{
  "schema_version": 1,
  "report_date": "2026-07-02",
  "title": "AI 日报 2026-07-02",
  "summary": "今天的主线集中在三类可执行更新：Google 把纽约 150 人 AI 教育峰会和 B3 的 Android Enterprise 案例写进公开博客；Alibaba Cloud 给出 DAS Agent + MCP Server + Dify 的跨账号数据库运维方案和云上 CI/CD 流水线；研究与模型侧则有 OpenAI 新经济分析、Claude Sonnet 5 开发者记录，以及 Microsoft HARC-Qwen2.5 模型页。",
  "hero_highlights": [
    {
      "title": "OpenAI 新经济分析聚焦 AI 在工作场景中的采用率",
      "url": "https://openai.com/global-affairs/new-economic-analysis/",
      "reason": "OpenAI 把美国在职成年人工作中使用 ChatGPT 的比例与 2023 年数据对比，适合关注 AI 采纳率和生产率衡量的人先看方法和样本。",
      "what_happened": "OpenAI 发布新的经济分析，讨论 AI 在不同职业任务中的采用、生产率影响和衡量方法，并提醒读者关注样本、评估方法和现实部署差异。",
      "why_watch": "OpenAI 把美国在职成年人工作中使用 ChatGPT 的比例与 2023 年数据对比，适合关注 AI 采纳率和生产率衡量的人先看方法和样本。",
      "category": "model_platform",
      "source_item_ref": "https://openai.com/global-affairs/new-economic-analysis/"
    },
    {
      "title": "B3 用 Android Enterprise 支撑安全 AI 办公",
      "url": "https://blog.google/products-and-platforms/products/android-enterprise/b3-android-enterprise/",
      "reason": "B3 案例把 AI 办公落到受管 Android 设备、企业移动管理和安全部署，采购方可以对照设备生命周期和合规要求。",
      "what_happened": "Google 介绍 B3 选择 Android Enterprise 支撑安全、可管理的 AI 办公设备，重点是企业移动设备管理、合规和生产力场景。",
      "why_watch": "B3 案例把 AI 办公落到受管 Android 设备、企业移动管理和安全部署，采购方可以对照设备生命周期和合规要求。",
      "category": "product_tool",
      "source_item_ref": "https://blog.google/products-and-platforms/products/android-enterprise/b3-android-enterprise/"
    },
    {
      "title": "Microsoft HARC Qwen2.5 7B 指令模型上架 Hugging Face",
      "url": "https://huggingface.co/microsoft/HARC-Qwen2.5-7B-Instruct",
      "reason": "模型评估和工程团队可以进入模型卡查看任务说明、权重许可、下载限制和推理成本，再决定是否纳入 PoC。",
      "what_happened": "Microsoft 在 Hugging Face 上架 HARC-Qwen2.5-7B-Instruct 模型页，公开入口显示其基座为 Qwen2.5-7B-Instruct。",
      "why_watch": "模型评估和工程团队可以进入模型卡查看任务说明、权重许可、下载限制和推理成本，再决定是否纳入 PoC。",
      "category": "china_open_source_community",
      "source_item_ref": "https://huggingface.co/microsoft/HARC-Qwen2.5-7B-Instruct"
    }
  ],
  "stories": [
    {
      "story_id": "story-content-google-keyword-new-york-city-educators-and-industry-leaders-gathered-at",
      "title": "Google 与纽约教育伙伴办 AI 课堂峰会，150 名教育和行业负责人参会",
      "importance": "general",
      "trend": "AI business",
      "event_date": "2026-07-01",
      "primary_entity": "Google Keyword Blog",
      "event_type": "signal",
      "object": "纽约 AI 教育峰会",
      "what_happened": "Google、New York Jobs CEO Council 和 Urban Assembly 在 Google 办公室举办 AI 教育峰会，聚集 150 名教育与行业负责人，讨论课堂 AI 培训、学校试点和产业合作。",
      "why_it_matters": "这条更新把教育机构、供应商和学校负责人放到同一场讨论里，能帮助学校判断教师支持、课堂试点和采购节奏。",
      "evidence_level": "primary",
      "sources": [
        {
          "label": "Google Keyword Blog",
          "url": "https://blog.google/products-and-platforms/products/education/nyc-ai-summit/",
          "type": "official"
        }
      ]
    },
    {
      "story_id": "story-content-huggingface-microsoft-microsoft-harc-qwen2-5-7b-instruct",
      "title": "Microsoft 在 Hugging Face 发布 HARC-Qwen2.5-7B-Instruct 模型页",
      "importance": "general",
      "trend": "open source AI",
      "event_date": "2026-07-01",
      "primary_entity": "Microsoft Hugging Face Organization",
      "event_type": "signal",
      "object": "HARC-Qwen2.5-7B-Instruct",
      "what_happened": "Microsoft 在 Hugging Face 上架 HARC-Qwen2.5-7B-Instruct 模型页，公开入口显示其基座为 Qwen2.5-7B-Instruct。",
      "why_it_matters": "模型评估和工程团队可以进入模型卡查看任务说明、权重许可、下载限制和推理成本，再决定是否纳入 PoC。",
      "evidence_level": "primary",
      "sources": [
        {
          "label": "Microsoft Hugging Face Organization",
          "url": "https://huggingface.co/microsoft/HARC-Qwen2.5-7B-Instruct",
          "type": "official"
        }
      ]
    },
    {
      "story_id": "story-content-google-keyword-why-b3-chose-android-for-secure-ai-enabled-productivity",
      "title": "B3 用 Android Enterprise 构建受管 AI 办公设备",
      "importance": "general",
      "trend": "AI products and developer workflow",
      "event_date": "2026-07-01",
      "primary_entity": "Google Keyword Blog",
      "event_type": "signal",
      "object": "B3 Android Enterprise 案例",
      "what_happened": "Google 介绍 B3 选择 Android Enterprise 来部署安全、可管理的 AI 办公体验，重点落在设备注册、企业移动管理和生产力场景。",
      "why_it_matters": "企业把 AI 办公带进受管终端时，采购方需要同时评估权限、设备生命周期、合规和远程管理成本。",
      "evidence_level": "primary",
      "sources": [
        {
          "label": "Google Keyword Blog",
          "url": "https://blog.google/products-and-platforms/products/android-enterprise/b3-android-enterprise/",
          "type": "official"
        }
      ]
    },
    {
      "story_id": "story-content-alibaba-cloud-blog-cross-account-intelligent-database-operations-integra",
      "title": "Alibaba Cloud 用 DAS Agent、MCP Server 和 Dify 做跨账号数据库运维",
      "importance": "general",
      "trend": "AI products and developer workflow",
      "event_date": "2026-07-01",
      "primary_entity": "Alibaba Cloud Blog",
      "event_type": "signal",
      "object": "跨账号智能数据库运维",
      "what_happened": "Alibaba Cloud 介绍把 DAS Agent、MCP Server 与 Dify 连接起来，对多个阿里云账号下的数据库实例做统一智能运维。",
      "why_it_matters": "数据库运维是 agent 接入企业系统的高风险场景，文章把账号隔离、权限连接和操作流程放到同一个方案里。",
      "evidence_level": "primary",
      "sources": [
        {
          "label": "Alibaba Cloud Blog",
          "url": "https://www.alibabacloud.com/blog/cross-account-intelligent-database-operations-integrating-das-agent-mcp-server-and-dify_603320",
          "type": "official"
        }
      ]
    },
    {
      "story_id": "story-content-mit-technology-review-the-download-anthropic-launches-claude-science-and",
      "title": "MIT Technology Review 简报同屏追踪 Claude Science 与加州减碳议题",
      "importance": "general",
      "trend": "AI industry",
      "event_date": "2026-07-01",
      "primary_entity": "MIT Technology Review",
      "event_type": "signal",
      "object": "The Download 科技简报",
      "what_happened": "MIT Technology Review 的 The Download 这期提到 Anthropic 推出 Claude Science，同时追踪加州利用粪肥减碳等科技政策议题。",
      "why_it_matters": "这类简报能帮助读者看到模型公司产品动作如何进入更广泛的科技、医疗和气候讨论。",
      "evidence_level": "primary",
      "sources": [
        {
          "label": "MIT Technology Review",
          "url": "https://www.technologyreview.com/2026/07/01/1139996/the-download-anthropic-claude-science-california-carbon-manure/",
          "type": "official"
        }
      ]
    },
    {
      "story_id": "story-content-builder-simon-willison-what-s-new-in-claude-sonnet-5",
      "title": "Simon Willison 逐条记录 Claude Sonnet 5 的开发者变化",
      "importance": "general",
      "trend": "AI industry",
      "event_date": "2026-06-30",
      "primary_entity": "Simon Willison's Weblog",
      "event_type": "signal",
      "object": "Claude Sonnet 5 开发者体验",
      "what_happened": "Simon Willison 记录 Claude Sonnet 5 发布后的开发者文档变化，重点查看工具调用、上下文、代码辅助和开发体验。",
      "why_it_matters": "独立开发者的逐条记录能帮助工程团队在正式接入前预判工具限制、迁移成本和测试重点。",
      "evidence_level": "primary",
      "sources": [
        {
          "label": "Simon Willison's Weblog",
          "url": "https://simonwillison.net/2026/Jun/30/claude-sonnet-5/#atom-everything",
          "type": "primary"
        }
      ]
    },
    {
      "story_id": "story-content-alibaba-cloud-blog-ci-cd-pipelines-on-alibaba-cloud-complete-devops-work",
      "title": "Alibaba Cloud 讲解云上 CI/CD 与 DevOps 流水线",
      "importance": "general",
      "trend": "AI products and developer workflow",
      "event_date": "2026-07-01",
      "primary_entity": "Alibaba Cloud Blog",
      "event_type": "signal",
      "object": "云上 CI/CD 流水线",
      "what_happened": "Alibaba Cloud 介绍如何用云原生 DevOps 与容器服务搭建生产级 CI/CD 流水线，覆盖代码提交、构建、部署和流程自动化。",
      "why_it_matters": "云厂商给出的 DevOps 路径会影响团队选择托管服务、权限配置、自动化发布方式和运维边界。",
      "evidence_level": "primary",
      "sources": [
        {
          "label": "Alibaba Cloud Blog",
          "url": "https://www.alibabacloud.com/blog/cicd-pipelines-on-alibaba-cloud-complete-devops-workflow_603318",
          "type": "official"
        }
      ]
    },
    {
      "story_id": "story-content-salvatorera-ml-news-week-openai-s-new-economic-analysis",
      "title": "OpenAI 新经济分析称 28% 美国在职成年人用 ChatGPT 工作",
      "importance": "general",
      "trend": "AI industry",
      "event_date": "2026-07-02",
      "primary_entity": "ML & AI News of the Week",
      "event_type": "research",
      "object": "OpenAI 经济分析",
      "what_happened": "OpenAI 发布新的经济分析，称美国在职成年人中用 ChatGPT 工作的比例从 2023 年 8% 升至 28%，并讨论 AI 采用率、工作任务和生产率衡量。",
      "why_it_matters": "这份分析给企业和研究者提供了观察 AI 进入工作场景的指标，但解读时仍要结合样本、方法和实际部署差异。",
      "evidence_level": "multi_source",
      "sources": [
        {
          "label": "ML & AI News of the Week",
          "url": "https://openai.com/global-affairs/new-economic-analysis/",
          "type": "official"
        },
        {
          "label": "ML News of the Week README",
          "url": "https://openai.com/global-affairs/new-economic-analysis/",
          "type": "github"
        }
      ]
    }
  ],
  "main_items": [
    {
      "title": "Google 与纽约教育伙伴办 AI 课堂峰会，150 名教育和行业负责人参会",
      "editorial_category": "company_business",
      "event_date": "2026-07-01",
      "url": "https://blog.google/products-and-platforms/products/education/nyc-ai-summit/",
      "source": "Google Keyword Blog",
      "tier": "T0",
      "entities": [
        "Google Keyword Blog"
      ],
      "summary": "Google、New York Jobs CEO Council 和 Urban Assembly 在 Google 办公室举办 AI 教育峰会，聚集 150 名教育与行业负责人，讨论课堂 AI 培训、学校试点和产业合作。",
      "bullets": [
        "Google、New York Jobs CEO Council 和 Urban Assembly 在 Google 办公室举办 AI 教育峰会，聚集 150 名教育与行业负责人，讨论课堂 AI 培训、学校试点和产业合作。",
        "这条更新把教育机构、供应商和学校负责人放到同一场讨论里，能帮助学校判断教师支持、课堂试点和采购节奏。",
        "来源为 Google Keyword Blog，原文链接已保留在标题中。"
      ],
      "importance": "general",
      "image_urls": []
    },
    {
      "title": "Microsoft 在 Hugging Face 发布 HARC-Qwen2.5-7B-Instruct 模型页",
      "editorial_category": "open_source",
      "event_date": "2026-07-01",
      "url": "https://huggingface.co/microsoft/HARC-Qwen2.5-7B-Instruct",
      "source": "Microsoft Hugging Face Organization",
      "tier": "T0",
      "entities": [
        "Microsoft Hugging Face Organization"
      ],
      "summary": "Microsoft 在 Hugging Face 上架 HARC-Qwen2.5-7B-Instruct 模型页，公开入口显示其基座为 Qwen2.5-7B-Instruct，并提供查看模型卡、权重、许可证和推理限制的入口。",
      "bullets": [
        "微软在 Hugging Face 的官方模型托管页发布该模型条目，名称显示它属于 HARC 系列，并基于 Qwen2.5-7B-Instruct 指令模型。",
        "工程团队查看模型卡时，应同时确认任务说明、权重许可、下载限制、推理成本和可商用边界。",
        "来源为 Microsoft Hugging Face Organization，原文链接已保留在标题中。"
      ],
      "importance": "general",
      "image_urls": []
    },
    {
      "title": "B3 用 Android Enterprise 构建受管 AI 办公设备",
      "editorial_category": "product_radar",
      "event_date": "2026-07-01",
      "url": "https://blog.google/products-and-platforms/products/android-enterprise/b3-android-enterprise/",
      "source": "Google Keyword Blog",
      "tier": "T0",
      "entities": [
        "Google Keyword Blog"
      ],
      "summary": "Google 介绍 B3 选择 Android Enterprise 来部署安全、可管理的 AI 办公体验，重点落在设备注册、企业移动管理和生产力场景。",
      "bullets": [
        "Google 介绍 B3 选择 Android Enterprise 来部署安全、可管理的 AI 办公体验，重点落在设备注册、企业移动管理和生产力场景。",
        "企业把 AI 办公带进受管终端时，采购方需要同时评估权限、设备生命周期、合规和远程管理成本。",
        "来源为 Google Keyword Blog，原文链接已保留在标题中。"
      ],
      "importance": "general",
      "image_urls": []
    },
    {
      "title": "Alibaba Cloud 用 DAS Agent、MCP Server 和 Dify 做跨账号数据库运维",
      "editorial_category": "engineering_toolchain",
      "event_date": "2026-07-01",
      "url": "https://www.alibabacloud.com/blog/cross-account-intelligent-database-operations-integrating-das-agent-mcp-server-and-dify_603320",
      "source": "Alibaba Cloud Blog",
      "tier": "T0",
      "entities": [
        "Alibaba Cloud Blog"
      ],
      "summary": "Alibaba Cloud 介绍把 DAS Agent、MCP Server 与 Dify 连接起来，对多个阿里云账号下的数据库实例做统一智能运维。",
      "bullets": [
        "Alibaba Cloud 介绍把 DAS Agent、MCP Server 与 Dify 连接起来，对多个阿里云账号下的数据库实例做统一智能运维。",
        "数据库运维是 agent 接入企业系统的高风险场景，文章把账号隔离、权限连接和操作流程放到同一个方案里。",
        "来源为 Alibaba Cloud Blog，原文链接已保留在标题中。"
      ],
      "importance": "general",
      "image_urls": []
    },
    {
      "title": "MIT Technology Review 简报同屏追踪 Claude Science 与加州减碳议题",
      "editorial_category": "ai_industry",
      "event_date": "2026-07-01",
      "url": "https://www.technologyreview.com/2026/07/01/1139996/the-download-anthropic-claude-science-california-carbon-manure/",
      "source": "MIT Technology Review",
      "tier": "T0",
      "entities": [
        "MIT Technology Review"
      ],
      "summary": "MIT Technology Review 的 The Download 这期提到 Anthropic 推出 Claude Science，同时追踪加州利用粪肥减碳等科技政策议题。",
      "bullets": [
        "MIT Technology Review 的 The Download 这期提到 Anthropic 推出 Claude Science，同时追踪加州利用粪肥减碳等科技政策议题。",
        "这类简报能帮助读者看到模型公司产品动作如何进入更广泛的科技、医疗和气候讨论。",
        "来源为 MIT Technology Review，原文链接已保留在标题中。"
      ],
      "importance": "general",
      "image_urls": []
    },
    {
      "title": "Simon Willison 逐条记录 Claude Sonnet 5 的开发者变化",
      "editorial_category": "ai_industry",
      "event_date": "2026-06-30",
      "url": "https://simonwillison.net/2026/Jun/30/claude-sonnet-5/#atom-everything",
      "source": "Simon Willison's Weblog",
      "tier": "T0",
      "entities": [
        "Simon Willison's Weblog"
      ],
      "summary": "Simon Willison 记录 Claude Sonnet 5 发布后的开发者文档变化，重点查看工具调用、上下文、代码辅助和开发体验。",
      "bullets": [
        "Simon Willison 记录 Claude Sonnet 5 发布后的开发者文档变化，重点查看工具调用、上下文、代码辅助和开发体验。",
        "独立开发者的逐条记录能帮助工程团队在正式接入前预判工具限制、迁移成本和测试重点。",
        "来源为 Simon Willison's Weblog，原文链接已保留在标题中。"
      ],
      "importance": "general",
      "image_urls": []
    },
    {
      "title": "Alibaba Cloud 讲解云上 CI/CD 与 DevOps 流水线",
      "editorial_category": "product_radar",
      "event_date": "2026-07-01",
      "url": "https://www.alibabacloud.com/blog/cicd-pipelines-on-alibaba-cloud-complete-devops-workflow_603318",
      "source": "Alibaba Cloud Blog",
      "tier": "T0",
      "entities": [
        "Alibaba Cloud Blog"
      ],
      "summary": "Alibaba Cloud 介绍如何用云原生 DevOps 与容器服务搭建生产级 CI/CD 流水线，覆盖代码提交、构建、部署和流程自动化。",
      "bullets": [
        "Alibaba Cloud 介绍如何用云原生 DevOps 与容器服务搭建生产级 CI/CD 流水线，覆盖代码提交、构建、部署和流程自动化。",
        "云厂商给出的 DevOps 路径会影响团队选择托管服务、权限配置、自动化发布方式和运维边界。",
        "来源为 Alibaba Cloud Blog，原文链接已保留在标题中。"
      ],
      "importance": "general",
      "image_urls": []
    },
    {
      "title": "OpenAI 新经济分析称 28% 美国在职成年人用 ChatGPT 工作",
      "editorial_category": "ai_industry",
      "event_date": "2026-07-02",
      "url": "https://openai.com/global-affairs/new-economic-analysis/",
      "source": "ML & AI News of the Week",
      "tier": "T0",
      "entities": [
        "ML & AI News of the Week"
      ],
      "summary": "OpenAI 发布新的经济分析，称美国在职成年人中用 ChatGPT 工作的比例从 2023 年 8% 升至 28%，并讨论 AI 采用率、工作任务和生产率衡量。",
      "bullets": [
        "OpenAI 发布新的经济分析，称美国在职成年人中用 ChatGPT 工作的比例从 2023 年 8% 升至 28%，并讨论 AI 采用率、工作任务和生产率衡量。",
        "这份分析给企业和研究者提供了观察 AI 进入工作场景的指标，但解读时仍要结合样本、方法和实际部署差异。",
        "来源为 ML & AI News of the Week，原文链接已保留在标题中。"
      ],
      "importance": "general",
      "image_urls": []
    }
  ],
  "github_trending": [
    {
      "name": "xbtlin/ai-berkshire",
      "repo": "xbtlin/ai-berkshire",
      "description": "ai-berkshire 是基于 Claude Code/Codex 的价值投资研究框架，把投资大师方法论、多 agent 对抗分析、公司资料整理和投资假设检查放进同一流程；适合关注 AI 投研的人看它如何产出可复核的研究结论与风险提示。落地时还要看数据来源、模型推理记录、投资建议边界和人工复核责任。",
      "readme_cache": {
        "key": "github-readme/xbtlin/ai-berkshire/main/unknown",
        "hit": true,
        "repo": "xbtlin/ai-berkshire",
        "sha": "unknown",
        "default_branch": "main",
        "source_url": "https://raw.githubusercontent.com/xbtlin/ai-berkshire/main/README.md"
      },
      "readme_fetch_status": "ok",
      "url": "https://github.com/xbtlin/ai-berkshire",
      "event_date": "2026-07-02",
      "source": "GitHub Trending weekly",
      "language": "all",
      "window": "weekly",
      "rank": 1,
      "source_rank": 1,
      "source_scope": "weekly:all",
      "previous_rank": null,
      "rank_delta": null,
      "trend": "new",
      "importance": "notable"
    },
    {
      "name": "simplex-chat/simplex-chat",
      "repo": "simplex-chat/simplex-chat",
      "description": "SimpleX Chat 是隐私即时通讯项目，重点是不用手机号或全局用户 ID 建立连接；适合关注端到端加密、去中心化通信和多端客户端工程的人看。评估时要看客户端覆盖、密钥恢复、群组能力和自托管服务的维护成本。",
      "readme_cache": {
        "key": "github-readme/simplex-chat/simplex-chat/master/unknown",
        "hit": true,
        "repo": "simplex-chat/simplex-chat",
        "sha": "unknown",
        "default_branch": "master",
        "source_url": "https://raw.githubusercontent.com/simplex-chat/simplex-chat/master/README.md"
      },
      "readme_fetch_status": "ok",
      "url": "https://github.com/simplex-chat/simplex-chat",
      "event_date": "2026-07-02",
      "source": "GitHub Trending weekly",
      "language": "all",
      "window": "weekly",
      "rank": 2,
      "source_rank": 2,
      "source_scope": "weekly:all",
      "previous_rank": 2,
      "rank_delta": 0,
      "trend": "same",
      "importance": "general"
    },
    {
      "name": "calesthio/OpenMontage",
      "repo": "calesthio/OpenMontage",
      "description": "OpenMontage 面向视频和多媒体剪辑自动化；适合关注 AI 辅助素材整理、片段拼接和创作工具链的读者先看输入格式、部署方式和示例。真正接入前还要核对它对素材时长、导出格式、字幕和批处理任务的支持程度。",
      "readme_cache": {
        "key": "github-readme/calesthio/openmontage/main/unknown",
        "hit": true,
        "repo": "calesthio/openmontage",
        "sha": "unknown",
        "default_branch": "main",
        "source_url": "https://raw.githubusercontent.com/calesthio/OpenMontage/main/README.md"
      },
      "readme_fetch_status": "ok",
      "url": "https://github.com/calesthio/OpenMontage",
      "event_date": "2026-07-02",
      "source": "GitHub Trending weekly",
      "language": "all",
      "window": "weekly",
      "rank": 3,
      "source_rank": 3,
      "source_scope": "weekly:all",
      "previous_rank": 1,
      "rank_delta": -2,
      "trend": "down",
      "importance": "general"
    },
    {
      "name": "google-labs-code/design.md",
      "repo": "google-labs-code/design.md",
      "description": "design.md 是 Google Labs Code 给 coding agents 使用的设计契约规范，把视觉识别、交互约束、品牌语气和验收标准写成仓库内文档；它对本项目的启发是把前端风格、内容边界和截图验收沉到版本化设计文件，而不是留在对话记忆里。后续应把它转化为本仓库 DESIGN.md 的必检项和页面验收用例。",
      "readme_cache": {
        "key": "github-readme/google-labs-code/design.md/main/unknown",
        "hit": true,
        "repo": "google-labs-code/design.md",
        "sha": "unknown",
        "default_branch": "main",
        "source_url": "https://raw.githubusercontent.com/google-labs-code/design.md/main/README.md"
      },
      "readme_fetch_status": "ok",
      "url": "https://github.com/google-labs-code/design.md",
      "event_date": "2026-07-02",
      "source": "GitHub Trending weekly",
      "language": "all",
      "window": "weekly",
      "rank": 4,
      "source_rank": 4,
      "source_scope": "weekly:all",
      "previous_rank": 3,
      "rank_delta": -1,
      "trend": "down",
      "importance": "general"
    },
    {
      "name": "DeusData/codebase-memory-mcp",
      "repo": "DeusData/codebase-memory-mcp",
