{
  "date": "2026-08-25",
  "stories": [
    {
      "story_id": "gh:1223170290",
      "title": "nexu-io/open-design: \ud83c\udfa8 Best DeepSeek Harness Design Plugin. The open-source Claude Design alternative. \ud83d\udda5\ufe0f Local-first desktop app. \ud83d\uddbc\ufe0f Your coding agent becomes the design engine: prototypes, landing pages, dashboards, slides, images & video \u2014 real files, HTML/PDF/PPTX/MP4 export. \ud83e\udd16 Claude Code / Codex / Cursor / DeepSeek Harness / OpenCode & 20+ CLIs via BYOK.",
      "url": "https://github.com/nexu-io/open-design",
      "overall": 8.13,
      "metrics": {
        "signal": 10.0,
        "novelty": 7.3,
        "impact": 7.81,
        "confidence": 7.03,
        "actionability": 6.5,
        "freshness": 9.97
      },
      "badges": {
        "Repo": "https://github.com/nexu-io/open-design",
        "Demo": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "github"
      ],
      "source": "github"
    },
    {
      "story_id": "gh:1170821064",
      "title": "paperclipai/paperclip: The open-source app everyone uses to manage agents at work",
      "url": "https://github.com/paperclipai/paperclip",
      "overall": 7.92,
      "metrics": {
        "signal": 10.0,
        "novelty": 6.2,
        "impact": 7.73,
        "confidence": 7.03,
        "actionability": 6.5,
        "freshness": 9.97
      },
      "badges": {
        "Repo": "https://github.com/paperclipai/paperclip",
        "Paper": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "github"
      ],
      "source": "github"
    },
    {
      "story_id": "gh:1197021090",
      "title": "ultraworkers/claw-code: An agent-managed museum exhibit, built in Rust with Gajae-Code / LazyCodex \u2014 developed and maintained with no human intervention.",
      "url": "https://github.com/ultraworkers/claw-code",
      "overall": 7.82,
      "metrics": {
        "signal": 10.0,
        "novelty": 5.1,
        "impact": 8.19,
        "confidence": 7.03,
        "actionability": 6.5,
        "freshness": 9.96
      },
      "badges": {
        "Repo": "https://github.com/ultraworkers/claw-code"
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "github"
      ],
      "source": "github"
    },
    {
      "story_id": "gh:1266797999",
      "title": "DietrichGebert/ponytail: Makes your AI agent think like the laziest senior dev in the room. The best code is the code you never wrote.",
      "url": "https://github.com/DietrichGebert/ponytail",
      "overall": 7.76,
      "metrics": {
        "signal": 10.0,
        "novelty": 5.1,
        "impact": 7.9,
        "confidence": 7.03,
        "actionability": 6.5,
        "freshness": 10.0
      },
      "badges": {
        "Repo": "https://github.com/DietrichGebert/ponytail"
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "github"
      ],
      "source": "github"
    },
    {
      "story_id": "gh:1197515131",
      "title": "VoltAgent/awesome-design-md: A collection of DESIGN.md files analysis by popular brand design systems. Drop one into your project and let coding agents generate a matching UI.",
      "url": "https://github.com/VoltAgent/awesome-design-md",
      "overall": 7.76,
      "metrics": {
        "signal": 10.0,
        "novelty": 5.1,
        "impact": 7.9,
        "confidence": 7.03,
        "actionability": 6.5,
        "freshness": 9.99
      },
      "badges": {
        "Repo": "https://github.com/VoltAgent/awesome-design-md"
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "github"
      ],
      "source": "github"
    },
    {
      "story_id": "gh:1158722119",
      "title": "addyosmani/agent-skills: Production-grade engineering skills for AI coding agents.",
      "url": "https://github.com/addyosmani/agent-skills",
      "overall": 7.74,
      "metrics": {
        "signal": 10.0,
        "novelty": 5.1,
        "impact": 7.8,
        "confidence": 7.03,
        "actionability": 6.5,
        "freshness": 10.0
      },
      "badges": {
        "Repo": "https://github.com/addyosmani/agent-skills"
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "github"
      ],
      "source": "github"
    },
    {
      "story_id": "gh:1174820787",
      "title": "karpathy/autoresearch: AI agents running research on single-GPU nanochat training automatically",
      "url": "https://github.com/karpathy/autoresearch",
      "overall": 7.74,
      "metrics": {
        "signal": 10.0,
        "novelty": 5.1,
        "impact": 7.82,
        "confidence": 7.03,
        "actionability": 6.5,
        "freshness": 9.98
      },
      "badges": {
        "Repo": "https://github.com/karpathy/autoresearch"
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "github"
      ],
      "source": "github"
    },
    {
      "story_id": "gh:1142983825",
