{
  "schemaVersion": "ai123box.daily-radar.v1",
  "updatedAt": "2026-10-16T00:56:58.858Z",
  "date": "2026-10-16",
  "count": 5979,
  "headline": {
    "en": "Ranked 5979 AI updates today. Top item: Voices / Melvin Vivas on Models.",
    "zh-CN": "今日已排序 5979 条 AI 更新，排在最前的是观点：Melvin Vivas 关于模型能力的观点。"
  },
  "items": [
    {
      "id": "voices-nu4jrv",
      "sectionId": "voices",
      "kind": "voices",
      "kindLabel": {
        "en": "Voices",
        "zh-CN": "观点"
      },
      "route": "/voices",
      "accent": "#8b5cf6",
      "name": "Melvin Vivas on Models",
      "title": "Melvin Vivas on Models",
      "description": "Melvin Vivas shared a note on models; open the source for full context.",
      "summary": "Melvin Vivas shared a note on models; open the source for full context.",
      "desc": "Melvin Vivas shared a note on models; open the source for full context.",
      "title_zh": "Melvin Vivas 关于模型能力的观点",
      "summary_zh": "Melvin Vivas 提到 模型能力 的一线判断，建议结合原帖查看完整语境。",
      "title_i18n": {
        "en": "Melvin Vivas on Models",
        "zh-CN": "Melvin Vivas 关于模型能力的观点"
      },
      "summary_i18n": {
        "en": "Melvin Vivas shared a note on models; open the source for full context.",
        "zh-CN": "Melvin Vivas 提到 模型能力 的一线判断，建议结合原帖查看完整语境。"
      },
      "reason_i18n": {
        "en": "@melvindvivas · signal 9.8/10",
        "zh-CN": "@melvindvivas · 信号 9.8/10"
      },
      "url": "https://x.com/melvindvivas/status/2109876908934787127",
      "page": null,
      "source": "@melvindvivas",
      "host": "x.com",
      "score": 97.3,
      "metrics": {
        "importance": 9.8,
        "likes": 0,
        "reposts": 83,
        "replies": 33,
        "views": 0
      },
      "topics": [
        "Models"
      ],
      "publishedAt": "2026-10-13T05:20:29.000Z",
      "updatedAt": "2026-10-13T05:20:29.000Z",
      "rawId": "attentionvc-2109876908934787127"
    },
    {
      "id": "news-i6lt9",
      "sectionId": "news",
      "kind": "news",
      "kindLabel": {
        "en": "News",
        "zh-CN": "新闻"
      },
      "route": "/ai-news",
      "accent": "#ef4444",
      "name": "Meta launches an open-weight AI model that runs on Macs",
      "title": "Meta launches an open-weight AI model that runs on Macs",
      "description": "Meta and Apple have teamed up for the launch of the social media giant's newest model, Muse Spark Open, which will run locally on Apple silicon.",
      "summary": "Meta and Apple have teamed up for the launch of the social media giant's newest model, Muse Spark Open, which will run locally on Apple silicon.",
      "desc": "Meta and Apple have teamed up for the launch of the social media giant's newest model, Muse Spark Open, which will run locally on Apple silicon.",
      "title_zh": "Meta launches an open-weight AI model that runs on Macs",
      "summary_zh": "Meta and Apple have teamed up for the launch of the social media giant's newest model, Muse Spark Open, which will run locally on Apple silicon.",
      "title_i18n": {
        "en": "Meta launches an open-weight AI model that runs on Macs",
        "zh-CN": "Meta launches an open-weight AI model that runs on Macs"
      },
      "summary_i18n": {
        "en": "Meta and Apple have teamed up for the launch of the social media giant's newest model, Muse Spark Open, which will run locally on Apple silicon.",
        "zh-CN": "Meta and Apple have teamed up for the launch of the social media giant's newest model, Muse Spark Open, which will run locally on Apple silicon."
      },
      "reason_i18n": {
        "en": "TechCrunch · AI · impact 10/10 · Models, Products",
        "zh-CN": "TechCrunch · AI · 重要度 10/10 · Models、Products"
      },
      "url": "https://techcrunch.com/2026/10/15/meta-launches-an-open-weight-model-that-runs-on-macs/",
      "page": null,
      "source": "TechCrunch · AI",
      "host": "techcrunch.com",
      "score": 97.2,
      "metrics": {
        "importance": 10,
        "weight": 4,
        "lang": "en"
      },
      "topics": [
        "Models",
        "Products"
      ],
      "publishedAt": "2026-10-15T20:04:53.000Z",
      "updatedAt": "2026-10-15T20:04:53.000Z",
      "rawId": "https://techcrunch.com/2026/10/15/meta-launches-an-open-weight-model-that-runs-on-macs/",
      "image": "https://techcrunch.com/wp-content/uploads/2026/09/GettyImages-2296215726.jpg?resize=1200,799",
      "thumbnail": "https://techcrunch.com/wp-content/uploads/2026/09/GettyImages-2296215726.jpg?resize=1200,799"
    },
    {
      "id": "news-19bbzkr",
      "sectionId": "news",
      "kind": "news",
      "kindLabel": {
        "en": "News",
        "zh-CN": "新闻"
      },
      "route": "/ai-news",
      "accent": "#ef4444",
      "name": "How Windows is being optimized for Nvidia’s RTX Spark laptops",
      "title": "How Windows is being optimized for Nvidia’s RTX Spark laptops",
      "description": "Microsoft and Nvidia held a launch party for the first wave of RTX Spark laptops last week in San Francisco, where a new wave of AI PCs took center stage amid a packed crowd and a…",
      "summary": "Microsoft and Nvidia held a launch party for the first wave of RTX Spark laptops last week in San Francisco, where a new wave of AI PCs took center stage amid a packed crowd and a…",
      "desc": "Microsoft and Nvidia held a launch party for the first wave of RTX Spark laptops last week in San Francisco, where a new wave of AI PCs took center stage amid a packed crowd and a…",
      "title_zh": "How Windows is being optimized for Nvidia’s RTX Spark laptops",
      "summary_zh": "Microsoft and Nvidia held a launch party for the first wave of RTX Spark laptops last week in San Francisco, where a new wave of AI PCs took center stage amid a packed crowd and a…",
      "title_i18n": {
        "en": "How Windows is being optimized for Nvidia’s RTX Spark laptops",
        "zh-CN": "How Windows is being optimized for Nvidia’s RTX Spark laptops"
      },
      "summary_i18n": {
        "en": "Microsoft and Nvidia held a launch party for the first wave of RTX Spark laptops last week in San Francisco, where a new wave of AI PCs took center stage amid a packed crowd and a…",
        "zh-CN": "Microsoft and Nvidia held a launch party for the first wave of RTX Spark laptops last week in San Francisco, where a new wave of AI PCs took center stage amid a packed crowd and a…"
      },
      "reason_i18n": {
        "en": "The Verge · AI · impact 10/10 · Products, Hardware",
        "zh-CN": "The Verge · AI · 重要度 10/10 · Products、Hardware"
      },
      "url": "https://www.theverge.com/tech/1011996/microsoft-windows-rtx-spark-platform-changes-notepad",
      "page": null,
      "source": "The Verge · AI",
      "host": "theverge.com",
      "score": 97.2,
      "metrics": {
        "importance": 10,
        "weight": 4,
        "lang": "en"
      },
      "topics": [
        "Products",
        "Hardware"
      ],
      "publishedAt": "2026-10-15T14:30:00.000Z",
      "updatedAt": "2026-10-15T14:30:00.000Z",
      "rawId": "https://www.theverge.com/tech/1011996/microsoft-windows-rtx-spark-platform-changes-notepad",
      "image": "https://platform.theverge.com/wp-content/uploads/sites/2/2025/08/STK109_WINDOWS_A.jpg?quality=90&strip=all&crop=0%2C10.732984293194%2C100%2C78.534031413613&w=1200",
      "thumbnail": "https://platform.theverge.com/wp-content/uploads/sites/2/2025/08/STK109_WINDOWS_A.jpg?quality=90&strip=all&crop=0%2C10.732984293194%2C100%2C78.534031413613&w=1200"
    },
    {
      "id": "voices-pntihk",
      "sectionId": "voices",
      "kind": "voices",
      "kindLabel": {
        "en": "Voices",
        "zh-CN": "观点"
      },
      "route": "/voices",
      "accent": "#8b5cf6",
      "name": "Eric Glyman on Models / Agents",
      "title": "Eric Glyman on Models / Agents",
      "description": "Eric Glyman shared a note on models; open the source for full context.",
      "summary": "Eric Glyman shared a note on models; open the source for full context.",
      "desc": "Eric Glyman shared a note on models; open the source for full context.",
      "title_zh": "Eric Glyman 关于模型能力的观点",
      "summary_zh": "Eric Glyman 提到 模型能力 的一线判断，建议结合原帖查看完整语境。",
      "title_i18n": {
        "en": "Eric Glyman on Models / Agents",
        "zh-CN": "Eric Glyman 关于模型能力的观点"
      },
      "summary_i18n": {
        "en": "Eric Glyman shared a note on models; open the source for full context.",
        "zh-CN": "Eric Glyman 提到 模型能力 的一线判断，建议结合原帖查看完整语境。"
      },
      "reason_i18n": {
        "en": "@eglyman · signal 9.8/10",
        "zh-CN": "@eglyman · 信号 9.8/10"
      },
      "url": "https://x.com/eglyman/status/2110007713396613568",
      "page": null,
      "source": "@eglyman",
      "host": "x.com",
      "score": 96.9,
      "metrics": {
        "importance": 9.8,
        "likes": 0,
        "reposts": 58,
        "replies": 42,
        "views": 0
      },
      "topics": [
        "Models",
        "Agents",
        "Engineering"
      ],
      "publishedAt": "2026-10-13T14:00:15.000Z",
      "updatedAt": "2026-10-13T14:00:15.000Z",
      "rawId": "attentionvc-2110007713396613568"
    },
    {
      "id": "voices-niewm3",
      "sectionId": "voices",
      "kind": "voices",
      "kindLabel": {
        "en": "Voices",
        "zh-CN": "观点"
      },
      "route": "/voices",
      "accent": "#8b5cf6",
      "name": "Google Cloud on Models / Agents",
      "title": "Google Cloud on Models / Agents",
      "description": "Google Cloud shared a note on models; open the source for full context.",
      "summary": "Google Cloud shared a note on models; open the source for full context.",
      "desc": "Google Cloud shared a note on models; open the source for full context.",
      "title_zh": "Google Cloud 关于模型能力的观点",
      "summary_zh": "Google Cloud 提到 模型能力 的一线判断，建议结合原帖查看完整语境。",
      "title_i18n": {
        "en": "Google Cloud on Models / Agents",
        "zh-CN": "Google Cloud 关于模型能力的观点"
      },
      "summary_i18n": {
        "en": "Google Cloud shared a note on models; open the source for full context.",
        "zh-CN": "Google Cloud 提到 模型能力 的一线判断，建议结合原帖查看完整语境。"
      },
      "reason_i18n": {
        "en": "@googlecloud · signal 9.5/10",
        "zh-CN": "@googlecloud · 信号 9.5/10"
      },
      "url": "https://x.com/googlecloud/status/2110039621878882558",
      "page": null,
      "source": "@googlecloud",
      "host": "x.com",
      "score": 95.5,
      "metrics": {
        "importance": 9.5,
        "likes": 0,
        "reposts": 70,
        "replies": 19,
        "views": 0
      },
      "topics": [
        "Models",
        "Agents"
      ],
      "publishedAt": "2026-10-13T16:07:03.000Z",
      "updatedAt": "2026-10-13T16:07:03.000Z",
      "rawId": "attentionvc-2110039621878882558"
    },
    {
      "id": "news-6cadgs",
      "sectionId": "news",
      "kind": "news",
      "kindLabel": {
        "en": "News",
        "zh-CN": "新闻"
      },
      "route": "/ai-news",
      "accent": "#ef4444",
      "name": "Google wants Gemini 4 Argon to be the only AI model robots need",
      "title": "Google wants Gemini 4 Argon to be the only AI model robots need",
      "description": "Google's Gemini 4 Argon controls robots, generates simulations from photos or sketches, and evaluates training videos. On Google's own ERQA benchmark, it scores 87 percent. But th…",
      "summary": "Google's Gemini 4 Argon controls robots, generates simulations from photos or sketches, and evaluates training videos. On Google's own ERQA benchmark, it scores 87 percent. But th…",
      "desc": "Google's Gemini 4 Argon controls robots, generates simulations from photos or sketches, and evaluates training videos. On Google's own ERQA benchmark, it scores 87 percent. But th…",
      "title_zh": "Google wants Gemini 4 Argon to be the only AI model robots need",
      "summary_zh": "Google's Gemini 4 Argon controls robots, generates simulations from photos or sketches, and evaluates training videos. On Google's own ERQA benchmark, it scores 87 percent. But th…",
      "title_i18n": {
        "en": "Google wants Gemini 4 Argon to be the only AI model robots need",
        "zh-CN": "Google wants Gemini 4 Argon to be the only AI model robots need"
      },
      "summary_i18n": {
        "en": "Google's Gemini 4 Argon controls robots, generates simulations from photos or sketches, and evaluates training videos. On Google's own ERQA benchmark, it scores 87 percent. But th…",