      "description": "codebase-memory-mcp 把代码库索引成持久知识图谱并通过 MCP 暴露查询能力，重点是多语言解析、SQLite 存储和低 token 上下文复用；适合评估 coding agent 跨会话理解架构、检索符号和避免重复读仓库的接入方式。落地时还要看索引增量更新、隐私边界、查询准确率和现有 codegraph 的重叠。",
      "readme_cache": {
        "key": "github-readme/deusdata/codebase-memory-mcp/main/unknown",
        "hit": true,
        "repo": "deusdata/codebase-memory-mcp",
        "sha": "unknown",
        "default_branch": "main",
        "source_url": "https://raw.githubusercontent.com/DeusData/codebase-memory-mcp/main/README.md"
      },
      "readme_fetch_status": "ok",
      "url": "https://github.com/DeusData/codebase-memory-mcp",
      "event_date": "2026-07-02",
      "source": "GitHub Trending weekly",
      "language": "all",
      "window": "weekly",
      "rank": 5,
      "source_rank": 5,
      "source_scope": "weekly:all",
      "previous_rank": 4,
      "rank_delta": -1,
      "trend": "down",
      "importance": "general"
    },
    {
      "name": "kunchenguid/no-mistakes",
      "repo": "kunchenguid/no-mistakes",
      "description": "no-mistakes 围绕 git push 前的失误拦截和工程预检查展开，关注点不是再写一段提示词，而是把常见错误、执行前验证和提交约束变成可运行规则；适合看 agent 工作流能否把事故复盘沉淀成自动化 guardrail。落地时应关注规则如何配置、误报怎么处理，以及是否能接进 CI 或 pre-push 钩子。",
      "readme_cache": {
        "key": "github-readme/kunchenguid/no-mistakes/main/unknown",
        "hit": true,
        "repo": "kunchenguid/no-mistakes",
        "sha": "unknown",
        "default_branch": "main",
        "source_url": "https://raw.githubusercontent.com/kunchenguid/no-mistakes/main/README.md"
      },
      "readme_fetch_status": "ok",
      "url": "https://github.com/kunchenguid/no-mistakes",
      "event_date": "2026-07-02",
      "source": "GitHub Trending weekly",
      "language": "all",
      "window": "weekly",
      "rank": 6,
      "source_rank": 6,
      "source_scope": "weekly:all",
      "previous_rank": 5,
      "rank_delta": -1,
      "trend": "down",
      "importance": "general"
    },
    {
      "name": "JCodesMore/ai-website-cloner-template",
      "repo": "JCodesMore/ai-website-cloner-template",
      "description": "ai-website-cloner-template 是用 AI coding agents 复刻网站的模板工程，覆盖抓取、页面还原和 Next.js/React 输出；前端团队更应关注布局还原、素材版权、响应式状态、组件可维护性和生成后能否进入正常工程迭代。真正使用时还要把原站授权、图片来源和设计差异审查纳入流程。",
      "readme_cache": {
        "key": "github-readme/jcodesmore/ai-website-cloner-template/master/unknown",
        "hit": true,
        "repo": "jcodesmore/ai-website-cloner-template",
        "sha": "unknown",
        "default_branch": "master",
        "source_url": "https://raw.githubusercontent.com/JCodesMore/ai-website-cloner-template/master/README.md"
      },
      "readme_fetch_status": "ok",
      "url": "https://github.com/JCodesMore/ai-website-cloner-template",
      "event_date": "2026-07-02",
      "source": "GitHub Trending weekly",
      "language": "all",
      "window": "weekly",
      "rank": 7,
      "source_rank": 7,
      "source_scope": "weekly:all",
      "previous_rank": 6,
      "rank_delta": -1,
      "trend": "down",
      "importance": "general"
    },
    {
      "name": "Robbyant/lingbot-map",
      "repo": "Robbyant/lingbot-map",
      "description": "lingbot-map 是用于从流式数据重建场景的 feed-forward 3D foundation model，重点在连续输入下恢复空间结构；适合关注 3D 重建、实时场景理解和机器人数据管线的人检查输入模态、推理成本、可视化结果和示例可复现性。落地时还要看流式延迟、场景尺度和对真实传感器噪声的鲁棒性。",
      "readme_cache": {
        "key": "github-readme/robbyant/lingbot-map/main/unknown",
        "hit": true,
        "repo": "robbyant/lingbot-map",
        "sha": "unknown",
        "default_branch": "main",
        "source_url": "https://raw.githubusercontent.com/Robbyant/lingbot-map/main/README.md"
      },
      "readme_fetch_status": "ok",
      "url": "https://github.com/Robbyant/lingbot-map",
      "event_date": "2026-07-02",
      "source": "GitHub Trending weekly",
      "language": "all",
      "window": "weekly",
      "rank": 8,
      "source_rank": 8,
      "source_scope": "weekly:all",
      "previous_rank": 11,
      "rank_delta": 3,
      "trend": "up",
      "importance": "general"
    },
    {
      "name": "ripienaar/free-for-dev",
      "repo": "ripienaar/free-for-dev",
      "description": "free-for-dev 是开发者免费资源清单，不是 AI 专项项目；上榜价值在于给原型或小团队查托管、数据库、监控等免费额度限制。它更适合作为工程采购前的成本索引，不能替代对服务条款、地区可用性和退出成本的核对。",
      "readme_cache": {
        "key": "github-readme/ripienaar/free-for-dev/master/unknown",
        "hit": true,
        "repo": "ripienaar/free-for-dev",
        "sha": "unknown",
        "default_branch": "master",
        "source_url": "https://raw.githubusercontent.com/ripienaar/free-for-dev/master/README.md"
      },
      "readme_fetch_status": "ok",
      "url": "https://github.com/ripienaar/free-for-dev",
      "event_date": "2026-07-02",
      "source": "GitHub Trending weekly",
      "language": "all",
      "window": "weekly",
      "rank": 9,
      "source_rank": 9,
      "source_scope": "weekly:all",
      "previous_rank": null,
      "rank_delta": null,
      "trend": "new",
      "importance": "general"
    },
    {
      "name": "stablyai/orca",
      "repo": "stablyai/orca",
      "description": "orca 是面向并行 coding agents 的 agent development environment，可在桌面和移动端调度多个 agent 与 worktree；适合评估团队如何同时运行多条编码任务、隔离会话状态、复用订阅额度，并在移动端接管长任务。关键还在于长任务中断恢复、并发结果对比和远程环境权限控制。",
      "readme_cache": {
        "key": "github-readme/stablyai/orca/main/unknown",
        "hit": true,
        "repo": "stablyai/orca",
        "sha": "unknown",
        "default_branch": "main",
        "source_url": "https://raw.githubusercontent.com/stablyai/orca/main/README.md"
      },
      "readme_fetch_status": "ok",
      "url": "https://github.com/stablyai/orca",
      "event_date": "2026-07-02",
      "source": "GitHub Trending weekly",
      "language": "all",
      "window": "weekly",
      "rank": 10,
      "source_rank": 10,
      "source_scope": "weekly:all",
      "previous_rank": 9,
      "rank_delta": -1,
      "trend": "down",
      "importance": "general"
    },
    {
      "name": "browser-use/video-use",
      "repo": "browser-use/video-use",
      "description": "video-use 是让 coding agents 参与视频编辑的 Python 项目，目标是把剪辑需求转成可执行的素材处理步骤；适合关注自动剪辑的人验证它如何描述视频任务、调用编辑工具、处理素材路径、保留中间产物并生成可复现输出。落地时还要看素材版权、渲染耗时和编辑结果是否便于人工微调。",
      "readme_cache": {
        "key": "github-readme/browser-use/video-use/main/unknown",
        "hit": true,
        "repo": "browser-use/video-use",
        "sha": "unknown",
        "default_branch": "main",
        "source_url": "https://raw.githubusercontent.com/browser-use/video-use/main/README.md"
      },
      "readme_fetch_status": "ok",
      "url": "https://github.com/browser-use/video-use",
      "event_date": "2026-07-02",
      "source": "GitHub Trending Python weekly",
      "language": "python",
      "window": "weekly",
      "rank": 11,
      "source_rank": 12,
      "source_scope": "weekly:python",
      "previous_rank": 13,
      "rank_delta": 1,
      "trend": "up",
      "importance": "general"
    },
    {
      "name": "diegosouzapw/OmniRoute",
      "repo": "diegosouzapw/OmniRoute",
      "description": "OmniRoute 是 AI gateway，提供统一 endpoint、模型供应商路由、token 压缩、fallback、MCP/A2A 和桌面/PWA 接入；适合工程团队评估多模型接入时的免费额度、自动降级、可靠性、隐私边界和与 Claude Code/Codex 等工具的兼容性。",
      "readme_cache": {
        "key": "github-readme/diegosouzapw/omniroute/main/unknown",
        "hit": true,
        "repo": "diegosouzapw/omniroute",
        "sha": "unknown",
        "default_branch": "main",
        "source_url": "https://raw.githubusercontent.com/diegosouzapw/OmniRoute/main/README.md"
      },
      "readme_fetch_status": "ok",
      "url": "https://github.com/diegosouzapw/OmniRoute",
      "event_date": "2026-07-02",
      "source": "GitHub Trending TypeScript weekly",
      "language": "typescript",
      "window": "weekly",
      "rank": 12,
      "source_rank": 7,
      "source_scope": "weekly:typescript",
      "previous_rank": 12,
      "rank_delta": 5,
      "trend": "up",
      "importance": "general"
    },
    {
      "name": "googleworkspace/cli",
      "repo": "googleworkspace/cli",
      "description": "Google Workspace CLI 是用一条命令操作 Drive、Gmail、Calendar、Sheets、Docs、Chat、Admin 等服务的 Rust CLI，并带 AI agent skills；适合评估企业协作系统自动化时的 OAuth 权限、审计边界、批量操作能力和 agent 工具接入方式。",
      "readme_cache": {
        "key": "github-readme/googleworkspace/cli/main/unknown",
        "hit": true,
        "repo": "googleworkspace/cli",
        "sha": "unknown",
        "default_branch": "main",
        "source_url": "https://raw.githubusercontent.com/googleworkspace/cli/main/README.md"
      },
      "readme_fetch_status": "ok",
      "url": "https://github.com/googleworkspace/cli",
      "event_date": "2026-07-02",
      "source": "GitHub Trending Rust weekly",
      "language": "rust",
      "window": "weekly",
      "rank": 13,
      "source_rank": 1,
      "source_scope": "weekly:rust",
      "previous_rank": 13,
      "rank_delta": 12,
      "trend": "up",
      "importance": "general"
    },
    {
      "name": "IceWhaleTech/CasaOS",
      "repo": "IceWhaleTech/CasaOS",
      "description": "CasaOS 是家庭服务器和自托管管理系统；与 AI 的关系更多在本地服务承载，适合查部署、插件生态和资源占用。若团队想把本地模型、检索服务或自动化任务放到自托管环境，它的账户、权限和备份能力才是重点。",
      "readme_cache": {
        "key": "github-readme/icewhaletech/casaos/main/unknown",
        "hit": true,
        "repo": "icewhaletech/casaos",
        "sha": "unknown",
        "default_branch": "main",
        "source_url": "https://raw.githubusercontent.com/IceWhaleTech/CasaOS/main/README.md"
      },
      "readme_fetch_status": "ok",
      "url": "https://github.com/IceWhaleTech/CasaOS",
      "event_date": "2026-07-02",
      "source": "GitHub Trending Go weekly",
      "language": "go",
      "window": "weekly",
      "rank": 14,
      "source_rank": 2,
      "source_scope": "weekly:go",
      "previous_rank": 8,
      "rank_delta": 6,
      "trend": "up",
      "importance": "general"
    },
    {
      "name": "Stirling-Tools/Stirling-PDF",
      "repo": "Stirling-Tools/Stirling-PDF",
      "description": "Stirling-PDF 是自托管 PDF 工具箱，覆盖编辑、转换、合并、OCR 等文档处理任务；对 AI 工作流的价值在于把文档拆分、压缩、转换、预处理和权限隔离留在本地环境，适合评估批处理接口和部署维护成本。如果接入 agent pipeline，应先明确文件存储、任务队列和敏感文档删除策略。",
      "readme_cache": {
        "key": "github-readme/stirling-tools/stirling-pdf/main/unknown",
        "hit": true,
        "repo": "stirling-tools/stirling-pdf",
        "sha": "unknown",
        "default_branch": "main",
        "source_url": "https://raw.githubusercontent.com/Stirling-Tools/Stirling-PDF/main/README.md"
      },
      "readme_fetch_status": "ok",
      "url": "https://github.com/Stirling-Tools/Stirling-PDF",
      "event_date": "2026-07-02",
      "source": "GitHub Trending Java weekly",
      "language": "java",
      "window": "weekly",
      "rank": 15,
      "source_rank": 1,
      "source_scope": "weekly:java",
      "previous_rank": 15,
      "rank_delta": 14,
      "trend": "up",
      "importance": "general"
    },
    {
      "name": "commaai/openpilot",
      "repo": "commaai/openpilot",
      "description": "openpilot 是开源驾驶辅助项目，参考价值在真实设备、感知控制链路、模型更新和长期维护实践；它不适合被简化成普通 AI 应用项目，读者更应关注硬件适配、安全边界、测试数据和道路部署责任。如果借鉴其工程模式，应关注安全验证、回滚流程和真实道路数据闭环。",
      "readme_cache": {
        "key": "github-readme/commaai/openpilot/master/unknown",
        "hit": true,
        "repo": "commaai/openpilot",
        "sha": "unknown",
        "default_branch": "master",
        "source_url": "https://raw.githubusercontent.com/commaai/openpilot/master/README.md"
      },
      "readme_fetch_status": "ok",
      "url": "https://github.com/commaai/openpilot",
      "event_date": "2026-07-02",
      "source": "GitHub Trending Python weekly",
      "language": "python",
      "window": "weekly",
      "rank": 16,
      "source_rank": 13,
      "source_scope": "weekly:python",
      "previous_rank": 12,
      "rank_delta": -1,
      "trend": "down",
      "importance": "general"
    },
    {
      "name": "every-app/open-seo",
      "repo": "every-app/open-seo",
      "description": "open-seo 是开源 SEO 工具，定位为 Semrush/Ahrefs 替代，覆盖关键词、反链、站点审计和 Google Search Console MCP；适合增长和内容工程团队评估数据来源、索引范围、审计准确性、自动化接入和与现有 CMS 的衔接。使用前还要确认爬取限制、搜索数据来源、反链质量和报告导出能力。",
      "readme_cache": {
        "key": "github-readme/every-app/open-seo/main/unknown",
        "hit": true,
        "repo": "every-app/open-seo",
        "sha": "unknown",
        "default_branch": "main",
        "source_url": "https://raw.githubusercontent.com/every-app/open-seo/main/README.md"
      },
      "readme_fetch_status": "ok",
      "url": "https://github.com/every-app/open-seo",
      "event_date": "2026-07-02",
      "source": "GitHub Trending TypeScript weekly",
      "language": "typescript",
      "window": "weekly",
      "rank": 17,
      "source_rank": 8,
      "source_scope": "weekly:typescript",
      "previous_rank": 10,
      "rank_delta": 2,
      "trend": "up",
      "importance": "general"
    },
    {
      "name": "ogulcancelik/herdr",
      "repo": "ogulcancelik/herdr",
      "description": "herdr 是终端里的 agent multiplexer，用 Rust/TUI 管理多个 coding agents 与工作区；适合评估多 agent 并行编码时的任务隔离、终端调度、会话切换、输出可见性和与 tmux/CLI 工具链的集成。真正可用性取决于任务面板、日志保留、失败恢复和与本地 worktree 策略的配合与集成成本。",
      "readme_cache": {
        "key": "github-readme/ogulcancelik/herdr/master/unknown",
        "hit": true,
        "repo": "ogulcancelik/herdr",
        "sha": "unknown",
        "default_branch": "master",
        "source_url": "https://raw.githubusercontent.com/ogulcancelik/herdr/master/README.md"
      },
      "readme_fetch_status": "ok",
      "url": "https://github.com/ogulcancelik/herdr",
      "event_date": "2026-07-02",
      "source": "GitHub Trending Rust weekly",
      "language": "rust",
      "window": "weekly",
      "rank": 18,
      "source_rank": 2,
      "source_scope": "weekly:rust",
      "previous_rank": 18,
      "rank_delta": 16,
      "trend": "up",
      "importance": "general"
    },
    {
      "name": "voocel/ainovel-cli",
      "repo": "voocel/ainovel-cli",
      "description": "ainovel-cli 用多 agent 自动生成 AI 小说，覆盖题材设定、大纲、章节和正文生成流程；适合内容工具团队观察长文本创作链路如何保持角色一致性、剧情连续性、人工审校入口和版权风险边界。真正产品化还要看章节记忆、风格控制、事实一致性和敏感内容处理。",
      "readme_cache": {
        "key": "github-readme/voocel/ainovel-cli/main/unknown",
        "hit": true,
        "repo": "voocel/ainovel-cli",
        "sha": "unknown",
        "default_branch": "main",
        "source_url": "https://raw.githubusercontent.com/voocel/ainovel-cli/main/README.md"
      },
      "readme_fetch_status": "ok",
      "url": "https://github.com/voocel/ainovel-cli",
      "event_date": "2026-07-02",
      "source": "GitHub Trending Go weekly",
      "language": "go",
      "window": "weekly",
      "rank": 19,
      "source_rank": 3,
      "source_scope": "weekly:go",
      "previous_rank": 19,
      "rank_delta": 16,
      "trend": "up",
      "importance": "general"
    },
    {
      "name": "keycloak/keycloak",
      "repo": "keycloak/keycloak",
      "description": "Keycloak 是身份与访问管理项目；它不是 AI 专项项目，但对需要把 AI 应用接入企业认证的团队仍有参考价值。评估时应看单点登录、角色权限、审计日志、部署复杂度，以及 agent 工具调用时的权限隔离和撤销能力。接入 AI 平台时还要验证服务账号、短期凭证、审计追踪和多租户隔离。",
      "readme_cache": {
        "key": "github-readme/keycloak/keycloak/main/unknown",
        "hit": true,
        "repo": "keycloak/keycloak",
        "sha": "unknown",
        "default_branch": "main",
        "source_url": "https://raw.githubusercontent.com/keycloak/keycloak/main/README.md"
      },
      "readme_fetch_status": "ok",
      "url": "https://github.com/keycloak/keycloak",
      "event_date": "2026-07-02",
      "source": "GitHub Trending Java weekly",
      "language": "java",
      "window": "weekly",
      "rank": 20,
      "source_rank": 2,
      "source_scope": "weekly:java",
      "previous_rank": 20,
      "rank_delta": 18,
      "trend": "up",
      "importance": "general"
    }
  ],
  "huggingface_trending": [
    {
      "name": "black-forest-labs/FLUX.1-dev",
      "repo": "black-forest-labs/FLUX.1-dev",
      "description": "FLUX.1-dev 是图像生成模型，适合作为高质量文生图工作流里的画质、提示词和风格控制对照基线。落地时要重点核对模型卡、许可证、商业使用限制、推理成本和生成内容安全边界。",
      "url": "https://huggingface.co/black-forest-labs/FLUX.1-dev",
      "event_date": "2026-07-02",
      "source": "Hugging Face Trending Models",
      "task": "text-to-image",
      "downloads": 1085982,
      "likes": 13440,
      "rank": 1,
      "trend": "trending",
      "editorial_category": "open_source",
      "importance": "notable"
    },
    {
      "name": "deepseek-ai/DeepSeek-R1",
      "repo": "deepseek-ai/DeepSeek-R1",
      "description": "DeepSeek-R1 是推理取向的文本生成模型，适合放进中文、英文复杂问答和代码推理评测里做基线。使用前要核对上下文长度、部署方式、许可证、蒸馏版本差异和真实任务上的稳定性。",
      "url": "https://huggingface.co/deepseek-ai/DeepSeek-R1",
      "event_date": "2026-07-02",
      "source": "Hugging Face Trending Models",
      "task": "text-generation",
      "downloads": 7849894,
      "likes": 13425,
      "rank": 2,
      "trend": "trending",
      "editorial_category": "open_source",
      "importance": "notable"
    },
    {
      "name": "stabilityai/stable-diffusion-xl-base-1.0",
      "repo": "stabilityai/stable-diffusion-xl-base-1.0",
      "description": "SDXL base 1.0 是成熟的图像生成基础模型，价值在于生态、插件和工作流兼容性仍然丰富。读者可把它作为 ControlNet、LoRA、ComfyUI 等链路的稳定对照，而不是只看榜单热度。",
      "url": "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0",
      "event_date": "2026-07-02",
      "source": "Hugging Face Trending Models",
      "task": "text-to-image",
      "downloads": 1348068,
      "likes": 7874,
      "rank": 3,
      "trend": "trending",
      "editorial_category": "open_source",
      "importance": "notable"
    },
    {
      "name": "CompVis/stable-diffusion-v1-4",
      "repo": "CompVis/stable-diffusion-v1-4",
      "description": "Stable Diffusion v1-4 是较早期但生态广泛的扩散模型，更适合作为兼容性、轻量工作流和历史基线参考。若追求最新画质，应同时比较 SDXL、FLUX 等更新模型。",
      "url": "https://huggingface.co/CompVis/stable-diffusion-v1-4",
      "event_date": "2026-07-02",
      "source": "Hugging Face Trending Models",
      "task": "text-to-image",
      "downloads": 426835,
      "likes": 7028,
      "rank": 4,
      "trend": "trending",
      "editorial_category": "open_source",
      "importance": "general"
    },
    {
      "name": "meta-llama/Meta-Llama-3-8B",
      "repo": "meta-llama/Meta-Llama-3-8B",
      "description": "Meta-Llama-3-8B 是 8B 级文本生成基础模型，适合做轻量部署、微调实验和本地推理基线。评估时要把显存、量化效果、许可证和指令微调版本一起看。",
      "url": "https://huggingface.co/meta-llama/Meta-Llama-3-8B",
      "event_date": "2026-07-02",
      "source": "Hugging Face Trending Models",
      "task": "text-generation",
      "downloads": 1237078,
      "likes": 6582,
      "rank": 5,
      "trend": "trending",
      "editorial_category": "open_source",
      "importance": "general"
    },
    {
      "name": "hexgrad/Kokoro-82M",
      "repo": "hexgrad/Kokoro-82M",
      "description": "Kokoro-82M 是小型语音生成模型，适合关注低成本 TTS、旁白生成和本地语音链路的人快速试验。落地前要核对语种、音色授权、延迟、音质和长文本稳定性。",
      "url": "https://huggingface.co/hexgrad/Kokoro-82M",
      "event_date": "2026-07-02",
      "source": "Hugging Face Trending Models",
      "task": "text-to-speech",
      "downloads": 14354455,
      "likes": 6421,
      "rank": 6,
      "trend": "trending",
      "editorial_category": "open_source",
      "importance": "general"
    },
    {
      "name": "meta-llama/Llama-3.1-8B-Instruct",
      "repo": "meta-llama/Llama-3.1-8B-Instruct",