      "title": "multica-ai/andrej-karpathy-skills: A single CLAUDE.md file to improve Claude Code behavior, derived from Andrej Karpathy's observations on LLM coding pitfalls.",
      "url": "https://github.com/multica-ai/andrej-karpathy-skills",
      "overall": 7.63,
      "metrics": {
        "signal": 10.0,
        "novelty": 4.0,
        "impact": 8.22,
        "confidence": 7.03,
        "actionability": 6.5,
        "freshness": 9.99
      },
      "badges": {
        "Repo": "https://github.com/multica-ai/andrej-karpathy-skills"
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "github"
      ],
      "source": "github"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2608.22817v1",
      "title": "Industrial-Instruction: An End-to-End Framework for Building Instruction-Tuning and Benchmark Datasets from Industrial Technical Reports",
      "url": "https://arxiv.org/abs/2608.22817",
      "overall": 6.59,
      "metrics": {
        "signal": 9.43,
        "novelty": 5.1,
        "impact": 2.0,
        "confidence": 9.5,
        "actionability": 6.5,
        "freshness": 8.34
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2608.22817",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2608.23564v1",
      "title": "SWE Refactor Bench: Can Coding Agents Complete a Long-Horizon, Whole-Repository Stack Migration?",
      "url": "https://arxiv.org/abs/2608.23564",
      "overall": 6.46,
      "metrics": {
        "signal": 9.43,
        "novelty": 5.1,
        "impact": 2.0,
        "confidence": 8.7,
        "actionability": 6.5,
        "freshness": 8.34
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2608.23564",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2608.15763v2",
      "title": "Training Agents to Evolve with Their Harness: TaoLive Digital Avatar Agent Technical Report",
      "url": "https://arxiv.org/abs/2608.15763",
      "overall": 6.46,
      "metrics": {
        "signal": 9.43,
        "novelty": 5.1,
        "impact": 2.0,
        "confidence": 8.7,
        "actionability": 6.5,
        "freshness": 8.34
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2608.15763",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2608.23061v1",
      "title": "Improving O-RADS Risk Stratification from Ultrasound Reports: A Comparative Evaluation of Hybrid versus End-to-End LLM Reasoning Strategies",
      "url": "https://arxiv.org/abs/2608.23061",
      "overall": 6.39,
      "metrics": {
        "signal": 9.43,
        "novelty": 4.0,
        "impact": 2.0,
        "confidence": 9.5,
        "actionability": 6.5,
        "freshness": 8.34
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2608.23061",
        "Demo": "",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2608.22713v1",
      "title": "A Source-Grounded Framework for Constructing and Evaluating Progressive Multimodal Diagnostic Dialogues from Clinical Case Reports",
      "url": "https://arxiv.org/abs/2608.22713",
      "overall": 6.39,
      "metrics": {
        "signal": 9.43,
        "novelty": 4.0,
        "impact": 2.0,
        "confidence": 9.5,
        "actionability": 6.5,
        "freshness": 8.34
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2608.22713",
        "Demo": "",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2608.21964v1",
      "title": "Repo2Skill-Evo: Repository Skills Go Stale in Silence",
      "url": "https://arxiv.org/abs/2608.21964",
      "overall": 6.26,
      "metrics": {
        "signal": 9.43,
        "novelty": 4.0,
        "impact": 2.0,
        "confidence": 8.7,
        "actionability": 6.5,
        "freshness": 8.34
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2608.21964",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2605.11533v4",
      "title": "Checkup2Action: A Multimodal Clinical Check-up Report Dataset for Patient-Oriented Action Card Generation",
      "url": "https://arxiv.org/abs/2605.11533",
      "overall": 6.26,
      "metrics": {
        "signal": 9.43,
        "novelty": 4.0,
        "impact": 2.0,
        "confidence": 8.7,
        "actionability": 6.5,
        "freshness": 8.34
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2605.11533",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2608.21836v1",
      "title": "LLM4LLM: Bridging Kernel Benchmarks and Real Deployment via Closed-Loop Agentic Optimization",
      "url": "https://arxiv.org/abs/2608.21836",
      "overall": 6.23,
      "metrics": {
        "signal": 9.43,
        "novelty": 6.2,
        "impact": 2.0,
        "confidence": 8.3,
        "actionability": 3.5,
        "freshness": 8.34
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2608.21836",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2608.23035v1",
      "title": "MobilePA-Bench: Benchmarking Mobile Planner Agents on Complex Real-World Tasks",
      "url": "https://arxiv.org/abs/2608.23035",
      "overall": 6.23,
      "metrics": {
