{
  "schema_version": 1,
  "report_date": "2026-06-25",
  "title": "AI 日报 2026-06-25",
  "summary": "今天最值得看的主线有 Meta Newsroom披露 Claude Code agent 工具工作流；Alibaba Cloud披露 agent 与开发者工具能力；微软研究院更新AI 产品、平台或工程实践；热门博客这轮主要看 agent 和开发工具的落地边界。",
  "hero_highlights": [
    {
      "title": "Google Keyword更新AI 产品、平台或工程实践",
      "url": "https://blog.google/innovation-and-ai/models-and-research/gemini-models/introducing-computer-use-gemini-3-5-flash/",
      "reason": "信号集中在 AI 产品、模型或平台策略的实际变化",
      "what_happened": "Google Keyword更新AI 产品、平台或工程实践，材料覆盖功能变化、使用场景、接入方式、限制条件和后续部署边界，边界落在公开材料仍需要回到原文核对入口、权限、价格和适用范围",
      "why_watch": "信号集中在 AI 产品、模型或平台策略的实际变化",
      "category": "model_platform",
      "source_item_ref": "https://blog.google/innovation-and-ai/models-and-research/gemini-models/introducing-computer-use-gemini-3-5-flash/"
    },
    {
      "title": "Meta 在亚太推出小企业 AI 与数字技能增长学院",
      "url": "https://about.fb.com/news/2026/06/launch-of-metas-small-business-growth-academy-across-asia-pacific/",
      "reason": "工程侧价值集中在 agent、开发工具和自动化工作流接入",
      "what_happened": "Meta Newsroom更新agent 工作流和开发工具能力，材料覆盖任务编排、上下文、权限控制、工程集成和失败恢复，边界落在落地质量取决于权限模型、评估回放、团队流程和可观测性",
      "why_watch": "工程侧价值集中在 agent、开发工具和自动化工作流接入",
      "category": "product_tool",
      "source_item_ref": "https://about.fb.com/news/2026/06/launch-of-metas-small-business-growth-academy-across-asia-pacific/"
    },
    {
      "title": "Alibaba Cloud披露 agent 与开发者工具能力",
      "url": "https://www.alibabacloud.com/blog/qwen-agentworld-language-world-models-for-general-agents_603304",
      "reason": "工程侧价值集中在 agent、开发工具和自动化工作流接入",
      "what_happened": "Qwen 团队更新agent 工作流和开发工具能力，材料覆盖任务编排、上下文、权限控制、工程集成和失败恢复，边界落在落地质量取决于权限模型、评估回放、团队流程和可观测性",
      "why_watch": "工程侧价值集中在 agent、开发工具和自动化工作流接入",
      "category": "china_open_source_community",
      "source_item_ref": "https://www.alibabacloud.com/blog/qwen-agentworld-language-world-models-for-general-agents_603304"
    }
  ],
  "stories": [
    {
      "story_id": "story-content-meta-newsroom-launch-of-meta-s-small-business-growth-academy-across-asia",
      "title": "Meta Newsroom发布 Claude Code agent 工具工作流",
      "importance": "general",
      "trend": "AI products and developer workflow",
      "event_date": "2026-06-23",
      "primary_entity": "Meta Newsroom",
      "event_type": "launch",
      "object": "Meta Newsroom披露 Claude Code agent 工具工作流",
      "what_happened": "Meta 6 月 23 日宣布在亚太推出 Small Business Growth Academy，面向小企业提供 AI 工具、广告和跨境增长培训，目标是帮助商家提升数字技能并扩大业务。",
      "why_it_matters": "工程侧价值集中在 agent、开发工具和自动化工作流接入",
      "evidence_level": "primary",
      "sources": [
        {
          "label": "Meta Newsroom",
          "url": "https://about.fb.com/news/2026/06/launch-of-metas-small-business-growth-academy-across-asia-pacific/",
          "type": "primary"
        }
      ]
    },
    {
      "story_id": "story-content-alibaba-cloud-blog-qwen-agentworld-language-world-models-for-general-age",
      "title": "Alibaba Cloud更新agent 与开发者工具能力",
      "importance": "general",
      "trend": "AI industry",
      "event_date": "2026-06-25",
      "primary_entity": "Alibaba Cloud Blog",