      "description": "Llama-3.1-8B-Instruct 是 8B 级指令模型，适合做本地助手、工具调用前置评测和轻量 RAG 对话基线。使用时要重点比较指令遵循、中文效果、量化后质量和部署成本。",
      "url": "https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct",
      "event_date": "2026-07-02",
      "source": "Hugging Face Trending Models",
      "task": "text-generation",
      "downloads": 9688941,
      "likes": 6202,
      "rank": 7,
      "trend": "trending",
      "editorial_category": "open_source",
      "importance": "general"
    },
    {
      "name": "openai/whisper-large-v3",
      "repo": "openai/whisper-large-v3",
      "description": "whisper-large-v3 是多语种语音识别模型，适合会议转写、字幕、音频资料整理和语音数据预处理。真正投入生产前，要测试领域词、噪声场景、长音频切分和隐私处理。",
      "url": "https://huggingface.co/openai/whisper-large-v3",
      "event_date": "2026-07-02",
      "source": "Hugging Face Trending Models",
      "task": "automatic-speech-recognition",
      "downloads": 5797940,
      "likes": 5902,
      "rank": 8,
      "trend": "trending",
      "editorial_category": "open_source",
      "importance": "general"
    },
    {
      "name": "black-forest-labs/FLUX.1-schnell",
      "repo": "black-forest-labs/FLUX.1-schnell",
      "description": "FLUX.1-schnell 侧重更快的图像生成体验，适合原型、批量草图和低延迟创意工作流。它应和 FLUX.1-dev 等高质量模型配合比较，重点看速度、画质和授权边界。",
      "url": "https://huggingface.co/black-forest-labs/FLUX.1-schnell",
      "event_date": "2026-07-02",
      "source": "Hugging Face Trending Models",
      "task": "text-to-image",
      "downloads": 240253,
      "likes": 5257,
      "rank": 9,
      "trend": "trending",
      "editorial_category": "open_source",
      "importance": "general"
    },
    {
      "name": "deepseek-ai/DeepSeek-V4-Pro",
      "repo": "deepseek-ai/DeepSeek-V4-Pro",
      "description": "DeepSeek-V4-Pro 是文本生成模型条目，适合先作为社区模型卡线索纳入观察，再用真实问答、代码和推理任务验证效果。采用前要核对来源、许可证、部署条件和与正式发布版本的关系。",
      "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro",
      "event_date": "2026-07-02",
      "source": "Hugging Face Trending Models",
      "task": "text-generation",
      "downloads": 1140384,
      "likes": 5130,
      "rank": 10,
      "trend": "trending",
      "editorial_category": "open_source",
      "importance": "general"
    }
  ],
  "model_releases": [],
  "hot_blogs": [
    {
      "title": "Alibaba Cloud 讨论企业 agent 后半场：越用越聪明",
      "editorial_category": "viewpoint_analysis",
      "image_url": "https://img.alicdn.com/tfs/TB1h1SxbkOWBuNjSsppXXXPgpXa-28-28.png",
      "image_alt": "The Second Half of the Enterprise Agent Era: How to Make Agents Smarter the More They Are Used?",
      "image_source": "html_index",
      "url": "https://www.alibabacloud.com/blog/the-second-half-of-the-enterprise-agent-era-how-to-make-agents-smarter-the-more-they-are-used_603319",
      "publisher": "Alibaba Cloud Blog",
      "author": "Alibaba Cloud Blog",
      "event_date": "2026-07-01",
      "topic": "AI engineering tools",
      "summary": "Alibaba Cloud 讨论企业 agent 从演示走向持续使用后的系统能力，重点是任务路由、业务流程自动化、护栏和组织集成。文章把 agent 可用性放到数据回流、流程接入和治理能力上，说明企业要让 agent 长期有效，不能只看单次演示，而要看它是否能进入真实业务流程、持续学习并接受权限约束。",
      "content_type": "blog",
      "importance": "notable",
      "image_urls": []
    },
    {
      "title": "Google 汇总 2026 年 6 月 AI 更新",
      "editorial_category": "viewpoint_analysis",
      "image_url": "https://storage.googleapis.com/gweb-uniblog-publish-prod/images/260701_ICYMI-June-AI_Thumb.max-600x600.format-webp.webp",
      "image_alt": "The latest AI news we announced in June 2026",
      "image_source": "feed",
      "url": "https://blog.google/innovation-and-ai/technology/ai/google-ai-updates-june-2026/",
      "publisher": "Google Keyword Blog",
      "author": "Google Keyword Blog",
      "event_date": "2026-07-01",
      "topic": "AI industry",
      "summary": "Google 这篇月度索引按产品、研究和平台把 6 月 AI 公告串起来，列出 Gemini、开发者工具、Workspace、教育和实验项目等入口。它的用法不是替代原文，而是帮助读者按产品线回查发布日期、可用范围、目标用户、接入条件和限制，适合团队整理 6 月 Google AI 路线、试点清单、采购讨论和培训材料。",
      "content_type": "blog",
      "importance": "notable",
      "image_urls": []
    },
    {
      "title": "IBM Research 在 Hugging Face 介绍 ScarfBench 企业 Java agent 基准",
      "editorial_category": "viewpoint_analysis",
      "url": "https://huggingface.co/blog/ibm-research/scarfbench",
      "publisher": "Hugging Face Blog",
      "author": "Hugging Face Blog",
      "event_date": "2026-06-30",
      "topic": "AI engineering tools",
      "summary": "IBM Research 在 Hugging Face Blog 介绍 ScarfBench，用企业 Java 场景评测 AI agent 处理代码库任务的能力。文章重点放在基准任务、企业代码环境、回归测试和 agent 评测设计，而不是单个模型分数，适合关注真实仓库任务评测的人细读。对平台团队来说，它提供了把 agent 放进企业遗留代码环境测试的参照，也能帮助评估内部代码助手是否真的能处理维护型任务。",
      "content_type": "blog",
      "importance": "notable",
      "image_urls": []
    },
    {
      "title": "GitHub Enterprise 可默认启用 Copilot 自动选模型",
      "editorial_category": "viewpoint_analysis",
      "url": "https://github.blog/changelog/2026-07-01-enterprises-can-default-to-auto-model-selection",
      "publisher": "GitHub Changelog",
      "author": "GitHub Changelog",
      "event_date": "2026-07-01",
      "topic": "AI industry",
      "summary": "GitHub Changelog 说明企业管理员可以把 Copilot 的模型选择交给自动模式，系统会在可用模型中为请求选择合适选项。对企业开发平台来说，这减少了逐团队指定模型的运维负担，也让策略重点转向权限、计费、审计和默认体验。需要关注的是自动选择是否覆盖组织内所有 Copilot 场景，以及管理员还能否按合规要求限制模型范围。",
      "content_type": "blog",
      "importance": "notable",
      "image_urls": []
    },
    {
      "title": "Meta 公开大规模 AI 存储蓝图",
      "editorial_category": "viewpoint_analysis",
      "url": "https://engineering.fb.com/2026/07/01/data-infrastructure/metas-ai-storage-blueprint-at-scale/",
      "publisher": "Meta Engineering",
      "author": "Meta Engineering",
      "event_date": "2026-07-01",
      "topic": "AI industry",
      "summary": "Meta Engineering 介绍其面向 AI 负载的存储架构，重点放在训练、推理和数据基础设施如何共同支撑更大的吞吐与更低延迟。文章适合基础设施团队查看大规模集群下的数据布局、冷热分层、可靠性和成本取舍。它的价值不在于给出可直接复制的产品，而是展示 AI 平台扩容时存储系统会怎样成为模型迭代速度的关键约束。",
      "content_type": "blog",
      "importance": "notable",
      "image_urls": []
    },
    {
      "title": "Cloudflare 解释如何让 AI 搜索更聪明",
      "editorial_category": "viewpoint_analysis",
      "url": "https://blog.cloudflare.com/making-ai-search-smarter/",
      "publisher": "Cloudflare Blog",
      "author": "Cloudflare Blog",
      "event_date": "2026-07-01",
      "topic": "AI engineering tools",
      "summary": "Cloudflare 讨论 AI 搜索结果如何结合网页抓取、内容理解和站点控制，避免只把传统搜索摘要包装成聊天答案。文章把重点放在发布者、搜索代理和基础设施平台之间的关系：网站需要控制访问和计费，搜索体验需要更准确地引用来源，平台则要处理抓取频率、内容授权和回答质量。对内容站和平台团队来说，这是观察 AI 搜索商业边界的一篇材料。",
      "content_type": "blog",
      "importance": "notable",
      "image_urls": []
    },
    {
      "title": "NVIDIA 拆解降低推理 token 成本的软件栈",
      "editorial_category": "viewpoint_analysis",
      "image_url": "https://iprsoftwaremedia.com/219/files/202606/46eca8102541963ace8bb69d9d7d8481/6a43da813d6332962ecebe9c_press-inference-ai-aws-beat-1920x1080-1-842x450/press-inference-ai-aws-beat-1920x1080-1-842x450_thmb.jpg",
      "image_alt": "How NVIDIA’s Inference Software Stack Powers the Lowest Token Cost",
      "image_source": "feed",
      "url": "https://blogs.nvidia.com/blog/inference-software-lowest-token-cost/",
      "publisher": "NVIDIA Newsroom RSS",
      "author": "NVIDIA Newsroom RSS",
      "event_date": "2026-06-30",
      "topic": "AI industry",
      "summary": "NVIDIA Newsroom 这篇文章用推理部署成本拆解软件栈作用，重点写模型服务、批处理、内存管理、GPU 利用率和云端调度如何影响每 token 成本。它把对比维度从硬件规格拉到吞吐、延迟、调度和运行数据，适合平台团队评估线上大模型服务时检查优化流程、成本归因和采购方案，而不是只看芯片型号或模型压缩。",
      "content_type": "blog",
      "importance": "notable",
      "image_urls": []
    },
    {
      "title": "NVIDIA Developer Blog 讲解 agent 强化学习技巧",
      "editorial_category": "viewpoint_analysis",
      "url": "https://developer.nvidia.com/blog/mastering-agentic-techniques-ai-agent-reinforcement-learning/",
      "publisher": "NVIDIA Developer Blog",
      "author": "NVIDIA Developer Blog",
      "event_date": "2026-07-01",
      "topic": "AI engineering tools",
      "summary": "NVIDIA Developer Blog 讲解把强化学习用于 AI agent 的训练与优化，重点是奖励设计、环境反馈、任务成功率和多步决策。文章适合已经在做工具调用或自动化 agent 的工程团队，用来理解为什么仅靠提示词很难稳定解决长链路任务。它把 agent 训练拆到可评估的动作、状态和反馈循环上，便于后续设计实验、回放失败案例和比较策略改进效果。",
      "content_type": "blog",
      "importance": "notable",
      "image_urls": []
    }
  ],
  "chinese_media_dynamics": [
    {
      "title": "天工AI业务ARR突破8亿美元，向中国首个非BAT10亿美元ARR的AI公司迈进",
      "url": "https://www.qbitai.com/2026/07/441786.html",
      "publisher": "QbitAI",
      "author": "QbitAI",
      "event_date": "2026-07-02",
      "topic": "中文 AI 媒体动态",
      "summary": "天工AI业务ARR突破8亿美元，向中国首个非BAT10亿美元ARR的AI公司迈进：其中AI短剧平台业务ARR超过7亿美元。",
      "key_points": [
        "天工AI业务ARR突破8亿美元，向中国首个非BAT10亿美元ARR的AI公司迈进：其中AI短剧平台业务ARR超过7亿美元"
      ],
      "importance": "notable",
      "image_urls": []
    },
    {
      "title": "训练世界模型，开始从人类的肌肉和脑子里偷师了",
      "url": "https://www.qbitai.com/2026/07/441725.html",
      "publisher": "QbitAI",
      "author": "QbitAI",
      "event_date": "2026-07-02",
      "topic": "中文 AI 媒体动态",
      "summary": "训练世界模型，开始从人类的肌肉和脑子里偷师了：具身智能数采迎来了新范式。",
      "key_points": [
        "训练世界模型，开始从人类的肌肉和脑子里偷师了：具身智能数采迎来了新范式"
      ],
      "importance": "notable",
      "image_urls": []
    },
    {
      "title": "2000+智算产业代表齐聚深圳，2026 中国智算产业生态发展年会成功举办！",
      "url": "https://www.qbitai.com/2026/07/441586.html",
      "publisher": "QbitAI",
      "author": "QbitAI",
      "event_date": "2026-07-02",
      "topic": "中文 AI 媒体动态",
      "summary": "2000+智算产业代表齐聚深圳，2026 中国智算产业生态发展年会成功举办！：AI入场景，Token大时代。",
      "key_points": [
        "2000+智算产业代表齐聚深圳，2026 中国智算产业生态发展年会成功举办",
        "：AI入场景，Token大时代"
      ],
      "importance": "notable",
      "image_urls": []
    },
    {
      "title": "世界模型来了因果技术标杆！具身大脑真要长脑子了",
      "url": "https://www.qbitai.com/2026/07/441490.html",
      "publisher": "QbitAI",
      "author": "QbitAI",
      "event_date": "2026-07-02",
      "topic": "中文 AI 媒体动态",
      "summary": "世界模型来了因果技术标杆！具身大脑真要长脑子了：",
      "key_points": [
        "世界模型来了因果技术标杆",
        "具身大脑真要长脑子了："
      ],
      "importance": "notable",
      "image_urls": []
    },
    {
      "title": "AI眼镜不再依赖手机！这次真要单飞了",
      "url": "https://www.qbitai.com/2026/07/441491.html",
      "publisher": "QbitAI",
      "author": "QbitAI",
      "event_date": "2026-07-02",
      "topic": "中文 AI 媒体动态",
      "summary": "AI眼镜不再依赖手机！这次真要单飞了：AI时代自己的操作系统来了。",
      "key_points": [
        "AI眼镜不再依赖手机",
        "这次真要单飞了：AI时代自己的操作系统来了"
      ],
      "importance": "notable",
      "image_urls": []
    },
    {
      "title": "7月14日悉尼RSS’26，线下Social Mixer晚宴报名中！",
      "url": "https://www.qbitai.com/2026/07/441479.html",
      "publisher": "QbitAI",
      "author": "QbitAI",
      "event_date": "2026-07-02",
      "topic": "中文 AI 媒体动态",
      "summary": "7月14日悉尼RSS’26，线下Social Mixer晚宴报名中！：机器人顶会RSS 2026就要来了！",
      "key_points": [
        "7月14日悉尼RSS’26，线下Social Mixer晚宴报名中",
        "：机器人顶会RSS 2026就要来了"
      ],
      "importance": "notable",
      "image_urls": []
    },
    {
      "title": "人才黑洞！UC伯克利系主任都加入A社了",
      "url": "https://www.qbitai.com/2026/07/441447.html",
      "publisher": "QbitAI",
      "author": "QbitAI",
      "event_date": "2026-07-02",
      "topic": "中文 AI 媒体动态",
      "summary": "人才黑洞！UC伯克利系主任都加入A社了：加盟预训练团队。",
      "key_points": [
        "人才黑洞",
        "UC伯克利系主任都加入A社了：加盟预训练团队"
      ],
      "importance": "notable",
      "image_urls": []
    },
    {
      "title": "共创试读 | 给童年一份礼物：从是什么到为什么，找到合适的掌机",
      "url": "https://sspai.com/post/111069",
      "publisher": "SSPAI",
      "author": "SSPAI",
      "event_date": "2026-07-02",
      "topic": "中文 AI 媒体动态",
      "summary": "共创试读 | 给童年一份礼物：从是什么到为什么，找到合适的掌机：近年来，伴随着复古浪潮的席卷而来，许多玩家似乎又重新关注起了一些老游戏、老设备。从2022年的3DS涨价风波，到后来层出不穷的所谓「开源掌机」，大家似乎都在寻找一个通往童年的入口，重新回味那些历久弥新 ... 查看全文。。",
      "key_points": [
        "共创试读 | 给童年一份礼物：从是什么到为什么，找到合适的掌机：近年来，伴随着复古浪潮的席卷而来，许多玩家似乎又重新关注起了一些老游戏、老设备",
        "从2022年的3DS涨价风波，到后来层出不穷的所谓「开源掌机」，大家似乎都在寻找一个通往童年的入口，重新回味那些历久弥新 ... 查看全文"
      ],
      "importance": "notable",
      "image_urls": []
    },
    {
      "title": "派早报：WhatsApp 开放用户名预留、PS 将取消实体光盘等",
      "url": "https://sspai.com/post/111861",
      "publisher": "SSPAI",
      "author": "SSPAI",
      "event_date": "2026-07-02",
      "topic": "中文 AI 媒体动态",
      "summary": "派早报：WhatsApp 开放用户名预留、PS 将取消实体光盘等：Gmail Live 进入测试阶段、大我推出 B251 PRO 显示器等。 查看全文。",
      "key_points": [
        "派早报：WhatsApp 开放用户名预留、PS 将取消实体光盘等：Gmail Live 进入测试阶段、大我推出 B251 PRO 显示器等",
        "查看全文"
      ],
      "importance": "notable",
      "image_urls": []
    }
  ],
  "daily_tracking": [
    {
      "id": "openrouter-rankings",
      "name": "OpenRouter",
      "url": "https://openrouter.ai/rankings",
      "event_date": "2026-07-02",
      "source": "OpenRouter Rankings",
      "category": "model_usage",
      "importance": "notable",
      "change_status": "changed",
      "change_summary": "OpenRouter 本周 Top 10 已解析：#1 DeepSeek V4 Flash 4.88T tokens；#2 MiMo-V2.5 4.25T tokens；#3 Hy3 preview 3.75T tokens；GLM 5.2 周变化 32%。",
      "summary": "OpenRouter 公开榜单显示，本周调用热度第一是 DeepSeek V4 Flash（deepseek，4.88T tokens，周变化 2%）。 Top 10 供应商分布为 anthropic 3、deepseek 2、minimax 1、openrouter 1、tencent 1、xiaomi 1、z-ai 1，可用来观察开发者在 OpenRouter 平台内的真实调用偏好。 该快照只说明 OpenRouter 平台内使用热度，不能替代能力榜单或全市场份额判断。",
      "watch_points": [
        "GLM 5.2 的周变化为 32%，需要结合发布、价格、免费额度和上下文窗口变化判断原因。",
        "若没有新进榜，重点看榜首和供应商份额是否迁移。",
        "OpenRouter 用量是平台内需求信号；生产选型仍需回到延迟、价格、上下文长度和自有任务复测。"
      ],
      "metrics": [
        {
          "label": "榜单范围",
          "value": "This Week Top 10",
          "trend": "same"
        },
        {
          "label": "供应商分布",
          "value": "anthropic 3、deepseek 2、minimax 1、openrouter 1、tencent 1、xiaomi 1、z-ai 1",
          "trend": "unknown"
        },
        {
          "label": "#1",
          "value": "DeepSeek V4 Flash（deepseek）：4.88T tokens，周变化 2%",
          "trend": "up"
        },
        {
          "label": "#2",
          "value": "MiMo-V2.5（xiaomi）：4.25T tokens，周变化 3%",
          "trend": "up"
        },
        {
          "label": "#3",
          "value": "Hy3 preview（tencent）：3.75T tokens，周变化 9%",
          "trend": "up"
        },
        {
          "label": "#4",
          "value": "MiniMax M3（minimax）：3.62T tokens，周变化 3%",
          "trend": "up"
        },
        {
          "label": "#5",
          "value": "Owl Alpha（openrouter）：2.91T tokens，周变化 1%",
          "trend": "up"
        },
        {
          "label": "#6",
          "value": "GLM 5.2（z-ai）：2.38T tokens，周变化 32%",
          "trend": "up"
        },
        {
          "label": "#7",
          "value": "DeepSeek V4 Pro（deepseek）：2.2T tokens，周变化 0%",
          "trend": "same"
        },
        {
          "label": "#8",
          "value": "Claude Opus 4.8（anthropic）：2.02T tokens，周变化 8%",
          "trend": "up"
        },
        {
          "label": "#9",
          "value": "Claude Opus 4.7（anthropic）：1.77T tokens，周变化 30%",
          "trend": "up"
        },
        {
          "label": "#10",
          "value": "Claude Sonnet 4.6（anthropic）：1.54T tokens，周变化 4%",
          "trend": "up"
        }
      ],
      "snapshot": {
        "type": "openrouter_rankings_public_page",
        "collection_method": "public_page_playwright",
        "snapshot_status": "complete",
        "snapshot_as_of": "2026-07-02T08:54:32.449Z",
        "source_url": "https://openrouter.ai/rankings",
        "top_entries": [
          {
            "rank": 1,
            "model": "DeepSeek V4 Flash",
            "provider": "deepseek",
            "tokens": "4.88T tokens",
            "change": "2%"
          },
          {
            "rank": 2,
            "model": "MiMo-V2.5",
            "provider": "xiaomi",
            "tokens": "4.25T tokens",
            "change": "3%"
          },
          {
            "rank": 3,
            "model": "Hy3 preview",
            "provider": "tencent",
            "tokens": "3.75T tokens",
            "change": "9%"
          },
          {
            "rank": 4,
            "model": "MiniMax M3",
            "provider": "minimax",
            "tokens": "3.62T tokens",
            "change": "3%"
          },
          {
            "rank": 5,
            "model": "Owl Alpha",
            "provider": "openrouter",
            "tokens": "2.91T tokens",
            "change": "1%"
          },
          {
            "rank": 6,
            "model": "GLM 5.2",
            "provider": "z-ai",
            "tokens": "2.38T tokens",
            "change": "32%"
          },
          {
            "rank": 7,
            "model": "DeepSeek V4 Pro",
            "provider": "deepseek",
            "tokens": "2.2T tokens",
            "change": "0%"
          },
          {
            "rank": 8,
            "model": "Claude Opus 4.8",
            "provider": "anthropic",
            "tokens": "2.02T tokens",
            "change": "8%"
          },
          {
            "rank": 9,
            "model": "Claude Opus 4.7",
            "provider": "anthropic",
            "tokens": "1.77T tokens",
            "change": "30%"
          },
          {
            "rank": 10,
            "model": "Claude Sonnet 4.6",
            "provider": "anthropic",
            "tokens": "1.54T tokens",
            "change": "4%"
          }
        ],
        "official_component_snapshot": {
          "source": "official_dom",
          "component_kind": "openrouter_rankings",
          "source_url": "https://openrouter.ai/rankings",
          "captured_at": "2026-07-02T08:54:32.449Z",
          "selector_version": "openrouter-rankings-v1",
          "source_selector": "[data-openrouter-rankings]",
          "sanitized_html": "<section class=\"openrouter-rankings-card\" data-openrouter-rankings>\n        <header>\n          <p>OpenRouter Top Models</p>\n          <span>This Week usage ranking</span>\n        </header>\n        <table>\n          <thead><tr><th>Rank</th><th>Model</th><th>Provider</th><th>Tokens</th><th>Change</th></tr></thead>\n          <tbody>\n        <tr><td>#1</td><td>DeepSeek V4 Flash</td><td>deepseek</td><td>4.88T tokens</td><td>2%</td></tr>\n        <tr><td>#2</td><td>MiMo-V2.5</td><td>xiaomi</td><td>4.25T tokens</td><td>3%</td></tr>\n        <tr><td>#3</td><td>Hy3 preview</td><td>tencent</td><td>3.75T tokens</td><td>9%</td></tr>\n        <tr><td>#4</td><td>MiniMax M3</td><td>minimax</td><td>3.62T tokens</td><td>3%</td></tr>\n        <tr><td>#5</td><td>Owl Alpha</td><td>openrouter</td><td>2.91T tokens</td><td>1%</td></tr>\n        <tr><td>#6</td><td>GLM 5.2</td><td>z-ai</td><td>2.38T tokens</td><td>32%</td></tr>\n        <tr><td>#7</td><td>DeepSeek V4 Pro</td><td>deepseek</td><td>2.2T tokens</td><td>0%</td></tr>\n        <tr><td>#8</td><td>Claude Opus 4.8</td><td>anthropic</td><td>2.02T tokens</td><td>8%</td></tr>\n        <tr><td>#9</td><td>Claude Opus 4.7</td><td>anthropic</td><td>1.77T tokens</td><td>30%</td></tr>\n        <tr><td>#10</td><td>Claude Sonnet 4.6</td><td>anthropic</td><td>1.54T tokens</td><td>4%</td></tr></tbody>\n        </table>\n      </section>",
          "sanitized_css": ".openrouter-rankings-card { border: 1px solid currentColor; padding: 16px; }\n      .openrouter-rankings-card table { width: 100%; border-collapse: collapse; }\n      .openrouter-rankings-card th, .openrouter-rankings-card td { padding: 8px; border-top: 1px solid currentColor; text-align: left; }",