        "zh-CN": "Google's Gemini 4 Argon controls robots, generates simulations from photos or sketches, and evaluates training videos. On Google's own ERQA benchmark, it scores 87 percent. But th…"
      },
      "reason_i18n": {
        "en": "The Decoder · impact 9/10 · Models, Research",
        "zh-CN": "The Decoder · 重要度 9/10 · Models、Research"
      },
      "url": "https://the-decoder.com/google-wants-gemini-4-argon-to-be-the-only-ai-model-robots-need/",
      "page": null,
      "source": "The Decoder",
      "host": "the-decoder.com",
      "score": 91.4,
      "metrics": {
        "importance": 9,
        "weight": 3,
        "lang": "en"
      },
      "topics": [
        "Models",
        "Research"
      ],
      "publishedAt": "2026-10-15T16:35:47.000Z",
      "updatedAt": "2026-10-15T16:35:47.000Z",
      "rawId": "https://the-decoder.com/google-wants-gemini-4-argon-to-be-the-only-ai-model-robots-need/",
      "image": "https://the-decoder.com/wp-content/uploads/2026/10/gemini-4-argon-robotics-03-spot-trash-bin.jpg",
      "thumbnail": "https://the-decoder.com/wp-content/uploads/2026/10/gemini-4-argon-robotics-03-spot-trash-bin.jpg"
    },
    {
      "id": "github-x9xv",
      "sectionId": "github",
      "kind": "github",
      "kindLabel": {
        "en": "GitHub",
        "zh-CN": "开源"
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      "route": "/github",
      "accent": "#10b981",
      "name": "KKKKhazix/AIHOT",
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      "description": "一个自己找热点、自己写日报的网站框架。把信源和精选标准换成你的，它就是你的行业热点站。",
      "summary": "一个自己找热点、自己写日报的网站框架。把信源和精选标准换成你的，它就是你的行业热点站。",
      "desc": "一个自己找热点、自己写日报的网站框架。把信源和精选标准换成你的，它就是你的行业热点站。",
      "title_zh": "AIHOT",
      "summary_zh": "一个自己找热点、自己写日报的网站框架。把信源和精选标准换成你的，它就是你的行业热点站。",
      "title_i18n": {
        "en": "KKKKhazix/AIHOT",
        "zh-CN": "AIHOT"
      },
      "summary_i18n": {
        "en": "一个自己找热点、自己写日报的网站框架。把信源和精选标准换成你的，它就是你的行业热点站。",
        "zh-CN": "一个自己找热点、自己写日报的网站框架。把信源和精选标准换成你的，它就是你的行业热点站。"
      },
      "reason_i18n": {
        "en": "7792 stars · TypeScript · new/rising",
        "zh-CN": "7792 星 · TypeScript · new/rising"
      },
      "url": "https://github.com/KKKKhazix/AIHOT",
      "page": null,
      "source": "GitHub",
      "host": "github.com",
      "score": 91,
      "metrics": {
        "stars": 7792,
        "forks": 1874,
        "language": "TypeScript",
        "badges": [
          "new",
          "rising",
          "hot"
        ]
      },
      "topics": [
        "ai",
        "chinese",
        "content-curation",
        "daily-digest",
        "docker-compose",
        "llm",
        "mcp",
        "news-aggregator"
      ],
      "publishedAt": "2026-09-28T23:40:05Z",
      "updatedAt": "2026-10-15T18:10:29Z",
      "rawId": "KKKKhazix/AIHOT"
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    {
      "id": "models-9tq5f1",
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      "kind": "models",
      "kindLabel": {
        "en": "Models",
        "zh-CN": "模型"
      },
      "route": "/models",
      "accent": "#f59e0b",
      "name": "Unlimited-OCR",
      "title": "Unlimited-OCR",
      "description": "Unlimited-OCR is a image-text-to-text by baidu.",
      "summary": "Unlimited-OCR is a image-text-to-text by baidu.",
      "desc": "Unlimited-OCR is a image-text-to-text by baidu.",
      "title_zh": "Unlimited-OCR",
      "summary_zh": "Unlimited-OCR",
      "title_i18n": {
        "en": "Unlimited-OCR",
        "zh-CN": "Unlimited-OCR"
      },
      "summary_i18n": {
        "en": "Unlimited-OCR is a image-text-to-text by baidu.",
        "zh-CN": "Unlimited-OCR"
      },
      "reason_i18n": {
        "en": "图文问答 · 多模态 · 4702 likes · 1163009 downloads",
        "zh-CN": "图文问答 · 多模态 · 4702 喜欢 · 1163009 下载"
      },
      "url": "https://huggingface.co/baidu/Unlimited-OCR",
      "page": null,
      "source": "HF Trending",
      "host": "huggingface.co",
      "score": 90.2,
      "metrics": {
        "likes": 4702,
        "downloads": 1163009,
        "trendingScore": 319,
        "pipeline": "image-text-to-text"
      },
      "topics": [
        "unlimited_ocr",
        "feature-extraction",
        "baidu",
        "vision-language",
        "ocr",
        "image-text-to-text",
        "multilingual",
        "arxiv:2606.23050"
      ],
      "publishedAt": "2026-06-19T09:40:33.000Z",
      "updatedAt": "2026-10-14T06:17:59.000Z",
      "rawId": "baidu/Unlimited-OCR"
    },
    {
      "id": "models-b4jatx",
      "sectionId": "models",
      "kind": "models",
      "kindLabel": {
        "en": "Models",
        "zh-CN": "模型"
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      "route": "/models",
      "accent": "#f59e0b",
      "name": "JEV-27B-VL",
      "title": "JEV-27B-VL",
      "description": "JEV-27B-VL is a image-text-to-text by autotrust.",
      "summary": "JEV-27B-VL is a image-text-to-text by autotrust.",
      "desc": "JEV-27B-VL is a image-text-to-text by autotrust.",
      "title_zh": "JEV-27B-VL",
      "summary_zh": "JEV-27B-VL",
      "title_i18n": {
        "en": "JEV-27B-VL",
        "zh-CN": "JEV-27B-VL"
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      "summary_i18n": {
        "en": "JEV-27B-VL is a image-text-to-text by autotrust.",
        "zh-CN": "JEV-27B-VL"
      },
      "reason_i18n": {
        "en": "图文问答 · 多模态 · 5543 likes · 1600363 downloads",
        "zh-CN": "图文问答 · 多模态 · 5543 喜欢 · 1600363 下载"
      },
      "url": "https://huggingface.co/autotrust/JEV-27B-VL",
      "page": null,
      "source": "HF Multimodal Trending",
      "host": "huggingface.co",
      "score": 90.2,
      "metrics": {
        "likes": 5543,
        "downloads": 1600363,
        "trendingScore": 143.5,
        "pipeline": "image-text-to-text"
      },
      "topics": [
        "qwen3_5",
        "image-text-to-text",
        "jev",
        "system-one",
        "system-two",
        "typed-decisions",
        "decision-model",
        "calibrated-probabilities"
      ],
      "publishedAt": "2026-09-30T05:02:23.000Z",
      "updatedAt": "2026-10-14T06:16:37.000Z",
      "rawId": "autotrust/JEV-27B-VL"
    },
    {
      "id": "models-1ire6xk",
      "sectionId": "models",
      "kind": "models",
      "kindLabel": {
        "en": "Models",
        "zh-CN": "模型"
      },
      "route": "/models",
      "accent": "#f59e0b",
      "name": "humanizer",
      "title": "humanizer",
      "description": "humanizer is a text-generation by jialinyyzz.",
      "summary": "humanizer is a text-generation by jialinyyzz.",
      "desc": "humanizer is a text-generation by jialinyyzz.",
      "title_zh": "humanizer",
      "summary_zh": "humanizer",
      "title_i18n": {
        "en": "humanizer",
        "zh-CN": "humanizer"
      },
      "summary_i18n": {
        "en": "humanizer is a text-generation by jialinyyzz.",
        "zh-CN": "humanizer"
      },
      "reason_i18n": {
        "en": "文本生成 · LLM · 1733 likes · 75253 downloads",
        "zh-CN": "文本生成 · LLM · 1733 喜欢 · 75253 下载"
      },
      "url": "https://huggingface.co/jialinyyzz/humanizer",
      "page": null,
      "source": "HF Trending",
      "host": "huggingface.co",
      "score": 89.3,
      "metrics": {
        "likes": 1733,
        "downloads": 75253,
        "trendingScore": 855,
        "pipeline": "text-generation"
      },
      "topics": [
        "gemma4_unified",
        "image-text-to-text",
        "humanizer",
        "text-rewriting",
        "rewriting",
        "paraphrase",
        "style-transfer",
        "llama.cpp"
      ],
      "publishedAt": "2026-09-11T05:00:16.000Z",
      "updatedAt": "2026-10-15T02:09:56.000Z",
      "rawId": "jialinyyzz/humanizer"
    },
    {
      "id": "github-1vptxfh",
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        "en": "GitHub",
        "zh-CN": "开源"
      },
      "route": "/github",
      "accent": "#10b981",
      "name": "ponytail",
      "title": "ponytail",
      "description": "Makes your AI agent think like the laziest senior dev in the room. The best code is the code you never wrote.",
      "summary": "Makes your AI agent think like the laziest senior dev in the room. The best code is the code you never wrote.",
      "desc": "Makes your AI agent think like the laziest senior dev in the room. The best code is the code you never wrote.",
      "title_zh": "ponytail",
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        "zh-CN": "开源公司研究 CLI，把公司名、官网域名和可选 ticker 生成带证据标签的商业模式报告"
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        "Competitive Analysis",
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      "desc": "Jeva is a decision-native architecture that jointly enforces null-awareness, permutation invariance, option scalability, and cross-request caching for structured decision-making.",
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        "en": "Jeva: System One Models from First Principles",
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      "accent": "#f97316",
      "name": "Ara",
      "title": "Ara",
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      "title_zh": "Ara",
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        "en": "Ara",
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        "en": "personal ai cloud computing for everyone",
        "zh-CN": "personal ai cloud computing for everyone"
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        "en": "infra · 已融资 · San Francisco, USA",
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      "desc": "Microsoft and Nvidia held a launch party for the first wave of RTX Spark laptops last week in San Francisco, where a new wave of AI PCs took center stage amid a packed crowd and a…",
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        "zh-CN": "Microsoft and Nvidia held a launch party for the first wave of RTX Spark laptops last week in San Francisco, where a new wave of AI PCs took center stage amid a packed crowd and a…"
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        "zh-CN": "The Verge · AI · 重要度 10/10 · Products、Hardware"
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      "page": null,
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      "host": "theverge.com",
      "score": 97.2,
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        "weight": 4,
        "lang": "en"
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        "Hardware"
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      "publishedAt": "2026-10-15T14:30:00.000Z",
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      "name": "Google Cloud on Models / Agents",
      "title": "Google Cloud on Models / Agents",
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      "summary": "Google Cloud shared a note on models; open the source for full context.",
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        "en": "Google Cloud on Models / Agents",
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      "name": "Google wants Gemini 4 Argon to be the only AI model robots need",
      "title": "Google wants Gemini 4 Argon to be the only AI model robots need",
      "description": "Google's Gemini 4 Argon controls robots, generates simulations from photos or sketches, and evaluates training videos. On Google's own ERQA benchmark, it scores 87 percent. But th…",
      "summary": "Google's Gemini 4 Argon controls robots, generates simulations from photos or sketches, and evaluates training videos. On Google's own ERQA benchmark, it scores 87 percent. But th…",
      "desc": "Google's Gemini 4 Argon controls robots, generates simulations from photos or sketches, and evaluates training videos. On Google's own ERQA benchmark, it scores 87 percent. But th…",
      "title_zh": "Google wants Gemini 4 Argon to be the only AI model robots need",
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      "title_i18n": {
        "en": "Google wants Gemini 4 Argon to be the only AI model robots need",
        "zh-CN": "Google wants Gemini 4 Argon to be the only AI model robots need"
      },
      "summary_i18n": {
        "en": "Google's Gemini 4 Argon controls robots, generates simulations from photos or sketches, and evaluates training videos. On Google's own ERQA benchmark, it scores 87 percent. But th…",