      "event_type": "launch",
      "object": "Alibaba Cloud披露 agent 与开发者工具能力",
      "what_happened": "阿里云 Qwen 团队 6 月 25 日发布 Qwen-AgentWorld，这是一个用语言模拟 agent 环境的 world model，覆盖七个领域，用来支持通用 agent 的训练和评估。",
      "why_it_matters": "工程侧价值集中在 agent、开发工具和自动化工作流接入",
      "evidence_level": "primary",
      "sources": [
        {
          "label": "Alibaba Cloud Blog",
          "url": "https://www.alibabacloud.com/blog/qwen-agentworld-language-world-models-for-general-agents_603304",
          "type": "official"
        }
      ]
    },
    {
      "story_id": "story-content-microsoft-research-talos-scaling-rare-disease-diagnosis-with-automated-i",
      "title": "微软研究院用 Talos 自动化基因组再分析扩展罕见病诊断",
      "importance": "general",
      "trend": "open source AI",
      "event_date": "2026-06-24",
      "primary_entity": "Microsoft Research Blog",
      "event_type": "research",
      "object": "微软研究院更新AI 产品、平台或工程实践",
      "what_happened": "微软研究院介绍 Talos，用自动化、迭代的基因组再分析缓解罕见病诊断中的人工审阅瓶颈；公开材料称它在范围内诊断中恢复了 90%，平均每位患者只向专家呈现 1.3 个候选变异。",
      "why_it_matters": "工程价值集中在代码、权重、示例和生态复用条件",
      "evidence_level": "primary",
      "sources": [
        {
          "label": "Microsoft Research Blog",
          "url": "https://www.microsoft.com/en-us/research/blog/talos-scaling-rare-disease-diagnosis-with-automated-iterative-genomic-reanalysis/",
          "type": "official"
        }
      ]
    },
    {
      "story_id": "story-content-alibaba-cloud-blog-fully-managed-polardb-x-mem0-service-enabling-infinit",
      "title": "Alibaba Cloud更新agent 与开发者工具能力",
      "importance": "general",
      "trend": "AI products and developer workflow",
      "event_date": "2026-06-24",
      "primary_entity": "Alibaba Cloud Blog",
      "event_type": "signal",
      "object": "Alibaba Cloud披露 agent 与开发者工具能力",
      "what_happened": "阿里云介绍全托管 PolarDB-X Mem0 服务，为 AI agent 提供长期记忆，采用语义数据和结构化数据的双通道架构，面向记忆扩展与双用途集成场景。",
      "why_it_matters": "工程侧价值集中在 agent、开发工具和自动化工作流接入",
      "evidence_level": "primary",
      "sources": [
        {
          "label": "Alibaba Cloud Blog",
          "url": "https://www.alibabacloud.com/blog/fully-managed-polardb-x-mem0-service-enabling-infinite-ai-memory-extension-and-dual-use-integration_603303",
          "type": "official"
        }
      ]
    },
    {
      "story_id": "story-content-google-keyword-introducing-computer-use-in-gemini-3-5-flash",
      "title": "Google 为 Gemini 模型新增电脑操作能力",
      "importance": "general",
      "trend": "AI industry",
      "event_date": "2026-06-24",
      "primary_entity": "Google Keyword Blog",
      "event_type": "signal",
      "object": "Google Keyword更新AI 产品、平台或工程实践",
      "what_happened": "Google 在 Keyword 博客介绍 Gemini 3.5 Flash 的 computer use 能力，公开入口指向模型在网页和工具操作类任务中的使用方式；具体可用范围、权限和限制仍需要回到原文核对。",
      "why_it_matters": "信号集中在 AI 产品、模型或平台策略的实际变化",
      "evidence_level": "primary",
      "sources": [
        {
          "label": "Google Keyword Blog",
          "url": "https://blog.google/innovation-and-ai/models-and-research/gemini-models/introducing-computer-use-gemini-3-5-flash/",
          "type": "official"
        }
      ]
    },
    {
