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      "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.",
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      "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.",
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        "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."
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        "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."
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        "Generalization outside curated tasks is still unclear."
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        "Reproduce one claim with a public baseline and fixed evaluation settings.",
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      "title": "The Prompt Engineering Report Distilled: Quick Start Guide for Life Sciences",
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      "whats_new": "To provide actionable guidelines and reduce the friction of navigating these various approaches, we distil this report to focus on 6 core techniques: zero-shot, few-shot approaches, thought generation, ensembling, self-criticism, and decomposition.",
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        "By deploying case-specific prompt engineering techniques that streamline frequently performed life sciences workflows, researchers could achieve substantial efficiency gains that far exceed the initial time investment required to master these techniques.",
        "The Prompt Report published in 2025 outlined 58 different text-based prompt engineering techniques, highlighting the numerous ways prompts could be constructed.",
        "To provide actionable guidelines and reduce the friction of navigating these various approaches, we distil this report to focus on 6 core techniques: zero-shot, few-shot approaches, thought generation, ensembling, self-criticism, and decomposition.",
        "We breakdown the significance of each approach and ground it in use cases relevant to life sciences, from literature summarization and data extraction to editorial tasks."
      ],
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        "arXiv:2509.11295v2 Announce Type: replace Abstract: Developing effective prompts demands significant cognitive investment to generate reliable, high-quality responses from Large Language Models (LLMs).",
        "The Prompt Report published in 2025 outlined 58 different text-based prompt engineering techniques, highlighting the numerous ways prompts could be constructed.",
        "To provide actionable guidelines and reduce the friction of navigating these various approaches, we distil this report to focus on 6 core techniques: zero-shot, few-shot approaches, thought generation, ensembling, self-criticism, and decomposition."
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      "whats_new": "Shared context board for teams and agents Try Kanwas for free at kanwas.ai Kanwas is a multiplayer workspace for AI work.",
      "key_details": [
        "Teams and an AI agent share the same documents, evidence, and decisions, with the agent's tool calls streaming into the same timeline everyone sees.",
        "Turn a fundraising deck, customer interviews, MVP spec, and hiring plan into one canvas where the agent helps across all of them.",
        "Less context to keep in your head, more output across many fronts.",
        "Drop interview snippets, tickets, and competitor screenshots on a board; get a discovery readout and a PRD with every claim traceable to its source."
      ],
      "results_evidence": [
        "Overall 6.2/10 with Signal 8.4 and Impact 3.1.",
        "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."
      ]
    }
  ],
  "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": 7.5,
          "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": 8.12,
          "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:2509.11295v2",
        "title": "The Prompt Engineering Report Distilled: Quick Start Guide for Life Sciences",
        "url": "https://arxiv.org/abs/2509.11295",
        "source_domain": "arxiv.org",
        "category_label": "Cs.Cl",
        "overall": 6.45,
        "metrics": {
          "signal": 9.43,
          "novelty": 4.0,
          "impact": 2.0,
          "confidence": 8.7,
          "actionability": 8.2
        },
        "badges": [
          "paper",
          "demo"
        ],
        "checklist": {
          "primary_source": "yes",
          "demo": "yes",
          "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:2509.26184v5",
        "title": "Auto-ARGUE: LLM-Based Report Generation Evaluation",
        "url": "https://arxiv.org/abs/2509.26184",
        "source_domain": "arxiv.org",
        "category_label": "Cs.Ai",
        "overall": 6.37,
        "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/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"
    }
  }
}