          "dom_hash": "sha256:962898c7ec1bf5f1fda2fa49399fd7339ca8f92516cdb114b308b98994509e5c",
          "css_hash": "sha256:e5df3dc0e07de42f5c2ca4021bbf59258d47d1ef0fa569d55d98b09876618e43"
        },
        "history_entries": []
      },
      "tracking_component_snapshot": {
        "source": "OpenRouter",
        "component_kind": "openrouter_rankings",
        "source_url": "https://openrouter.ai/rankings",
        "collected_at": "2026-07-02T08:54:32.449Z",
        "selector_version": "openrouter-rankings-v1",
        "raw_dom_hash": "sha256:c9a236047036e5adf9bf7b5181724694dc606c41e33a3d0e2c6dc7cf97277ed4",
        "data_hash": "sha256:03d618d587f5d14609f32959350fb9169c10f3e31d04e458bbe794fcee31bf7c",
        "tabs": [
          {
            "id": "top-models",
            "label": "七日排名",
            "view": "line_multi",
            "fallback_reason": ""
          },
          {
            "id": "leaderboard",
            "label": "当前榜单",
            "view": "leaderboard",
            "fallback_reason": ""
          }
        ],
        "series": [
          {
            "id": "openrouter-top-models-weekly-usage",
            "tab_id": "top-models",
            "label": "OpenRouter Top models rank trend",
            "chart": "line_multi",
            "rows": [
              {
                "rank": 1,
                "model": "DeepSeek V4 Flash",
                "provider": "deepseek",
                "value": 4880000000000,
                "value_label": "4.88T tokens",
                "change": "2%",
                "metric": "2026-07-02"
              },
              {
                "rank": 2,
                "model": "MiMo-V2.5",
                "provider": "xiaomi",
                "value": 4250000000000,
                "value_label": "4.25T tokens",
                "change": "3%",
                "metric": "2026-07-02"
              },
              {
                "rank": 3,
                "model": "Hy3 preview",
                "provider": "tencent",
                "value": 3750000000000,
                "value_label": "3.75T tokens",
                "change": "9%",
                "metric": "2026-07-02"
              },
              {
                "rank": 4,
                "model": "MiniMax M3",
                "provider": "minimax",
                "value": 3620000000000,
                "value_label": "3.62T tokens",
                "change": "3%",
                "metric": "2026-07-02"
              },
              {
                "rank": 5,
                "model": "Owl Alpha",
                "provider": "openrouter",
                "value": 2910000000000,
                "value_label": "2.91T tokens",
                "change": "1%",
                "metric": "2026-07-02"
              },
              {
                "rank": 6,
                "model": "GLM 5.2",
                "provider": "z-ai",
                "value": 2380000000000,
                "value_label": "2.38T tokens",
                "change": "32%",
                "metric": "2026-07-02"
              },
              {
                "rank": 7,
                "model": "DeepSeek V4 Pro",
                "provider": "deepseek",
                "value": 2200000000000,
                "value_label": "2.2T tokens",
                "change": "0%",
                "metric": "2026-07-02"
              },
              {
                "rank": 8,
                "model": "Claude Opus 4.8",
                "provider": "anthropic",
                "value": 2020000000000,
                "value_label": "2.02T tokens",
                "change": "8%",
                "metric": "2026-07-02"
              },
              {
                "rank": 9,
                "model": "Claude Opus 4.7",
                "provider": "anthropic",
                "value": 1770000000000,
                "value_label": "1.77T tokens",
                "change": "30%",
                "metric": "2026-07-02"
              },
              {
                "rank": 10,
                "model": "Claude Sonnet 4.6",
                "provider": "anthropic",
                "value": 1540000000000,
                "value_label": "1.54T tokens",
                "change": "4%",
                "metric": "2026-07-02"
              }
            ],
            "fallback_reason": ""
          },
          {
            "id": "openrouter-llm-leaderboard",
            "tab_id": "leaderboard",
            "label": "LLM Leaderboard",
            "chart": "leaderboard",
            "rows": [
              {
                "rank": 1,
                "model": "DeepSeek V4 Flash",
                "provider": "deepseek",
                "value": 4880000000000,
                "value_label": "4.88T tokens",
                "change": "2%"
              },
              {
                "rank": 2,
                "model": "MiMo-V2.5",
                "provider": "xiaomi",
                "value": 4250000000000,
                "value_label": "4.25T tokens",
                "change": "3%"
              },
              {
                "rank": 3,
                "model": "Hy3 preview",
                "provider": "tencent",
                "value": 3750000000000,
                "value_label": "3.75T tokens",
                "change": "9%"
              },
              {
                "rank": 4,
                "model": "MiniMax M3",
                "provider": "minimax",
                "value": 3620000000000,
                "value_label": "3.62T tokens",
                "change": "3%"
              },
              {
                "rank": 5,
                "model": "Owl Alpha",
                "provider": "openrouter",
                "value": 2910000000000,
                "value_label": "2.91T tokens",
                "change": "1%"
              },
              {
                "rank": 6,
                "model": "GLM 5.2",
                "provider": "z-ai",
                "value": 2380000000000,
                "value_label": "2.38T tokens",
                "change": "32%"
              },
              {
                "rank": 7,
                "model": "DeepSeek V4 Pro",
                "provider": "deepseek",
                "value": 2200000000000,
                "value_label": "2.2T tokens",
                "change": "0%"
              },
              {
                "rank": 8,
                "model": "Claude Opus 4.8",
                "provider": "anthropic",
                "value": 2020000000000,
                "value_label": "2.02T tokens",
                "change": "8%"
              },
              {
                "rank": 9,
                "model": "Claude Opus 4.7",
                "provider": "anthropic",
                "value": 1770000000000,
                "value_label": "1.77T tokens",
                "change": "30%"
              },
              {
                "rank": 10,
                "model": "Claude Sonnet 4.6",
                "provider": "anthropic",
                "value": 1540000000000,
                "value_label": "1.54T tokens",
                "change": "4%"
              }
            ],
            "fallback_reason": ""
          }
        ],
        "rows": [
          {
            "rank": 1,
            "model": "DeepSeek V4 Flash",
            "provider": "deepseek",
            "value": 4880000000000,
            "value_label": "4.88T tokens",
            "change": "2%"
          },
          {
            "rank": 2,
            "model": "MiMo-V2.5",
            "provider": "xiaomi",
            "value": 4250000000000,
            "value_label": "4.25T tokens",
            "change": "3%"
          },
          {
            "rank": 3,
            "model": "Hy3 preview",
            "provider": "tencent",
            "value": 3750000000000,
            "value_label": "3.75T tokens",
            "change": "9%"
          },
          {
            "rank": 4,
            "model": "MiniMax M3",
            "provider": "minimax",
            "value": 3620000000000,
            "value_label": "3.62T tokens",
            "change": "3%"
          },
          {
            "rank": 5,
            "model": "Owl Alpha",
            "provider": "openrouter",
            "value": 2910000000000,
            "value_label": "2.91T tokens",
            "change": "1%"
          },
          {
            "rank": 6,
            "model": "GLM 5.2",
            "provider": "z-ai",
            "value": 2380000000000,
            "value_label": "2.38T tokens",
            "change": "32%"
          },
          {
            "rank": 7,
            "model": "DeepSeek V4 Pro",
            "provider": "deepseek",
            "value": 2200000000000,
            "value_label": "2.2T tokens",
            "change": "0%"
          },
          {
            "rank": 8,
            "model": "Claude Opus 4.8",
            "provider": "anthropic",
            "value": 2020000000000,
            "value_label": "2.02T tokens",
            "change": "8%"
          },
          {
            "rank": 9,
            "model": "Claude Opus 4.7",
            "provider": "anthropic",
            "value": 1770000000000,
            "value_label": "1.77T tokens",
            "change": "30%"
          },
          {
            "rank": 10,
            "model": "Claude Sonnet 4.6",
            "provider": "anthropic",
            "value": 1540000000000,
            "value_label": "1.54T tokens",
            "change": "4%"
          }
        ],
        "previous_snapshot": null,
        "diff": {
          "summary": "暂无上一版组件快照可对比。",
          "changed_rows": [],
          "new_entries": [
            "DeepSeek V4 Flash",
            "MiMo-V2.5",
            "Hy3 preview",
            "MiniMax M3",
            "Owl Alpha",
            "GLM 5.2",
            "DeepSeek V4 Pro",
            "Claude Opus 4.8",
            "Claude Opus 4.7",
            "Claude Sonnet 4.6"
          ]
        },
        "cache_path": "",
        "fallback_reason": "",
        "official_component_snapshot": {
          "source": "official_dom",
          "component_kind": "openrouter_rankings",
          "source_url": "https://openrouter.ai/rankings",
          "captured_at": "2026-07-02T08:54:32.449Z",
          "selector_version": "openrouter-rankings-v1",
          "source_selector": "[data-openrouter-rankings]",
          "sanitized_html": "<section class=\"openrouter-rankings-card\" data-openrouter-rankings>\n        <header>\n          <p>OpenRouter Top Models</p>\n          <span>This Week usage ranking</span>\n        </header>\n        <table>\n          <thead><tr><th>Rank</th><th>Model</th><th>Provider</th><th>Tokens</th><th>Change</th></tr></thead>\n          <tbody>\n        <tr><td>#1</td><td>DeepSeek V4 Flash</td><td>deepseek</td><td>4.88T tokens</td><td>2%</td></tr>\n        <tr><td>#2</td><td>MiMo-V2.5</td><td>xiaomi</td><td>4.25T tokens</td><td>3%</td></tr>\n        <tr><td>#3</td><td>Hy3 preview</td><td>tencent</td><td>3.75T tokens</td><td>9%</td></tr>\n        <tr><td>#4</td><td>MiniMax M3</td><td>minimax</td><td>3.62T tokens</td><td>3%</td></tr>\n        <tr><td>#5</td><td>Owl Alpha</td><td>openrouter</td><td>2.91T tokens</td><td>1%</td></tr>\n        <tr><td>#6</td><td>GLM 5.2</td><td>z-ai</td><td>2.38T tokens</td><td>32%</td></tr>\n        <tr><td>#7</td><td>DeepSeek V4 Pro</td><td>deepseek</td><td>2.2T tokens</td><td>0%</td></tr>\n        <tr><td>#8</td><td>Claude Opus 4.8</td><td>anthropic</td><td>2.02T tokens</td><td>8%</td></tr>\n        <tr><td>#9</td><td>Claude Opus 4.7</td><td>anthropic</td><td>1.77T tokens</td><td>30%</td></tr>\n        <tr><td>#10</td><td>Claude Sonnet 4.6</td><td>anthropic</td><td>1.54T tokens</td><td>4%</td></tr></tbody>\n        </table>\n      </section>",
          "sanitized_css": ".openrouter-rankings-card { border: 1px solid currentColor; padding: 16px; }\n      .openrouter-rankings-card table { width: 100%; border-collapse: collapse; }\n      .openrouter-rankings-card th, .openrouter-rankings-card td { padding: 8px; border-top: 1px solid currentColor; text-align: left; }",
          "dom_hash": "sha256:962898c7ec1bf5f1fda2fa49399fd7339ca8f92516cdb114b308b98994509e5c",
          "css_hash": "sha256:e5df3dc0e07de42f5c2ca4021bbf59258d47d1ef0fa569d55d98b09876618e43"
        },
        "public_trace": {
          "source_url": "https://openrouter.ai/rankings",
          "collected_at": "2026-07-02T08:54:32.449Z",
          "selector_version": "openrouter-rankings-v1",
          "data_hash": "sha256:03d618d587f5d14609f32959350fb9169c10f3e31d04e458bbe794fcee31bf7c",
          "top_rows": [
            {
              "rank": 1,
              "model": "DeepSeek V4 Flash",
              "provider": "deepseek",
              "value_label": "4.88T tokens",
              "change": "2%"
            },
            {
              "rank": 2,
              "model": "MiMo-V2.5",
              "provider": "xiaomi",
              "value_label": "4.25T tokens",
              "change": "3%"
            },
            {
              "rank": 3,
              "model": "Hy3 preview",
              "provider": "tencent",
              "value_label": "3.75T tokens",
              "change": "9%"
            },
            {
              "rank": 4,
              "model": "MiniMax M3",
              "provider": "minimax",
              "value_label": "3.62T tokens",
              "change": "3%"
            },
            {
              "rank": 5,
              "model": "Owl Alpha",
              "provider": "openrouter",
              "value_label": "2.91T tokens",
              "change": "1%"
            },
            {
              "rank": 6,
              "model": "GLM 5.2",
              "provider": "z-ai",
              "value_label": "2.38T tokens",
              "change": "32%"
            },
            {
              "rank": 7,
              "model": "DeepSeek V4 Pro",
              "provider": "deepseek",
              "value_label": "2.2T tokens",
              "change": "0%"
            },
            {
              "rank": 8,
              "model": "Claude Opus 4.8",
              "provider": "anthropic",
              "value_label": "2.02T tokens",
              "change": "8%"
            },
            {
              "rank": 9,
              "model": "Claude Opus 4.7",
              "provider": "anthropic",
              "value_label": "1.77T tokens",
              "change": "30%"
            },
            {
              "rank": 10,
              "model": "Claude Sonnet 4.6",
              "provider": "anthropic",
              "value_label": "1.54T tokens",
              "change": "4%"
            }
          ],
          "diff": {
            "summary": "暂无上一版组件快照可对比。",
            "changed_rows": [],
            "new_entries": [
              "DeepSeek V4 Flash",
              "MiMo-V2.5",
              "Hy3 preview",
              "MiniMax M3",
              "Owl Alpha",
              "GLM 5.2",
              "DeepSeek V4 Pro",
              "Claude Opus 4.8",
              "Claude Opus 4.7",
              "Claude Sonnet 4.6"
            ]
          },
          "cache_status": "live",
          "fallback_reason": ""
        }
      }
    },
    {
      "id": "artificial-analysis-intelligence-index",
      "name": "Artificial Analysis",
      "url": "https://artificialanalysis.ai/evaluations/artificial-analysis-intelligence-index",
      "event_date": "2026-07-02",
      "source": "Artificial Analysis Intelligence Index",
      "category": "model_benchmark",
      "importance": "notable",
      "change_status": "changed",
      "change_summary": "Artificial Analysis Intelligence Index Top 10 已解析：#1 Claude Fable 5 (with fallback) 60 分，#2 Claude Opus 4.8 (max) 56 分，#3 GPT-5.5 (xhigh) 55 分。",
      "summary": "Artificial Analysis 公开榜单显示，当前 Intelligence Index 第一是 Claude Fable 5 (with fallback)（anthropic，60 分）。 前三名为 Claude Fable 5 (with fallback) 60 分、Claude Opus 4.8 (max) 56 分、GPT-5.5 (xhigh) 55 分，Top 10 供应商分布为 anthropic 4、google 2、alibaba 1、minimax 1、openai 1、zhipu 1。 这个榜单适合做模型 shortlist 和能力变化监测，但生产选型仍要结合延迟、价格、上下文长度和自有任务复测。",
      "watch_points": [
        "榜首 Claude Fable 5 (with fallback) 的综合分为 60 分，需要继续看它在代码、长上下文和 agentic task 分项上的表现。",
        "Top 10 内部竞争接近：46 分有 2 个模型，不要只按一个名次做选型。",
        "把 Intelligence Index 与价格、延迟、吞吐和可用地区一起看，避免用综合分替代真实 workload 复测。"
      ],
      "metrics": [
        {
          "label": "榜单范围",
          "value": "Intelligence Index Top 10",
          "trend": "same"
        },
        {
          "label": "供应商分布",
          "value": "anthropic 4、google 2、alibaba 1、minimax 1、openai 1、zhipu 1",
          "trend": "unknown"
        },
        {
          "label": "#1",
          "value": "Claude Fable 5 (with fallback)（anthropic）：60 分",
          "trend": "unknown"
        },
        {
          "label": "#2",
          "value": "Claude Opus 4.8 (max)（anthropic）：56 分",
          "trend": "unknown"
        },
        {
          "label": "#3",
          "value": "GPT-5.5 (xhigh)（openai）：55 分",
          "trend": "unknown"
        },
        {
          "label": "#4",
          "value": "Claude Opus 4.7 (max)（anthropic）：54 分",
          "trend": "unknown"
        },
        {
          "label": "#5",
          "value": "Claude Sonnet 5 (max)（anthropic）：53 分",
          "trend": "unknown"
        },
        {
          "label": "#6",
          "value": "GLM-5.2 (max)（zhipu）：51 分",
          "trend": "unknown"
        },
        {
          "label": "#7",
          "value": "Gemini 3.5 Flash（google）：50 分",
          "trend": "unknown"
        },
        {
          "label": "#8",
          "value": "Gemini 3.1 Pro Preview（google）：46 分",
          "trend": "unknown"
        },
        {
          "label": "#9",
          "value": "Qwen3.7 Max（alibaba）：46 分",
          "trend": "unknown"
        },
        {
          "label": "#10",
          "value": "MiniMax-M3（minimax）：44 分",
          "trend": "unknown"
        }
      ],
      "snapshot": {
        "type": "artificial_analysis_intelligence_index_public_page",
        "collection_method": "public_page_playwright",
        "snapshot_status": "complete",
        "snapshot_as_of": "2026-07-02T08:54:32.449Z",
        "source_url": "https://artificialanalysis.ai/evaluations/artificial-analysis-intelligence-index",
        "top_entries": [
          {
            "rank": 1,
            "model": "Claude Fable 5 (with fallback)",
            "provider": "anthropic",
            "tokens": "60 分",
            "change": "AA Index"
          },
          {
            "rank": 2,
            "model": "Claude Opus 4.8 (max)",
            "provider": "anthropic",
            "tokens": "56 分",
            "change": "AA Index"
          },
          {
            "rank": 3,
            "model": "GPT-5.5 (xhigh)",
            "provider": "openai",
            "tokens": "55 分",
            "change": "AA Index"
          },
          {
            "rank": 4,
            "model": "Claude Opus 4.7 (max)",
            "provider": "anthropic",
            "tokens": "54 分",
            "change": "AA Index"
          },
          {
            "rank": 5,
            "model": "Claude Sonnet 5 (max)",
