{
  "date": "2026-05-02",
  "stories": [
    {
      "story_id": "gh:1136590548",
      "title": "affaan-m/everything-claude-code: 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/everything-claude-code",
      "overall": 8.01,
      "metrics": {
        "signal": 10.0,
        "novelty": 6.2,
        "impact": 8.13,
        "confidence": 7.03,
        "actionability": 6.5,
        "freshness": 10.0
      },
      "badges": {
        "Repo": "https://github.com/affaan-m/everything-claude-code"
      },
      "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": 7.51,
        "confidence": 7.83,
        "actionability": 6.5,
        "freshness": 9.96
      },
      "badges": {
        "Repo": "https://github.com/MemPalace/mempalace",
        "Benchmarks": ""
      },
      "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": 7.72,
      "metrics": {
        "signal": 10.0,
        "novelty": 5.1,
        "impact": 7.73,
        "confidence": 7.03,
        "actionability": 6.5,
        "freshness": 9.98
      },
      "badges": {
        "Repo": "https://github.com/karpathy/autoresearch"
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "github"
      ],
      "source": "github"
    },
    {
      "story_id": "gh:1197515131",
      "title": "VoltAgent/awesome-design-md: A collection of DESIGN.md files inspired 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": 7.71,
      "metrics": {
        "signal": 10.0,
        "novelty": 5.1,
        "impact": 7.67,
        "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:1176011916",
      "title": "HKUDS/CLI-Anything: \"CLI-Anything: Making ALL Software Agent-Native\" -- CLI-Hub: https://clianything.cc/",
      "url": "https://github.com/HKUDS/CLI-Anything",
      "overall": 7.63,
      "metrics": {
        "signal": 10.0,
        "novelty": 5.1,
        "impact": 7.29,
        "confidence": 7.03,
        "actionability": 6.5,
        "freshness": 9.99
      },
      "badges": {
        "Repo": "https://github.com/HKUDS/CLI-Anything"
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "github"
      ],
      "source": "github"
    },
    {
      "story_id": "gh:1134426800",
      "title": "sickn33/antigravity-awesome-skills: Installable GitHub library of 1,400+ agentic skills for Claude Code, Cursor, Codex CLI, Gemini CLI, Antigravity, and more. Includes installer CLI, bundles, workflows, and official/community skill collections.",
      "url": "https://github.com/sickn33/antigravity-awesome-skills",
      "overall": 7.63,
      "metrics": {
        "signal": 10.0,
        "novelty": 5.1,
        "impact": 7.33,
        "confidence": 7.03,
        "actionability": 6.5,
        "freshness": 9.98
      },
      "badges": {
        "Repo": "https://github.com/sickn33/antigravity-awesome-skills"
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "github"
      ],
      "source": "github"
    },
    {
      "story_id": "gh:1142983825",
      "title": "forrestchang/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/forrestchang/andrej-karpathy-skills",
      "overall": 7.56,
      "metrics": {
        "signal": 10.0,
        "novelty": 4.0,
        "impact": 7.88,
        "confidence": 7.03,
        "actionability": 6.5,
        "freshness": 10.0
      },
      "badges": {
        "Repo": "https://github.com/forrestchang/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": 7.45,
      "metrics": {
        "signal": 10.0,
        "novelty": 4.0,
        "impact": 7.38,
        "confidence": 7.03,
        "actionability": 6.5,
        "freshness": 10.0
      },
      "badges": {
        "Repo": "https://github.com/rtk-ai/rtk"
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "github"
      ],
      "source": "github"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2604.19606v2",
      "title": "AblateCell: A Reproduce-then-Ablate Agent for Virtual Cell Repositories",
      "url": "https://arxiv.org/abs/2604.19606",
      "overall": 6.46,
      "metrics": {
        "signal": 9.43,
        "novelty": 5.1,
        "impact": 2.0,
        "confidence": 8.7,
        "actionability": 6.5,
        "freshness": 8.3
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2604.19606",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2604.28093v1",
      "title": "What Makes a Good Terminal-Agent Benchmark Task: A Guideline for Adversarial, Difficult, and Legible Evaluation Design",
      "url": "https://arxiv.org/abs/2604.28093",
      "overall": 6.43,
      "metrics": {
        "signal": 9.43,
        "novelty": 6.2,
        "impact": 2.0,
        "confidence": 8.3,
        "actionability": 5.2,
        "freshness": 8.3
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2604.28093",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2604.27011v1",
      "title": "Automatic Causal Fairness Analysis with LLM-Generated Reporting",
