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      "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",
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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.",
      "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 182K+ stars | 28K+ forks | 170+ contributors | 12+ language ecosystems | Anthropic Hackathon Winner Language / \u8bed\u8a00 / \u8a9e\u8a00 / Dil / \u042f\u0437\u044b\u043a / Ng\u00f4n ng\u1eef English | P...",
        "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 | \u0420\u0443\u0441\u0441\u043a\u0438\u0439 | Ti\u1ebfng Vi\u1ec7t | \u0e44\u0e17\u0e22 182K+ stars | 28K+ forks | 170+ contributors | 12+ language ecosystems | Anthropic Hackathon Winner Language / \u8bed\u8a00 / \u8a9e\u8a00 / Dil / \u042f\u0437\u044b\u043a / Ng\u00f4n ng\u1eef English | P...",
        "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.",
        "Check robustness on out-of-distribution or long-context cases.",
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      ]
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      "title": "PromptRad: Knowledge-Enhanced Multi-Label Prompt-Tuning for Low-Resource Radiology Report Labeling",
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      "context": "arXiv:2605.20052v2 Announce Type: replace-cross Abstract: Automatic report labeling facilitates the identification of clinical findings from unstructured text and enables large-scale annotation for medical imaging research.",
      "whats_new": "In this paper, we propose PromptRad, a knowledge-enhanced multi-label \\textbf{prompt}-tuning approach for \\textbf{rad}iology report labeling under low-resource settings.",
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        "Existing rule-based labelers struggle with the diverse descriptions in clinical reports, while fine-tuning pre-trained language models (PLMs) requires large amounts of labeled data that are often unavailable in clinical settings.",
        "In this paper, we propose PromptRad, a knowledge-enhanced multi-label \\textbf{prompt}-tuning approach for \\textbf{rad}iology report labeling under low-resource settings.",
        "PromptRad reformulates multi-label classification as masked language modeling and incorporates synonyms from the UMLS Metathesaurus into a multi-word verbalizer to enrich category representations.",
        "By fine-tuning the PLM without additional classification layers, PromptRad requires substantially less labeled data than conventional fine-tuning."
      ],
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        "arXiv:2605.20052v2 Announce Type: replace-cross Abstract: Automatic report labeling facilitates the identification of clinical findings from unstructured text and enables large-scale annotation for medical imaging research.",
        "Experiments on liver CT (computed tomography) reports show that PromptRad outperforms dictionary-based and fine-tuning baselines with only 32 labeled training examples, and achieves competitive performance with GPT-4 despite using a much smaller model.",
        "Computer Science > Computation and Language [Submitted on 19 May 2026 (v1), last revised 20 May 2026 (this version, v2)] Title:PromptRad: Knowledge-Enhanced Multi-Label Prompt-Tuning for Low-Resource Radiology Report Labeling View PDF HTML (experimental)Abs..."
      ],
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        "Generalization outside curated tasks is still unclear."
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    {
      "story_id": "hn:48234838",
      "title": "Show HN: OTA \u2013 a readiness contract for software repos",
      "url": "https://ota.run",
      "source_domain": "ota.run",
      "category_label": "Hn",
      "overall": 6.07,
      "metrics": {
        "signal": 8.37,
        "novelty": 4.0,
        "impact": 2.7,
        "confidence": 7.45,
        "actionability": 6.5
      },
      "why_made_cut": "Signal 8.4, Confidence 7.5, and Impact 2.7 combined to rank this in the top set.",
      "badges": [
        "repo"
      ],
      "context": "It makes software repositories runnable and trustworthy for human, CI, and AI agents.<p>The problem we kept seeing is that the real truth of how a repo gets set up and run is usually scattered across READMEs, scripts, CI config, env files, and maintainer me...",
      "whats_new": "Hi HN, We&#x27;re building Ota.<p>Ota is open repo readiness infrastructure.",
      "key_details": [
        "It makes software repositories runnable and trustworthy for human, CI, and AI agents.<p>The problem we kept seeing is that the real truth of how a repo gets set up and run is usually scattered across READMEs, scripts, CI config, env files, and maintainer me...",
        "That slows onboarding, causes local and CI drift, and makes automation brittle.<p>Ota gives each repo one explicit operational contract for what it needs, how it becomes ready, and how tasks should run.<p>The core flow is:<p>- `ota doctor` to diagnose what...",
        "We think repo readiness is its own layer: something between the repo, the developer, CI, and now agents.<p>We&#x27;d especially love feedback on:<p>- whether this problem feels real in your repos - whether the contract model feels like the right abstraction..."
      ],
      "results_evidence": [
        "Overall 6.1/10 with Signal 8.4 and Impact 2.7.",
        "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": 7.53,
          "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": 8.02,
        "metrics": {
          "signal": 10.0,
          "novelty": 6.2,
          "impact": 8.17,
          "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:2605.20052v2",
        "title": "PromptRad: Knowledge-Enhanced Multi-Label Prompt-Tuning for Low-Resource Radiology Report Labeling",
        "url": "https://arxiv.org/abs/2605.20052",
        "source_domain": "arxiv.org",
        "category_label": "Cs.Ai",
        "overall": 6.43,
        "metrics": {
          "signal": 9.43,
          "novelty": 4.0,
          "impact": 2.0,
          "confidence": 8.7,
          "actionability": 8.2
        },
        "badges": [
          "repo",
          "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:2605.20901v1",
        "title": "VISTA: Technical Report for the Ego4D Short-Term Object Interaction Anticipation at EgoVis 2026",
        "url": "https://arxiv.org/abs/2605.20901",
        "source_domain": "arxiv.org",
        "category_label": "Cs.Ai",
        "overall": 6.23,
        "metrics": {
          "signal": 9.43,
          "novelty": 4.0,
          "impact": 2.0,
          "confidence": 8.7,
          "actionability": 6.5
        },
        "badges": [
          "repo",
          "paper",
          "demo"
        ],
        "checklist": {
          "primary_source": "yes",
          "demo": "yes",
          "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/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"
    }
  }
}