        "zh-CN": "Google's Gemini 4 Argon controls robots, generates simulations from photos or sketches, and evaluates training videos. On Google's own ERQA benchmark, it scores 87 percent. But th…"
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      "page": null,
      "source": "The Decoder",
      "host": "the-decoder.com",
      "score": 91.4,
      "metrics": {
        "importance": 9,
        "weight": 3,
        "lang": "en"
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        "Research"
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      "publishedAt": "2026-10-15T16:35:47.000Z",
      "updatedAt": "2026-10-15T16:35:47.000Z",
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        "zh-CN": "开源"
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      "route": "/github",
      "accent": "#10b981",
      "name": "KKKKhazix/AIHOT",
      "title": "KKKKhazix/AIHOT",
      "description": "一个自己找热点、自己写日报的网站框架。把信源和精选标准换成你的，它就是你的行业热点站。",
      "summary": "一个自己找热点、自己写日报的网站框架。把信源和精选标准换成你的，它就是你的行业热点站。",
      "desc": "一个自己找热点、自己写日报的网站框架。把信源和精选标准换成你的，它就是你的行业热点站。",
      "title_zh": "AIHOT",
      "summary_zh": "一个自己找热点、自己写日报的网站框架。把信源和精选标准换成你的，它就是你的行业热点站。",
      "title_i18n": {
        "en": "KKKKhazix/AIHOT",
        "zh-CN": "AIHOT"
      },
      "summary_i18n": {
        "en": "一个自己找热点、自己写日报的网站框架。把信源和精选标准换成你的，它就是你的行业热点站。",
        "zh-CN": "一个自己找热点、自己写日报的网站框架。把信源和精选标准换成你的，它就是你的行业热点站。"
      },
      "reason_i18n": {
        "en": "7792 stars · TypeScript · new/rising",
        "zh-CN": "7792 星 · TypeScript · new/rising"
      },
      "url": "https://github.com/KKKKhazix/AIHOT",
      "page": null,
      "source": "GitHub",
      "host": "github.com",
      "score": 91,
      "metrics": {
        "stars": 7792,
        "forks": 1874,
        "language": "TypeScript",
        "badges": [
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      },
      "topics": [
        "ai",
        "chinese",
        "content-curation",
        "daily-digest",
        "docker-compose",
        "llm",
        "mcp",
        "news-aggregator"
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      "publishedAt": "2026-09-28T23:40:05Z",
      "updatedAt": "2026-10-15T18:10:29Z",
      "rawId": "KKKKhazix/AIHOT"
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      "id": "models-9tq5f1",
      "sectionId": "models",
      "kind": "models",
      "kindLabel": {
        "en": "Models",
        "zh-CN": "模型"
      },
      "route": "/models",
      "accent": "#f59e0b",
      "name": "Unlimited-OCR",
      "title": "Unlimited-OCR",
      "description": "Unlimited-OCR is a image-text-to-text by baidu.",
      "summary": "Unlimited-OCR is a image-text-to-text by baidu.",
      "desc": "Unlimited-OCR is a image-text-to-text by baidu.",
      "title_zh": "Unlimited-OCR",
      "summary_zh": "Unlimited-OCR",
      "title_i18n": {
        "en": "Unlimited-OCR",
        "zh-CN": "Unlimited-OCR"
      },
      "summary_i18n": {
        "en": "Unlimited-OCR is a image-text-to-text by baidu.",
        "zh-CN": "Unlimited-OCR"
      },
      "reason_i18n": {
        "en": "图文问答 · 多模态 · 4702 likes · 1163009 downloads",
        "zh-CN": "图文问答 · 多模态 · 4702 喜欢 · 1163009 下载"
      },
      "url": "https://huggingface.co/baidu/Unlimited-OCR",
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      "host": "huggingface.co",
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      "metrics": {
        "likes": 4702,
        "downloads": 1163009,
        "trendingScore": 319,
        "pipeline": "image-text-to-text"
      },
      "topics": [
        "unlimited_ocr",
        "feature-extraction",
        "baidu",
        "vision-language",
        "ocr",
        "image-text-to-text",
        "multilingual",
        "arxiv:2606.23050"
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      "publishedAt": "2026-06-19T09:40:33.000Z",
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      "rawId": "baidu/Unlimited-OCR"
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      "route": "/models",
      "accent": "#f59e0b",
      "name": "JEV-27B-VL",
      "title": "JEV-27B-VL",
      "description": "JEV-27B-VL is a image-text-to-text by autotrust.",
      "summary": "JEV-27B-VL is a image-text-to-text by autotrust.",
      "desc": "JEV-27B-VL is a image-text-to-text by autotrust.",
      "title_zh": "JEV-27B-VL",
      "summary_zh": "JEV-27B-VL",
      "title_i18n": {
        "en": "JEV-27B-VL",
        "zh-CN": "JEV-27B-VL"
      },
      "summary_i18n": {
        "en": "JEV-27B-VL is a image-text-to-text by autotrust.",
        "zh-CN": "JEV-27B-VL"
      },
      "reason_i18n": {
        "en": "图文问答 · 多模态 · 5543 likes · 1600363 downloads",
        "zh-CN": "图文问答 · 多模态 · 5543 喜欢 · 1600363 下载"
      },
      "url": "https://huggingface.co/autotrust/JEV-27B-VL",
      "page": null,
      "source": "HF Multimodal Trending",
      "host": "huggingface.co",
      "score": 90.2,
      "metrics": {
        "likes": 5543,
        "downloads": 1600363,
        "trendingScore": 143.5,
        "pipeline": "image-text-to-text"
      },
      "topics": [
        "qwen3_5",
        "image-text-to-text",
        "jev",
        "system-one",
        "system-two",
        "typed-decisions",
        "decision-model",
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      ],
      "publishedAt": "2026-09-30T05:02:23.000Z",
      "updatedAt": "2026-10-14T06:16:37.000Z",
      "rawId": "autotrust/JEV-27B-VL"
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      "id": "models-1ire6xk",
      "sectionId": "models",
      "kind": "models",
      "kindLabel": {
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        "zh-CN": "模型"
      },
      "route": "/models",
      "accent": "#f59e0b",
      "name": "humanizer",
      "title": "humanizer",
      "description": "humanizer is a text-generation by jialinyyzz.",
      "summary": "humanizer is a text-generation by jialinyyzz.",
      "desc": "humanizer is a text-generation by jialinyyzz.",
      "title_zh": "humanizer",
      "summary_zh": "humanizer",
      "title_i18n": {
        "en": "humanizer",
        "zh-CN": "humanizer"
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      "summary_i18n": {
        "en": "humanizer is a text-generation by jialinyyzz.",
        "zh-CN": "humanizer"
      },
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        "en": "文本生成 · LLM · 1733 likes · 75253 downloads",
        "zh-CN": "文本生成 · LLM · 1733 喜欢 · 75253 下载"
      },
      "url": "https://huggingface.co/jialinyyzz/humanizer",
      "page": null,
      "source": "HF Trending",
      "host": "huggingface.co",
      "score": 89.3,
      "metrics": {
        "likes": 1733,
        "downloads": 75253,
        "trendingScore": 855,
        "pipeline": "text-generation"
      },
      "topics": [
        "gemma4_unified",
        "image-text-to-text",
        "humanizer",
        "text-rewriting",
        "rewriting",
        "paraphrase",
        "style-transfer",
        "llama.cpp"
      ],
      "publishedAt": "2026-09-11T05:00:16.000Z",
      "updatedAt": "2026-10-15T02:09:56.000Z",
      "rawId": "jialinyyzz/humanizer"
    },
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      "id": "github-1vptxfh",
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      "kind": "github",
      "kindLabel": {
        "en": "GitHub",
        "zh-CN": "开源"
      },
      "route": "/github",
      "accent": "#10b981",
      "name": "ponytail",
      "title": "ponytail",
      "description": "Makes your AI agent think like the laziest senior dev in the room. The best code is the code you never wrote.",
      "summary": "Makes your AI agent think like the laziest senior dev in the room. The best code is the code you never wrote.",
      "desc": "Makes your AI agent think like the laziest senior dev in the room. The best code is the code you never wrote.",
      "title_zh": "ponytail",
      "summary_zh": "ponytail",
      "title_i18n": {
        "en": "ponytail",
        "zh-CN": "ponytail"
      },
      "summary_i18n": {
        "en": "Makes your AI agent think like the laziest senior dev in the room. The best code is the code you never wrote.",
        "zh-CN": "ponytail"
      },
      "reason_i18n": {
        "en": "164431 stars · JavaScript · rising/classic",
        "zh-CN": "164431 星 · JavaScript · rising/classic"
      },
      "url": "https://github.com/DietrichGebert/ponytail",
      "page": null,
      "source": "GitHub",
      "host": "github.com",
      "score": 89,
      "metrics": {
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        "forks": 8864,
        "language": "JavaScript",
        "badges": [
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      },
      "topics": [
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        "ai-agents",
        "claude",
        "claude-code",
        "claude-code-plugin",
        "cursor-rules",
        "developer-tools",
        "llm"
      ],
      "publishedAt": "2026-06-12T00:52:37Z",
      "updatedAt": "2026-10-15T01:02:15Z",
      "rawId": "DietrichGebert/ponytail"
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      "id": "tools-1on4ggn",
      "sectionId": "tools",
      "kind": "tools",
      "kindLabel": {
        "en": "Tools",
        "zh-CN": "工具"
      },
      "route": "/tool/openbusiness",
      "accent": "#2563eb",
      "name": "OpenBusiness",
      "title": "OpenBusiness",
      "description": "开源公司研究 CLI，把公司名、官网域名和可选 ticker 生成带证据标签的商业模式报告",
      "summary": "开源公司研究 CLI，把公司名、官网域名和可选 ticker 生成带证据标签的商业模式报告",
      "desc": "开源公司研究 CLI，把公司名、官网域名和可选 ticker 生成带证据标签的商业模式报告",
      "title_zh": "OpenBusiness",
      "summary_zh": "开源公司研究 CLI，把公司名、官网域名和可选 ticker 生成带证据标签的商业模式报告",
      "title_i18n": {
        "en": "OpenBusiness",
        "zh-CN": "OpenBusiness"
      },
      "summary_i18n": {
        "en": "开源公司研究 CLI，把公司名、官网域名和可选 ticker 生成带证据标签的商业模式报告",
        "zh-CN": "开源公司研究 CLI，把公司名、官网域名和可选 ticker 生成带证据标签的商业模式报告"
      },
      "reason_i18n": {
        "en": "AI 科研 · 4.7 rating · hot",
        "zh-CN": "AI 科研 · 评分 4.7 · 热门"
      },
      "url": "https://github.com/wanikua/OpenBusiness",
      "page": "https://ai123box.com/tool/openbusiness",
      "source": "AI 科研",
      "host": "github.com",
      "score": 79.8,
      "metrics": {
        "rating": 4.7,
        "hot": true,
        "isNew": true,
        "users": null
      },
      "topics": [
        "Company Research",
        "Business Model",
        "Competitive Analysis",
        "Open Source",
        "Python CLI",
        "LangGraph"
      ],
      "publishedAt": "2026-06-02",
      "updatedAt": "2026-06-02",
      "rawId": "openbusiness"
    },
    {
      "id": "papers-rn18es",
      "sectionId": "papers",
      "kind": "papers",
      "kindLabel": {
        "en": "Papers",
        "zh-CN": "论文"
      },
      "route": "/papers",
      "accent": "#6366f1",
      "name": "Jeva: System One Models from First Principles",
      "title": "Jeva: System One Models from First Principles",
      "description": "Jeva is a decision-native architecture that jointly enforces null-awareness, permutation invariance, option scalability, and cross-request caching for structured decision-making.",
      "summary": "Jeva is a decision-native architecture that jointly enforces null-awareness, permutation invariance, option scalability, and cross-request caching for structured decision-making.",
      "desc": "Jeva is a decision-native architecture that jointly enforces null-awareness, permutation invariance, option scalability, and cross-request caching for structured decision-making.",
      "title_zh": "Jeva: System One Models from First Principles",
      "summary_zh": "Jeva is a decision-native architecture that jointly enforces null-awareness, permutation invariance, option scalability, and cross-request caching for structured decision-making.",
      "title_i18n": {
        "en": "Jeva: System One Models from First Principles",
        "zh-CN": "Jeva: System One Models from First Principles"
      },
      "summary_i18n": {
        "en": "Jeva is a decision-native architecture that jointly enforces null-awareness, permutation invariance, option scalability, and cross-request caching for structured decision-making.",
        "zh-CN": "Jeva is a decision-native architecture that jointly enforces null-awareness, permutation invariance, option scalability, and cross-request caching for structured decision-making."