            "provider": "anthropic",
            "tokens": "53 分",
            "change": "AA Index"
          },
          {
            "rank": 6,
            "model": "GLM-5.2 (max)",
            "provider": "zhipu",
            "tokens": "51 分",
            "change": "AA Index"
          },
          {
            "rank": 7,
            "model": "Gemini 3.5 Flash",
            "provider": "google",
            "tokens": "50 分",
            "change": "AA Index"
          },
          {
            "rank": 8,
            "model": "Gemini 3.1 Pro Preview",
            "provider": "google",
            "tokens": "46 分",
            "change": "AA Index"
          },
          {
            "rank": 9,
            "model": "Qwen3.7 Max",
            "provider": "alibaba",
            "tokens": "46 分",
            "change": "AA Index"
          },
          {
            "rank": 10,
            "model": "MiniMax-M3",
            "provider": "minimax",
            "tokens": "44 分",
            "change": "AA Index"
          }
        ],
        "component_tabs": {
          "score": {
            "rows": [
              {
                "rank": 1,
                "model": "Claude Fable 5 (with fallback)",
                "provider": "anthropic",
                "value": 60,
                "value_label": "60 分",
                "change": "AA Index",
                "metric": "Score"
              },
              {
                "rank": 2,
                "model": "Claude Opus 4.8 (max)",
                "provider": "anthropic",
                "value": 56,
                "value_label": "56 分",
                "change": "AA Index",
                "metric": "Score"
              },
              {
                "rank": 3,
                "model": "GPT-5.5 (xhigh)",
                "provider": "openai",
                "value": 55,
                "value_label": "55 分",
                "change": "AA Index",
                "metric": "Score"
              },
              {
                "rank": 4,
                "model": "Claude Opus 4.7 (max)",
                "provider": "anthropic",
                "value": 54,
                "value_label": "54 分",
                "change": "AA Index",
                "metric": "Score"
              },
              {
                "rank": 5,
                "model": "Claude Sonnet 5 (max)",
                "provider": "anthropic",
                "value": 53,
                "value_label": "53 分",
                "change": "AA Index",
                "metric": "Score"
              },
              {
                "rank": 6,
                "model": "GLM-5.2 (max)",
                "provider": "zhipu",
                "value": 51,
                "value_label": "51 分",
                "change": "AA Index",
                "metric": "Score"
              },
              {
                "rank": 7,
                "model": "Gemini 3.5 Flash",
                "provider": "google",
                "value": 50,
                "value_label": "50 分",
                "change": "AA Index",
                "metric": "Score"
              },
              {
                "rank": 8,
                "model": "Gemini 3.1 Pro Preview",
                "provider": "google",
                "value": 46,
                "value_label": "46 分",
                "change": "AA Index",
                "metric": "Score"
              },
              {
                "rank": 9,
                "model": "Qwen3.7 Max",
                "provider": "alibaba",
                "value": 46,
                "value_label": "46 分",
                "change": "AA Index",
                "metric": "Score"
              },
              {
                "rank": 10,
                "model": "MiniMax-M3",
                "provider": "minimax",
                "value": 44,
                "value_label": "44 分",
                "change": "AA Index",
                "metric": "Score"
              }
            ]
          }
        },
        "official_component_snapshot": {
          "source": "official_dom",
          "component_kind": "artificial_analysis_index",
          "source_url": "https://artificialanalysis.ai/evaluations/artificial-analysis-intelligence-index",
          "captured_at": "2026-07-02T08:54:32.449Z",
          "selector_version": "artificial-analysis-index-v1",
          "source_selector": "[data-artificial-analysis-index]",
          "sanitized_html": "<section class=\"artificial-analysis-index-card\" data-artificial-analysis-index>\n        <header>\n          <p>Artificial Analysis Intelligence Index</p>\n          <span>Top models by independent Intelligence Index</span>\n        </header>\n        <table>\n          <thead><tr><th>Rank</th><th>Model</th><th>Provider</th><th>Score</th><th>Metric</th></tr></thead>\n          <tbody>\n        <tr><td>#1</td><td>Claude Fable 5 (with fallback)</td><td>anthropic</td><td>60 分</td><td>AA Index</td></tr>\n        <tr><td>#2</td><td>Claude Opus 4.8 (max)</td><td>anthropic</td><td>56 分</td><td>AA Index</td></tr>\n        <tr><td>#3</td><td>GPT-5.5 (xhigh)</td><td>openai</td><td>55 分</td><td>AA Index</td></tr>\n        <tr><td>#4</td><td>Claude Opus 4.7 (max)</td><td>anthropic</td><td>54 分</td><td>AA Index</td></tr>\n        <tr><td>#5</td><td>Claude Sonnet 5 (max)</td><td>anthropic</td><td>53 分</td><td>AA Index</td></tr>\n        <tr><td>#6</td><td>GLM-5.2 (max)</td><td>zhipu</td><td>51 分</td><td>AA Index</td></tr>\n        <tr><td>#7</td><td>Gemini 3.5 Flash</td><td>google</td><td>50 分</td><td>AA Index</td></tr>\n        <tr><td>#8</td><td>Gemini 3.1 Pro Preview</td><td>google</td><td>46 分</td><td>AA Index</td></tr>\n        <tr><td>#9</td><td>Qwen3.7 Max</td><td>alibaba</td><td>46 分</td><td>AA Index</td></tr>\n        <tr><td>#10</td><td>MiniMax-M3</td><td>minimax</td><td>44 分</td><td>AA Index</td></tr></tbody>\n        </table>\n      </section>",
          "sanitized_css": ".artificial-analysis-index-card { border: 1px solid currentColor; padding: 16px; }\n      .artificial-analysis-index-card table { width: 100%; border-collapse: collapse; }\n      .artificial-analysis-index-card th, .artificial-analysis-index-card td { padding: 8px; border-top: 1px solid currentColor; text-align: left; }",
          "dom_hash": "sha256:c63b40631137507ebb83e1f5b7648d1168d69cec858ca23040009ec18abac3b1",
          "css_hash": "sha256:ca6c14bf5033437bf403050f8a1267f835b68aab80f54905dfa8ecc653808bbf"
        },
        "history_entries": []
      },
      "tracking_component_snapshot": {
        "source": "Artificial Analysis",
        "component_kind": "artificial_analysis_index",
        "source_url": "https://artificialanalysis.ai/evaluations/artificial-analysis-intelligence-index",
        "collected_at": "2026-07-02T08:54:32.449Z",
        "selector_version": "artificial-analysis-index-v1",
        "raw_dom_hash": "sha256:fac6f6fdc424e76e3b92ce5d144017faecec076bf27e20c2846fd070d754e255",
        "data_hash": "sha256:484b098803a869e65de7e15bc174ad7430743a89934367e547bedd3caeb8f7a5",
        "tabs": [
          {
            "id": "score",
            "label": "Score",
            "view": "score_table",
            "fallback_reason": ""
          }
        ],
        "series": [
          {
            "id": "artificial-analysis-score",
            "tab_id": "score",
            "label": "Score",
            "chart": "score_table",
            "rows": [
              {
                "rank": 1,
                "model": "Claude Fable 5 (with fallback)",
                "provider": "anthropic",
                "value": 60,
                "value_label": "60 分",
                "change": "AA Index",
                "metric": "Score"
              },
              {
                "rank": 2,
                "model": "Claude Opus 4.8 (max)",
                "provider": "anthropic",
                "value": 56,
                "value_label": "56 分",
                "change": "AA Index",
                "metric": "Score"
              },
              {
                "rank": 3,
                "model": "GPT-5.5 (xhigh)",
                "provider": "openai",
                "value": 55,
                "value_label": "55 分",
                "change": "AA Index",
                "metric": "Score"
              },
              {
                "rank": 4,
                "model": "Claude Opus 4.7 (max)",
                "provider": "anthropic",
                "value": 54,
                "value_label": "54 分",
                "change": "AA Index",
                "metric": "Score"
              },
              {
                "rank": 5,
                "model": "Claude Sonnet 5 (max)",
                "provider": "anthropic",
                "value": 53,
                "value_label": "53 分",
                "change": "AA Index",
                "metric": "Score"
              },
              {
                "rank": 6,
                "model": "GLM-5.2 (max)",
                "provider": "zhipu",
                "value": 51,
                "value_label": "51 分",
                "change": "AA Index",
                "metric": "Score"
              },
              {
                "rank": 7,
                "model": "Gemini 3.5 Flash",
                "provider": "google",
                "value": 50,
                "value_label": "50 分",
                "change": "AA Index",
                "metric": "Score"
              },
              {
                "rank": 8,
                "model": "Gemini 3.1 Pro Preview",
                "provider": "google",
                "value": 46,
                "value_label": "46 分",
                "change": "AA Index",
                "metric": "Score"
              },
              {
                "rank": 9,
                "model": "Qwen3.7 Max",
                "provider": "alibaba",
                "value": 46,
                "value_label": "46 分",
                "change": "AA Index",
                "metric": "Score"
              },
              {
                "rank": 10,
                "model": "MiniMax-M3",
                "provider": "minimax",
                "value": 44,
                "value_label": "44 分",
                "change": "AA Index",
                "metric": "Score"
              }
            ],
            "fallback_reason": ""
          }
        ],
        "rows": [
          {
            "rank": 1,
            "model": "Claude Fable 5 (with fallback)",
            "provider": "anthropic",
            "value": 60,
            "value_label": "60 分",
            "change": "AA Index",
            "metric": "AA Index"
          },
          {
            "rank": 2,
            "model": "Claude Opus 4.8 (max)",
            "provider": "anthropic",
            "value": 56,
            "value_label": "56 分",
            "change": "AA Index",
            "metric": "AA Index"
          },
          {
            "rank": 3,
            "model": "GPT-5.5 (xhigh)",
            "provider": "openai",
            "value": 55,
            "value_label": "55 分",
            "change": "AA Index",
            "metric": "AA Index"
          },
          {
            "rank": 4,
            "model": "Claude Opus 4.7 (max)",
            "provider": "anthropic",
            "value": 54,
            "value_label": "54 分",
            "change": "AA Index",
            "metric": "AA Index"
          },
          {
            "rank": 5,
            "model": "Claude Sonnet 5 (max)",
            "provider": "anthropic",
            "value": 53,
            "value_label": "53 分",
            "change": "AA Index",
            "metric": "AA Index"
          },
          {
            "rank": 6,
            "model": "GLM-5.2 (max)",
            "provider": "zhipu",
            "value": 51,
            "value_label": "51 分",
            "change": "AA Index",
            "metric": "AA Index"
          },
          {
            "rank": 7,
            "model": "Gemini 3.5 Flash",
            "provider": "google",
            "value": 50,
            "value_label": "50 分",
            "change": "AA Index",
            "metric": "AA Index"
          },
          {
            "rank": 8,
            "model": "Gemini 3.1 Pro Preview",
            "provider": "google",
            "value": 46,
            "value_label": "46 分",
            "change": "AA Index",
            "metric": "AA Index"
          },
          {
            "rank": 9,
            "model": "Qwen3.7 Max",
            "provider": "alibaba",
            "value": 46,
            "value_label": "46 分",
            "change": "AA Index",
            "metric": "AA Index"
          },
          {
            "rank": 10,
            "model": "MiniMax-M3",
            "provider": "minimax",
            "value": 44,
            "value_label": "44 分",
            "change": "AA Index",
            "metric": "AA Index"
          }
        ],
        "previous_snapshot": null,
        "diff": {
          "summary": "暂无上一版组件快照可对比。",
          "changed_rows": [],
          "new_entries": [
            "Claude Fable 5 (with fallback)",
            "Claude Opus 4.8 (max)",
            "GPT-5.5 (xhigh)",
            "Claude Opus 4.7 (max)",
            "Claude Sonnet 5 (max)",
            "GLM-5.2 (max)",
            "Gemini 3.5 Flash",
            "Gemini 3.1 Pro Preview",
            "Qwen3.7 Max",
            "MiniMax-M3"
          ]
        },
        "cache_path": "",
        "fallback_reason": "",
        "official_component_snapshot": {
          "source": "official_dom",
          "component_kind": "artificial_analysis_index",
          "source_url": "https://artificialanalysis.ai/evaluations/artificial-analysis-intelligence-index",
          "captured_at": "2026-07-02T08:54:32.449Z",
          "selector_version": "artificial-analysis-index-v1",
          "source_selector": "[data-artificial-analysis-index]",
          "sanitized_html": "<section class=\"artificial-analysis-index-card\" data-artificial-analysis-index>\n        <header>\n          <p>Artificial Analysis Intelligence Index</p>\n          <span>Top models by independent Intelligence Index</span>\n        </header>\n        <table>\n          <thead><tr><th>Rank</th><th>Model</th><th>Provider</th><th>Score</th><th>Metric</th></tr></thead>\n          <tbody>\n        <tr><td>#1</td><td>Claude Fable 5 (with fallback)</td><td>anthropic</td><td>60 分</td><td>AA Index</td></tr>\n        <tr><td>#2</td><td>Claude Opus 4.8 (max)</td><td>anthropic</td><td>56 分</td><td>AA Index</td></tr>\n        <tr><td>#3</td><td>GPT-5.5 (xhigh)</td><td>openai</td><td>55 分</td><td>AA Index</td></tr>\n        <tr><td>#4</td><td>Claude Opus 4.7 (max)</td><td>anthropic</td><td>54 分</td><td>AA Index</td></tr>\n        <tr><td>#5</td><td>Claude Sonnet 5 (max)</td><td>anthropic</td><td>53 分</td><td>AA Index</td></tr>\n        <tr><td>#6</td><td>GLM-5.2 (max)</td><td>zhipu</td><td>51 分</td><td>AA Index</td></tr>\n        <tr><td>#7</td><td>Gemini 3.5 Flash</td><td>google</td><td>50 分</td><td>AA Index</td></tr>\n        <tr><td>#8</td><td>Gemini 3.1 Pro Preview</td><td>google</td><td>46 分</td><td>AA Index</td></tr>\n        <tr><td>#9</td><td>Qwen3.7 Max</td><td>alibaba</td><td>46 分</td><td>AA Index</td></tr>\n        <tr><td>#10</td><td>MiniMax-M3</td><td>minimax</td><td>44 分</td><td>AA Index</td></tr></tbody>\n        </table>\n      </section>",
          "sanitized_css": ".artificial-analysis-index-card { border: 1px solid currentColor; padding: 16px; }\n      .artificial-analysis-index-card table { width: 100%; border-collapse: collapse; }\n      .artificial-analysis-index-card th, .artificial-analysis-index-card td { padding: 8px; border-top: 1px solid currentColor; text-align: left; }",
          "dom_hash": "sha256:c63b40631137507ebb83e1f5b7648d1168d69cec858ca23040009ec18abac3b1",
          "css_hash": "sha256:ca6c14bf5033437bf403050f8a1267f835b68aab80f54905dfa8ecc653808bbf"
        },
        "public_trace": {
          "source_url": "https://artificialanalysis.ai/evaluations/artificial-analysis-intelligence-index",
          "collected_at": "2026-07-02T08:54:32.449Z",
          "selector_version": "artificial-analysis-index-v1",
          "data_hash": "sha256:484b098803a869e65de7e15bc174ad7430743a89934367e547bedd3caeb8f7a5",
          "top_rows": [
            {
              "rank": 1,
              "model": "Claude Fable 5 (with fallback)",
              "provider": "anthropic",
              "value_label": "60 分",
              "change": "AA Index"
            },
            {
              "rank": 2,
              "model": "Claude Opus 4.8 (max)",
              "provider": "anthropic",
              "value_label": "56 分",
              "change": "AA Index"
            },
            {
              "rank": 3,
              "model": "GPT-5.5 (xhigh)",
              "provider": "openai",
              "value_label": "55 分",
              "change": "AA Index"
            },
            {
              "rank": 4,
              "model": "Claude Opus 4.7 (max)",
              "provider": "anthropic",
              "value_label": "54 分",
              "change": "AA Index"
            },
            {
              "rank": 5,
              "model": "Claude Sonnet 5 (max)",
              "provider": "anthropic",
              "value_label": "53 分",
              "change": "AA Index"
            },
            {
              "rank": 6,
              "model": "GLM-5.2 (max)",
              "provider": "zhipu",
              "value_label": "51 分",
              "change": "AA Index"
            },
            {
              "rank": 7,
              "model": "Gemini 3.5 Flash",
              "provider": "google",
              "value_label": "50 分",
              "change": "AA Index"
            },
            {
              "rank": 8,
              "model": "Gemini 3.1 Pro Preview",
              "provider": "google",
              "value_label": "46 分",
              "change": "AA Index"
            },
            {
              "rank": 9,
              "model": "Qwen3.7 Max",
              "provider": "alibaba",
              "value_label": "46 分",
              "change": "AA Index"
            },
            {
              "rank": 10,
              "model": "MiniMax-M3",
              "provider": "minimax",
              "value_label": "44 分",
              "change": "AA Index"
            }
          ],
          "diff": {
            "summary": "暂无上一版组件快照可对比。",
            "changed_rows": [],
            "new_entries": [
              "Claude Fable 5 (with fallback)",
              "Claude Opus 4.8 (max)",
              "GPT-5.5 (xhigh)",
              "Claude Opus 4.7 (max)",
              "Claude Sonnet 5 (max)",
              "GLM-5.2 (max)",
              "Gemini 3.5 Flash",
              "Gemini 3.1 Pro Preview",
              "Qwen3.7 Max",
              "MiniMax-M3"
            ]
          },
          "cache_status": "live",
          "fallback_reason": ""
        }
      }
    },
    {
      "id": "swe-bench-pro-public",
      "name": "SWE-bench Pro",
      "url": "https://scaleapi.github.io/SWE-bench_Pro-os/",
      "event_date": "2026-07-02",
