        "signal": 10.0,
        "novelty": 6.2,
        "impact": 8.21,
        "confidence": 7.03,
        "actionability": 6.5
      },
      "why_made_cut": "Signal 10.0, Confidence 7.0, and Impact 8.2 combined to rank this in the top set.",
      "badges": [
        "repo"
      ],
      "context": "| Topic | What You'll Learn | |---|---| | Token Optimization | Model selection, system prompt slimming, background processes | | Memory Persistence | Hooks that save/load context across sessions automatically | | Continuous Learning | Auto-extract patterns...",
      "whats_new": "Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.",
      "key_details": [
        "Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.",
        "Language: English | Portugu\u00eas (Brasil) | \u7b80\u4f53\u4e2d\u6587 | \u7e41\u9ad4\u4e2d\u6587 | \u65e5\u672c\u8a9e | \ud55c\uad6d\uc5b4 | T\u00fcrk\u00e7e | \u0420\u0443\u0441\u0441\u043a\u0438\u0439 | Ti\u1ebfng Vi\u1ec7t | \u0e44\u0e17\u0e22 | Deutsch 182K+ stars | 28K+ forks | 170+ contributors | 12+ language ecosystems | Cross-harness agent workflows Language / \u8bed\u8a00 / \u8a9e\u8a00 / Dil / \u042f\u0437\u044b\u043a / Ng\u00f4n ng...",
        "Built from real-world multi-harness engineering workflows.",
        "A complete system: skills, instincts, memory optimization, continuous learning, security scanning, and research-first development."
      ],
      "results_evidence": [
        "Language: English | Portugu\u00eas (Brasil) | \u7b80\u4f53\u4e2d\u6587 | \u7e41\u9ad4\u4e2d\u6587 | \u65e5\u672c\u8a9e | \ud55c\uad6d\uc5b4 | T\u00fcrk\u00e7e | \u0420\u0443\u0441\u0441\u043a\u0438\u0439 | Ti\u1ebfng Vi\u1ec7t | \u0e44\u0e17\u0e22 | Deutsch 182K+ stars | 28K+ forks | 170+ contributors | 12+ language ecosystems | Cross-harness agent workflows Language / \u8bed\u8a00 / \u8a9e\u8a00 / Dil / \u042f\u0437\u044b\u043a / Ng\u00f4n ng...",
        "Production-ready agents, skills, hooks, rules, MCP configurations, and legacy command shims evolved over 10+ months of intensive daily use building real products.",
        "ECC v2.0.0-rc.1 adds the public Hermes operator story on top of that reusable layer: start with the Hermes setup guide, then review the rc.1 release notes and cross-harness architecture."
      ],
      "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:2603.19005v2",
      "title": "AgentDS Technical Report: Benchmarking the Future of Human-AI Collaboration in Domain-Specific Data Science",
      "url": "https://arxiv.org/abs/2603.19005",
      "source_domain": "arxiv.org",
      "category_label": "Cs.Ai",
      "overall": 6.72,
      "metrics": {
        "signal": 9.43,
        "novelty": 6.2,
        "impact": 2.0,
        "confidence": 9.5,
        "actionability": 6.5
      },
      "why_made_cut": "Signal 9.4, Confidence 9.5, and Impact 2.0 combined to rank this in the top set.",
      "badges": [
        "paper"
      ],
      "context": "AgentDS consists of 17 challenges across six industries: commerce, food production, healthcare, insurance, manufacturing, and retail banking.",
      "whats_new": "We conducted an open competition involving 29 teams and 80 participants, enabling systematic comparison between human-AI collaborative approaches and AI-only baselines.",
      "key_details": [
        "Recent developments in large language models (LLMs) and artificial intelligence (AI) agents have significantly automated data science workflow.",
        "However, it remains unclear to what extent AI agents can match the performance of human experts on domain-specific data science tasks, and in which aspects human expertise continues to provide advantages.",
        "We introduce AgentDS, a benchmark and competition designed to evaluate both AI agents and human-AI collaboration performance in domain-specific data science.",
        "AgentDS consists of 17 challenges across six industries: commerce, food production, healthcare, insurance, manufacturing, and retail banking."
      ],
      "results_evidence": [
        "arXiv:2603.19005v2 Announce Type: replace-cross Abstract: Data science plays a critical role in transforming complex data into actionable insights across numerous domains.",
        "AgentDS consists of 17 challenges across six industries: commerce, food production, healthcare, insurance, manufacturing, and retail banking.",
        "We conducted an open competition involving 29 teams and 80 participants, enabling systematic comparison between human-AI collaborative approaches and AI-only baselines."