      "url": "https://arxiv.org/abs/2604.27011",
      "overall": 6.26,
      "metrics": {
        "signal": 9.43,
        "novelty": 4.0,
        "impact": 2.0,
        "confidence": 8.7,
        "actionability": 6.5,
        "freshness": 8.3
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2604.27011",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2604.27559v1",
      "title": "RIHA: Report-Image Hierarchical Alignment for Radiology Report Generation",
      "url": "https://arxiv.org/abs/2604.27559",
      "overall": 6.26,
      "metrics": {
        "signal": 9.43,
        "novelty": 4.0,
        "impact": 2.0,
        "confidence": 8.7,
        "actionability": 6.5,
        "freshness": 8.3
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2604.27559",
        "Demo": "",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2601.00376v2",
      "title": "In Line with Context: Repository-Level Code Generation via Context Inlining",
      "url": "https://arxiv.org/abs/2601.00376",
      "overall": 6.26,
      "metrics": {
        "signal": 9.43,
        "novelty": 4.0,
        "impact": 2.0,
        "confidence": 8.7,
        "actionability": 6.5,
        "freshness": 8.3
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2601.00376",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2604.27776v1",
      "title": "WindowsWorld: A Process-Centric Benchmark of Autonomous GUI Agents in Professional Cross-Application Environments",
      "url": "https://arxiv.org/abs/2604.27776",
      "overall": 6.23,
      "metrics": {
        "signal": 9.43,
        "novelty": 6.2,
        "impact": 2.0,
        "confidence": 8.3,
        "actionability": 3.5,
        "freshness": 8.3
      },
      "badges": {
        "Repo": "",
        "Paper": "https://arxiv.org/abs/2604.27776",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2604.28139v1",
      "title": "Claw-Eval-Live: A Live Agent Benchmark for Evolving Real-World Workflows",
      "url": "https://arxiv.org/abs/2604.28139",
      "overall": 6.23,
      "metrics": {
        "signal": 9.43,
        "novelty": 6.2,
        "impact": 2.0,
        "confidence": 8.3,
        "actionability": 3.5,
        "freshness": 8.3
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2604.28139",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2604.09408v3",
      "title": "HiL-Bench (Human-in-Loop Benchmark): Do Agents Know When to Ask for Help?",
      "url": "https://arxiv.org/abs/2604.09408",
      "overall": 6.23,
      "metrics": {
        "signal": 9.43,
        "novelty": 6.2,
        "impact": 2.0,
        "confidence": 8.3,
        "actionability": 3.5,
        "freshness": 8.3
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2604.09408",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2601.08611v2",
      "title": "VeriTaS: The First Dynamic Benchmark for Multimodal Automated Fact-Checking",
      "url": "https://arxiv.org/abs/2601.08611",
      "overall": 6.23,
      "metrics": {
        "signal": 9.43,
        "novelty": 6.2,
        "impact": 2.0,
        "confidence": 8.3,
        "actionability": 3.5,
        "freshness": 8.3
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2601.08611",
        "Demo": "https://veritas.mai.informatik.tu-darmstadt.de",
        "Benchmarks": "https://veritas.mai.informatik.tu-darmstadt.de"
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2604.27300v1",
      "title": "METASYMBO: Multi-Agent Language-Guided Metamaterial Discovery via Symbolic Latent Evolution",
      "url": "https://arxiv.org/abs/2604.27300",
      "overall": 6.11,
      "metrics": {
        "signal": 9.43,
        "novelty": 5.1,
        "impact": 2.0,
        "confidence": 7.5,
        "actionability": 5.2,
        "freshness": 8.3
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2604.27300",
        "Demo": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2604.27891v1",
      "title": "In-Context Prompting Obsoletes Agent Orchestration for Procedural Tasks",
      "url": "https://arxiv.org/abs/2604.27891",
      "overall": 6.11,
      "metrics": {
        "signal": 9.43,
        "novelty": 5.1,
        "impact": 2.0,
        "confidence": 7.5,
        "actionability": 5.2,
        "freshness": 8.3
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2604.27891",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2604.24021v2",
      "title": "QED: An Open-Source Multi-Agent System for Generating Mathematical Proofs on Open Problems",
      "url": "https://arxiv.org/abs/2604.24021",
      "overall": 6.1,
      "metrics": {
        "signal": 9.43,
        "novelty": 6.2,
        "impact": 2.0,
        "confidence": 7.5,
        "actionability": 3.5,
        "freshness": 8.3
      },
      "badges": {
        "Repo": "",
        "Paper": "https://arxiv.org/abs/2604.24021",
        "Benchmarks": "https://github.com/proofQED/QED."