      },
      "reason_i18n": {
        "en": "276 HF votes · 76 GitHub stars",
        "zh-CN": "HF 276 赞 · GitHub 76 星"
      },
      "url": "https://huggingface.co/papers/2610.18773",
      "page": null,
      "source": "HF Daily Papers",
      "host": "huggingface.co",
      "score": 72.8,
      "metrics": {
        "upvotes": 276,
        "comments": 4,
        "githubStars": 76
      },
      "topics": [
        "encoder",
        "decision stack",
        "symmetric aggregation",
        "permutation-equivariant",
        "shared scoring",
        "coverage estimate"
      ],
      "publishedAt": "2026-10-14T00:00:00.000Z",
      "updatedAt": "2026-10-14T00:00:00.000Z",
      "rawId": "2610.18773"
    },
    {
      "id": "startups-1qazeic",
      "sectionId": "startups",
      "kind": "startups",
      "kindLabel": {
        "en": "Startups",
        "zh-CN": "公司"
      },
      "route": "/startups",
      "accent": "#f97316",
      "name": "Ara",
      "title": "Ara",
      "description": "personal ai cloud computing for everyone",
      "summary": "personal ai cloud computing for everyone",
      "desc": "personal ai cloud computing for everyone",
      "title_zh": "Ara",
      "summary_zh": "personal ai cloud computing for everyone",
      "title_i18n": {
        "en": "Ara",
        "zh-CN": "Ara"
      },
      "summary_i18n": {
        "en": "personal ai cloud computing for everyone",
        "zh-CN": "personal ai cloud computing for everyone"
      },
      "reason_i18n": {
        "en": "infra · 已融资 · San Francisco, USA",
        "zh-CN": "infra · 已融资 · San Francisco, USA"
      },
      "url": "https://ara.so",
      "page": null,
      "source": "YC",
      "host": "ara.so",
      "score": 65,
      "metrics": {
        "track": "infra",
        "stage": "已融资",
        "year": 2026
      },
      "topics": [
        "infra",
        "已融资",
        "San Francisco, USA"
      ],
      "publishedAt": "2026-01-01T00:00:00.000Z",
      "updatedAt": "2026-01-01T00:00:00.000Z",
      "rawId": "https://ara.so"
    }
  ],
  "sections": [
    {
      "id": "news",
      "title": {
        "en": "News",
        "zh-CN": "新闻"
      },
      "route": "/ai-news",
      "accent": "#ef4444",
      "count": 50,
      "sourceUpdatedAt": "2026-10-16T00:56:02.551Z",
      "items": [
        {
          "id": "news-i6lt9",
          "sectionId": "news",
          "kind": "news",
          "kindLabel": {
            "en": "News",
            "zh-CN": "新闻"
          },
          "route": "/ai-news",
          "accent": "#ef4444",
          "name": "Meta launches an open-weight AI model that runs on Macs",
          "title": "Meta launches an open-weight AI model that runs on Macs",
          "description": "Meta and Apple have teamed up for the launch of the social media giant's newest model, Muse Spark Open, which will run locally on Apple silicon.",
          "summary": "Meta and Apple have teamed up for the launch of the social media giant's newest model, Muse Spark Open, which will run locally on Apple silicon.",
          "desc": "Meta and Apple have teamed up for the launch of the social media giant's newest model, Muse Spark Open, which will run locally on Apple silicon.",
          "title_zh": "Meta launches an open-weight AI model that runs on Macs",
          "summary_zh": "Meta and Apple have teamed up for the launch of the social media giant's newest model, Muse Spark Open, which will run locally on Apple silicon.",
          "title_i18n": {
            "en": "Meta launches an open-weight AI model that runs on Macs",
            "zh-CN": "Meta launches an open-weight AI model that runs on Macs"
          },
          "summary_i18n": {
            "en": "Meta and Apple have teamed up for the launch of the social media giant's newest model, Muse Spark Open, which will run locally on Apple silicon.",
            "zh-CN": "Meta and Apple have teamed up for the launch of the social media giant's newest model, Muse Spark Open, which will run locally on Apple silicon."
          },
          "reason_i18n": {
            "en": "TechCrunch · AI · impact 10/10 · Models, Products",
            "zh-CN": "TechCrunch · AI · 重要度 10/10 · Models、Products"
          },
          "url": "https://techcrunch.com/2026/10/15/meta-launches-an-open-weight-model-that-runs-on-macs/",
          "page": null,
          "source": "TechCrunch · AI",
          "host": "techcrunch.com",
          "score": 97.2,
          "metrics": {
            "importance": 10,
            "weight": 4,
            "lang": "en"
          },
          "topics": [
            "Models",
            "Products"
          ],
          "publishedAt": "2026-10-15T20:04:53.000Z",
          "updatedAt": "2026-10-15T20:04:53.000Z",
          "rawId": "https://techcrunch.com/2026/10/15/meta-launches-an-open-weight-model-that-runs-on-macs/",
          "image": "https://techcrunch.com/wp-content/uploads/2026/09/GettyImages-2296215726.jpg?resize=1200,799",
          "thumbnail": "https://techcrunch.com/wp-content/uploads/2026/09/GettyImages-2296215726.jpg?resize=1200,799"
        },
        {
          "id": "news-19bbzkr",
          "sectionId": "news",
          "kind": "news",
          "kindLabel": {
            "en": "News",
            "zh-CN": "新闻"
          },
          "route": "/ai-news",
          "accent": "#ef4444",
          "name": "How Windows is being optimized for Nvidia’s RTX Spark laptops",
          "title": "How Windows is being optimized for Nvidia’s RTX Spark laptops",
          "description": "Microsoft and Nvidia held a launch party for the first wave of RTX Spark laptops last week in San Francisco, where a new wave of AI PCs took center stage amid a packed crowd and a…",
          "summary": "Microsoft and Nvidia held a launch party for the first wave of RTX Spark laptops last week in San Francisco, where a new wave of AI PCs took center stage amid a packed crowd and a…",
          "desc": "Microsoft and Nvidia held a launch party for the first wave of RTX Spark laptops last week in San Francisco, where a new wave of AI PCs took center stage amid a packed crowd and a…",
          "title_zh": "How Windows is being optimized for Nvidia’s RTX Spark laptops",
          "summary_zh": "Microsoft and Nvidia held a launch party for the first wave of RTX Spark laptops last week in San Francisco, where a new wave of AI PCs took center stage amid a packed crowd and a…",
          "title_i18n": {
            "en": "How Windows is being optimized for Nvidia’s RTX Spark laptops",
            "zh-CN": "How Windows is being optimized for Nvidia’s RTX Spark laptops"
          },
          "summary_i18n": {
            "en": "Microsoft and Nvidia held a launch party for the first wave of RTX Spark laptops last week in San Francisco, where a new wave of AI PCs took center stage amid a packed crowd and a…",
            "zh-CN": "Microsoft and Nvidia held a launch party for the first wave of RTX Spark laptops last week in San Francisco, where a new wave of AI PCs took center stage amid a packed crowd and a…"
          },
          "reason_i18n": {
            "en": "The Verge · AI · impact 10/10 · Products, Hardware",
            "zh-CN": "The Verge · AI · 重要度 10/10 · Products、Hardware"
          },
          "url": "https://www.theverge.com/tech/1011996/microsoft-windows-rtx-spark-platform-changes-notepad",
          "page": null,
          "source": "The Verge · AI",
          "host": "theverge.com",
          "score": 97.2,
          "metrics": {
            "importance": 10,
            "weight": 4,
            "lang": "en"
          },
          "topics": [
            "Products",
            "Hardware"
          ],
          "publishedAt": "2026-10-15T14:30:00.000Z",
          "updatedAt": "2026-10-15T14:30:00.000Z",
          "rawId": "https://www.theverge.com/tech/1011996/microsoft-windows-rtx-spark-platform-changes-notepad",
          "image": "https://platform.theverge.com/wp-content/uploads/sites/2/2025/08/STK109_WINDOWS_A.jpg?quality=90&strip=all&crop=0%2C10.732984293194%2C100%2C78.534031413613&w=1200",
          "thumbnail": "https://platform.theverge.com/wp-content/uploads/sites/2/2025/08/STK109_WINDOWS_A.jpg?quality=90&strip=all&crop=0%2C10.732984293194%2C100%2C78.534031413613&w=1200"
        },
        {
          "id": "news-6cadgs",
          "sectionId": "news",
          "kind": "news",
          "kindLabel": {
            "en": "News",
            "zh-CN": "新闻"
          },
          "route": "/ai-news",
          "accent": "#ef4444",
          "name": "Google wants Gemini 4 Argon to be the only AI model robots need",
          "title": "Google wants Gemini 4 Argon to be the only AI model robots need",
          "description": "Google's Gemini 4 Argon controls robots, generates simulations from photos or sketches, and evaluates training videos. On Google's own ERQA benchmark, it scores 87 percent. But th…",
          "summary": "Google's Gemini 4 Argon controls robots, generates simulations from photos or sketches, and evaluates training videos. On Google's own ERQA benchmark, it scores 87 percent. But th…",
          "desc": "Google's Gemini 4 Argon controls robots, generates simulations from photos or sketches, and evaluates training videos. On Google's own ERQA benchmark, it scores 87 percent. But th…",
          "title_zh": "Google wants Gemini 4 Argon to be the only AI model robots need",
          "summary_zh": "Google's Gemini 4 Argon controls robots, generates simulations from photos or sketches, and evaluates training videos. On Google's own ERQA benchmark, it scores 87 percent. But th…",
          "title_i18n": {
            "en": "Google wants Gemini 4 Argon to be the only AI model robots need",
            "zh-CN": "Google wants Gemini 4 Argon to be the only AI model robots need"
          },
          "summary_i18n": {
            "en": "Google's Gemini 4 Argon controls robots, generates simulations from photos or sketches, and evaluates training videos. On Google's own ERQA benchmark, it scores 87 percent. But th…",
            "zh-CN": "Google's Gemini 4 Argon controls robots, generates simulations from photos or sketches, and evaluates training videos. On Google's own ERQA benchmark, it scores 87 percent. But th…"
          },
          "reason_i18n": {
            "en": "The Decoder · impact 9/10 · Models, Research",
            "zh-CN": "The Decoder · 重要度 9/10 · Models、Research"
          },
          "url": "https://the-decoder.com/google-wants-gemini-4-argon-to-be-the-only-ai-model-robots-need/",
          "page": null,
          "source": "The Decoder",
          "host": "the-decoder.com",
          "score": 91.4,
          "metrics": {
            "importance": 9,
            "weight": 3,
            "lang": "en"
          },
          "topics": [
            "Models",
            "Research"
          ],
          "publishedAt": "2026-10-15T16:35:47.000Z",
          "updatedAt": "2026-10-15T16:35:47.000Z",
          "rawId": "https://the-decoder.com/google-wants-gemini-4-argon-to-be-the-only-ai-model-robots-need/",
          "image": "https://the-decoder.com/wp-content/uploads/2026/10/gemini-4-argon-robotics-03-spot-trash-bin.jpg",
          "thumbnail": "https://the-decoder.com/wp-content/uploads/2026/10/gemini-4-argon-robotics-03-spot-trash-bin.jpg"
        },
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          "id": "news-1at8jj5",
          "sectionId": "news",
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          "kindLabel": {
            "en": "News",
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          },
          "route": "/ai-news",
          "accent": "#ef4444",
          "name": "Crossing the Threshold: How OpenAI and Anthropic Approach Autonomy and Safety in Financial Agents",
          "title": "Crossing the Threshold: How OpenAI and Anthropic Approach Autonomy and Safety in Financial Agents",
          "description": "At AI Engineer New York, frontier labs and financial firms explored how to give agents useful autonomy while keeping consequential decisions under control.",
          "summary": "At AI Engineer New York, frontier labs and financial firms explored how to give agents useful autonomy while keeping consequential decisions under control.",
          "desc": "At AI Engineer New York, frontier labs and financial firms explored how to give agents useful autonomy while keeping consequential decisions under control.",
          "title_zh": "Crossing the Threshold: How OpenAI and Anthropic Approach Autonomy and Safety in Financial Agents",
          "summary_zh": "At AI Engineer New York, frontier labs and financial firms explored how to give agents useful autonomy while keeping consequential decisions under control.",
          "title_i18n": {
            "en": "Crossing the Threshold: How OpenAI and Anthropic Approach Autonomy and Safety in Financial Agents",
            "zh-CN": "Crossing the Threshold: How OpenAI and Anthropic Approach Autonomy and Safety in Financial Agents"
          },
          "summary_i18n": {
            "en": "At AI Engineer New York, frontier labs and financial firms explored how to give agents useful autonomy while keeping consequential decisions under control.",
            "zh-CN": "At AI Engineer New York, frontier labs and financial firms explored how to give agents useful autonomy while keeping consequential decisions under control."