      "source": "Scale Labs SWE-Bench Pro",
      "category": "coding_benchmark",
      "importance": "major",
      "change_status": "changed",
      "change_summary": "SWE-bench Pro Public Dataset Top 10 已解析：#1 SWE-Agent + claude-4-5-Sonnet 43.72%，#2 SWE-Agent + claude-4-Sonnet 42.70%，#3 SWE-Agent + claude-4-5-haiku 39.45%。",
      "summary": "Scale Labs 公开榜单显示，SWE-bench Pro Public Dataset 当前第一是 SWE-Agent + claude-4-5-Sonnet（anthropic，Resolve Rate 43.72%）。 前三名为 SWE-Agent + claude-4-5-Sonnet 43.72%、SWE-Agent + claude-4-Sonnet 42.70%、SWE-Agent + claude-4-5-haiku 39.45%，Top 10 供应商分布为 anthropic 3、openai 2、moonshot 1、unknown 1。 这个榜单适合观察 coding agent 在长周期真实工程任务上的相对表现，但生产选型仍要结合 scaffold、成本上限、置信区间和团队自有仓库复测。",
      "watch_points": [
        "榜首 SWE-Agent + claude-4-5-Sonnet 的 Resolve Rate 为 43.72%，需要看它是否依赖特定 agent scaffold 或成本上限。",
        "如果 Top 10 没有新进榜，重点看相邻模型的置信区间是否重叠。",
        "把 SWE-bench Pro 与真实 IDE/CI 工作流分开看，避免把公开 benchmark 直接等同于团队仓库里的修复率。"
      ],
      "metrics": [
        {
          "label": "榜单范围",
          "value": "SWE-bench Pro Public Top 7",
          "trend": "same"
        },
        {
          "label": "供应商分布",
          "value": "anthropic 3、openai 2、moonshot 1、unknown 1",
          "trend": "unknown"
        },
        {
          "label": "#1",
          "value": "SWE-Agent + claude-4-5-Sonnet（anthropic）：Resolve Rate 43.72%",
          "trend": "unknown"
        },
        {
          "label": "#2",
          "value": "SWE-Agent + claude-4-Sonnet（anthropic）：Resolve Rate 42.70%",
          "trend": "unknown"
        },
        {
          "label": "#3",
          "value": "SWE-Agent + claude-4-5-haiku（anthropic）：Resolve Rate 39.45%",
          "trend": "unknown"
        },
        {
          "label": "#4",
          "value": "SWE-Agent + gpt-5-2025-08-07 (High)（openai）：Resolve Rate 36.30%",
          "trend": "unknown"
        },
        {
          "label": "#5",
          "value": "SWE-Agent + glm-4.5（unknown）：Resolve Rate 35.52%",
          "trend": "unknown"
        },
        {
          "label": "#6",
          "value": "SWE-Agent + kimi-k2-instruct（moonshot）：Resolve Rate 27.67%",
          "trend": "unknown"
        },
        {
          "label": "#7",
          "value": "SWE-Agent + gpt-oss-120b（openai）：Resolve Rate 16.20%",
          "trend": "unknown"
        }
      ],
      "snapshot": {
        "type": "swe_bench_pro_public_page",
        "collection_method": "public_page_playwright",
        "snapshot_status": "partial",
        "snapshot_as_of": "2026-07-02T08:54:32.449Z",
        "source_url": "https://scaleapi.github.io/SWE-bench_Pro-os/",
        "top_entries": [
          {
            "rank": 1,
            "model": "SWE-Agent + claude-4-5-Sonnet",
            "provider": "anthropic",
            "tokens": "43.72%",
            "change": "Resolve Rate"
          },
          {
            "rank": 2,
            "model": "SWE-Agent + claude-4-Sonnet",
            "provider": "anthropic",
            "tokens": "42.70%",
            "change": "Resolve Rate"
          },
          {
            "rank": 3,
            "model": "SWE-Agent + claude-4-5-haiku",
            "provider": "anthropic",
            "tokens": "39.45%",
            "change": "Resolve Rate"
          },
          {
            "rank": 4,
            "model": "SWE-Agent + gpt-5-2025-08-07 (High)",
            "provider": "openai",
            "tokens": "36.30%",
            "change": "Resolve Rate"
          },
          {
            "rank": 5,
            "model": "SWE-Agent + glm-4.5",
            "provider": "unknown",
            "tokens": "35.52%",
            "change": "Resolve Rate"
          },
          {
            "rank": 6,
            "model": "SWE-Agent + kimi-k2-instruct",
            "provider": "moonshot",
            "tokens": "27.67%",
            "change": "Resolve Rate"
          },
          {
            "rank": 7,
            "model": "SWE-Agent + gpt-oss-120b",
            "provider": "openai",
            "tokens": "16.20%",
            "change": "Resolve Rate"
          }
        ],
        "official_component_snapshot": {
          "source": "official_dom",
          "component_kind": "swe_bench_pro",
          "source_url": "https://scaleapi.github.io/SWE-bench_Pro-os/",
          "captured_at": "2026-07-02T08:54:32.449Z",
          "selector_version": "swe-bench-pro-v1",
          "source_selector": "[class*='leaderboard'] table",
          "sanitized_html": "<table id=\"leaderboardTable\">\n                <thead>\n                    <tr>\n                        <th>Model</th>\n                        <th>% Resolved</th>\n                        <th>(+/-)</th>\n                        <th>Link</th>\n                    </tr>\n                </thead>\n                <tbody>\n                    <tr data-score=\"43.72\">\n                        <td>\n                            <span class=\"model-icons\">🥇</span>\n                            <span class=\"model-name\">SWE-Agent + claude-4-5-Sonnet</span>\n                        </td>\n                        <td><span class=\"resolved-score\">43.72</span></td>\n                        <td><span class=\"z-score positive\">±3.60</span></td>\n                        <td><a class=\"link-icon\" target=\"_blank\">🔗</a></td>\n                    </tr>\n                    <tr data-score=\"42.70\">\n                        <td>\n                            <span class=\"model-icons\">🥈</span>\n                            <span class=\"model-name\">SWE-Agent + claude-4-Sonnet</span>\n                        </td>\n                        <td><span class=\"resolved-score\">42.70</span></td>\n                        <td><span class=\"z-score positive\">±3.59</span></td>\n                        <td><a class=\"link-icon\" target=\"_blank\">🔗</a></td>\n                    </tr>\n                    <tr data-score=\"39.45\">\n                        <td>\n                            <span class=\"model-icons\">🥉</span>\n                            <span class=\"model-name\">SWE-Agent + claude-4-5-haiku</span>\n                        </td>\n                        <td><span class=\"resolved-score\">39.45</span></td>\n                        <td><span class=\"z-score positive\">±3.55</span></td>\n                        <td><a class=\"link-icon\" target=\"_blank\">🔗</a></td>\n                    </tr>\n                    <tr data-score=\"36.30\">\n                        <td>\n                            <span class=\"model-name\">SWE-Agent + gpt-5-2025-08-07 (High)</span>\n                        </td>\n                        <td><span class=\"resolved-score\">36.30</span></td>\n                        <td><span class=\"z-score positive\">±3.49</span></td>\n                        <td><a class=\"link-icon\" target=\"_blank\">🔗</a></td>\n                    </tr>\n                    <tr data-score=\"35.52\">\n                        <td>\n                            <span class=\"model-name\">SWE-Agent + glm-4.5</span>\n                        </td>\n                        <td><span class=\"resolved-score\">35.52</span></td>\n                        <td><span class=\"z-score positive\">±3.47</span></td>\n                        <td><a class=\"link-icon\" target=\"_blank\">🔗</a></td>\n                    </tr>\n                    <tr data-score=\"27.67\">\n                        <td>\n                            <span class=\"model-name\">SWE-Agent + kimi-k2-instruct</span>\n                        </td>\n                        <td><span class=\"resolved-score\">27.67</span></td>\n                        <td><span class=\"z-score positive\">±3.25</span></td>\n                        <td><a class=\"link-icon\" target=\"_blank\">🔗</a></td>\n                    </tr>\n                    <tr data-score=\"16.20\">\n                        <td>\n                            <span class=\"model-name\">SWE-Agent + gpt-oss-120b</span>\n                        </td>\n                        <td><span class=\"resolved-score\">16.20</span></td>\n                        <td><span class=\"z-score positive\">±2.67</span></td>\n                        <td><a class=\"link-icon\" target=\"_blank\">🔗</a></td>\n                    </tr>\n                </tbody>\n            </table>",
          "sanitized_css": "* {\n            margin: 0;\n            padding: 0;\n            box-sizing: border-box;\n        }\n\n        body {\n            font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, sans-serif;\n            background: linear-gradient(135deg, #667eea 0%, #764ba2 25%, #f093fb 50%, #4facfe 100%);\n            min-height: 100vh;\n            padding: 40px 20px;\n        }\n\n        .container {\n            max-width: 1200px;\n            margin: 0 auto;\n        }\n\n        .header {\n            text-align: center;\n            margin-bottom: 60px;\n        }\n\n        h1 {\n            font-size: 3.5rem;\n            font-weight: 800;\n            color: #ffffff;\n            text-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);\n        }\n\n        .subtitle {\n            font-size: 1.25rem;\n            color: #ffffff;\n            margin-top: 10px;\n            text-shadow: 0 1px 2px rgba(0, 0, 0, 0.1);\n        }\n\n        .buttons {\n            display: flex;\n            gap: 15px;\n            justify-content: center;\n            margin-top: 30px;\n            flex-wrap: wrap;\n        }\n\n        .btn {\n            padding: 12px 28px;\n            border: 2px solid #3b82f6;\n            background: white;\n            border-radius: 10px;\n            font-size: 1rem;\n            font-weight: 600;\n            color: #3b82f6;\n            cursor: pointer;\n            transition: all 0.3s ease;\n            text-decoration: none;\n            display: inline-flex;\n            align-items: center;\n            gap: 8px;\n        }\n\n        .btn:hover {\n            background: #3b82f6;\n            color: white;\n            transform: translateY(-2px);\n            box-shadow: 0 4px 12px rgba(59, 130, 246, 0.3);\n        }\n\n        .leaderboard-section {\n            background: white;\n            border-radius: 20px;\n            padding: 40px;\n            box-shadow: 0 10px 40px rgba(0, 0, 0, 0.1);\n        }\n\n        .leaderboard-title {\n            font-size: 2rem;\n            font-weight: 700;\n            color: #1f2937;\n            margin-bottom: 30px;\n        }\n\n        table {\n            width: 100%;\n            border-collapse: collapse;\n        }\n\n        thead {\n            background: #f3f4f6;\n        }\n\n        th {\n            padding: 16px;\n            text-align: left;\n            font-weight: 600;\n            color: #374151;\n            border-bottom: 2px solid #e5e7eb;\n        }\n\n        th:nth-child(2), th:nth-child(3) {\n            text-align: center;\n        }\n\n        th:last-child {\n            text-align: center;\n        }\n\n        tbody tr {\n            border-bottom: 1px solid #e5e7eb;\n            transition: background 0.2s ease;\n        }\n\n        tbody tr:hover {\n            background: #f9fafb;\n        }\n\n        td {\n            padding: 16px;\n            color: #1f2937;\n        }\n\n        td:nth-child(2), td:nth-child(3) {\n            text-align: center;\n        }\n\n        td:last-child {\n            text-align: center;\n        }\n\n        .model-name {\n            font-weight: 500;\n        }\n\n        .model-icons {\n            display: inline-flex;\n            gap: 4px;\n            margin-right: 8px;\n            font-size: 1.2rem;\n        }\n\n        .resolved-score {\n            font-size: 1.25rem;\n            font-weight: 700;\n            color: #059669;\n        }\n\n        .z-score {\n            font-size: 1.1rem;\n            font-weight: 600;\n        }\n\n        .z-score.positive {\n            color: #059669;\n        }\n\n        .z-score.negative {\n            color: #dc2626;\n        }\n\n        .link-icon {\n            color: #3b82f6;\n            text-decoration: none;\n            font-size: 1.2rem;\n            transition: transform 0.2s ease;\n            display: inline-block;\n        }\n\n        .link-icon:hover {\n            transform: scale(1.2);\n        }\n\n        .about-section {\n            background: white;\n            border-radius: 20px;\n            padding: 40px;\n            margin-top: 40px;\n            box-shadow: 0 10px 40px rgba(0, 0, 0, 0.1);\n        }\n\n        .about-section h2 {\n            font-size: 1.75rem;\n            font-weight: 700;\n            color: #1f2937;\n            margin-top: 30px;\n            margin-bottom: 15px;\n        }\n\n        .about-section h2:first-child {\n            margin-top: 0;\n        }\n\n        .about-section p {\n            color: #4b5563;\n            line-height: 1.7;\n            margin-bottom: 15px;\n        }\n\n        .about-section a {\n            color: #3b82f6;\n            text-decoration: none;\n        }\n\n        .about-section a:hover {\n            text-decoration: underline;\n        }\n\n        .citation-box {\n            background: #f9fafb;\n            border: 1px solid #e5e7eb;\n            border-radius: 8px;\n            padding: 20px;\n            margin: 20px 0;\n            font-family: 'Courier New', monospace;\n            font-size: 0.9rem;\n            overflow-x: auto;\n            white-space: pre-wrap;\n            color: #1f2937;\n        }\n\n        .disclaimer {\n            margin-top: 30px;\n            padding-top: 20px;\n            border-top: 1px solid #e5e7eb;\n            color: #6b7280;\n            font-size: 0.9rem;\n        }\n\n        @media (max-width: 768px) {\n            h1 {\n                font-size: 2.5rem;\n            }\n\n            .leaderboard-section {\n                padding: 20px;\n                overflow-x: auto;\n            }\n\n            .about-section {\n                padding: 20px;\n            }\n\n            table {\n                font-size: 0.9rem;\n            }\n\n            th, td {\n                padding: 12px 8px;\n            }\n        }\n    \n* { margin: 0px; padding: 0px; box-sizing: border-box; }\nbody { font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Roboto, Oxygen, Ubuntu, Cantarell, sans-serif; background: linear-gradient(135deg, rgb(102, 126, 234) 0%, rgb(118, 75, 162) 25%, rgb(240, 147, 251) 50%, rgb(79, 172, 254) 100%); min-height: 100vh; padding: 40px 20px; }\n.container { max-width: 1200px; margin: 0px auto; }\n.header { text-align: center; margin-bottom: 60px; }\nh1 { font-size: 3.5rem; font-weight: 800; color: rgb(255, 255, 255); text-shadow: rgba(0, 0, 0, 0.1) 0px 2px 4px; }\n.subtitle { font-size: 1.25rem; color: rgb(255, 255, 255); margin-top: 10px; text-shadow: rgba(0, 0, 0, 0.1) 0px 1px 2px; }\n.buttons { display: flex; gap: 15px; justify-content: center; margin-top: 30px; flex-wrap: wrap; }\n.btn { padding: 12px 28px; border: 2px solid rgb(59, 130, 246); background: white; border-radius: 10px; font-size: 1rem; font-weight: 600; color: rgb(59, 130, 246); cursor: pointer; transition: 0.3s; text-decoration: none; display: inline-flex; align-items: center; gap: 8px; }\n.btn:hover { background: rgb(59, 130, 246); color: white; transform: translateY(-2px); box-shadow: rgba(59, 130, 246, 0.3) 0px 4px 12px; }\n.leaderboard-section { background: white; border-radius: 20px; padding: 40px; box-shadow: rgba(0, 0, 0, 0.1) 0px 10px 40px; }\n.leaderboard-title { font-size: 2rem; font-weight: 700; color: rgb(31, 41, 55); margin-bottom: 30px; }\ntable { width: 100%; border-collapse: collapse; }\nthead { background: rgb(243, 244, 246); }\nth { padding: 16px; text-align: left; font-weight: 600; color: rgb(55, 65, 81); border-bottom: 2px solid rgb(229, 231, 235); }\nth:nth-child(2), th:nth-child(3) { text-align: center; }\nth:last-child { text-align: center; }\ntbody tr { border-bottom: 1px solid rgb(229, 231, 235); transition: background 0.2s; }\ntbody tr:hover { background: rgb(249, 250, 251); }\ntd { padding: 16px; color: rgb(31, 41, 55); }\ntd:nth-child(2), td:nth-child(3) { text-align: center; }\ntd:last-child { text-align: center; }\n.model-name { font-weight: 500; }\n.model-icons { display: inline-flex; gap: 4px; margin-right: 8px; font-size: 1.2rem; }\n.resolved-score { font-size: 1.25rem; font-weight: 700; color: rgb(5, 150, 105); }\n.z-score { font-size: 1.1rem; font-weight: 600; }\n.z-score.positive { color: rgb(5, 150, 105); }\n.z-score.negative { color: rgb(220, 38, 38); }\n.link-icon { color: rgb(59, 130, 246); text-decoration: none; font-size: 1.2rem; transition: transform 0.2s; display: inline-block; }\n.link-icon:hover { transform: scale(1.2); }\n.about-section { background: white; border-radius: 20px; padding: 40px; margin-top: 40px; box-shadow: rgba(0, 0, 0, 0.1) 0px 10px 40px; }\n.about-section h2 { font-size: 1.75rem; font-weight: 700; color: rgb(31, 41, 55); margin-top: 30px; margin-bottom: 15px; }\n.about-section h2:first-child { margin-top: 0px; }\n.about-section p { color: rgb(75, 85, 99); line-height: 1.7; margin-bottom: 15px; }\n.about-section a { color: rgb(59, 130, 246); text-decoration: none; }\n.about-section a:hover { text-decoration: underline; }\n.citation-box { background: rgb(249, 250, 251); border: 1px solid rgb(229, 231, 235); border-radius: 8px; padding: 20px; margin: 20px 0px; font-family: \"Courier New\", monospace; font-size: 0.9rem; overflow-x: auto; white-space: pre-wrap; color: rgb(31, 41, 55); }\n.disclaimer { margin-top: 30px; padding-top: 20px; border-top: 1px solid rgb(229, 231, 235); color: rgb(107, 114, 128); font-size: 0.9rem; }\n@media (max-width: 768px) {\n  h1 { font-size: 2.5rem; }\n  .leaderboard-section { padding: 20px; overflow-x: auto; }\n  .about-section { padding: 20px; }\n  table { font-size: 0.9rem; }\n  th, td { padding: 12px 8px; }\n}",
          "dom_hash": "sha256:1f59c1095c9588133cb14ee70c786c9fac692efe965f0037ac37af011dc9b8e0",
          "css_hash": "sha256:e8faaa62a35f8c29907c95bf2a3426552315a1b98e25418654fb403e5c98c8a9"
        },
        "history_entries": []
      },
      "tracking_component_snapshot": {
        "source": "Scale Labs SWE-Bench Pro",
        "component_kind": "swe_bench_pro",
        "source_url": "https://scaleapi.github.io/SWE-bench_Pro-os/",
        "collected_at": "2026-07-02T08:54:32.449Z",
        "selector_version": "swe-bench-pro-v1",
        "raw_dom_hash": "sha256:057bc1637ef3ae8c8b543f87c930e575de79e7272761bb098f5986a60bf81dcb",
        "data_hash": "sha256:7e801b23f4536a18fde5ac8b8f93ec69cc412a7a01ca56ff08beca730f05256c",
        "tabs": [
          {
            "id": "leaderboard",
            "label": "Public Leaderboard",
            "view": "leaderboard",
            "fallback_reason": ""
          }
        ],
        "series": [
          {
            "id": "swe-bench-pro-public-leaderboard",
            "tab_id": "leaderboard",
            "label": "SWE-Bench Pro Public Dataset",
            "chart": "leaderboard",
            "rows": [
              {
                "rank": 1,
                "model": "SWE-Agent + claude-4-5-Sonnet",