        "signal": 9.43,
        "novelty": 6.2,
        "impact": 2.0,
        "confidence": 8.3,
        "actionability": 3.5,
        "freshness": 8.34
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2608.23035",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2608.23525v1",
      "title": "EarthVerse: Benchmarking Scientific Agents Across Dynamic Earth Systems and Natural Hazards",
      "url": "https://arxiv.org/abs/2608.23525",
      "overall": 6.23,
      "metrics": {
        "signal": 9.43,
        "novelty": 6.2,
        "impact": 2.0,
        "confidence": 8.3,
        "actionability": 3.5,
        "freshness": 8.34
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2608.23525",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2608.23179v1",
      "title": "NetConfArena: An Executable Benchmark for LLM Agents in Closed-Loop Network Configuration",
      "url": "https://arxiv.org/abs/2608.23179",
      "overall": 6.23,
      "metrics": {
        "signal": 9.43,
        "novelty": 6.2,
        "impact": 2.0,
        "confidence": 8.3,
        "actionability": 3.5,
        "freshness": 8.34
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2608.23179",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2607.20510v2",
      "title": "Telco-GAIA: Bilingual Benchmark for Agents in Telecom Domain",
      "url": "https://arxiv.org/abs/2607.20510",
      "overall": 6.23,
      "metrics": {
        "signal": 9.43,
        "novelty": 6.2,
        "impact": 2.0,
        "confidence": 8.3,
        "actionability": 3.5,
        "freshness": 8.34
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2607.20510",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2608.15389v2",
      "title": "Agentic-SQL Revisited: Autonomy-Based Taxonomy and Empirical Benchmark Analysis for LLM Text-to-SQL",
      "url": "https://arxiv.org/abs/2608.15389",
      "overall": 6.23,
      "metrics": {
        "signal": 9.43,
        "novelty": 6.2,
        "impact": 2.0,
        "confidence": 8.3,
        "actionability": 3.5,
        "freshness": 8.34
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2608.15389",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2607.19261v3",
      "title": "PathAgentBench: Benchmarking Evidence-Seeking Vision-Language Models on Whole-Slide Pathology Image",
      "url": "https://arxiv.org/abs/2607.19261",
      "overall": 6.23,
      "metrics": {
        "signal": 9.43,
        "novelty": 6.2,
        "impact": 2.0,
        "confidence": 8.3,
        "actionability": 3.5,
        "freshness": 8.34
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2607.19261",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2608.22331v1",
      "title": "Noise Floor Audit for Agent Benchmarks",
      "url": "https://arxiv.org/abs/2608.22331",
      "overall": 6.23,
      "metrics": {
        "signal": 9.43,
        "novelty": 6.2,
        "impact": 2.0,
        "confidence": 8.3,
        "actionability": 3.5,
        "freshness": 8.34
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2608.22331",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2608.23067v1",
      "title": "Signal or Noise? A Benchmark Study of Agent Skills in Web Development",
      "url": "https://arxiv.org/abs/2608.23067",
      "overall": 6.23,
      "metrics": {
        "signal": 9.43,
        "novelty": 6.2,
        "impact": 2.0,
        "confidence": 8.3,
        "actionability": 3.5,
        "freshness": 8.34
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2608.23067",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    }
  ],
  "deep_dives": [
    {
      "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": 7.92,
      "metrics": {
        "signal": 10.0,
        "novelty": 6.2,
        "impact": 7.73,
        "confidence": 7.03,
        "actionability": 6.5
      },
      "why_made_cut": "Signal 10.0, Confidence 7.0, and Impact 7.7 combined to rank this in the top set.",
      "badges": [
        "repo",
        "paper"
      ],
      "context": "The open-source app everyone uses to manage agents at work Quickstart \u00b7 Docs \u00b7 GitHub \u00b7 Discord \u00b7 Twitter \u00b7 Website full-tour.webm Open-source orchestration for teams of AI agents.",
      "whats_new": "The open-source app everyone uses to manage agents at work Quickstart \u00b7 Docs \u00b7 GitHub \u00b7 Discord \u00b7 Twitter \u00b7 Website full-tour.webm Open-source orchestration for teams of AI agents.",
      "key_details": [
        "If OpenClaw is an employee, Paperclip is the company.",
        "Paperclip is a Node.js server and React UI that orchestrates a team of AI agents to run a business.",
        "Bring your own agents, assign goals, and track work and costs from one dashboard.",
        "Under the hood: org charts, budgets, governance, goal alignment, and agent coordination."