          },
          "reason_i18n": {
            "en": "Latent Space · impact 9/10 · Safety",
            "zh-CN": "Latent Space · 重要度 9/10 · Safety"
          },
          "url": "https://www.latent.space/p/crossing-the-threshold",
          "page": null,
          "source": "Latent Space",
          "host": "latent.space",
          "score": 90.1,
          "metrics": {
            "importance": 9,
            "weight": 5,
            "lang": "en"
          },
          "topics": [
            "Safety"
          ],
          "publishedAt": "2026-10-14T21:40:52.000Z",
          "updatedAt": "2026-10-14T21:40:52.000Z",
          "rawId": "https://www.latent.space/p/crossing-the-threshold",
          "image": "https://substackcdn.com/image/fetch/$s_!rtS2!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4aa91ce4-8821-4844-aa0f-56d4bd3c8d7e_1280x720.jpeg",
          "thumbnail": "https://substackcdn.com/image/fetch/$s_!rtS2!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4aa91ce4-8821-4844-aa0f-56d4bd3c8d7e_1280x720.jpeg"
        },
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          "id": "news-jwxkqm",
          "sectionId": "news",
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          "route": "/ai-news",
          "accent": "#ef4444",
          "name": "Anthropic's Q3 revenue hits $16.8B but growth slows ahead of planned November IPO",
          "title": "Anthropic's Q3 revenue hits $16.8B but growth slows ahead of planned November IPO",
          "description": "Anthropic hit $16.8 billion in third-quarter revenue, a 45 percent gain that still falls far short of Q2's 140 percent surge. With a November IPO approaching, some investors now e…",
          "summary": "Anthropic hit $16.8 billion in third-quarter revenue, a 45 percent gain that still falls far short of Q2's 140 percent surge. With a November IPO approaching, some investors now e…",
          "desc": "Anthropic hit $16.8 billion in third-quarter revenue, a 45 percent gain that still falls far short of Q2's 140 percent surge. With a November IPO approaching, some investors now e…",
          "title_zh": "Anthropic's Q3 revenue hits $16.8B but growth slows ahead of planned November IPO",
          "summary_zh": "Anthropic hit $16.8 billion in third-quarter revenue, a 45 percent gain that still falls far short of Q2's 140 percent surge. With a November IPO approaching, some investors now e…",
          "title_i18n": {
            "en": "Anthropic's Q3 revenue hits $16.8B but growth slows ahead of planned November IPO",
            "zh-CN": "Anthropic's Q3 revenue hits $16.8B but growth slows ahead of planned November IPO"
          },
          "summary_i18n": {
            "en": "Anthropic hit $16.8 billion in third-quarter revenue, a 45 percent gain that still falls far short of Q2's 140 percent surge. With a November IPO approaching, some investors now e…",
            "zh-CN": "Anthropic hit $16.8 billion in third-quarter revenue, a 45 percent gain that still falls far short of Q2's 140 percent surge. With a November IPO approaching, some investors now e…"
          },
          "reason_i18n": {
            "en": "The Decoder · impact 9/10 · Business",
            "zh-CN": "The Decoder · 重要度 9/10 · Business"
          },
          "url": "https://the-decoder.com/anthropics-q3-revenue-hits-16-8b-but-growth-slows-ahead-of-planned-november-ipo/",
          "page": null,
          "source": "The Decoder",
          "host": "the-decoder.com",
          "score": 89.9,
          "metrics": {
            "importance": 9,
            "weight": 3,
            "lang": "en"
          },
          "topics": [
            "Business"
          ],
          "publishedAt": "2026-10-15T09:50:43.000Z",
          "updatedAt": "2026-10-15T09:50:43.000Z",
          "rawId": "https://the-decoder.com/anthropics-q3-revenue-hits-16-8b-but-growth-slows-ahead-of-planned-november-ipo/",
          "image": "https://the-decoder.com/wp-content/uploads/2026/09/anthropic_amodei.png",
          "thumbnail": "https://the-decoder.com/wp-content/uploads/2026/09/anthropic_amodei.png"
        },
        {
          "id": "news-11n9uio",
          "sectionId": "news",
          "kind": "news",
          "kindLabel": {
            "en": "News",
            "zh-CN": "新闻"
          },
          "route": "/ai-news",
          "accent": "#ef4444",
          "name": "Google’s Universal Cart shopping tool launches in the US",
          "title": "Google’s Universal Cart shopping tool launches in the US",
          "description": "The new shopping tool is available in the U.S. across Google Search and the Gemini app.",
          "summary": "The new shopping tool is available in the U.S. across Google Search and the Gemini app.",
          "desc": "The new shopping tool is available in the U.S. across Google Search and the Gemini app.",
          "title_zh": "Google’s Universal Cart shopping tool launches in the US",
          "summary_zh": "The new shopping tool is available in the U.S. across Google Search and the Gemini app.",
          "title_i18n": {
            "en": "Google’s Universal Cart shopping tool launches in the US",
            "zh-CN": "Google’s Universal Cart shopping tool launches in the US"
          },
          "summary_i18n": {
            "en": "The new shopping tool is available in the U.S. across Google Search and the Gemini app.",
            "zh-CN": "The new shopping tool is available in the U.S. across Google Search and the Gemini app."
          },
          "reason_i18n": {
            "en": "TechCrunch · AI · impact 8/10 · Models, Products",
            "zh-CN": "TechCrunch · AI · 重要度 8/10 · Models、Products"
          },
          "url": "https://techcrunch.com/2026/10/15/googles-universal-cart-shopping-tool-launches-in-the-us/",
          "page": null,
          "source": "TechCrunch · AI",
          "host": "techcrunch.com",
          "score": 89.2,
          "metrics": {
            "importance": 8,
            "weight": 4,
            "lang": "en"
          },
          "topics": [
            "Models",
            "Products"
          ],
          "publishedAt": "2026-10-15T14:29:33.000Z",
          "updatedAt": "2026-10-15T14:29:33.000Z",
          "rawId": "https://techcrunch.com/2026/10/15/googles-universal-cart-shopping-tool-launches-in-the-us/",
          "image": "https://techcrunch.com/wp-content/uploads/2026/10/Google-UniversalCart.png?w=1200",
          "thumbnail": "https://techcrunch.com/wp-content/uploads/2026/10/Google-UniversalCart.png?w=1200"
        },
        {
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          "sectionId": "news",
          "kind": "news",
          "kindLabel": {
            "en": "News",
            "zh-CN": "新闻"
          },
          "route": "/ai-news",
          "accent": "#ef4444",
          "name": "ChatGPT can peek at your Chrome tabs now",
          "title": "ChatGPT can peek at your Chrome tabs now",
          "description": "OpenAI's ChatGPT Atlas browser is gone, but now ChatGPT's Google Chrome extension is getting some upgrades that allow it to use the browser tabs you already have open. With the ex…",
          "summary": "OpenAI's ChatGPT Atlas browser is gone, but now ChatGPT's Google Chrome extension is getting some upgrades that allow it to use the browser tabs you already have open. With the ex…",
          "desc": "OpenAI's ChatGPT Atlas browser is gone, but now ChatGPT's Google Chrome extension is getting some upgrades that allow it to use the browser tabs you already have open. With the ex…",
          "title_zh": "ChatGPT can peek at your Chrome tabs now",
          "summary_zh": "OpenAI's ChatGPT Atlas browser is gone, but now ChatGPT's Google Chrome extension is getting some upgrades that allow it to use the browser tabs you already have open. With the ex…",
          "title_i18n": {
            "en": "ChatGPT can peek at your Chrome tabs now",
            "zh-CN": "ChatGPT can peek at your Chrome tabs now"
          },
          "summary_i18n": {
            "en": "OpenAI's ChatGPT Atlas browser is gone, but now ChatGPT's Google Chrome extension is getting some upgrades that allow it to use the browser tabs you already have open. With the ex…",
            "zh-CN": "OpenAI's ChatGPT Atlas browser is gone, but now ChatGPT's Google Chrome extension is getting some upgrades that allow it to use the browser tabs you already have open. With the ex…"
          },
          "reason_i18n": {
            "en": "The Verge · AI · impact 8/10 · AI",
            "zh-CN": "The Verge · AI · 重要度 8/10 · AI"
          },
          "url": "https://www.theverge.com/ai-artificial-intelligence/1012510/openai-chatgpt-google-chrome-tabs",
          "page": null,
          "source": "The Verge · AI",
          "host": "theverge.com",
          "score": 87.7,
          "metrics": {
            "importance": 8,
            "weight": 4,
            "lang": "en"
          },
          "topics": [
            "AI"
          ],
          "publishedAt": "2026-10-15T22:43:23.000Z",
          "updatedAt": "2026-10-15T22:43:23.000Z",
          "rawId": "https://www.theverge.com/ai-artificial-intelligence/1012510/openai-chatgpt-google-chrome-tabs",
          "image": "https://platform.theverge.com/wp-content/uploads/sites/2/2026/10/Screenshot-2026-10-15-at-6.35.44-PM.png?quality=90&strip=all&crop=0%2C0%2C100%2C93.077370564282&w=1200",
          "thumbnail": "https://platform.theverge.com/wp-content/uploads/sites/2/2026/10/Screenshot-2026-10-15-at-6.35.44-PM.png?quality=90&strip=all&crop=0%2C0%2C100%2C93.077370564282&w=1200"
        },
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          "kind": "news",
          "kindLabel": {
            "en": "News",
            "zh-CN": "新闻"
          },
          "route": "/ai-news",
          "accent": "#ef4444",
          "name": "Anthropic is letting METR researchers look under the hood with employee-level access",
          "title": "Anthropic is letting METR researchers look under the hood with employee-level access",
          "description": "METR will publish its own assessments of Anthropic’s risks, incidents, and practices, and the frontier AI model maker has given up the right to block them.",
          "summary": "METR will publish its own assessments of Anthropic’s risks, incidents, and practices, and the frontier AI model maker has given up the right to block them.",
          "desc": "METR will publish its own assessments of Anthropic’s risks, incidents, and practices, and the frontier AI model maker has given up the right to block them.",
          "title_zh": "Anthropic is letting METR researchers look under the hood with employee-level access",
          "summary_zh": "METR will publish its own assessments of Anthropic’s risks, incidents, and practices, and the frontier AI model maker has given up the right to block them.",
          "title_i18n": {
            "en": "Anthropic is letting METR researchers look under the hood with employee-level access",
            "zh-CN": "Anthropic is letting METR researchers look under the hood with employee-level access"
          },
          "summary_i18n": {
            "en": "METR will publish its own assessments of Anthropic’s risks, incidents, and practices, and the frontier AI model maker has given up the right to block them.",
            "zh-CN": "METR will publish its own assessments of Anthropic’s risks, incidents, and practices, and the frontier AI model maker has given up the right to block them."
          },
          "reason_i18n": {
            "en": "TechCrunch · AI · impact 8/10 · Models",
            "zh-CN": "TechCrunch · AI · 重要度 8/10 · Models"
          },
          "url": "https://techcrunch.com/2026/10/15/anthropic-is-letting-metr-researchers-look-under-the-hood-with-employee-level-access/",
          "page": null,
          "source": "TechCrunch · AI",
          "host": "techcrunch.com",
          "score": 87.7,
          "metrics": {
            "importance": 8,
            "weight": 4,
            "lang": "en"
          },
          "topics": [
            "Models"
          ],
          "publishedAt": "2026-10-15T19:14:26.000Z",
          "updatedAt": "2026-10-15T19:14:26.000Z",
          "rawId": "https://techcrunch.com/2026/10/15/anthropic-is-letting-metr-researchers-look-under-the-hood-with-employee-level-access/",
          "image": "https://techcrunch.com/wp-content/uploads/2026/06/GettyImages-2278736523.jpeg?resize=1200,798",
          "thumbnail": "https://techcrunch.com/wp-content/uploads/2026/06/GettyImages-2278736523.jpeg?resize=1200,798"
        },
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          "sectionId": "news",
          "kind": "news",
          "kindLabel": {
            "en": "News",
            "zh-CN": "新闻"
          },
          "route": "/ai-news",
          "accent": "#ef4444",
          "name": "Meta and Apple team up to make Muse AI models run better on Macs",
          "title": "Meta and Apple team up to make Muse AI models run better on Macs",
          "description": "Meta announced the Muse Spark Open AI model today, claiming that it's the \"most capable US open-weight model to date.\" But the company also says that it's the \"the first model in…",
          "summary": "Meta announced the Muse Spark Open AI model today, claiming that it's the \"most capable US open-weight model to date.\" But the company also says that it's the \"the first model in…",
          "desc": "Meta announced the Muse Spark Open AI model today, claiming that it's the \"most capable US open-weight model to date.\" But the company also says that it's the \"the first model in…",
          "title_zh": "Meta and Apple team up to make Muse AI models run better on Macs",
          "summary_zh": "Meta announced the Muse Spark Open AI model today, claiming that it's the \"most capable US open-weight model to date.\" But the company also says that it's the \"the first model in…",
          "title_i18n": {
            "en": "Meta and Apple team up to make Muse AI models run better on Macs",
            "zh-CN": "Meta and Apple team up to make Muse AI models run better on Macs"
          },
          "summary_i18n": {
            "en": "Meta announced the Muse Spark Open AI model today, claiming that it's the \"most capable US open-weight model to date.\" But the company also says that it's the \"the first model in…",
            "zh-CN": "Meta announced the Muse Spark Open AI model today, claiming that it's the \"most capable US open-weight model to date.\" But the company also says that it's the \"the first model in…"
          },
          "reason_i18n": {
            "en": "The Verge · AI · impact 8/10 · Models",
            "zh-CN": "The Verge · AI · 重要度 8/10 · Models"
          },
          "url": "https://www.theverge.com/news/1012293/meta-apple-muse-spark-open-ai-model-mlx",
          "page": null,
          "source": "The Verge · AI",
          "host": "theverge.com",
          "score": 87.7,
          "metrics": {
            "importance": 8,
            "weight": 4,
            "lang": "en"
          },
          "topics": [
            "Models"
          ],
          "publishedAt": "2026-10-15T18:15:36.000Z",
          "updatedAt": "2026-10-15T18:15:36.000Z",
          "rawId": "https://www.theverge.com/news/1012293/meta-apple-muse-spark-open-ai-model-mlx",
          "image": "https://platform.theverge.com/wp-content/uploads/sites/2/2026/09/268742_Mac_Studio_AKrales_0025.jpg?quality=90&strip=all&crop=0%2C10.732984293194%2C100%2C78.534031413613&w=1200",
          "thumbnail": "https://platform.theverge.com/wp-content/uploads/sites/2/2026/09/268742_Mac_Studio_AKrales_0025.jpg?quality=90&strip=all&crop=0%2C10.732984293194%2C100%2C78.534031413613&w=1200"
        },
        {
          "id": "news-15l1ajn",
          "sectionId": "news",
          "kind": "news",
          "kindLabel": {
            "en": "News",
            "zh-CN": "新闻"
          },
          "route": "/ai-news",
          "accent": "#ef4444",
          "name": "Sullivan & Cromwell brings legal expertise into agreement review",
          "title": "Sullivan & Cromwell brings legal expertise into agreement review",
          "description": "In collaboration with OpenAI, Sullivan & Cromwell developed Agreement Analyzer, an AI tool for identifying issues in a draft agreement.",
          "summary": "In collaboration with OpenAI, Sullivan & Cromwell developed Agreement Analyzer, an AI tool for identifying issues in a draft agreement.",
          "desc": "In collaboration with OpenAI, Sullivan & Cromwell developed Agreement Analyzer, an AI tool for identifying issues in a draft agreement.",
          "title_zh": "Sullivan & Cromwell brings legal expertise into agreement review",
          "summary_zh": "In collaboration with OpenAI, Sullivan & Cromwell developed Agreement Analyzer, an AI tool for identifying issues in a draft agreement.",
          "title_i18n": {
            "en": "Sullivan & Cromwell brings legal expertise into agreement review",
            "zh-CN": "Sullivan & Cromwell brings legal expertise into agreement review"
          },
          "summary_i18n": {
            "en": "In collaboration with OpenAI, Sullivan & Cromwell developed Agreement Analyzer, an AI tool for identifying issues in a draft agreement.",
            "zh-CN": "In collaboration with OpenAI, Sullivan & Cromwell developed Agreement Analyzer, an AI tool for identifying issues in a draft agreement."
