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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": 8.14, "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:2605.04458v1", "title": "DoGMaTiQ: Automated Generation of Question-and-Answer Nuggets for Report Evaluation", "url": "https://arxiv.org/abs/2605.04458", "source_domain": "arxiv.org", "category_label": "Cs.Cl", "overall": 6.37, "metrics": { "signal": 9.43, "novelty": 4.0, "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": [ "repo", "paper" ], "context": "A persistent challenge for nugget-based evaluation is the need to manually curate sets of nuggets for each topic in a test collection -- a laborious process that scales poorly to novel information needs.", "whats_new": "arXiv:2605.04458v1 Announce Type: new Abstract: Evaluation of long-form, citation-backed reports has lately received significant attention due to the wide-scale adoption of retrieval-augmented generation (RAG) systems.", "key_details": [ "Core to many evaluation frameworks is the use of atomic facts, or nuggets, to assess a report's coverage of query-relevant information attested in the underlying collection.", "While nuggets have traditionally been represented as short statements, recent work has used question-answer (QA) representations, enabling fine-grained evaluations that decouple the information need (i.e.", "the question) from the potentially diverse content that satisfies it (i.e.", "A persistent challenge for nugget-based evaluation is the need to manually curate sets of nuggets for each topic in a test collection -- a laborious process that scales poorly to novel information needs." ], "results_evidence": [ "arXiv:2605.04458v1 Announce Type: new Abstract: Evaluation of long-form, citation-backed reports has lately received significant attention due to the wide-scale adoption of retrieval-augmented generation (RAG) systems.", "Accordingly, we introduce DoGMaTiQ, a pipeline for generating high-quality QA-based nugget sets in three stages: (1) document-grounded nugget generation, (2) paraphrase clustering, and (3) nugget subselection based on principled quality criteria.", "Computer Science > Computation and Language [Submitted on 6 May 2026] Title:DoGMaTiQ: Automated Generation of Question-and-Answer Nuggets for Report Evaluation View PDFAbstract:Evaluation of long-form, citation-backed reports has lately received significant..." ], "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:48048647", "title": "Show HN: Memory system for AI agents with associations, forgetting, synthesis", "url": "https://github.com/jarimustonen/formative-memory", "source_domain": "github.com", "category_label": "Hn", "overall": 5.84, "metrics": { "signal": 8.36, "novelty": 5.1, "impact": 2.35, "confidence": 7.45, "actionability": 3.5 }, "why_made_cut": "Signal 8.4, Confidence 7.5, and Impact 2.4 combined to rank this in the top set.", "badges": [ "repo" ], "context": "- *Reconsolidation ("coloring")* \u2014 when a newer memory contradicts an older one, the older memory is rewritten in light of the new context.", "whats_new": "- *Reconsolidation ("coloring")* \u2014 when a newer memory contradicts an older one, the older memory is rewritten in light of the new context.", "key_details": [ "If we let an agent gather memories, it allows the agent to show features that look like a "personality".", "The basic architecture is to let the memory system form more complex memories from the initial facts that get remembered.", "To build this consolidation, memories are forgotten, and memories that are retrieved often get stronger.", "There's also a structure that builds associations, used both in retrieval and in spotting memories that are candidates for consolidation.

This is what consolidation here looks like:

- *Weighted bidirectional associations* between memory pairs, buil..." ], "results_evidence": [ "- *Content-addressed identity* (SHA-256), so the same fact from two sources collapses to one object at write time.

Today it ships as an OpenClaw plugin (v0.5.5, MIT)." ], "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": 8.14, "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:2605.04458v1", "title": "DoGMaTiQ: Automated Generation of Question-and-Answer Nuggets for Report Evaluation", "url": "https://arxiv.org/abs/2605.04458", "source_domain": "arxiv.org", "category_label": "Cs.Cl", "overall": 6.37, "metrics": { "signal": 9.43, "novelty": 4.0, "impact": 2.0, "confidence": 9.5, "actionability": 6.5 }, "badges": [ "repo", "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:2604.25370v2", "title": "GPT-Image-2 in the Wild: A Twitter Dataset of Self-Reported AI-Generated Images from the First Week of Deployment", "url": "https://arxiv.org/abs/2604.25370", "source_domain": "arxiv.org", "category_label": "Cs.Ai", "overall": 6.44, "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": "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." } ] }, "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" } } }