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "hn:47983873",
      "title": "Spirit Airlines Is Winding Down All Operations",
      "url": "https://www.spiritrestructuring.com/",
      "overall": 6.07,
      "metrics": {
        "signal": 8.59,
        "novelty": 4.0,
        "impact": 5.29,
        "confidence": 6.25,
        "actionability": 3.5,
        "freshness": 8.81
      },
      "badges": {},
      "corroboration_count": 1,
      "corroboration_sources": [
        "hackernews"
      ],
      "source": "hackernews"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2604.27637v1",
      "title": "Optimization before Evaluation: Evaluation with Unoptimised Prompts Can be Misleading",
      "url": "https://arxiv.org/abs/2604.27637",
      "overall": 6.04,
      "metrics": {
        "signal": 9.43,
        "novelty": 4.0,
        "impact": 2.0,
        "confidence": 8.3,
        "actionability": 5.2,
        "freshness": 8.3
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2604.27637",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2604.27309v1",
      "title": "End-to-End Evaluation and Governance of an EHR-Embedded AI Agent for Clinicians",
      "url": "https://arxiv.org/abs/2604.27309",
      "overall": 6.03,
      "metrics": {
        "signal": 9.43,
        "novelty": 5.1,
        "impact": 2.0,
        "confidence": 8.3,
        "actionability": 3.5,
        "freshness": 8.3
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2604.27309",
        "Demo": "",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2604.27763v1",
      "title": "Intent2Tx: Benchmarking LLMs for Translating Natural Language Intents into Ethereum Transactions",
      "url": "https://arxiv.org/abs/2604.27763",
      "overall": 6.03,
      "metrics": {
        "signal": 9.43,
        "novelty": 5.1,
        "impact": 2.0,
        "confidence": 8.3,
        "actionability": 3.5,
        "freshness": 8.3
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2604.27763",
        "Demo": "https://anonymous.4open.science/r/Intent2Tx_Bench-97FF",
        "Benchmarks": "https://anonymous.4open.science/r/Intent2Tx_Bench-97FF"
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    }
  ],
  "deep_dives": [
    {
      "story_id": "gh:1136590548",
      "title": "affaan-m/everything-claude-code: 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/everything-claude-code",
      "source_domain": "github.com",
      "category_label": "Agent",
      "overall": 8.01,
      "metrics": {
        "signal": 10.0,
        "novelty": 6.2,
        "impact": 8.13,
        "confidence": 7.03,
        "actionability": 6.5
      },
      "why_made_cut": "Signal 10.0, Confidence 7.0, and Impact 8.1 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 140K+ stars | 21K+ forks | 170+ contributors | 12+ language ecosystems | Anthropic Hackathon Winner The performance optimization system for AI agent harnesses.",
        "From an Anthropic hackathon winner.",
        "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 140K+ stars | 21K+ forks | 170+ contributors | 12+ language ecosystems | Anthropic Hackathon Winner The performance optimization system for AI agent harnesses.",
        "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:2604.19606v2",
      "title": "AblateCell: A Reproduce-then-Ablate Agent for Virtual Cell Repositories",
      "url": "https://arxiv.org/abs/2604.19606",
      "source_domain": "arxiv.org",
      "category_label": "Cs.Ai",
      "overall": 6.46,
      "metrics": {
        "signal": 9.43,
        "novelty": 5.1,
        "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:2604.19606v2 Announce Type: replace Abstract: Systematic ablations are essential to attribute performance gains in AI Virtual Cells, yet they are rarely performed because biological repositories are under-standardized and tightly coupled to domain-spe...",
      "whats_new": "AblateCell first reproduces reported baselines end-to-end by auto-configuring environments, resolving dependency and data issues, and rerunning official evaluations while emitting verifiable artifacts.",
      "key_details": [
        "While recent coding agents can translate ideas into implementations, they typically stop at producing code and lack a verifier that can reproduce strong baselines and rigorously test which components truly matter.",
        "We introduce AblateCell, a reproduce-then-ablate agent for virtual cell repositories that closes this verification gap.",
        "AblateCell first reproduces reported baselines end-to-end by auto-configuring environments, resolving dependency and data issues, and rerunning official evaluations while emitting verifiable artifacts.",
        "It then conducts closed-loop ablation by generating a graph of isolated repository mutations and adaptively selecting experiments under a reward that trades off performance impact and execution cost."