          },
          "reason_i18n": {
            "en": "OpenAI · impact 8/10 · AI",
            "zh-CN": "OpenAI · 重要度 8/10 · AI"
          },
          "url": "https://openai.com/index/sullivan-cromwell",
          "page": null,
          "source": "OpenAI",
          "host": "openai.com",
          "score": 86.1,
          "metrics": {
            "importance": 8,
            "weight": 5,
            "lang": "en"
          },
          "topics": [
            "AI"
          ],
          "publishedAt": "2026-10-15T00:00:00.000Z",
          "updatedAt": "2026-10-15T00:00:00.000Z",
          "rawId": "https://openai.com/index/sullivan-cromwell"
        },
        {
          "id": "news-wd7wnq",
          "sectionId": "news",
          "kind": "news",
          "kindLabel": {
            "en": "News",
            "zh-CN": "新闻"
          },
          "route": "/ai-news",
          "accent": "#ef4444",
          "name": "Nvidia's Nemotron 3.5 Super VL is an open model built to run enterprise AI agents nonstop",
          "title": "Nvidia's Nemotron 3.5 Super VL is an open model built to run enterprise AI agents nonstop",
          "description": "With Nemotron 3.5 Super VL, Nvidia is releasing an open multimodal model with 120 billion parameters for enterprise agents. Two compact Bolt models bring agents to local machines…",
          "summary": "With Nemotron 3.5 Super VL, Nvidia is releasing an open multimodal model with 120 billion parameters for enterprise agents. Two compact Bolt models bring agents to local machines…",
          "desc": "With Nemotron 3.5 Super VL, Nvidia is releasing an open multimodal model with 120 billion parameters for enterprise agents. Two compact Bolt models bring agents to local machines…",
          "title_zh": "Nvidia's Nemotron 3.5 Super VL is an open model built to run enterprise AI agents nonstop",
          "summary_zh": "With Nemotron 3.5 Super VL, Nvidia is releasing an open multimodal model with 120 billion parameters for enterprise agents. Two compact Bolt models bring agents to local machines…",
          "title_i18n": {
            "en": "Nvidia's Nemotron 3.5 Super VL is an open model built to run enterprise AI agents nonstop",
            "zh-CN": "Nvidia's Nemotron 3.5 Super VL is an open model built to run enterprise AI agents nonstop"
          },
          "summary_i18n": {
            "en": "With Nemotron 3.5 Super VL, Nvidia is releasing an open multimodal model with 120 billion parameters for enterprise agents. Two compact Bolt models bring agents to local machines…",
            "zh-CN": "With Nemotron 3.5 Super VL, Nvidia is releasing an open multimodal model with 120 billion parameters for enterprise agents. Two compact Bolt models bring agents to local machines…"
          },
          "reason_i18n": {
            "en": "The Decoder · impact 7/10 · Models, Hardware",
            "zh-CN": "The Decoder · 重要度 7/10 · Models、Hardware"
          },
          "url": "https://the-decoder.com/nvidias-nemotron-3-5-super-vl-is-an-open-model-built-to-run-enterprise-ai-agents-nonstop/",
          "page": null,
          "source": "The Decoder",
          "host": "the-decoder.com",
          "score": 84.9,
          "metrics": {
            "importance": 7,
            "weight": 3,
            "lang": "en"
          },
          "topics": [
            "Models",
            "Hardware",
            "Media"
          ],
          "publishedAt": "2026-10-15T16:09:44.000Z",
          "updatedAt": "2026-10-15T16:09:44.000Z",
          "rawId": "https://the-decoder.com/nvidias-nemotron-3-5-super-vl-is-an-open-model-built-to-run-enterprise-ai-agents-nonstop/",
          "image": "https://the-decoder.com/wp-content/uploads/2026/08/nvidia_logo.png",
          "thumbnail": "https://the-decoder.com/wp-content/uploads/2026/08/nvidia_logo.png"
        },
        {
          "id": "news-1hiertd",
          "sectionId": "news",
          "kind": "news",
          "kindLabel": {
            "en": "News",
            "zh-CN": "新闻"
          },
          "route": "/ai-news",
          "accent": "#ef4444",
          "name": "Meta’s Muse Spark Open now available in Amazon SageMaker JumpStart",
          "title": "Meta’s Muse Spark Open now available in Amazon SageMaker JumpStart",
          "description": "Muse Spark Open, Meta's coding-optimized open-weight model, is now available in Amazon SageMaker JumpStart. Deploy it to a SageMaker AI endpoint in your own AWS account using Sage…",
          "summary": "Muse Spark Open, Meta's coding-optimized open-weight model, is now available in Amazon SageMaker JumpStart. Deploy it to a SageMaker AI endpoint in your own AWS account using Sage…",
          "desc": "Muse Spark Open, Meta's coding-optimized open-weight model, is now available in Amazon SageMaker JumpStart. Deploy it to a SageMaker AI endpoint in your own AWS account using Sage…",
          "title_zh": "Meta’s Muse Spark Open now available in Amazon SageMaker JumpStart",
          "summary_zh": "Muse Spark Open, Meta's coding-optimized open-weight model, is now available in Amazon SageMaker JumpStart. Deploy it to a SageMaker AI endpoint in your own AWS account using Sage…",
          "title_i18n": {
            "en": "Meta’s Muse Spark Open now available in Amazon SageMaker JumpStart",
            "zh-CN": "Meta’s Muse Spark Open now available in Amazon SageMaker JumpStart"
          },
          "summary_i18n": {
            "en": "Muse Spark Open, Meta's coding-optimized open-weight model, is now available in Amazon SageMaker JumpStart. Deploy it to a SageMaker AI endpoint in your own AWS account using Sage…",
            "zh-CN": "Muse Spark Open, Meta's coding-optimized open-weight model, is now available in Amazon SageMaker JumpStart. Deploy it to a SageMaker AI endpoint in your own AWS account using Sage…"
          },
          "reason_i18n": {
            "en": "AWS ML Blog · impact 7/10 · Models",
            "zh-CN": "AWS ML Blog · 重要度 7/10 · Models"
          },
          "url": "https://aws.amazon.com/blogs/machine-learning/metas-muse-spark-open-now-available-in-amazon-sagemaker-jumpstart/",
          "page": null,
          "source": "AWS ML Blog",
          "host": "aws.amazon.com",
          "score": 81.9,
          "metrics": {
            "importance": 7,
            "weight": 3,
            "lang": "en"
          },
          "topics": [
            "Models"
          ],
          "publishedAt": "2026-10-15T17:43:35.000Z",
          "updatedAt": "2026-10-15T17:43:35.000Z",
          "rawId": "https://aws.amazon.com/blogs/machine-learning/metas-muse-spark-open-now-available-in-amazon-sagemaker-jumpstart/",
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          "name": "Report: FTC probes Anthropic, OpenAI after Trump dismissed AI fears as “hoax”",
          "title": "Report: FTC probes Anthropic, OpenAI after Trump dismissed AI fears as “hoax”",
          "description": "FTC may fine AI firms on par with companies that delay data breach reports.",
          "summary": "FTC may fine AI firms on par with companies that delay data breach reports.",
          "desc": "FTC may fine AI firms on par with companies that delay data breach reports.",
          "title_zh": "Report: FTC probes Anthropic, OpenAI after Trump dismissed AI fears as “hoax”",
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          "name": "[AINews] Modal’s $1B ARR, $16B Series D makes it NYC’s newest AI Infra Decacorn, 3x usage and 3x valuation in 5 months, 15x in 1 yr",
          "title": "[AINews] Modal’s $1B ARR, $16B Series D makes it NYC’s newest AI Infra Decacorn, 3x usage and 3x valuation in 5 months, 15x in 1 yr",
          "description": "Sandboxes are all you need.",
          "summary": "Sandboxes are all you need.",
          "desc": "Sandboxes are all you need.",
          "title_zh": "[AINews] Modal’s $1B ARR, $16B Series D makes it NYC’s newest AI Infra Decacorn, 3x usage and 3x valuation in 5 months, 15x in 1 yr",
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            "zh-CN": "Latent Space · 重要度 6/10 · AI"
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          "name": "Jeva: System One Models from First Principles",
          "title": "Jeva: System One Models from First Principles",
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          "desc": "Jeva is a decision-native architecture that jointly enforces null-awareness, permutation invariance, option scalability, and cross-request caching for structured decision-making.",
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          "title": "Player-1: Learning to Play from Synthetic Experience",
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          "desc": "Player-1 improves game control by combining supervised fine-tuning on gameplay trajectories with reinforcement learning across synthetic environments, achieving substantial gains over demonstration-only baselines.",
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          "url": "https://huggingface.co/papers/2610.20584",
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          "name": "LeWAM: End-to-End World and Action Modeling with JEPAs",
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          "description": "LeWAM is an end-to-end joint-embedding predictive architecture that jointly learns latent dynamics and action flow matching for efficient planning and direct control.",
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          "desc": "LeWAM is an end-to-end joint-embedding predictive architecture that jointly learns latent dynamics and action flow matching for efficient planning and direct control.",
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            "zh-CN": "HF 65 赞 · GitHub 240 星"
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          "url": "https://huggingface.co/papers/2610.12818",
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            "JEPA",
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            "latent dynamics"
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          "rawId": "2610.12818"
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          "description": "Multimodal language models use an intrinsic linear subspace called S-Space to encode object coordinates, supporting spatial perception, reasoning, and response generation, while also revealing bottlenecks in viewpoint transformation.",
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          "desc": "Multimodal language models use an intrinsic linear subspace called S-Space to encode object coordinates, supporting spatial perception, reasoning, and response generation, while also revealing bottlenecks in viewpoint transformation.",
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            "zh-CN": "Multimodal language models use an intrinsic linear subspace called S-Space to encode object coordinates, supporting spatial perception, reasoning, and response generation, while also revealing bottlenecks in viewpoint transformation."
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            "spatial reasoning",
            "causal interventions"
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          "publishedAt": "2026-10-12T00:00:00.000Z",
          "updatedAt": "2026-10-12T00:00:00.000Z",
          "rawId": "2610.13527"
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          "name": "Urbex: Agentic Spatial Grounding for City-Scale 3D Scenes",
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          "description": "Urbex reframes city-scale 3D grounding as an active search process using a vision-language agent with reinforcement learning, improving accuracy and speed without precomputed proposals.",
          "summary": "Urbex reframes city-scale 3D grounding as an active search process using a vision-language agent with reinforcement learning, improving accuracy and speed without precomputed proposals.",
          "desc": "Urbex reframes city-scale 3D grounding as an active search process using a vision-language agent with reinforcement learning, improving accuracy and speed without precomputed proposals.",
          "title_zh": "Urbex: Agentic Spatial Grounding for City-Scale 3D Scenes",
          "summary_zh": "Urbex reframes city-scale 3D grounding as an active search process using a vision-language agent with reinforcement learning, improving accuracy and speed without precomputed proposals.",
          "title_i18n": {
            "en": "Urbex: Agentic Spatial Grounding for City-Scale 3D Scenes",
            "zh-CN": "Urbex: Agentic Spatial Grounding for City-Scale 3D Scenes"
          },
          "summary_i18n": {
            "en": "Urbex reframes city-scale 3D grounding as an active search process using a vision-language agent with reinforcement learning, improving accuracy and speed without precomputed proposals.",
            "zh-CN": "Urbex reframes city-scale 3D grounding as an active search process using a vision-language agent with reinforcement learning, improving accuracy and speed without precomputed proposals."