      ],
      "results_evidence": [
        "| | Step | Example | |---|---|---| | 01 | Define the goal | \"Build the #1 AI note-taking app to $1M MRR.\" | | 02 | Hire the team | CEO, CTO, engineers, designers, marketers \u2014 any bot, any provider.",
        "| | 03 | Approve and run | Review strategy.",
        "| - \u2705 You want to build autonomous AI companies - \u2705 You coordinate many different agents (OpenClaw, Codex, Claude, Cursor) toward a common goal - \u2705 You have 20 simultaneous Claude Code terminals open and lose track of what everyone is doing - \u2705 You want age..."
      ],
      "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."
      ]
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2608.22817v1",
      "title": "Industrial-Instruction: An End-to-End Framework for Building Instruction-Tuning and Benchmark Datasets from Industrial Technical Reports",
      "url": "https://arxiv.org/abs/2608.22817",
      "source_domain": "arxiv.org",
      "category_label": "Cs.Cl",
      "overall": 6.59,
      "metrics": {
        "signal": 9.43,
        "novelty": 5.1,
        "impact": 2.0,
        "confidence": 9.5,
        "actionability": 6.5
      },
      "why_made_cut": "Signal 9.4, Confidence 9.5, and Impact 2.0 combined to rank this in the top set.",
      "badges": [
        "paper"
      ],
      "context": "arXiv:2608.22817v1 Announce Type: new Abstract: Industrial technical reports contain high-value knowledge for maintenance, troubleshooting, and product engineering, but their heterogeneous structure (dense prose, specifications, tables) makes them difficult...",
      "whats_new": "arXiv:2608.22817v1 Announce Type: new Abstract: Industrial technical reports contain high-value knowledge for maintenance, troubleshooting, and product engineering, but their heterogeneous structure (dense prose, specifications, tables) makes them difficult...",
      "key_details": [
        "We address this gap with Industrial-Instruction, contributing (i) two open QA datasets built from real industrial technical reports and (ii) the end-to-end pipeline that produces them.",
        "Using 906 public Panasonic documents (7,525 pages), we apply layout-aware extraction, build a semantic retrieval index, and synthesize multiple-choice QA grounded in retrieved evidence under five query-document relationships (irrelevant retrieval, single-/m...",
        "After filtering an initial 23.9k generated samples, each dataset provides approximately 13.6k QA pairs with source documents and a held-out benchmark split.",
        "Fine-tuning small open LLMs (under 10B parameters) improves Set-Match Accuracy from 28.5% to 42.0% and F1 from 46.6% to 63.5% on the Panasonic benchmark."
      ],
      "results_evidence": [
        "arXiv:2608.22817v1 Announce Type: new Abstract: Industrial technical reports contain high-value knowledge for maintenance, troubleshooting, and product engineering, but their heterogeneous structure (dense prose, specifications, tables) makes them difficult...",
        "Using 906 public Panasonic documents (7,525 pages), we apply layout-aware extraction, build a semantic retrieval index, and synthesize multiple-choice QA grounded in retrieved evidence under five query-document relationships (irrelevant retrieval, single-/m...",
        "After filtering an initial 23.9k generated samples, each dataset provides approximately 13.6k QA pairs with source documents and a held-out benchmark split."
      ],
      "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."