      "story_id": "story-content-alibaba-cloud-blog-the-constraint-infrastructure-growing-on-alibaba-clou",
      "title": "Alibaba Cloud更新agent 与开发者工具能力",
      "importance": "general",
      "trend": "AI products and developer workflow",
      "event_date": "2026-06-25",
      "primary_entity": "Alibaba Cloud Blog",
      "event_type": "signal",
      "object": "Alibaba Cloud披露 agent 与开发者工具能力",
      "what_happened": "阿里云介绍 Agent Infra 上的约束基础设施，强调让 AI agent 的运行时行为可控、可观测并持续演进，用于约束和监控 agent 执行过程。",
      "why_it_matters": "工程侧价值集中在 agent、开发工具和自动化工作流接入",
      "evidence_level": "primary",
      "sources": [
        {
          "label": "Alibaba Cloud Blog",
          "url": "https://www.alibabacloud.com/blog/the-constraint-infrastructure-growing-on-alibaba-cloud-agent-infra_603305",
          "type": "official"
        }
      ]
    },
    {
      "story_id": "story-content-openai-news-openai-and-broadcom-unveil-llm-optimized-inference-chip",
      "title": "OpenAI公布模型能力和推理入口变化",
      "importance": "general",
      "trend": "AI industry",
      "event_date": "2026-06-24",
      "primary_entity": "OpenAI News RSS",
      "event_type": "launch",
      "object": "OpenAI披露模型能力和推理入口变化",
      "what_happened": "OpenAI 与 Broadcom 介绍面向 LLM 推理优化的定制 AI 芯片 Jalapeno，目标是提升 AI 系统的性能、效率和规模化能力。",
      "why_it_matters": "信号集中在 AI 产品、模型或平台策略的实际变化",
      "evidence_level": "multi_source",
      "sources": [
        {
          "label": "OpenAI News RSS",
          "url": "https://openai.com/index/openai-broadcom-jalapeno-inference-chip",
          "type": "official"
        },
        {
          "label": "OpenAI Blog RSS",
          "url": "https://openai.com/index/openai-broadcom-jalapeno-inference-chip",
          "type": "official"
        },
        {
          "label": "OpenAI Company News RSS",
          "url": "https://openai.com/index/openai-broadcom-jalapeno-inference-chip",
          "type": "official"
        }
      ]
    },
    {
      "story_id": "story-content-microsoft-official-blog-inside-microsoft-s-two-decade-push-to-cut-water",
      "title": "微软回顾降低数据中心用水强度的长期工程实践",
      "importance": "general",
      "trend": "AI products and developer workflow",
      "event_date": "2026-06-24",
      "primary_entity": "Official Microsoft Blog",
      "event_type": "signal",
      "object": "微软研究院更新agent 工作流和开发工具能力",
      "what_happened": "微软官方博客回顾在云和 AI 服务需求增长下减少数据中心用水强度的长期工程实践，并解释基础设施扩张与本地水资源影响之间的关系。",
      "why_it_matters": "信号集中在 AI 产品、模型或平台策略的实际变化",
      "evidence_level": "primary",
      "sources": [
        {
          "label": "Official Microsoft Blog",
          "url": "https://blogs.microsoft.com/blog/2026/06/24/inside-microsofts-two-decade-push-to-cut-water-intensity-while-scaling-for-growth/",
          "type": "official"
        }
      ]
    }
  ],
  "main_items": [
    {
      "title": "Meta Newsroom发布 Claude Code agent 工具工作流",
      "editorial_category": "engineering_toolchain",
      "event_date": "2026-06-23",
      "url": "https://about.fb.com/news/2026/06/launch-of-metas-small-business-growth-academy-across-asia-pacific/",
      "source": "Meta Newsroom",
      "tier": "T0",
      "entities": [
