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      "title": "MemPalace/mempalace: The best-benchmarked open-source AI memory system. And it's free.",
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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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        "Track whether independent teams report matching results."
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      "whats_new": "We further introduce autocomplete tasks, a new class of predictive objectives that require models to infer missing attribute values directly within relational tables while respecting temporal constraints, expanding beyond traditional forecasting tasks const...",
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        "As this paradigm evolves toward larger models and relational foundation models, scalable and realistic benchmarks are essential for enabling systematic evaluation and progress.",
        "In this paper, we introduce RelBench v2, a major expansion of the RelBench benchmark for RDL.",
        "RelBench v2 adds four large-scale relational datasets spanning scholarly publications, enterprise resource planning, consumer platforms, and clinical records, increasing the benchmark to 11 datasets comprising over 22 million rows across 29 tables.",
        "We further introduce autocomplete tasks, a new class of predictive objectives that require models to infer missing attribute values directly within relational tables while respecting temporal constraints, expanding beyond traditional forecasting tasks const..."
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        "arXiv:2602.12606v2 Announce Type: replace Abstract: Relational deep learning (RDL) has emerged as a powerful paradigm for learning directly on relational databases by modeling entities and their relationships across multiple interconnected tables.",
        "RelBench v2 adds four large-scale relational datasets spanning scholarly publications, enterprise resource planning, consumer platforms, and clinical records, increasing the benchmark to 11 datasets comprising over 22 million rows across 29 tables.",
        "Computer Science > Machine Learning [Submitted on 13 Feb 2026 (v1), last revised 9 May 2026 (this version, v2)] Title:RelBench v2: A Large-Scale Benchmark and Repository for Relational Data View PDF HTML (experimental)Abstract:Relational deep learning (RDL)..."
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        "Reproduce one claim with a public baseline and fixed evaluation settings.",
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      "title": "Three teams shipped the same fix for AI agents losing cross-repo context",
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      "source_domain": "riftmap.dev",
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      "context": "The phrase that\u2019s settled into the conversation since isn\u2019t \u201cblast radius\u201d or \u201cservice catalog.\u201d It\u2019s cross-repo context.",
      "whats_new": "Three weeks ago, the Cortex 2026 Engineering in the Age of AI Benchmark put incidents per pull request up 23.5% and change failure rates up roughly 30% since AI adoption accelerated.",
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        "I wrote about that data and what it means for blast radius shortly after it landed.",
        "What I underestimated at the time was how fast the language was going to shift.",
        "The phrase that\u2019s settled into the conversation since isn\u2019t \u201cblast radius\u201d or \u201cservice catalog.\u201d It\u2019s cross-repo context.",
        "And it\u2019s almost always being used in the same sentence as \u201cAI coding agents.\u201d The reason becomes obvious once you read what teams operating AI coding agents at scale are publishing right now."
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        "Three weeks ago, the Cortex 2026 Engineering in the Age of AI Benchmark put incidents per pull request up 23.5% and change failure rates up roughly 30% since AI adoption accelerated.",
        "What three teams just shipped Neilos (@neil_agentic on dev.to), March 27.",
        "A solo founder running 15+ repositories across Go, Rust, TypeScript, Python, and C++, coordinating ten specialised Claude Code agents through Telegram."
      ],
      "limitations_unknowns": [
        "Three weeks ago, the Cortex 2026 Engineering in the Age of AI Benchmark put incidents per pull request up 23.5% and change failure rates up roughly 30% since AI adoption accelerated."
      ],
      "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": 7.52,
          "confidence": 7.83,
          "actionability": 6.5
        },
        "badges": [
          "repo"
        ],
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          "primary_source": "yes",
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          "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": 8.15,
          "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:2602.12606v2",
        "title": "RelBench v2: A Large-Scale Benchmark and Repository for Relational Data",
        "url": "https://arxiv.org/abs/2602.12606",
        "source_domain": "arxiv.org",
        "category_label": "Cs.Lg",
        "overall": 6.56,
        "metrics": {
          "signal": 9.43,
          "novelty": 5.1,
          "impact": 2.0,
          "confidence": 9.5,
          "actionability": 6.5
        },
        "badges": [
          "paper",
          "demo"
        ],
        "checklist": {
          "primary_source": "yes",
          "demo": "yes",
          "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:2603.19254v2",
        "title": "FinReasoning: A Hierarchical Benchmark for Reliable Financial Research Reporting",
        "url": "https://arxiv.org/abs/2603.19254",
        "source_domain": "arxiv.org",
        "category_label": "Cs.Cl",
        "overall": 6.56,
        "metrics": {
          "signal": 9.43,
          "novelty": 5.1,
          "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."
      }
    ]
  },
  "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"
    }
  }
}