          },
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            "zh-CN": "HF 116 赞 · GitHub 92 星"
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          "url": "https://huggingface.co/papers/2610.18356",
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          "score": 67.7,
          "metrics": {
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            "VLM agent",
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            "reinforcement learning",
            "bounding box",
            "cross-attention"
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          "publishedAt": "2026-10-14T00:00:00.000Z",
          "updatedAt": "2026-10-14T00:00:00.000Z",
          "rawId": "2610.18356"
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          "accent": "#6366f1",
          "name": "DiffusionART: Efficient, Stable and Scalable Reinforcement of Diffusion Models",
          "title": "DiffusionART: Efficient, Stable and Scalable Reinforcement of Diffusion Models",
          "description": "DiffusionART improves reinforcement learning fine-tuning of diffusion models by removing hidden consistency forces and enabling stable, efficient training without EMA tuning or differentiable rewards.",
          "summary": "DiffusionART improves reinforcement learning fine-tuning of diffusion models by removing hidden consistency forces and enabling stable, efficient training without EMA tuning or differentiable rewards.",
          "desc": "DiffusionART improves reinforcement learning fine-tuning of diffusion models by removing hidden consistency forces and enabling stable, efficient training without EMA tuning or differentiable rewards.",
          "title_zh": "DiffusionART: Efficient, Stable and Scalable Reinforcement of Diffusion Models",
          "summary_zh": "DiffusionART improves reinforcement learning fine-tuning of diffusion models by removing hidden consistency forces and enabling stable, efficient training without EMA tuning or differentiable rewards.",
          "title_i18n": {
            "en": "DiffusionART: Efficient, Stable and Scalable Reinforcement of Diffusion Models",
            "zh-CN": "DiffusionART: Efficient, Stable and Scalable Reinforcement of Diffusion Models"
          },
          "summary_i18n": {
            "en": "DiffusionART improves reinforcement learning fine-tuning of diffusion models by removing hidden consistency forces and enabling stable, efficient training without EMA tuning or differentiable rewards.",
            "zh-CN": "DiffusionART improves reinforcement learning fine-tuning of diffusion models by removing hidden consistency forces and enabling stable, efficient training without EMA tuning or differentiable rewards."
          },
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            "zh-CN": "HF 148 赞 · GitHub 130 星"
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          "url": "https://huggingface.co/papers/2610.12572",
          "page": null,
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          "score": 67.5,
          "metrics": {
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            "comments": 4,
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            "Flow-GRPO",
            "DiffusionNFT",
            "exponential-moving-average",
            "NFT-style objectives"
          ],
          "publishedAt": "2026-10-12T00:00:00.000Z",
          "updatedAt": "2026-10-12T00:00:00.000Z",
          "rawId": "2610.12572"
        },
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          "sectionId": "papers",
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            "en": "Papers",
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          "accent": "#6366f1",
          "name": "Agent-Reflex: Teaching Language Agents to Act Reflectively",
          "title": "Agent-Reflex: Teaching Language Agents to Act Reflectively",
          "description": "Agent-Reflex trains interactive agents to reflect and recover from noisy tool feedback and user corrections through skill discovery, supervised fine-tuning, and on-policy optimization.",
          "summary": "Agent-Reflex trains interactive agents to reflect and recover from noisy tool feedback and user corrections through skill discovery, supervised fine-tuning, and on-policy optimization.",
          "desc": "Agent-Reflex trains interactive agents to reflect and recover from noisy tool feedback and user corrections through skill discovery, supervised fine-tuning, and on-policy optimization.",
          "title_zh": "Agent-Reflex: Teaching Language Agents to Act Reflectively",
          "summary_zh": "Agent-Reflex trains interactive agents to reflect and recover from noisy tool feedback and user corrections through skill discovery, supervised fine-tuning, and on-policy optimization.",
          "title_i18n": {
            "en": "Agent-Reflex: Teaching Language Agents to Act Reflectively",
            "zh-CN": "Agent-Reflex: Teaching Language Agents to Act Reflectively"
          },
          "summary_i18n": {
            "en": "Agent-Reflex trains interactive agents to reflect and recover from noisy tool feedback and user corrections through skill discovery, supervised fine-tuning, and on-policy optimization.",
            "zh-CN": "Agent-Reflex trains interactive agents to reflect and recover from noisy tool feedback and user corrections through skill discovery, supervised fine-tuning, and on-policy optimization."
          },
          "reason_i18n": {
            "en": "49 HF votes · 75 GitHub stars",
            "zh-CN": "HF 49 赞 · GitHub 75 星"
          },
          "url": "https://huggingface.co/papers/2610.17953",
          "page": null,
          "source": "HF Daily Papers",
          "host": "huggingface.co",
          "score": 67,
          "metrics": {
            "upvotes": 49,
            "comments": 3,
            "githubStars": 75
          },
          "topics": [
            "Agent-Reflex",
            "Reflection Skill Discovery",
            "Reflective Supervised Fine-Tuning",
            "On-Policy Reflective Policy Optimization",
            "long-horizon reasoning",
            "tool-use"
          ],
          "publishedAt": "2026-10-14T00:00:00.000Z",
          "updatedAt": "2026-10-14T00:00:00.000Z",
          "rawId": "2610.17953"
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          "name": "GroundingPI: A Grounding Foundation Model towards Physical Intelligence with Visual Primitives",
          "title": "GroundingPI: A Grounding Foundation Model towards Physical Intelligence with Visual Primitives",
          "description": "GroundingPI is a 4B grounding foundation model that improves precise object localization and downstream robotic and autonomous driving performance through multimodal pretraining and reinforcement learning.",
          "summary": "GroundingPI is a 4B grounding foundation model that improves precise object localization and downstream robotic and autonomous driving performance through multimodal pretraining and reinforcement learning.",
          "desc": "GroundingPI is a 4B grounding foundation model that improves precise object localization and downstream robotic and autonomous driving performance through multimodal pretraining and reinforcement learning.",
          "title_zh": "GroundingPI: A Grounding Foundation Model towards Physical Intelligence with Visual Primitives",
          "summary_zh": "GroundingPI is a 4B grounding foundation model that improves precise object localization and downstream robotic and autonomous driving performance through multimodal pretraining and reinforcement learning.",
          "title_i18n": {
            "en": "GroundingPI: A Grounding Foundation Model towards Physical Intelligence with Visual Primitives",
            "zh-CN": "GroundingPI: A Grounding Foundation Model towards Physical Intelligence with Visual Primitives"
          },
          "summary_i18n": {
            "en": "GroundingPI is a 4B grounding foundation model that improves precise object localization and downstream robotic and autonomous driving performance through multimodal pretraining and reinforcement learning.",
            "zh-CN": "GroundingPI is a 4B grounding foundation model that improves precise object localization and downstream robotic and autonomous driving performance through multimodal pretraining and reinforcement learning."
          },
          "reason_i18n": {
            "en": "314 HF votes · 93 GitHub stars",
            "zh-CN": "HF 314 赞 · GitHub 93 星"
          },
          "url": "https://huggingface.co/papers/2609.39601",
          "page": null,
          "source": "HF Daily Papers",
          "host": "huggingface.co",
          "score": 67,
          "metrics": {
            "upvotes": 314,
            "comments": 2,
            "githubStars": 93
          },
          "topics": [
            "vision-language-action",
            "world-action models",
            "grounding foundation model",
            "quantized coordinates",
            "multimodal pretraining",
            "supervised fine-tuning"
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          "publishedAt": "2026-10-13T00:00:00.000Z",
          "updatedAt": "2026-10-13T00:00:00.000Z",
          "rawId": "2609.39601"
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          "accent": "#6366f1",
          "name": "Vela: User Behavior as Supervision Sparks LLM Intelligence",
          "title": "Vela: User Behavior as Supervision Sparks LLM Intelligence",
          "description": "Vela converts real-world user behavior records into training tasks and trajectories, improving large language model performance on user-related and general benchmarks through supervised fine-tuning and reinforcement learning.",
          "summary": "Vela converts real-world user behavior records into training tasks and trajectories, improving large language model performance on user-related and general benchmarks through supervised fine-tuning and reinforcement learning.",
          "desc": "Vela converts real-world user behavior records into training tasks and trajectories, improving large language model performance on user-related and general benchmarks through supervised fine-tuning and reinforcement learning.",
          "title_zh": "Vela: User Behavior as Supervision Sparks LLM Intelligence",
          "summary_zh": "Vela converts real-world user behavior records into training tasks and trajectories, improving large language model performance on user-related and general benchmarks through supervised fine-tuning and reinforcement learning.",
          "title_i18n": {
            "en": "Vela: User Behavior as Supervision Sparks LLM Intelligence",
            "zh-CN": "Vela: User Behavior as Supervision Sparks LLM Intelligence"
          },
          "summary_i18n": {
            "en": "Vela converts real-world user behavior records into training tasks and trajectories, improving large language model performance on user-related and general benchmarks through supervised fine-tuning and reinforcement learning.",
            "zh-CN": "Vela converts real-world user behavior records into training tasks and trajectories, improving large language model performance on user-related and general benchmarks through supervised fine-tuning and reinforcement learning."
          },
          "reason_i18n": {
            "en": "71 HF votes · 17 GitHub stars",
            "zh-CN": "HF 71 赞 · GitHub 17 星"
          },
          "url": "https://huggingface.co/papers/2610.14709",
          "page": null,
          "source": "HF Daily Papers",
          "host": "huggingface.co",
          "score": 66.5,
          "metrics": {
            "upvotes": 71,
            "comments": 6,
            "githubStars": 17
          },
          "topics": [
            "large language models",
            "user behavior supervision",
            "Vela",
            "fine-tuning",
            "reinforcement learning",
            "Qwen3.6-35B-A3B"
          ],
          "publishedAt": "2026-10-14T00:00:00.000Z",
          "updatedAt": "2026-10-14T00:00:00.000Z",
          "rawId": "2610.14709"
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          "accent": "#6366f1",
          "name": "Multics: Generative Multi-Player World Model with Hierarchical Communication",
          "title": "Multics: Generative Multi-Player World Model with Hierarchical Communication",
          "description": "Multics introduces hierarchical group-level communication and dynamic grouping to scale multi-player world models efficiently while reducing interference and maintaining consistency.",
          "summary": "Multics introduces hierarchical group-level communication and dynamic grouping to scale multi-player world models efficiently while reducing interference and maintaining consistency.",
          "desc": "Multics introduces hierarchical group-level communication and dynamic grouping to scale multi-player world models efficiently while reducing interference and maintaining consistency.",
          "title_zh": "Multics: Generative Multi-Player World Model with Hierarchical Communication",
          "summary_zh": "Multics introduces hierarchical group-level communication and dynamic grouping to scale multi-player world models efficiently while reducing interference and maintaining consistency.",
          "title_i18n": {
            "en": "Multics: Generative Multi-Player World Model with Hierarchical Communication",
            "zh-CN": "Multics: Generative Multi-Player World Model with Hierarchical Communication"
          },
          "summary_i18n": {
            "en": "Multics introduces hierarchical group-level communication and dynamic grouping to scale multi-player world models efficiently while reducing interference and maintaining consistency.",
            "zh-CN": "Multics introduces hierarchical group-level communication and dynamic grouping to scale multi-player world models efficiently while reducing interference and maintaining consistency."