        "Meta Newsroom"
      ],
      "summary": "Meta Newsroom更新agent 工作流和开发工具能力，材料覆盖任务编排、上下文、权限控制、工程集成和失败恢复，边界落在落地质量取决于权限模型、评估回放、团队流程和可观测性。",
      "bullets": [
        "**Meta Newsroom披露 Claude Code**：材料把agent 工作流和开发工具能力落到任务编排、上下文、权限控制、工程集成和失败恢复，已披露事实集中在agent 工作流、开发工具入口、权限控制和工程集成。",
        "当前公开的是部署方式、权限、上下文管理和失败恢复边界。",
        "这会影响研发团队安排 agent 工具接入顺序、权限设计、评估回放和落地成本。"
      ],
      "importance": "general",
      "image_urls": []
    },
    {
      "title": "Alibaba Cloud更新agent 与开发者工具能力",
      "editorial_category": "ai_industry",
      "event_date": "2026-06-25",
      "url": "https://www.alibabacloud.com/blog/qwen-agentworld-language-world-models-for-general-agents_603304",
      "source": "Alibaba Cloud Blog",
      "tier": "T0",
      "entities": [
        "Alibaba Cloud Blog"
      ],
      "summary": "Qwen 团队更新agent 工作流和开发工具能力，材料覆盖任务编排、上下文、权限控制、工程集成和失败恢复，边界落在落地质量取决于权限模型、评估回放、团队流程和可观测性。",
      "bullets": [
        "**Alibaba Cloud披露 agent 与开发者工**：材料把agent 工作流和开发工具能力落到任务编排、上下文、权限控制、工程集成和失败恢复，已披露事实集中在agent 工作流、开发工具入口、权限控制和工程集成。",
        "当前公开的是部署方式、权限、上下文管理和失败恢复边界。",
        "这会影响研发团队安排 agent 工具接入顺序、权限设计、评估回放和落地成本。"
      ],
      "importance": "general",
      "image_urls": []
    },
    {
      "title": "微软研究院用 Talos 自动化基因组再分析扩展罕见病诊断",
      "editorial_category": "open_source",
      "event_date": "2026-06-24",
      "url": "https://www.microsoft.com/en-us/research/blog/talos-scaling-rare-disease-diagnosis-with-automated-iterative-genomic-reanalysis/",
      "source": "Microsoft Research Blog",
      "tier": "T0",
      "entities": [
        "Microsoft Research Blog"
      ],
      "summary": "微软研究院更新AI 产品、平台或工程实践，材料覆盖功能变化、使用场景、接入方式、限制条件和后续部署边界，边界落在公开材料仍需要回到原文核对入口、权限、价格和适用范围。",
      "bullets": [
        "**微软研究院更新AI 产品、平台或工程实践**：材料把AI 产品、平台或工程实践落到功能变化、使用场景、接入方式、限制条件和后续部署边界，已披露事实集中在功能变化、适用场景、接入方式和限制条件。",
        "当前公开的是代码接口、许可证、维护节奏、集成门槛和团队可复用边界。",
        "这会影响研发团队是否把它放进 PoC、评估清单、现有工作流或长期维护计划。"
      ],
      "importance": "general",
      "image_urls": []
    },
    {
      "title": "Alibaba Cloud更新agent 与开发者工具能力",
      "editorial_category": "engineering_toolchain",
      "event_date": "2026-06-24",
      "url": "https://www.alibabacloud.com/blog/fully-managed-polardb-x-mem0-service-enabling-infinite-ai-memory-extension-and-dual-use-integration_603303",
      "source": "Alibaba Cloud Blog",
      "tier": "T0",
      "entities": [
        "Alibaba Cloud Blog"
      ],
      "summary": "Alibaba Cloud更新agent 工作流和开发工具能力，材料覆盖任务编排、上下文、权限控制、工程集成和失败恢复，边界落在落地质量取决于权限模型、评估回放、团队流程和可观测性。",
      "bullets": [
        "**Alibaba Cloud披露 agent 与开发者工**：材料把agent 工作流和开发工具能力落到任务编排、上下文、权限控制、工程集成和失败恢复，已披露事实集中在agent 工作流、开发工具入口、权限控制和工程集成。",
        "当前公开的是部署方式、权限、上下文管理和失败恢复边界。",
        "这会影响研发团队安排 agent 工具接入顺序、权限设计、评估回放和落地成本。"
      ],
      "importance": "general",
      "image_urls": []
    },
    {
      "title": "Google 为 Gemini 模型新增电脑操作能力",
      "editorial_category": "ai_industry",
      "event_date": "2026-06-24",
      "url": "https://blog.google/innovation-and-ai/models-and-research/gemini-models/introducing-computer-use-gemini-3-5-flash/",
      "source": "Google Keyword Blog",
      "tier": "T0",
