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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...",
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        "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!"
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        "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."
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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": "PromptRad: Knowledge-Enhanced Multi-Label Prompt-Tuning for Low-Resource Radiology Report Labeling",
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      "context": "arXiv:2605.20052v1 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.",
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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:2605.20052v1 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 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] Title:PromptRad: Knowledge-Enhanced Multi-Label Prompt-Tuning for Low-Resource Radiology Report Labeling View PDF HTML (experimental)Abstract:Automatic report labeling facilitates the id..."
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      "title": "Show HN: Agyn, an open-source Kubernetes runtime for AI agents",
      "url": "https://github.com/agynio/platform",
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      "context": "| Problem | Agyn | |---|---| | Agents run on individual laptops | Centralized deployment on your infrastructure | | Secrets passed directly to models | Secrets isolated, never exposed to the model | | No budget visibility or limits | Spend caps at any level...",
      "whats_new": "Each agent is a first-class citizen: - Isolated sandbox \u2014 own container, filesystem, env vars, secrets - MCPs in separate containers \u2014 full process isolation per tool - Observability built in \u2014 token usage, compute, activity logs - Auto-scaling \u2014 agents spi...",
      "key_details": [
        "Agyn is an open-source, Kubernetes-native platform that moves agents from laptops to company infrastructure with the controls enterprises need.",
        "| Problem | Agyn | |---|---| | Agents run on individual laptops | Centralized deployment on your infrastructure | | Secrets passed directly to models | Secrets isolated, never exposed to the model | | No budget visibility or limits | Spend caps at any level...",
        "Want a ready-made fleet to play with?",
        "Apply agynio/demo-agent \u2014 a Terraform config that provisions a support, marketing, and data-engineer agent in one command."
      ],
      "results_evidence": [
        "resource \"agyn_agent\" \"support\" { organization_id = agyn_organization.acme.id name = \"Support\" nickname = \"support\" model = agyn_llm_model.gpt_4o.name image = \"ghcr.io/agynio/agent-runtime:v1.0.0\" init_image = \"ghcr.io/agynio/agent-init-codex:v1.0.0\" idle_t..."
      ],
      "limitations_unknowns": [
        "| Problem | Agyn | |---|---| | Agents run on individual laptops | Centralized deployment on your infrastructure | | Secrets passed directly to models | Secrets isolated, never exposed to the model | | No budget visibility or limits | Spend caps at any level..."
      ],
      "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": "arxiv:oai:arXiv.org:2605.20052v1",
        "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": "gh:1170821064",
        "title": "paperclipai/paperclip: The open-source app everyone uses to manage agents at work",
        "url": "https://github.com/paperclipai/paperclip",
        "source_domain": "github.com",
        "category_label": "Agent",
        "overall": 7.9,
        "metrics": {
          "signal": 10.0,
          "novelty": 6.2,
          "impact": 7.65,
          "confidence": 7.03,
          "actionability": 6.5
        },
        "badges": [
          "repo",
          "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."
      },
      {
        "story_id": "arxiv:oai:arXiv.org:2604.16503v2",
        "title": "Motif-Video 2B: Technical Report",
        "url": "https://arxiv.org/abs/2604.16503",
        "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": [
          "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": "MemPalace/mempalace: The best-benchmarked open-source AI memory system. And it's free.",
      "url": "https://github.com/MemPalace/mempalace",
      "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"
    }
  }
}