      ],
      "limitations_unknowns": [
        "However, it remains unclear to what extent AI agents can match the performance of human experts on domain-specific data science tasks, and in which aspects human expertise continues to provide advantages."
      ],
      "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": "gh:1170821064",
      "title": "paperclipai/paperclip: The open-source app everyone uses to manage agents at work",
      "url": "https://github.com/paperclipai/paperclip",
      "source_domain": "github.com",
      "category_label": "Agent",
      "overall": 7.91,
      "metrics": {
        "signal": 10.0,
        "novelty": 6.2,
        "impact": 7.66,
        "confidence": 7.03,
        "actionability": 6.5
      },
      "why_made_cut": "Signal 10.0, Confidence 7.0, and Impact 7.7 combined to rank this in the top set.",
      "badges": [
        "repo",
        "paper"
      ],
      "context": "The open-source app everyone uses to manage agents at work Quickstart \u00b7 Docs \u00b7 GitHub \u00b7 Discord \u00b7 Twitter \u00b7 Website full-tour.webm Open-source orchestration for teams of AI agents.",
      "whats_new": "The open-source app everyone uses to manage agents at work Quickstart \u00b7 Docs \u00b7 GitHub \u00b7 Discord \u00b7 Twitter \u00b7 Website full-tour.webm Open-source orchestration for teams of AI agents.",
      "key_details": [
        "If OpenClaw is an employee, Paperclip is the company.",
        "Paperclip is a Node.js server and React UI that orchestrates a team of AI agents to run a business.",
        "Bring your own agents, assign goals, and track work and costs from one dashboard.",
        "Under the hood: org charts, budgets, governance, goal alignment, and agent coordination."
      ],
      "results_evidence": [
        "| Step | Example | | |---|---|---| | 01 | Define the goal | \"Build the #1 AI note-taking app to $1M MRR.\" | | 02 | Hire the team | CEO, CTO, engineers, designers, marketers \u2014 any bot, any provider.",
        "| | 03 | Approve and run | Review strategy.",
        "| - \u2705 You want to build autonomous AI companies - \u2705 You coordinate many different agents (OpenClaw, Codex, Claude, Cursor) toward a common goal - \u2705 You have 20 simultaneous Claude Code terminals open and lose track of what everyone is doing - \u2705 You want age..."
      ],
      "limitations_unknowns": [
        "When they hit the limit, they stop."
      ],
      "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:1201656210",
        "title": "MemPalace/mempalace: The best-benchmarked open-source AI memory system. And it's free.",
        "url": "https://github.com/MemPalace/mempalace",
        "source_domain": "github.com",
        "category_label": "Benchmark",
        "overall": 8.0,
        "metrics": {
          "signal": 10.0,
          "novelty": 6.2,
          "impact": 7.53,
          "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:1136590548",
        "title": "affaan-m/ECC: The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.",
        "url": "https://github.com/affaan-m/ECC",
        "source_domain": "github.com",
        "category_label": "Agent",
        "overall": 8.03,
        "metrics": {
          "signal": 10.0,
          "novelty": 6.2,
          "impact": 8.21,
          "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:2603.19005v2",
        "title": "AgentDS Technical Report: Benchmarking the Future of Human-AI Collaboration in Domain-Specific Data Science",
        "url": "https://arxiv.org/abs/2603.19005",
        "source_domain": "arxiv.org",
        "category_label": "Cs.Ai",
        "overall": 6.72,
        "metrics": {
          "signal": 9.43,
          "novelty": 6.2,
          "impact": 2.0,
          "confidence": 9.5,
          "actionability": 6.5
        },
        "badges": [
          "paper"
        ],
        "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": "arxiv:oai:arXiv.org:2511.21140v4",
        "title": "How to Correctly Report LLM-as-a-Judge Evaluations",
        "url": "https://arxiv.org/abs/2511.21140",
        "source_domain": "arxiv.org",
        "category_label": "Cs.Cl",
        "overall": 6.33,
        "metrics": {
          "signal": 9.43,
          "novelty": 4.0,
          "impact": 2.0,
          "confidence": 9.5,
          "actionability": 6.5
        },
        "badges": [
          "paper"
        ],
        "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."
      }
    ]
  },
  "lab_notes": {
    "tool_repo_of_the_day": {
      "title": "affaan-m/ECC: The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.",
      "url": "https://github.com/affaan-m/ECC",
      "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": [
      "agent",
      "llm",
      "cs.ai",
      "cs.lg",
      "rss",
      "cs.cl",
      "python",
      "benchmark"
    ],
    "subscribe": {
      "label": "Subscribe for Daily Emails",
      "url": "mailto:morning-singularity-digest@localhost?subject=Subscribe%20for%20Daily%20Emails"
    }
  }
}