      "entities": [
        "Google Keyword Blog"
      ],
      "summary": "Google Keyword更新AI 产品、平台或工程实践，材料覆盖功能变化、使用场景、接入方式、限制条件和后续部署边界，边界落在公开材料仍需要回到原文核对入口、权限、价格和适用范围。",
      "bullets": [
        "**Google Keyword更新AI 产品、平台或工程**：材料把AI 产品、平台或工程实践落到功能变化、使用场景、接入方式、限制条件和后续部署边界，已披露事实集中在功能变化、适用场景、接入方式和限制条件。",
        "已披露细节覆盖适用对象、证据来源、执行安排、后续时间表和风险边界。",
        "这会影响产品团队判断路线优先级、接入时机、资源投入和后续风险预案。"
      ],
      "importance": "general",
      "image_urls": []
    },
    {
      "title": "Alibaba Cloud更新agent 与开发者工具能力",
      "editorial_category": "engineering_toolchain",
      "event_date": "2026-06-25",
      "url": "https://www.alibabacloud.com/blog/the-constraint-infrastructure-growing-on-alibaba-cloud-agent-infra_603305",
      "source": "Alibaba Cloud Blog",
      "tier": "T0",
      "entities": [
        "Alibaba Cloud Blog"
      ],
      "summary": "Alibaba Cloud更新agent 工作流和开发工具能力，材料覆盖任务编排、上下文、权限控制、工程集成和失败恢复，边界落在落地质量取决于权限模型、评估回放、团队流程和可观测性。",
      "bullets": [
        "**Alibaba Cloud披露 agent 与开发者工**：材料把agent 工作流和开发工具能力落到任务编排、上下文、权限控制、工程集成和失败恢复，已披露事实集中在agent 工作流、开发工具入口、权限控制和工程集成。",
        "当前公开的是部署方式、权限、上下文管理和失败恢复边界。",
        "这会影响研发团队安排 agent 工具接入顺序、权限设计、评估回放和落地成本。"
      ],
      "importance": "general",
      "image_urls": []
    },
    {
      "title": "OpenAI公布模型能力和推理入口变化",
      "editorial_category": "ai_industry",
      "event_date": "2026-06-24",
      "url": "https://openai.com/index/openai-broadcom-jalapeno-inference-chip",
      "source": "OpenAI News RSS",
      "tier": "T0",
      "entities": [
        "OpenAI News RSS"
      ],
      "summary": "OpenAI披露模型能力和评估方法更新，材料覆盖能力边界、评估设置、数据来源、使用场景和限制说明，边界落在结论仍要依赖可复现评测、真实任务和公开限制。",
      "bullets": [
        "**OpenAI披露模型能力和推理入口变化**：材料把模型能力和评估方法更新落到能力边界、评估设置、数据来源、使用场景和限制说明，已披露事实集中在模型能力、评估设置、数据来源和限制说明。",
        "已披露细节覆盖适用对象、证据来源、执行安排、后续时间表和风险边界。",
        "这会影响产品团队判断路线优先级、接入时机、资源投入和后续风险预案。"
      ],
      "importance": "general",
      "image_urls": []
    },
    {
      "title": "微软回顾降低数据中心用水强度的长期工程实践",
      "editorial_category": "engineering_toolchain",
      "event_date": "2026-06-24",
      "url": "https://blogs.microsoft.com/blog/2026/06/24/inside-microsofts-two-decade-push-to-cut-water-intensity-while-scaling-for-growth/",
      "source": "Official Microsoft Blog",
      "tier": "T0",
      "entities": [
        "Official Microsoft Blog"
      ],
      "summary": "微软研究院更新agent 工作流和开发工具能力，材料覆盖任务编排、上下文、权限控制、工程集成和失败恢复，边界落在落地质量取决于权限模型、评估回放、团队流程和可观测性。",
      "bullets": [
        "**微软研究院更新agent 工作流和开发工具能力**：材料把agent 工作流和开发工具能力落到任务编排、上下文、权限控制、工程集成和失败恢复，已披露事实集中在agent 工作流、开发工具入口、权限控制和工程集成。",
        "已披露细节覆盖适用对象、证据来源、执行安排、后续时间表和风险边界。",
        "这会影响产品团队判断路线优先级、接入时机、资源投入和后续风险预案。"
      ],
      "importance": "general",
      "image_urls": []
    }
  ],
  "github_trending": [
    {
      "name": "calesthio/OpenMontage",
      "repo": "calesthio/OpenMontage",
      "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": "2026-06-25",
      "source": "GitHub Trending weekly",
      "language": "all",
      "window": "weekly",
      "rank": 1,
      "source_rank": 1,
      "source_scope": "weekly:all",
      "previous_rank": 2,
      "rank_delta": 1,