          },
          "reason_i18n": {
            "en": "57 HF votes · 68 GitHub stars",
            "zh-CN": "HF 57 赞 · GitHub 68 星"
          },
          "url": "https://huggingface.co/papers/2610.17201",
          "page": null,
          "source": "HF Daily Papers",
          "host": "huggingface.co",
          "score": 66.2,
          "metrics": {
            "upvotes": 57,
            "comments": 2,
            "githubStars": 68
          },
          "topics": [
            "multi-player interactive world models",
            "hierarchical communication",
            "group tokens",
            "self-attention",
            "global tokens",
            "budget-constrained dynamic grouping"
          ],
          "publishedAt": "2026-10-14T00:00:00.000Z",
          "updatedAt": "2026-10-14T00:00:00.000Z",
          "rawId": "2610.17201"
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          "id": "papers-rmz0ls",
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          "route": "/papers",
          "accent": "#6366f1",
          "name": "More Methods, Better Agents? A Reality Check on Self-Distillation in Agentic Reinforcement Learning",
          "title": "More Methods, Better Agents? A Reality Check on Self-Distillation in Agentic Reinforcement Learning",
          "description": "Researchers benchmarked ten self-distillation methods against GRPO across multiple agent tasks and model sizes, finding limited consistent gains, hidden training instability, and no reliable cost-to-performance benefit.",
          "summary": "Researchers benchmarked ten self-distillation methods against GRPO across multiple agent tasks and model sizes, finding limited consistent gains, hidden training instability, and no reliable cost-to-performance benefit.",
          "desc": "Researchers benchmarked ten self-distillation methods against GRPO across multiple agent tasks and model sizes, finding limited consistent gains, hidden training instability, and no reliable cost-to-performance benefit.",
          "title_zh": "More Methods, Better Agents? A Reality Check on Self-Distillation in Agentic Reinforcement Learning",
          "summary_zh": "Researchers benchmarked ten self-distillation methods against GRPO across multiple agent tasks and model sizes, finding limited consistent gains, hidden training instability, and no reliable cost-to-performance benefit.",
          "title_i18n": {
            "en": "More Methods, Better Agents? A Reality Check on Self-Distillation in Agentic Reinforcement Learning",
            "zh-CN": "More Methods, Better Agents? A Reality Check on Self-Distillation in Agentic Reinforcement Learning"
          },
          "summary_i18n": {
            "en": "Researchers benchmarked ten self-distillation methods against GRPO across multiple agent tasks and model sizes, finding limited consistent gains, hidden training instability, and no reliable cost-to-performance benefit.",
            "zh-CN": "Researchers benchmarked ten self-distillation methods against GRPO across multiple agent tasks and model sizes, finding limited consistent gains, hidden training instability, and no reliable cost-to-performance benefit."
          },
          "reason_i18n": {
            "en": "72 HF votes · 32 GitHub stars",
            "zh-CN": "HF 72 赞 · GitHub 32 星"
          },
          "url": "https://huggingface.co/papers/2610.19789",
          "page": null,
          "source": "HF Daily Papers",
          "host": "huggingface.co",
          "score": 65.6,
          "metrics": {
            "upvotes": 72,
            "comments": 2,
            "githubStars": 32
          },
          "topics": [
            "self-distillation",
            "agentic reinforcement learning",
            "GRPO",
            "ALFWorld",
            "ScienceWorld",
            "Search-QA"
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          "publishedAt": "2026-10-15T00:00:00.000Z",
          "updatedAt": "2026-10-15T00:00:00.000Z",
          "rawId": "2610.19789"
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          "sectionId": "papers",
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          "accent": "#6366f1",
          "name": "Doc2Harness: Benchmarking LLMs on Long-Horizon Harness Engineering of Coding Agents",
          "title": "Doc2Harness: Benchmarking LLMs on Long-Horizon Harness Engineering of Coding Agents",
          "description": "Doc2Harness is a deterministic two-stage benchmark that evaluates long-horizon harness engineering by having models build and run harnesses across isolated containers, revealing substantial performance gaps among frontier models.",
          "summary": "Doc2Harness is a deterministic two-stage benchmark that evaluates long-horizon harness engineering by having models build and run harnesses across isolated containers, revealing substantial performance gaps among frontier models.",
          "desc": "Doc2Harness is a deterministic two-stage benchmark that evaluates long-horizon harness engineering by having models build and run harnesses across isolated containers, revealing substantial performance gaps among frontier models.",
          "title_zh": "Doc2Harness: Benchmarking LLMs on Long-Horizon Harness Engineering of Coding Agents",
          "summary_zh": "Doc2Harness is a deterministic two-stage benchmark that evaluates long-horizon harness engineering by having models build and run harnesses across isolated containers, revealing substantial performance gaps among frontier models.",
          "title_i18n": {
            "en": "Doc2Harness: Benchmarking LLMs on Long-Horizon Harness Engineering of Coding Agents",
            "zh-CN": "Doc2Harness: Benchmarking LLMs on Long-Horizon Harness Engineering of Coding Agents"
          },
          "summary_i18n": {
            "en": "Doc2Harness is a deterministic two-stage benchmark that evaluates long-horizon harness engineering by having models build and run harnesses across isolated containers, revealing substantial performance gaps among frontier models.",
            "zh-CN": "Doc2Harness is a deterministic two-stage benchmark that evaluates long-horizon harness engineering by having models build and run harnesses across isolated containers, revealing substantial performance gaps among frontier models."
          },
          "reason_i18n": {
            "en": "93 HF votes · 33 GitHub stars",
            "zh-CN": "HF 93 赞 · GitHub 33 星"
          },
          "url": "https://huggingface.co/papers/2610.20344",
          "page": null,
          "source": "HF Daily Papers",
          "host": "huggingface.co",
          "score": 65.4,
          "metrics": {
            "upvotes": 93,
            "comments": 1,
            "githubStars": 33
          },
          "topics": [
            "Doc2Harness",
            "harness engineering",
            "long-horizon agents",
            "build model",
            "interface-only contract skeleton",
            "mock model"
          ],
          "publishedAt": "2026-10-15T00:00:00.000Z",
          "updatedAt": "2026-10-15T00:00:00.000Z",
          "rawId": "2610.20344"
        }
      ]
    },
    {
      "id": "models",
      "title": {
        "en": "Models",
        "zh-CN": "模型"
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      "sourceUpdatedAt": "2026-10-16T00:56:22.853Z",
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          "kindLabel": {
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            "zh-CN": "模型"
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          "name": "Unlimited-OCR",
          "title": "Unlimited-OCR",
          "description": "Unlimited-OCR is a image-text-to-text by baidu.",
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          "desc": "Unlimited-OCR is a image-text-to-text by baidu.",
          "title_zh": "Unlimited-OCR",
          "summary_zh": "Unlimited-OCR",
          "title_i18n": {
            "en": "Unlimited-OCR",
            "zh-CN": "Unlimited-OCR"
          },
          "summary_i18n": {
            "en": "Unlimited-OCR is a image-text-to-text by baidu.",
            "zh-CN": "Unlimited-OCR"
          },
          "reason_i18n": {
            "en": "图文问答 · 多模态 · 4702 likes · 1163009 downloads",
            "zh-CN": "图文问答 · 多模态 · 4702 喜欢 · 1163009 下载"
          },
          "url": "https://huggingface.co/baidu/Unlimited-OCR",
          "page": null,
          "source": "HF Trending",
          "host": "huggingface.co",
          "score": 90.2,
          "metrics": {
            "likes": 4702,
            "downloads": 1163009,
            "trendingScore": 319,
            "pipeline": "image-text-to-text"
          },
          "topics": [
            "unlimited_ocr",
            "feature-extraction",
            "baidu",
            "vision-language",
            "ocr",
            "image-text-to-text",
            "multilingual",
            "arxiv:2606.23050"
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          "publishedAt": "2026-06-19T09:40:33.000Z",
          "updatedAt": "2026-10-14T06:17:59.000Z",
          "rawId": "baidu/Unlimited-OCR"
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          "kindLabel": {
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          "route": "/models",
          "accent": "#f59e0b",
          "name": "JEV-27B-VL",
          "title": "JEV-27B-VL",
          "description": "JEV-27B-VL is a image-text-to-text by autotrust.",
          "summary": "JEV-27B-VL is a image-text-to-text by autotrust.",
          "desc": "JEV-27B-VL is a image-text-to-text by autotrust.",
          "title_zh": "JEV-27B-VL",
          "summary_zh": "JEV-27B-VL",
          "title_i18n": {
            "en": "JEV-27B-VL",
            "zh-CN": "JEV-27B-VL"
          },
          "summary_i18n": {
            "en": "JEV-27B-VL is a image-text-to-text by autotrust.",
            "zh-CN": "JEV-27B-VL"
          },
          "reason_i18n": {
            "en": "图文问答 · 多模态 · 5543 likes · 1600363 downloads",
            "zh-CN": "图文问答 · 多模态 · 5543 喜欢 · 1600363 下载"
          },
          "url": "https://huggingface.co/autotrust/JEV-27B-VL",
          "page": null,
          "source": "HF Multimodal Trending",
          "host": "huggingface.co",
          "score": 90.2,
          "metrics": {
            "likes": 5543,
            "downloads": 1600363,
            "trendingScore": 143.5,
            "pipeline": "image-text-to-text"
          },
          "topics": [
            "qwen3_5",
            "image-text-to-text",
            "jev",
            "system-one",
            "system-two",
            "typed-decisions",
            "decision-model",
            "calibrated-probabilities"
          ],
          "publishedAt": "2026-09-30T05:02:23.000Z",
          "updatedAt": "2026-10-14T06:16:37.000Z",
          "rawId": "autotrust/JEV-27B-VL"
        },
        {
          "id": "models-1ire6xk",
          "sectionId": "models",
          "kind": "models",
          "kindLabel": {
            "en": "Models",
            "zh-CN": "模型"
          },
          "route": "/models",
          "accent": "#f59e0b",
          "name": "humanizer",
          "title": "humanizer",
          "description": "humanizer is a text-generation by jialinyyzz.",
          "summary": "humanizer is a text-generation by jialinyyzz.",
          "desc": "humanizer is a text-generation by jialinyyzz.",
          "title_zh": "humanizer",
          "summary_zh": "humanizer",
          "title_i18n": {
            "en": "humanizer",
            "zh-CN": "humanizer"
          },
          "summary_i18n": {
            "en": "humanizer is a text-generation by jialinyyzz.",
            "zh-CN": "humanizer"
          },
          "reason_i18n": {
            "en": "文本生成 · LLM · 1733 likes · 75253 downloads",
            "zh-CN": "文本生成 · LLM · 1733 喜欢 · 75253 下载"
          },
          "url": "https://huggingface.co/jialinyyzz/humanizer",
          "page": null,
          "source": "HF Trending",
          "host": "huggingface.co",
          "score": 89.3,
          "metrics": {
            "likes": 1733,
            "downloads": 75253,
            "trendingScore": 855,
            "pipeline": "text-generation"
          },
          "topics": [
            "gemma4_unified",
            "image-text-to-text",
            "humanizer",
            "text-rewriting",
            "rewriting",
            "paraphrase",
            "style-transfer",
            "llama.cpp"
          ],
          "publishedAt": "2026-09-11T05:00:16.000Z",
          "updatedAt": "2026-10-15T02:09:56.000Z",
          "rawId": "jialinyyzz/humanizer"
        },
        {
          "id": "models-ohxk4f",
          "sectionId": "models",
          "kind": "models",
          "kindLabel": {
            "en": "Models",
            "zh-CN": "模型"
          },
          "route": "/models",
          "accent": "#f59e0b",
          "name": "Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NEO-CODER-MAX-MTP-GGUF",
          "title": "Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NEO-CODER-MAX-MTP-GGUF",
          "description": "Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NEO-CODER-MAX-MTP-GGUF is a image-text-to-text by DavidAU.",
          "summary": "Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NEO-CODER-MAX-MTP-GGUF is a image-text-to-text by DavidAU.",
          "desc": "Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NEO-CODER-MAX-MTP-GGUF is a image-text-to-text by DavidAU.",
          "title_zh": "Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NEO-CODER-MAX-MTP-GGUF",
          "summary_zh": "Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NEO-CODER-MAX-MTP-GGUF",
          "title_i18n": {
            "en": "Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NEO-CODER-MAX-MTP-GGUF",
            "zh-CN": "Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NEO-CODER-MAX-MTP-GGUF"
          },
          "summary_i18n": {
            "en": "Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NEO-CODER-MAX-MTP-GGUF is a image-text-to-text by DavidAU.",
            "zh-CN": "Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NEO-CODER-MAX-MTP-GGUF"
          },
          "reason_i18n": {
            "en": "图文问答 · 多模态 · 1796 likes · 1856283 downloads",
            "zh-CN": "图文问答 · 多模态 · 1796 喜欢 · 1856283 下载"
          },
          "url": "https://huggingface.co/DavidAU/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NEO-CODER-MAX-MTP-GGUF",
          "page": null,
          "source": "HF Multimodal Trending",
          "host": "huggingface.co",
          "score": 88.8,
          "metrics": {
            "likes": 1796,
            "downloads": 1856283,
            "trendingScore": 159,
            "pipeline": "image-text-to-text"
          },
          "topics": [
            "unsloth",
            "fine tune",
            "heretic",
            "uncensored",
            "abliterated",
            "ara",
            "MTP GGUF Quants",
            "Regular GGUF Quants"
          ],
          "publishedAt": "2026-09-01T07:01:09.000Z",
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            "zh-CN": "一个自己找热点、自己写日报的网站框架。把信源和精选标准换成你的，它就是你的行业热点站。"
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          "summary": "A growing collection of viral videos made with Claude Opus 5.5 and the prompts behind them. Watch each original next to a live remake on Skillry. Updated regularly.",
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            "zh-CN": "Claude Code skill for short films with no video model: 43 film styles, each a style prompt plus a demo film made entirely in code by Claude Opus 5.5 (incl. OPUSCAR 98). By Lemomo. · Opus5.5 x 43 种影片风格：风格提示词 + 纯代码样片 + 导演与技术指南"
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