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      "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",
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      "badges": [
        "repo"
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      "context": "# Mine content into the palace mempalace mine ~/projects/myapp # project files mempalace mine ~/.claude/projects/ --mode convos # Claude Code sessions (scope with --wing per project) # Search mempalace search \"why did we switch to GraphQL\" # Load context fo...",
      "whats_new": "The best-benchmarked open-source AI memory system.",
      "key_details": [
        "The only official sources for MemPalace are this GitHub repository, the PyPI package, and the docs site at mempalaceofficial.com.",
        "Any other domain \u2014 including mempalace.tech \u2014 is an impostor and may distribute malware.",
        "Details and timeline: docs/HISTORY.md.",
        "Important \ud83d\udea8 Claude Code sessions expire in 30 days w/out auto-save hooks wired!"
      ],
      "results_evidence": [
        "Important \ud83d\udea8 Claude Code sessions expire in 30 days w/out auto-save hooks wired!",
        "Verbatim storage, pluggable backend, 96.6% R@5 raw on LongMemEval \u2014 zero API calls."
      ],
      "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."
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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: 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.",
      "key_details": [
        "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: 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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        "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:48222366",
      "title": "Hating AI Is Good",
      "url": "https://www.thehandbasket.co/p/hating-ai-is-good-actually",
      "source_domain": "thehandbasket.co",
      "category_label": "Hn",
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      "badges": [],
      "context": "- The Handbasket - Posts - Hating AI is good, actually Hating AI is good, actually LinkedIn may be awash with boosters, but shunning AI is the human choice.",
      "whats_new": "At the same time this new partnership was revealed, Peretti announced he\u2019d be stepping down as CEO of Buzzfeed to serve in a new role as President of Buzzfeed AI.",
      "key_details": [
        "[Ex-Google CEO Eric Schmidt while being booed] Jonah Peretti is very lucky.",
        "Buzzfeed\u2014the viral media company he founded 20 years ago and was once valued at $1.6 billion\u2014was running out of cash when billionaire Byron Allen agreed to buy 52% of its shares.",
        "At the same time this new partnership was revealed, Peretti announced he\u2019d be stepping down as CEO of Buzzfeed to serve in a new role as President of Buzzfeed AI.",
        "So Allen will continue to bankroll the former media titan\u2019s obsession, as he promises (without evidence) that AI will right the ship."
      ],
      "results_evidence": [
        "Buzzfeed\u2014the viral media company he founded 20 years ago and was once valued at $1.6 billion\u2014was running out of cash when billionaire Byron Allen agreed to buy 52% of its shares."
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      ],
      "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",
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        "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"
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          "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",
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          "impact": 8.17,
          "confidence": 7.03,
          "actionability": 6.5
        },
        "badges": [
          "repo"
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          "primary_source": "yes",
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          "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,
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          "signal": 9.43,
          "novelty": 4.0,
          "impact": 2.0,
          "confidence": 8.7,
          "actionability": 8.2
        },
        "badges": [
          "repo",
          "paper",
          "demo"
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          "primary_source": "yes",
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          "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:2510.04905v3",
        "title": "Retrieval-Augmented Code Generation: A Survey with Focus on Repository-Level Approaches",
        "url": "https://arxiv.org/abs/2510.04905",
        "source_domain": "arxiv.org",
        "category_label": "Cs.Cl",
        "overall": 6.35,
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          "signal": 9.43,
          "novelty": 4.0,
          "impact": 2.0,
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          "actionability": 6.5
        },
        "badges": [
          "paper"
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        "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"
    }
  }
}