      "trend": "up",
      "importance": "notable",
      "description": "OpenMontage 是代理式自动化、工具调用和开发者工作流相关的开源项目，公开说明提到Agent 构建、工具调用和工作流编排等能力，并提供部署说明；评估时核对安装步骤、许可证、示例质量、近期维护和接入边界。 它把相关能力沉淀为代码、示例和集成入口，方便和同类方案做功能与工程成本比较。"
    },
    {
      "name": "DeusData/codebase-memory-mcp",
      "repo": "DeusData/codebase-memory-mcp",
      "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": "2026-06-25",
      "source": "GitHub Trending weekly",
      "language": "all",
      "window": "weekly",
      "rank": 2,
      "source_rank": 2,
      "source_scope": "weekly:all",
      "previous_rank": 1,
      "rank_delta": -1,
      "trend": "down",
      "importance": "general",
      "description": "DeusData/codebase-memory-mcp 本周出现在开源榜单 weekly #2，本周 +9,589 stars；当前只能确认榜单动量，正式采用前还要核对 README、许可证、维护状态和 issue 反馈。"
    },
    {
      "name": "google-research/timesfm",
      "repo": "google-research/timesfm",
      "readme_cache": {
        "key": "github-readme/google-research/timesfm/master/unknown",
        "hit": true,
        "repo": "google-research/timesfm",
        "sha": "unknown",
        "default_branch": "master",
        "source_url": "https://raw.githubusercontent.com/google-research/timesfm/master/README.md"
      },
      "readme_fetch_status": "ok",
      "url": "https://github.com/google-research/timesfm",
      "event_date": "2026-06-25",
      "source": "GitHub Trending weekly",
      "language": "all",
      "window": "weekly",
      "rank": 3,
      "source_rank": 3,
      "source_scope": "weekly:all",
      "previous_rank": 3,
      "rank_delta": 0,
      "trend": "same",
      "importance": "general",
      "description": "google-research/timesfm 本周出现在开源榜单 weekly #3，本周 +3,915 stars；当前只能确认榜单动量，正式采用前还要核对 README、许可证、维护状态和 issue 反馈。"
    },
    {
      "name": "n0-computer/iroh",
      "repo": "n0-computer/iroh",
      "readme_cache": {
        "key": "github-readme/n0-computer/iroh/main/unknown",
        "hit": true,
        "repo": "n0-computer/iroh",
        "sha": "unknown",
        "default_branch": "main",
        "source_url": "https://raw.githubusercontent.com/n0-computer/iroh/main/README.md"
      },
      "readme_fetch_status": "ok",
      "url": "https://github.com/n0-computer/iroh",
      "event_date": "2026-06-25",
      "source": "GitHub Trending weekly",
      "language": "all",
      "window": "weekly",
      "rank": 4,
      "source_rank": 4,
      "source_scope": "weekly:all",
      "previous_rank": 5,
      "rank_delta": 1,
      "trend": "up",
      "importance": "general",
      "description": "n0-computer/iroh 本周出现在开源榜单 weekly #4，本周 +1,196 stars；当前只能确认榜单动量，正式采用前还要核对 README、许可证、维护状态和 issue 反馈。"
    },
    {
      "name": "koala73/worldmonitor",
      "repo": "koala73/worldmonitor",
      "readme_cache": {
        "key": "github-readme/koala73/worldmonitor/main/unknown",
        "hit": true,
        "repo": "koala73/worldmonitor",
        "sha": "unknown",
        "default_branch": "main",
        "source_url": "https://raw.githubusercontent.com/koala73/worldmonitor/main/README.md"
      },
      "readme_fetch_status": "ok",
      "url": "https://github.com/koala73/worldmonitor",