      ]
    },
    {
      "story_id": "hn:49430614",
      "title": "Retrieval is a measurement instrument, and nobody reports its coverage",
      "url": "https://ai.bedvibe.studio/retrieval-coverage/",
      "source_domain": "ai.bedvibe.studio",
      "category_label": "Hn",
      "overall": 6.18,
      "metrics": {
        "signal": 8.37,
        "novelty": 4.0,
        "impact": 2.76,
        "confidence": 8.25,
        "actionability": 6.5
      },
      "why_made_cut": "Signal 8.4, Confidence 8.2, and Impact 2.8 combined to rank this in the top set.",
      "badges": [],
      "context": "My Agent Answers From 0.6% of Its Corpus and Reports It Like a Full Read Retrieval is not the evidence.",
      "whats_new": "My Agent Answers From 0.6% of Its Corpus and Reports It Like a Full Read Retrieval is not the evidence.",
      "key_details": [
        "It is a measurement instrument, and nobody reports its coverage.",
        "My portfolio agent held 1,003 indexed chunks when I measured this on 25 August 2026.",
        "When someone asks it a question it retrieves six of them and answers.",
        "The corpus has grown since, which is the ordinary fate of a corpus and the reason the date is here rather than a bare number."
      ],
      "results_evidence": [
        "My Agent Answers From 0.6% of Its Corpus and Reports It Like a Full Read Retrieval is not the evidence.",
        "My portfolio agent held 1,003 indexed chunks when I measured this on 25 August 2026.",
        "It reads exactly the same as an answer built from reading all 1,003  same tone, same citations, same confidence."
      ],
      "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:1223170290",
        "title": "nexu-io/open-design: \ud83c\udfa8 Best DeepSeek Harness Design Plugin. The open-source Claude Design alternative. \ud83d\udda5\ufe0f Local-first desktop app. \ud83d\uddbc\ufe0f Your coding agent becomes the design engine: prototypes, landing pages, dashboards, slides, images & video \u2014 real files, HTML/PDF/PPTX/MP4 export. \ud83e\udd16 Claude Code / Codex / Cursor / DeepSeek Harness / OpenCode & 20+ CLIs via BYOK.",
        "url": "https://github.com/nexu-io/open-design",
        "source_domain": "github.com",
        "category_label": "Agent",
        "overall": 8.13,
        "metrics": {
          "signal": 10.0,
          "novelty": 7.3,
          "impact": 7.81,
          "confidence": 7.03,
          "actionability": 6.5
        },
        "badges": [
          "repo",
          "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": 7.92,
        "metrics": {
          "signal": 10.0,
          "novelty": 6.2,
          "impact": 7.73,
          "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:2608.22817v1",
        "title": "Industrial-Instruction: An End-to-End Framework for Building Instruction-Tuning and Benchmark Datasets from Industrial Technical Reports",
        "url": "https://arxiv.org/abs/2608.22817",
        "source_domain": "arxiv.org",
        "category_label": "Cs.Cl",
        "overall": 6.59,
        "metrics": {
          "signal": 9.43,
          "novelty": 5.1,
          "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."
      },
      {
        "story_id": "arxiv:oai:arXiv.org:2608.23061v1",
        "title": "Improving O-RADS Risk Stratification from Ultrasound Reports: A Comparative Evaluation of Hybrid versus End-to-End LLM Reasoning Strategies",
        "url": "https://arxiv.org/abs/2608.23061",
        "source_domain": "arxiv.org",
        "category_label": "Cs.Ai",
        "overall": 6.39,
        "metrics": {
          "signal": 9.43,
          "novelty": 4.0,
          "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."
      }
    ]
  },
  "lab_notes": {
    "tool_repo_of_the_day": {
      "title": "nexu-io/open-design: \ud83c\udfa8 Best DeepSeek Harness Design Plugin. The open-source Claude Design alternative. \ud83d\udda5\ufe0f Local-first desktop app. \ud83d\uddbc\ufe0f Your coding agent becomes the design engine: prototypes, landing pages, dashboards, slides, images & video \u2014 real files, HTML/PDF/PPTX/MP4 export. \ud83e\udd16 Claude Code / Codex / Cursor / DeepSeek Harness / OpenCode & 20+ CLIs via BYOK.",
      "url": "https://github.com/nexu-io/open-design",
      "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": [
      "cs.ai",
      "cs.lg",
      "rss",
      "cs.cl",
      "python",
      "benchmark",
      "eval",
      "repo"
    ],
    "subscribe": {
      "label": "Subscribe for Daily Emails",
      "url": "mailto:morning-singularity-digest@localhost?subject=Subscribe%20for%20Daily%20Emails"
    }
  }
}