      ],
      "results_evidence": [
        "arXiv:2604.19606v2 Announce Type: replace Abstract: Systematic ablations are essential to attribute performance gains in AI Virtual Cells, yet they are rarely performed because biological repositories are under-standardized and tightly coupled to domain-spe...",
        "Evaluated on three single-cell perturbation prediction repositories (CPA, GEARS, BioLORD), AblateCell achieves 88.9% (+29.9% to human expert) end-to-end workflow success and 93.3% (+53.3% to heuristic) accuracy in recovering ground-truth critical components.",
        "Computer Science > Artificial Intelligence [Submitted on 21 Apr 2026 (v1), last revised 30 Apr 2026 (this version, v2)] Title:AblateCell: A Reproduce-then-Ablate Agent for Virtual Cell Repositories View PDF HTML (experimental)Abstract:Systematic ablations a..."
      ],
      "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": "hn:47983873",
      "title": "Spirit Airlines Is Winding Down All Operations",
      "url": "https://www.spiritrestructuring.com/",
      "source_domain": "spiritrestructuring.com",
      "category_label": "Hn",
      "overall": 6.07,
      "metrics": {
        "signal": 8.59,
        "novelty": 4.0,
        "impact": 5.29,
        "confidence": 6.25,
        "actionability": 3.5
      },
      "why_made_cut": "Signal 8.6, Confidence 6.2, and Impact 5.3 combined to rank this in the top set.",
      "badges": [],
      "context": "Spirit Is Winding Down All Operations Guests Spirit Guests should not go to the airport.",
      "whats_new": "Spirit Is Winding Down All Operations Guests Spirit Guests should not go to the airport.",
      "key_details": [
        "For more information regarding your refund status and next steps, please visit our Guests page.",
        "Vendors For more information and FAQs, please visit our Vendors page.",
        "Announcement It is with great disappointment that on May 2, 2026, Spirit Airlines started an orderly wind-down of our operations, effective immediately.",
        "To our Guests: all flights have been cancelled, and customer service is no longer available."
      ],
      "results_evidence": [
        "Announcement It is with great disappointment that on May 2, 2026, Spirit Airlines started an orderly wind-down of our operations, effective immediately.",
        "We are proud of the impact of our ultra-low-cost model on the industry over the last 34 years and had hoped to serve our Guests for many years to come.",
        "If you have any further questions, please contact our claims agent, Epiq, by emailing [email protected] or calling (855) 952-6606 (for toll-free U.S."
      ],
      "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."
      ]
    }
  ],
  "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": 7.51,
          "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/everything-claude-code: 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/everything-claude-code",
        "source_domain": "github.com",
        "category_label": "Agent",
        "overall": 8.01,
        "metrics": {
          "signal": 10.0,
          "novelty": 6.2,
          "impact": 8.13,
          "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:2604.19606v2",
        "title": "AblateCell: A Reproduce-then-Ablate Agent for Virtual Cell Repositories",
        "url": "https://arxiv.org/abs/2604.19606",
        "source_domain": "arxiv.org",
        "category_label": "Cs.Ai",
        "overall": 6.46,
        "metrics": {
          "signal": 9.43,
          "novelty": 5.1,
          "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:2604.27011v1",
        "title": "Automatic Causal Fairness Analysis with LLM-Generated Reporting",
        "url": "https://arxiv.org/abs/2604.27011",
        "source_domain": "arxiv.org",
        "category_label": "Cs.Ai",
        "overall": 6.26,
        "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": "affaan-m/everything-claude-code: 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/everything-claude-code",
      "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"
    }
  }
}