      "event_date": "2026-06-25",
      "source": "GitHub Trending weekly",
      "language": "all",
      "window": "weekly",
      "rank": 5,
      "source_rank": 5,
      "source_scope": "weekly:all",
      "previous_rank": 7,
      "rank_delta": 2,
      "trend": "up",
      "importance": "general",
      "description": "koala73/worldmonitor 本周出现在开源榜单 weekly #5，本周 +2,899 stars；当前只能确认榜单动量，正式采用前还要核对 README、许可证、维护状态和 issue 反馈。"
    },
    {
      "name": "asgeirtj/system_prompts_leaks",
      "repo": "asgeirtj/system_prompts_leaks",
      "readme_cache": {
        "key": "github-readme/asgeirtj/system_prompts_leaks/main/unknown",
        "hit": true,
        "repo": "asgeirtj/system_prompts_leaks",
        "sha": "unknown",
        "default_branch": "main",
        "source_url": "https://raw.githubusercontent.com/asgeirtj/system_prompts_leaks/main/README.md"
      },
      "readme_fetch_status": "ok",
      "url": "https://github.com/asgeirtj/system_prompts_leaks",
      "event_date": "2026-06-25",
      "source": "GitHub Trending weekly",
      "language": "all",
      "window": "weekly",
      "rank": 6,
      "source_rank": 6,
      "source_scope": "weekly:all",
      "previous_rank": 6,
      "rank_delta": 0,
      "trend": "same",
      "importance": "general",
      "description": "asgeirtj/system_prompts_leaks 本周出现在开源榜单 weekly #6，本周 +2,662 stars；当前只能确认榜单动量，正式采用前还要核对 README、许可证、维护状态和 issue 反馈。"
    },
    {
      "name": "Panniantong/Agent-Reach",
      "repo": "Panniantong/Agent-Reach",
      "readme_cache": {
        "key": "github-readme/panniantong/agent-reach/main/unknown",
        "hit": true,
        "repo": "panniantong/agent-reach",
        "sha": "unknown",
        "default_branch": "main",
        "source_url": "https://raw.githubusercontent.com/Panniantong/Agent-Reach/main/README.md"
      },
      "readme_fetch_status": "ok",
      "url": "https://github.com/Panniantong/Agent-Reach",
      "event_date": "2026-06-25",
      "source": "GitHub Trending weekly",
      "language": "all",
      "window": "weekly",
      "rank": 7,
      "source_rank": 7,
      "source_scope": "weekly:all",
      "previous_rank": 4,
      "rank_delta": -3,
      "trend": "down",
      "importance": "general",
      "description": "Panniantong/Agent-Reach 本周出现在开源榜单 weekly #7，本周 +6,752 stars；当前只能确认榜单动量，正式采用前还要核对 README、许可证、维护状态和 issue 反馈。"
    },
    {
      "name": "penpot/penpot",
      "repo": "penpot/penpot",
      "readme_cache": {
        "key": "github-readme/penpot/penpot/main/unknown",
        "hit": true,
        "repo": "penpot/penpot",
        "sha": "unknown",
        "default_branch": "main",
        "source_url": "https://raw.githubusercontent.com/penpot/penpot/main/README.md"
      },
      "readme_fetch_status": "ok",
      "url": "https://github.com/penpot/penpot",
      "event_date": "2026-06-25",
      "source": "GitHub Trending weekly",
      "language": "all",
      "window": "weekly",
      "rank": 8,
      "source_rank": 8,
      "source_scope": "weekly:all",
      "previous_rank": 9,
      "rank_delta": 1,
      "trend": "up",
      "importance": "general",