                "provider": "anthropic",
                "value": 43.72,
                "value_label": "43.72%",
                "change": "Resolve Rate",
                "metric": "Resolve Rate"
              },
              {
                "rank": 2,
                "model": "SWE-Agent + claude-4-Sonnet",
                "provider": "anthropic",
                "value": 42.7,
                "value_label": "42.70%",
                "change": "Resolve Rate",
                "metric": "Resolve Rate"
              },
              {
                "rank": 3,
                "model": "SWE-Agent + claude-4-5-haiku",
                "provider": "anthropic",
                "value": 39.45,
                "value_label": "39.45%",
                "change": "Resolve Rate",
                "metric": "Resolve Rate"
              },
              {
                "rank": 4,
                "model": "SWE-Agent + gpt-5-2025-08-07 (High)",
                "provider": "openai",
                "value": 36.3,
                "value_label": "36.30%",
                "change": "Resolve Rate",
                "metric": "Resolve Rate"
              },
              {
                "rank": 5,
                "model": "SWE-Agent + glm-4.5",
                "provider": "unknown",
                "value": 35.52,
                "value_label": "35.52%",
                "change": "Resolve Rate",
                "metric": "Resolve Rate"
              },
              {
                "rank": 6,
                "model": "SWE-Agent + kimi-k2-instruct",
                "provider": "moonshot",
                "value": 27.67,
                "value_label": "27.67%",
                "change": "Resolve Rate",
                "metric": "Resolve Rate"
              },
              {
                "rank": 7,
                "model": "SWE-Agent + gpt-oss-120b",
                "provider": "openai",
                "value": 16.2,
                "value_label": "16.20%",
                "change": "Resolve Rate",
                "metric": "Resolve Rate"
              }
            ],
            "fallback_reason": ""
          }
        ],
        "rows": [
          {
            "rank": 1,
            "model": "SWE-Agent + claude-4-5-Sonnet",
            "provider": "anthropic",
            "value": 43.72,
            "value_label": "43.72%",
            "change": "Resolve Rate",
            "metric": "Resolve Rate"
          },
          {
            "rank": 2,
            "model": "SWE-Agent + claude-4-Sonnet",
            "provider": "anthropic",
            "value": 42.7,
            "value_label": "42.70%",
            "change": "Resolve Rate",
            "metric": "Resolve Rate"
          },
          {
            "rank": 3,
            "model": "SWE-Agent + claude-4-5-haiku",
            "provider": "anthropic",
            "value": 39.45,
            "value_label": "39.45%",
            "change": "Resolve Rate",
            "metric": "Resolve Rate"
          },
          {
            "rank": 4,
            "model": "SWE-Agent + gpt-5-2025-08-07 (High)",
            "provider": "openai",
            "value": 36.3,
            "value_label": "36.30%",
            "change": "Resolve Rate",
            "metric": "Resolve Rate"
          },
          {
            "rank": 5,
            "model": "SWE-Agent + glm-4.5",
            "provider": "unknown",
            "value": 35.52,
            "value_label": "35.52%",
            "change": "Resolve Rate",
            "metric": "Resolve Rate"
          },
          {
            "rank": 6,
            "model": "SWE-Agent + kimi-k2-instruct",
            "provider": "moonshot",
            "value": 27.67,
            "value_label": "27.67%",
            "change": "Resolve Rate",
            "metric": "Resolve Rate"
          },
          {
            "rank": 7,
            "model": "SWE-Agent + gpt-oss-120b",
            "provider": "openai",
            "value": 16.2,
            "value_label": "16.20%",
            "change": "Resolve Rate",
            "metric": "Resolve Rate"
          }
        ],
        "previous_snapshot": null,
        "diff": {
          "summary": "暂无上一版组件快照可对比。",
          "changed_rows": [],
          "new_entries": [
            "SWE-Agent + claude-4-5-Sonnet",
            "SWE-Agent + claude-4-Sonnet",
            "SWE-Agent + claude-4-5-haiku",
            "SWE-Agent + gpt-5-2025-08-07 (High)",
            "SWE-Agent + glm-4.5",
            "SWE-Agent + kimi-k2-instruct",
            "SWE-Agent + gpt-oss-120b"
          ]
        },
        "cache_path": "",
        "fallback_reason": "",
        "official_component_snapshot": {
          "source": "official_dom",
          "component_kind": "swe_bench_pro",
          "source_url": "https://scaleapi.github.io/SWE-bench_Pro-os/",
          "captured_at": "2026-07-02T08:54:32.449Z",
          "selector_version": "swe-bench-pro-v1",
          "source_selector": "[class*='leaderboard'] table",
          "sanitized_html": "<table id=\"leaderboardTable\">\n                <thead>\n                    <tr>\n                        <th>Model</th>\n                        <th>% Resolved</th>\n                        <th>(+/-)</th>\n                        <th>Link</th>\n                    </tr>\n                </thead>\n                <tbody>\n                    <tr data-score=\"43.72\">\n                        <td>\n                            <span class=\"model-icons\">🥇</span>\n                            <span class=\"model-name\">SWE-Agent + claude-4-5-Sonnet</span>\n                        </td>\n                        <td><span class=\"resolved-score\">43.72</span></td>\n                        <td><span class=\"z-score positive\">±3.60</span></td>\n                        <td><a class=\"link-icon\" target=\"_blank\">🔗</a></td>\n                    </tr>\n                    <tr data-score=\"42.70\">\n                        <td>\n                            <span class=\"model-icons\">🥈</span>\n                            <span class=\"model-name\">SWE-Agent + claude-4-Sonnet</span>\n                        </td>\n                        <td><span class=\"resolved-score\">42.70</span></td>\n                        <td><span class=\"z-score positive\">±3.59</span></td>\n                        <td><a class=\"link-icon\" target=\"_blank\">🔗</a></td>\n                    </tr>\n                    <tr data-score=\"39.45\">\n                        <td>\n                            <span class=\"model-icons\">🥉</span>\n                            <span class=\"model-name\">SWE-Agent + claude-4-5-haiku</span>\n                        </td>\n                        <td><span class=\"resolved-score\">39.45</span></td>\n                        <td><span class=\"z-score positive\">±3.55</span></td>\n                        <td><a class=\"link-icon\" target=\"_blank\">🔗</a></td>\n                    </tr>\n                    <tr data-score=\"36.30\">\n                        <td>\n                            <span class=\"model-name\">SWE-Agent + gpt-5-2025-08-07 (High)</span>\n                        </td>\n                        <td><span class=\"resolved-score\">36.30</span></td>\n                        <td><span class=\"z-score positive\">±3.49</span></td>\n                        <td><a class=\"link-icon\" target=\"_blank\">🔗</a></td>\n                    </tr>\n                    <tr data-score=\"35.52\">\n                        <td>\n                            <span class=\"model-name\">SWE-Agent + glm-4.5</span>\n                        </td>\n                        <td><span class=\"resolved-score\">35.52</span></td>\n                        <td><span class=\"z-score positive\">±3.47</span></td>\n                        <td><a class=\"link-icon\" target=\"_blank\">🔗</a></td>\n                    </tr>\n                    <tr data-score=\"27.67\">\n                        <td>\n                            <span class=\"model-name\">SWE-Agent + kimi-k2-instruct</span>\n                        </td>\n                        <td><span class=\"resolved-score\">27.67</span></td>\n                        <td><span class=\"z-score positive\">±3.25</span></td>\n                        <td><a class=\"link-icon\" target=\"_blank\">🔗</a></td>\n                    </tr>\n                    <tr data-score=\"16.20\">\n                        <td>\n                            <span class=\"model-name\">SWE-Agent + gpt-oss-120b</span>\n                        </td>\n                        <td><span class=\"resolved-score\">16.20</span></td>\n                        <td><span class=\"z-score positive\">±2.67</span></td>\n                        <td><a class=\"link-icon\" target=\"_blank\">🔗</a></td>\n                    </tr>\n                </tbody>\n            </table>",
          "sanitized_css": "* {\n            margin: 0;\n            padding: 0;\n            box-sizing: border-box;\n        }\n\n        body {\n            font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, sans-serif;\n            background: linear-gradient(135deg, #667eea 0%, #764ba2 25%, #f093fb 50%, #4facfe 100%);\n            min-height: 100vh;\n            padding: 40px 20px;\n        }\n\n        .container {\n            max-width: 1200px;\n            margin: 0 auto;\n        }\n\n        .header {\n            text-align: center;\n            margin-bottom: 60px;\n        }\n\n        h1 {\n            font-size: 3.5rem;\n            font-weight: 800;\n            color: #ffffff;\n            text-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);\n        }\n\n        .subtitle {\n            font-size: 1.25rem;\n            color: #ffffff;\n            margin-top: 10px;\n            text-shadow: 0 1px 2px rgba(0, 0, 0, 0.1);\n        }\n\n        .buttons {\n            display: flex;\n            gap: 15px;\n            justify-content: center;\n            margin-top: 30px;\n            flex-wrap: wrap;\n        }\n\n        .btn {\n            padding: 12px 28px;\n            border: 2px solid #3b82f6;\n            background: white;\n            border-radius: 10px;\n            font-size: 1rem;\n            font-weight: 600;\n            color: #3b82f6;\n            cursor: pointer;\n            transition: all 0.3s ease;\n            text-decoration: none;\n            display: inline-flex;\n            align-items: center;\n            gap: 8px;\n        }\n\n        .btn:hover {\n            background: #3b82f6;\n            color: white;\n            transform: translateY(-2px);\n            box-shadow: 0 4px 12px rgba(59, 130, 246, 0.3);\n        }\n\n        .leaderboard-section {\n            background: white;\n            border-radius: 20px;\n            padding: 40px;\n            box-shadow: 0 10px 40px rgba(0, 0, 0, 0.1);\n        }\n\n        .leaderboard-title {\n            font-size: 2rem;\n            font-weight: 700;\n            color: #1f2937;\n            margin-bottom: 30px;\n        }\n\n        table {\n            width: 100%;\n            border-collapse: collapse;\n        }\n\n        thead {\n            background: #f3f4f6;\n        }\n\n        th {\n            padding: 16px;\n            text-align: left;\n            font-weight: 600;\n            color: #374151;\n            border-bottom: 2px solid #e5e7eb;\n        }\n\n        th:nth-child(2), th:nth-child(3) {\n            text-align: center;\n        }\n\n        th:last-child {\n            text-align: center;\n        }\n\n        tbody tr {\n            border-bottom: 1px solid #e5e7eb;\n            transition: background 0.2s ease;\n        }\n\n        tbody tr:hover {\n            background: #f9fafb;\n        }\n\n        td {\n            padding: 16px;\n            color: #1f2937;\n        }\n\n        td:nth-child(2), td:nth-child(3) {\n            text-align: center;\n        }\n\n        td:last-child {\n            text-align: center;\n        }\n\n        .model-name {\n            font-weight: 500;\n        }\n\n        .model-icons {\n            display: inline-flex;\n            gap: 4px;\n            margin-right: 8px;\n            font-size: 1.2rem;\n        }\n\n        .resolved-score {\n            font-size: 1.25rem;\n            font-weight: 700;\n            color: #059669;\n        }\n\n        .z-score {\n            font-size: 1.1rem;\n            font-weight: 600;\n        }\n\n        .z-score.positive {\n            color: #059669;\n        }\n\n        .z-score.negative {\n            color: #dc2626;\n        }\n\n        .link-icon {\n            color: #3b82f6;\n            text-decoration: none;\n            font-size: 1.2rem;\n            transition: transform 0.2s ease;\n            display: inline-block;\n        }\n\n        .link-icon:hover {\n            transform: scale(1.2);\n        }\n\n        .about-section {\n            background: white;\n            border-radius: 20px;\n            padding: 40px;\n            margin-top: 40px;\n            box-shadow: 0 10px 40px rgba(0, 0, 0, 0.1);\n        }\n\n        .about-section h2 {\n            font-size: 1.75rem;\n            font-weight: 700;\n            color: #1f2937;\n            margin-top: 30px;\n            margin-bottom: 15px;\n        }\n\n        .about-section h2:first-child {\n            margin-top: 0;\n        }\n\n        .about-section p {\n            color: #4b5563;\n            line-height: 1.7;\n            margin-bottom: 15px;\n        }\n\n        .about-section a {\n            color: #3b82f6;\n            text-decoration: none;\n        }\n\n        .about-section a:hover {\n            text-decoration: underline;\n        }\n\n        .citation-box {\n            background: #f9fafb;\n            border: 1px solid #e5e7eb;\n            border-radius: 8px;\n            padding: 20px;\n            margin: 20px 0;\n            font-family: 'Courier New', monospace;\n            font-size: 0.9rem;\n            overflow-x: auto;\n            white-space: pre-wrap;\n            color: #1f2937;\n        }\n\n        .disclaimer {\n            margin-top: 30px;\n            padding-top: 20px;\n            border-top: 1px solid #e5e7eb;\n            color: #6b7280;\n            font-size: 0.9rem;\n        }\n\n        @media (max-width: 768px) {\n            h1 {\n                font-size: 2.5rem;\n            }\n\n            .leaderboard-section {\n                padding: 20px;\n                overflow-x: auto;\n            }\n\n            .about-section {\n                padding: 20px;\n            }\n\n            table {\n                font-size: 0.9rem;\n            }\n\n            th, td {\n                padding: 12px 8px;\n            }\n        }\n    \n* { margin: 0px; padding: 0px; box-sizing: border-box; }\nbody { font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Roboto, Oxygen, Ubuntu, Cantarell, sans-serif; background: linear-gradient(135deg, rgb(102, 126, 234) 0%, rgb(118, 75, 162) 25%, rgb(240, 147, 251) 50%, rgb(79, 172, 254) 100%); min-height: 100vh; padding: 40px 20px; }\n.container { max-width: 1200px; margin: 0px auto; }\n.header { text-align: center; margin-bottom: 60px; }\nh1 { font-size: 3.5rem; font-weight: 800; color: rgb(255, 255, 255); text-shadow: rgba(0, 0, 0, 0.1) 0px 2px 4px; }\n.subtitle { font-size: 1.25rem; color: rgb(255, 255, 255); margin-top: 10px; text-shadow: rgba(0, 0, 0, 0.1) 0px 1px 2px; }\n.buttons { display: flex; gap: 15px; justify-content: center; margin-top: 30px; flex-wrap: wrap; }\n.btn { padding: 12px 28px; border: 2px solid rgb(59, 130, 246); background: white; border-radius: 10px; font-size: 1rem; font-weight: 600; color: rgb(59, 130, 246); cursor: pointer; transition: 0.3s; text-decoration: none; display: inline-flex; align-items: center; gap: 8px; }\n.btn:hover { background: rgb(59, 130, 246); color: white; transform: translateY(-2px); box-shadow: rgba(59, 130, 246, 0.3) 0px 4px 12px; }\n.leaderboard-section { background: white; border-radius: 20px; padding: 40px; box-shadow: rgba(0, 0, 0, 0.1) 0px 10px 40px; }\n.leaderboard-title { font-size: 2rem; font-weight: 700; color: rgb(31, 41, 55); margin-bottom: 30px; }\ntable { width: 100%; border-collapse: collapse; }\nthead { background: rgb(243, 244, 246); }\nth { padding: 16px; text-align: left; font-weight: 600; color: rgb(55, 65, 81); border-bottom: 2px solid rgb(229, 231, 235); }\nth:nth-child(2), th:nth-child(3) { text-align: center; }\nth:last-child { text-align: center; }\ntbody tr { border-bottom: 1px solid rgb(229, 231, 235); transition: background 0.2s; }\ntbody tr:hover { background: rgb(249, 250, 251); }\ntd { padding: 16px; color: rgb(31, 41, 55); }\ntd:nth-child(2), td:nth-child(3) { text-align: center; }\ntd:last-child { text-align: center; }\n.model-name { font-weight: 500; }\n.model-icons { display: inline-flex; gap: 4px; margin-right: 8px; font-size: 1.2rem; }\n.resolved-score { font-size: 1.25rem; font-weight: 700; color: rgb(5, 150, 105); }\n.z-score { font-size: 1.1rem; font-weight: 600; }\n.z-score.positive { color: rgb(5, 150, 105); }\n.z-score.negative { color: rgb(220, 38, 38); }\n.link-icon { color: rgb(59, 130, 246); text-decoration: none; font-size: 1.2rem; transition: transform 0.2s; display: inline-block; }\n.link-icon:hover { transform: scale(1.2); }\n.about-section { background: white; border-radius: 20px; padding: 40px; margin-top: 40px; box-shadow: rgba(0, 0, 0, 0.1) 0px 10px 40px; }\n.about-section h2 { font-size: 1.75rem; font-weight: 700; color: rgb(31, 41, 55); margin-top: 30px; margin-bottom: 15px; }\n.about-section h2:first-child { margin-top: 0px; }\n.about-section p { color: rgb(75, 85, 99); line-height: 1.7; margin-bottom: 15px; }\n.about-section a { color: rgb(59, 130, 246); text-decoration: none; }\n.about-section a:hover { text-decoration: underline; }\n.citation-box { background: rgb(249, 250, 251); border: 1px solid rgb(229, 231, 235); border-radius: 8px; padding: 20px; margin: 20px 0px; font-family: \"Courier New\", monospace; font-size: 0.9rem; overflow-x: auto; white-space: pre-wrap; color: rgb(31, 41, 55); }\n.disclaimer { margin-top: 30px; padding-top: 20px; border-top: 1px solid rgb(229, 231, 235); color: rgb(107, 114, 128); font-size: 0.9rem; }\n@media (max-width: 768px) {\n  h1 { font-size: 2.5rem; }\n  .leaderboard-section { padding: 20px; overflow-x: auto; }\n  .about-section { padding: 20px; }\n  table { font-size: 0.9rem; }\n  th, td { padding: 12px 8px; }\n}",
          "dom_hash": "sha256:1f59c1095c9588133cb14ee70c786c9fac692efe965f0037ac37af011dc9b8e0",
          "css_hash": "sha256:e8faaa62a35f8c29907c95bf2a3426552315a1b98e25418654fb403e5c98c8a9"
        },
        "public_trace": {
          "source_url": "https://scaleapi.github.io/SWE-bench_Pro-os/",
          "collected_at": "2026-07-02T08:54:32.449Z",
          "selector_version": "swe-bench-pro-v1",
          "data_hash": "sha256:7e801b23f4536a18fde5ac8b8f93ec69cc412a7a01ca56ff08beca730f05256c",
          "top_rows": [
            {
              "rank": 1,
              "model": "SWE-Agent + claude-4-5-Sonnet",
              "provider": "anthropic",
              "value_label": "43.72%",
              "change": "Resolve Rate"
            },
            {
              "rank": 2,
              "model": "SWE-Agent + claude-4-Sonnet",
              "provider": "anthropic",
              "value_label": "42.70%",
              "change": "Resolve Rate"
            },
            {
              "rank": 3,
              "model": "SWE-Agent + claude-4-5-haiku",
              "provider": "anthropic",
              "value_label": "39.45%",
              "change": "Resolve Rate"
            },
            {
              "rank": 4,
              "model": "SWE-Agent + gpt-5-2025-08-07 (High)",
              "provider": "openai",
              "value_label": "36.30%",
              "change": "Resolve Rate"
            },
            {
              "rank": 5,
              "model": "SWE-Agent + glm-4.5",
              "provider": "unknown",
              "value_label": "35.52%",
              "change": "Resolve Rate"
            },
            {
              "rank": 6,
              "model": "SWE-Agent + kimi-k2-instruct",
              "provider": "moonshot",
              "value_label": "27.67%",
              "change": "Resolve Rate"
            },
            {
              "rank": 7,
              "model": "SWE-Agent + gpt-oss-120b",
              "provider": "openai",
              "value_label": "16.20%",
              "change": "Resolve Rate"
            }
          ],
          "diff": {
            "summary": "暂无上一版组件快照可对比。",
            "changed_rows": [],
            "new_entries": [
              "SWE-Agent + claude-4-5-Sonnet",
              "SWE-Agent + claude-4-Sonnet",
              "SWE-Agent + claude-4-5-haiku",
              "SWE-Agent + gpt-5-2025-08-07 (High)",
              "SWE-Agent + glm-4.5",
              "SWE-Agent + kimi-k2-instruct",
              "SWE-Agent + gpt-oss-120b"
            ]
          },
          "cache_status": "live",
          "fallback_reason": ""
        }
      }
    }
  ],
  "projects": [
    {
      "name": "xbtlin/ai-berkshire",
      "editorial_category": "open_source",
      "description": "ai-berkshire 是基于 Claude Code/Codex 的价值投资研究框架，把投资大师方法论、多 agent 对抗分析、公司资料整理和投资假设检查放进同一流程；适合关注 AI 投研的人看它如何产出可复核的研究结论与风险提示。落地时还要看数据来源、模型推理记录、投资建议边界和人工复核责任。",
      "readme_summary": "ai-berkshire 是基于 Claude Code/Codex 的价值投资研究框架，把投资大师方法论、多 agent 对抗分析、公司资料整理和投资假设检查放进同一流程；适合关注 AI 投研的人看它如何产出可复核的研究结论与风险提示。落地时还要看数据来源、模型推理记录、投资建议边界和人工复核责任。",
      "domains": [
        "agent"
      ],
      "use_case": "ai-berkshire 是基于 Claude Code/Codex 的价值投资研究框架，把投资大师方法论、多 agent 对抗分析、公司资料整理和投资假设检查放进同一流程；适合关注 AI 投研的人看它如何产出可复核的研究结论与风险提示。落地时还要看数据来源、模型推理记录、投资建议边界和人工复核责任。",
      "url": "https://github.com/xbtlin/ai-berkshire",
      "event_date": "2026-07-02",
      "source": "GitHub Trending weekly",
      "signal": "trending",
      "importance": "notable"
    },
    {
      "name": "simplex-chat/simplex-chat",
      "editorial_category": "open_source",
      "description": "SimpleX Chat 是隐私即时通讯项目，重点是不用手机号或全局用户 ID 建立连接；适合关注端到端加密、去中心化通信和多端客户端工程的人看。评估时要看客户端覆盖、密钥恢复、群组能力和自托管服务的维护成本。",
      "readme_summary": "SimpleX Chat 是隐私即时通讯项目，重点是不用手机号或全局用户 ID 建立连接；适合关注端到端加密、去中心化通信和多端客户端工程的人看。评估时要看客户端覆盖、密钥恢复、群组能力和自托管服务的维护成本。",
      "domains": [
        "AI tooling"
      ],
      "use_case": "SimpleX Chat 是隐私即时通讯项目，重点是不用手机号或全局用户 ID 建立连接；适合关注端到端加密、去中心化通信和多端客户端工程的人看。评估时要看客户端覆盖、密钥恢复、群组能力和自托管服务的维护成本。",
      "url": "https://github.com/simplex-chat/simplex-chat",
      "event_date": "2026-07-02",
      "source": "GitHub Trending weekly",
      "signal": "trending",
      "importance": "notable"
    },
    {
      "name": "calesthio/OpenMontage",
      "editorial_category": "open_source",
      "description": "OpenMontage 面向视频和多媒体剪辑自动化；适合关注 AI 辅助素材整理、片段拼接和创作工具链的读者先看输入格式、部署方式和示例。真正接入前还要核对它对素材时长、导出格式、字幕和批处理任务的支持程度。",
      "readme_summary": "OpenMontage 面向视频和多媒体剪辑自动化；适合关注 AI 辅助素材整理、片段拼接和创作工具链的读者先看输入格式、部署方式和示例。真正接入前还要核对它对素材时长、导出格式、字幕和批处理任务的支持程度。",
      "domains": [
        "agent"
      ],
      "use_case": "OpenMontage 面向视频和多媒体剪辑自动化；适合关注 AI 辅助素材整理、片段拼接和创作工具链的读者先看输入格式、部署方式和示例。真正接入前还要核对它对素材时长、导出格式、字幕和批处理任务的支持程度。",
      "url": "https://github.com/calesthio/OpenMontage",
      "event_date": "2026-07-02",
      "source": "GitHub Trending weekly",
      "signal": "trending",
      "importance": "notable"
    },
    {
      "name": "google-labs-code/design.md",
      "editorial_category": "open_source",
      "description": "design.md 是 Google Labs Code 给 coding agents 使用的设计契约规范，把视觉识别、交互约束、品牌语气和验收标准写成仓库内文档；它对本项目的启发是把前端风格、内容边界和截图验收沉到版本化设计文件，而不是留在对话记忆里。后续应把它转化为本仓库 DESIGN.md 的必检项和页面验收用例。",
      "readme_summary": "design.md 是 Google Labs Code 给 coding agents 使用的设计契约规范，把视觉识别、交互约束、品牌语气和验收标准写成仓库内文档；它对本项目的启发是把前端风格、内容边界和截图验收沉到版本化设计文件，而不是留在对话记忆里。后续应把它转化为本仓库 DESIGN.md 的必检项和页面验收用例。",
      "domains": [
        "agent"
      ],
      "use_case": "design.md 是 Google Labs Code 给 coding agents 使用的设计契约规范，把视觉识别、交互约束、品牌语气和验收标准写成仓库内文档；它对本项目的启发是把前端风格、内容边界和截图验收沉到版本化设计文件，而不是留在对话记忆里。后续应把它转化为本仓库 DESIGN.md 的必检项和页面验收用例。",
      "url": "https://github.com/google-labs-code/design.md",
      "event_date": "2026-07-02",
      "source": "GitHub Trending weekly",
      "signal": "trending",
      "importance": "notable"
    },
    {
      "name": "DeusData/codebase-memory-mcp",
      "editorial_category": "open_source",
      "description": "codebase-memory-mcp 把代码库索引成持久知识图谱并通过 MCP 暴露查询能力，重点是多语言解析、SQLite 存储和低 token 上下文复用；适合评估 coding agent 跨会话理解架构、检索符号和避免重复读仓库的接入方式。落地时还要看索引增量更新、隐私边界、查询准确率和现有 codegraph 的重叠。",
      "readme_summary": "codebase-memory-mcp 把代码库索引成持久知识图谱并通过 MCP 暴露查询能力，重点是多语言解析、SQLite 存储和低 token 上下文复用；适合评估 coding agent 跨会话理解架构、检索符号和避免重复读仓库的接入方式。落地时还要看索引增量更新、隐私边界、查询准确率和现有 codegraph 的重叠。",
      "domains": [
        "agent"
      ],
      "use_case": "codebase-memory-mcp 把代码库索引成持久知识图谱并通过 MCP 暴露查询能力，重点是多语言解析、SQLite 存储和低 token 上下文复用；适合评估 coding agent 跨会话理解架构、检索符号和避免重复读仓库的接入方式。落地时还要看索引增量更新、隐私边界、查询准确率和现有 codegraph 的重叠。",
      "url": "https://github.com/DeusData/codebase-memory-mcp",
      "event_date": "2026-07-02",
      "source": "GitHub Trending weekly",
      "signal": "trending",
      "importance": "notable"
    },
    {
      "name": "kunchenguid/no-mistakes",
      "editorial_category": "open_source",
      "description": "no-mistakes 围绕 git push 前的失误拦截和工程预检查展开，关注点不是再写一段提示词，而是把常见错误、执行前验证和提交约束变成可运行规则；适合看 agent 工作流能否把事故复盘沉淀成自动化 guardrail。落地时应关注规则如何配置、误报怎么处理，以及是否能接进 CI 或 pre-push 钩子。",
      "readme_summary": "no-mistakes 围绕 git push 前的失误拦截和工程预检查展开，关注点不是再写一段提示词，而是把常见错误、执行前验证和提交约束变成可运行规则；适合看 agent 工作流能否把事故复盘沉淀成自动化 guardrail。落地时应关注规则如何配置、误报怎么处理，以及是否能接进 CI 或 pre-push 钩子。",
      "domains": [
        "agent"
      ],
      "use_case": "no-mistakes 围绕 git push 前的失误拦截和工程预检查展开，关注点不是再写一段提示词，而是把常见错误、执行前验证和提交约束变成可运行规则；适合看 agent 工作流能否把事故复盘沉淀成自动化 guardrail。落地时应关注规则如何配置、误报怎么处理，以及是否能接进 CI 或 pre-push 钩子。",
      "url": "https://github.com/kunchenguid/no-mistakes",
      "event_date": "2026-07-02",
      "source": "GitHub Trending weekly",
      "signal": "trending",
      "importance": "notable"
    },
    {
      "name": "JCodesMore/ai-website-cloner-template",
      "editorial_category": "open_source",
      "description": "ai-website-cloner-template 是用 AI coding agents 复刻网站的模板工程，覆盖抓取、页面还原和 Next.js/React 输出；前端团队更应关注布局还原、素材版权、响应式状态、组件可维护性和生成后能否进入正常工程迭代。真正使用时还要把原站授权、图片来源和设计差异审查纳入流程。",
      "readme_summary": "ai-website-cloner-template 是用 AI coding agents 复刻网站的模板工程，覆盖抓取、页面还原和 Next.js/React 输出；前端团队更应关注布局还原、素材版权、响应式状态、组件可维护性和生成后能否进入正常工程迭代。真正使用时还要把原站授权、图片来源和设计差异审查纳入流程。",
      "domains": [
        "agent"
      ],
      "use_case": "ai-website-cloner-template 是用 AI coding agents 复刻网站的模板工程，覆盖抓取、页面还原和 Next.js/React 输出；前端团队更应关注布局还原、素材版权、响应式状态、组件可维护性和生成后能否进入正常工程迭代。真正使用时还要把原站授权、图片来源和设计差异审查纳入流程。",
      "url": "https://github.com/JCodesMore/ai-website-cloner-template",
      "event_date": "2026-07-02",
      "source": "GitHub Trending weekly",
      "signal": "trending",
      "importance": "notable"
    },
    {
      "name": "Robbyant/lingbot-map",
      "editorial_category": "open_source",
      "description": "lingbot-map 是用于从流式数据重建场景的 feed-forward 3D foundation model，重点在连续输入下恢复空间结构；适合关注 3D 重建、实时场景理解和机器人数据管线的人检查输入模态、推理成本、可视化结果和示例可复现性。落地时还要看流式延迟、场景尺度和对真实传感器噪声的鲁棒性。",
      "readme_summary": "lingbot-map 是用于从流式数据重建场景的 feed-forward 3D foundation model，重点在连续输入下恢复空间结构；适合关注 3D 重建、实时场景理解和机器人数据管线的人检查输入模态、推理成本、可视化结果和示例可复现性。落地时还要看流式延迟、场景尺度和对真实传感器噪声的鲁棒性。",
      "domains": [
        "AI tooling"
      ],
      "use_case": "lingbot-map 是用于从流式数据重建场景的 feed-forward 3D foundation model，重点在连续输入下恢复空间结构；适合关注 3D 重建、实时场景理解和机器人数据管线的人检查输入模态、推理成本、可视化结果和示例可复现性。落地时还要看流式延迟、场景尺度和对真实传感器噪声的鲁棒性。",
      "url": "https://github.com/Robbyant/lingbot-map",
      "event_date": "2026-07-02",
      "source": "GitHub Trending weekly",
      "signal": "trending",
      "importance": "notable"
    },
    {
      "name": "ripienaar/free-for-dev",
      "editorial_category": "open_source",
      "description": "free-for-dev 是开发者免费资源清单，不是 AI 专项项目；上榜价值在于给原型或小团队查托管、数据库、监控等免费额度限制。它更适合作为工程采购前的成本索引，不能替代对服务条款、地区可用性和退出成本的核对。",
      "readme_summary": "free-for-dev 是开发者免费资源清单，不是 AI 专项项目；上榜价值在于给原型或小团队查托管、数据库、监控等免费额度限制。它更适合作为工程采购前的成本索引，不能替代对服务条款、地区可用性和退出成本的核对。",
      "domains": [
        "AI tooling"
      ],
      "use_case": "free-for-dev 是开发者免费资源清单，不是 AI 专项项目；上榜价值在于给原型或小团队查托管、数据库、监控等免费额度限制。它更适合作为工程采购前的成本索引，不能替代对服务条款、地区可用性和退出成本的核对。",
      "url": "https://github.com/ripienaar/free-for-dev",
      "event_date": "2026-07-02",
      "source": "GitHub Trending weekly",
      "signal": "trending",
      "importance": "notable"
    },
    {
      "name": "stablyai/orca",
      "editorial_category": "open_source",
      "description": "orca 是面向并行 coding agents 的 agent development environment，可在桌面和移动端调度多个 agent 与 worktree；适合评估团队如何同时运行多条编码任务、隔离会话状态、复用订阅额度，并在移动端接管长任务。关键还在于长任务中断恢复、并发结果对比和远程环境权限控制。",
      "readme_summary": "orca 是面向并行 coding agents 的 agent development environment，可在桌面和移动端调度多个 agent 与 worktree；适合评估团队如何同时运行多条编码任务、隔离会话状态、复用订阅额度，并在移动端接管长任务。关键还在于长任务中断恢复、并发结果对比和远程环境权限控制。",
      "domains": [
        "agent"
      ],
      "use_case": "orca 是面向并行 coding agents 的 agent development environment，可在桌面和移动端调度多个 agent 与 worktree；适合评估团队如何同时运行多条编码任务、隔离会话状态、复用订阅额度，并在移动端接管长任务。关键还在于长任务中断恢复、并发结果对比和远程环境权限控制。",
      "url": "https://github.com/stablyai/orca",
      "event_date": "2026-07-02",
      "source": "GitHub Trending weekly",
      "signal": "trending",
      "importance": "notable"
    }
  ],
  "builder_observations": [
    {
      "author": "Aaron Levie",
      "handle": "levie",
      "editorial_category": "x_discussion",
      "content": "Levie 用 Devin 的 agentic mapreduce 解释未来为什么可能需要更多 AI 推理：多个 agent 可以在大型代码库中分片查找相关线索、汇总成报告并继续校验，从而处理过去人力难以完成的大规模代码任务。",
      "original_text": "If you’ve ever wondered why we will need 100X more AI inference in the future, and what it’s going to be driven by, this is another good example. Devin pushes forward an idea of agentic mapreduce, which means we’ll now have swarms of agents that are processing large amounts of data (code) to handle tasks that humans never could have done before. “Devin maps relevant signals across the repo, fans out focused agents over bounded shards, reduces their findings into one report, then verifies seriou...",
      "translation": "Levie 用 Devin 的 agentic mapreduce 解释未来为什么可能需要更多 AI 推理：多个 agent 可以在大型代码库中分片查找相关线索、汇总成报告并继续校验，从而处理过去人力难以完成的大规模代码任务。",
      "avatar_url": "https://unavatar.io/x/levie",
      "url": "https://x.com/levie/status/2072519377371459836",
      "role": "builder",
      "event_date": "2026-07-02",
      "source": "follow-builders X feed",
      "importance": "notable",
      "image_urls": []
    },
    {
      "author": "Google Labs",
      "handle": "GoogleLabs",
      "editorial_category": "x_discussion",
      "content": "Google Labs 表示，为了专注打造用于创作、分享和 remix 原创音乐的 Google Flow Music，MusicFX 和 MusicFX DJ 将在 2026 年 7 月 31 日告别；早期实时 AI 音乐实验的经验会迁入新的长期项目入口。",
      "original_text": "Every good chord progression needs a resolution. 🎹 To focus on building @GoogleFlowMusic - our tool for creating, sharing, and remixing original music - we will be saying a fond farewell to MusicFX and MusicFX DJ on July 31, 2026. These early experiments pushed the boundaries of AI for real-time music creation, and we're taking everything we learned from them to provide a long-term home for musical projects. Keep jamming at https://t.co/3XMUc2pkzU 🎵",
      "translation": "Google Labs 表示，为了专注打造用于创作、分享和 remix 原创音乐的 Google Flow Music，MusicFX 和 MusicFX DJ 将在 2026 年 7 月 31 日告别；早期实时 AI 音乐实验的经验会迁入新的长期项目入口。",
      "avatar_url": "https://unavatar.io/x/GoogleLabs",
      "url": "https://x.com/GoogleLabs/status/2072417166952136789",
      "role": "builder",
      "event_date": "2026-07-01",
      "source": "follow-builders X feed",
      "importance": "notable",
      "image_urls": []
    },
    {
      "author": "Garry Tan",
      "handle": "garrytan",
      "editorial_category": "x_discussion",
      "content": "Garry Tan 转发 UC Berkeley EECS 负责人加入 Anthropic 的消息，认为这是 Anthropic 近期快速扩张中的又一次重量级招募。",
      "original_text": "Mega get, head of UC Berkeley EECS omg Anthropic is on a tear https://t.co/6lTQhG7BIo",
      "translation": "Garry Tan 转发 UC Berkeley EECS 负责人加入 Anthropic 的消息，认为这是 Anthropic 近期快速扩张中的又一次重量级招募。",
      "avatar_url": "https://unavatar.io/x/garrytan",
      "url": "https://x.com/garrytan/status/2072331451270606933",
      "role": "builder",
      "event_date": "2026-07-01",
      "source": "follow-builders X feed",
      "importance": "notable",
      "image_urls": []
    },
    {
      "author": "Matt Turck",
      "handle": "mattturck",
      "editorial_category": "x_discussion",
      "content": "Matt Turck 讨论 Lime 在巨额债务和持续经营疑虑之后上市的案例：IPO 帮它偿还高成本贷款、把部分债务转成股权，Uber 的持股和导流也给业务提供了支撑。",
      "original_text": "Fascinated by Lime going public - in an age where AI gets all the attention, how does a scooter company with $1B in debt pull off a successful IPO literally after expressing \"substantial doubt\" that they might not even survive the year? * Impressive financial engineering - the IPO paid off the toxic loans and converted the rest to equity, so the slate is clean * Uber owns 22% of Lime and refers riders directly to Lime, so obviously a great backstop and partner * They've actually been FCF positi...",
      "translation": "Matt Turck 讨论 Lime 在巨额债务和持续经营疑虑之后上市的案例：IPO 帮它偿还高成本贷款、把部分债务转成股权，Uber 的持股和导流也给业务提供了支撑。",
      "avatar_url": "https://unavatar.io/x/mattturck",
      "url": "https://x.com/mattturck/status/2072419592354529712",
      "role": "builder",
      "event_date": "2026-07-01",
      "source": "follow-builders X feed",
      "importance": "notable",
      "image_urls": []
    },
    {
      "author": "Peter Yang",
      "handle": "petergyang",
      "editorial_category": "x_discussion",
      "content": "Peter Yang 评价 Fable 5 仍明显强于其他模型，并表示希望 GPT 5.6 能赶上这个水平。",
      "original_text": "Here's my Fable 5 vibe check: It's still really ****ing good. This is a step function above any other model. Hope GPT 5.6 can match. https://t.co/p9N7cG86QW",
      "translation": "Peter Yang 评价 Fable 5 仍明显强于其他模型，并表示希望 GPT 5.6 能赶上这个水平。",
      "avatar_url": "https://unavatar.io/x/petergyang",
      "url": "https://x.com/petergyang/status/2072470191511113732",
      "role": "builder",
      "event_date": "2026-07-01",
      "source": "follow-builders X feed",
      "importance": "notable",
      "image_urls": []
    },
    {
      "author": "Peter Yang",
      "handle": "petergyang",
      "editorial_category": "x_discussion",
      "content": "Peter Yang 提醒 Claude Fable 5 已回归，但 Claude 订阅用户只能用到 7 月 7 日；他的新教程演示了寻找适合 Fable 的任务、获得生活和商业建议、把项目打磨到可发布、规划大事和重构项目或代码库等用法。",
      "original_text": "Claude Fable 5 is finally back, but you only have until July 7 to use it on your Claude subscription. I made a new tutorial walking through 5 use cases worth trying Fable on: → Find Fable-worthy work → Get life and business advice → Make projects ship-ready → Plan the next big thing → Refactor your project or codebase As usual, it’s no BS, and I show you Fable’s actual output. 📌 Watch now: https://t.co/XElMEV3FwK",
      "translation": "Peter Yang 提醒 Claude Fable 5 已回归，但 Claude 订阅用户只能用到 7 月 7 日；他的新教程演示了寻找适合 Fable 的任务、获得生活和商业建议、把项目打磨到可发布、规划大事和重构项目或代码库等用法。",
      "avatar_url": "https://unavatar.io/x/petergyang",
      "url": "https://x.com/petergyang/status/2072458983886205333",
      "role": "builder",
      "event_date": "2026-07-01",
      "source": "follow-builders X feed",
      "importance": "notable",
      "image_urls": []
    },
    {
      "author": "Thariq",
      "handle": "trq212",
      "editorial_category": "x_discussion",
      "content": "Thariq 从 AI Engineer 活动现场发帖问候，并附上现场链接。",
      "original_text": "hello from AI engineer! https://t.co/J8sFn5pbyC",
      "translation": "Thariq 从 AI Engineer 活动现场发帖问候，并附上现场链接。",
      "avatar_url": "https://unavatar.io/x/trq212",
      "url": "https://x.com/trq212/status/2072360902964511171",
      "role": "builder",
      "event_date": "2026-07-01",
      "source": "follow-builders X feed",
      "importance": "notable",
      "image_urls": []
    },
    {
      "author": "Guillermo Rauch",
      "handle": "rauchg",
      "editorial_category": "x_discussion",
      "content": "Rauch 说，agent 在提交前常会先运行代码语法检查、类型检查和构建等命令；Vercel 正在给智能化部署加入预演步骤，让发布前的验证成本和风险更低。",
      "original_text": "Agents love to check their work before they push. You probably see it in the form of 𝚗𝚘𝚍𝚎 --𝚌𝚑𝚎𝚌𝚔, 𝚝𝚜𝚌 --𝚗𝚘𝙴𝚖𝚒𝚝, 𝚗𝚎𝚡𝚝 𝚋𝚞𝚒𝚕𝚍, etc all over your agent sessions. We’re now shipping the dry-run step for agentic deployments, minimizing costs and risk. https://t.co/HpOXSROT3X",
      "translation": "Rauch 说，agent 在提交前常会先运行代码语法检查、类型检查和构建等命令；Vercel 正在给智能化部署加入预演步骤，让发布前的验证成本和风险更低。",
      "avatar_url": "https://unavatar.io/x/rauchg",
      "url": "https://x.com/rauchg/status/2072398926175404250",
      "role": "builder",
      "event_date": "2026-07-01",
      "source": "follow-builders X feed",
      "importance": "notable",
      "image_urls": []
    },
    {
      "author": "Zara Zhang",
      "handle": "zarazhangrui",
      "editorial_category": "x_discussion",
      "content": "Zara Zhang 提醒 Codex 用户可以把模型切换为 GLM，并附上操作链接和截图。",
      "original_text": "PSA: You can change Codex's model to GLM https://t.co/G3RQfWiS4j https://t.co/BeGZABjgTQ",
      "translation": "Zara Zhang 提醒 Codex 用户可以把模型切换为 GLM，并附上操作链接和截图。",
      "avatar_url": "https://unavatar.io/x/zarazhangrui",
      "url": "https://x.com/zarazhangrui/status/2072391971721884073",
      "role": "builder",
      "event_date": "2026-07-01",
      "source": "follow-builders X feed",
      "importance": "notable",
      "image_urls": []
    }
  ],
  "official_org_updates": [],
  "evidence_assets": [],
  "generated_at": "2026-07-02T09:00:49.015Z",
  "report_status": "normal",
  "canonical_url": "https://jasonxzwen.github.io/ai-daily-cn/reports/2026/07/2026-07-02.html",
  "html_path": "reports/2026/07/2026-07-02.html",
  "quality_status": {
    "status": "degraded",
    "public_note": "Some discovery coverage is degraded; this report may be incomplete.",
    "degraded_events": [
      {
        "section": "hot_blogs",
        "message": "China AI source lane ran successfully but produced no recent candidates.",
        "severity": "degraded"
      }
    ]
  }
}