      "description": "penpot 是代理式自动化、工具调用和开发者工作流相关的开源项目，公开说明提到工具调用和工作流编排等能力，并提供部署说明；评估时核对安装步骤、许可证、示例质量、近期维护和接入边界。 它把相关能力沉淀为代码、示例和集成入口，方便和同类方案做功能与工程成本比较。"
    },
    {
      "name": "withastro/flue",
      "repo": "withastro/flue",
      "readme_cache": {
        "key": "github-readme/withastro/flue/main/unknown",
        "hit": true,
        "repo": "withastro/flue",
        "sha": "unknown",
        "default_branch": "main",
        "source_url": "https://raw.githubusercontent.com/withastro/flue/main/README.md"
      },
      "readme_fetch_status": "ok",
      "url": "https://github.com/withastro/flue",
      "event_date": "2026-06-25",
      "source": "GitHub Trending weekly",
      "language": "all",
      "window": "weekly",
      "rank": 9,
      "source_rank": 9,
      "source_scope": "weekly:all",
      "previous_rank": 10,
      "rank_delta": 1,
      "trend": "up",
      "importance": "general",
      "description": "withastro/flue 本周出现在开源榜单 weekly #9，本周 +1,415 stars；当前只能确认榜单动量，正式采用前还要核对 README、许可证、维护状态和 issue 反馈。"
    },
    {
      "name": "jamiepine/voicebox",
      "repo": "jamiepine/voicebox",
      "readme_cache": {
        "key": "github-readme/jamiepine/voicebox/main/unknown",
        "hit": true,
        "repo": "jamiepine/voicebox",
        "sha": "unknown",
        "default_branch": "main",
        "source_url": "https://raw.githubusercontent.com/jamiepine/voicebox/main/README.md"
      },
      "readme_fetch_status": "ok",
      "url": "https://github.com/jamiepine/voicebox",
      "event_date": "2026-06-25",
      "source": "GitHub Trending weekly",
      "language": "all",
      "window": "weekly",
      "rank": 10,
      "source_rank": 10,
      "source_scope": "weekly:all",
      "previous_rank": 17,
      "rank_delta": 7,
      "trend": "up",
      "importance": "general",
      "description": "voicebox 是代理式自动化、工具调用和开发者工作流相关的开源项目，公开说明提到Agent 构建、API/SDK 适配等能力，并提供示例；评估时核对安装步骤、许可证、示例质量、近期维护和接入边界。 它把相关能力沉淀为代码、示例和集成入口，方便和同类方案做功能与工程成本比较。"
    },
    {
      "name": "LMCache/LMCache",
      "repo": "LMCache/LMCache",
      "readme_cache": {
        "key": "github-readme/lmcache/lmcache/main/unknown",
        "hit": true,
        "repo": "lmcache/lmcache",
        "sha": "unknown",
        "default_branch": "main",
        "source_url": "https://raw.githubusercontent.com/LMCache/LMCache/main/README.md"
      },
      "readme_fetch_status": "ok",
      "url": "https://github.com/LMCache/LMCache",
      "event_date": "2026-06-25",
      "source": "GitHub Trending Python weekly",
      "language": "python",
      "window": "weekly",
      "rank": 11,
      "source_rank": 8,
      "source_scope": "weekly:python",
      "previous_rank": 11,
      "rank_delta": 3,
      "trend": "up",
      "importance": "general",
      "description": "LMCache/LMCache 本周出现在开源榜单 Python weekly #11，本周 +551 stars；当前只能确认榜单动量，正式采用前还要核对 README、许可证、维护状态和 issue 反馈。"
    },
    {
      "name": "continuedev/continue",
      "repo": "continuedev/continue",
      "readme_cache": {
        "key": "github-readme/continuedev/continue/main/unknown",
        "hit": true,
        "repo": "continuedev/continue",
        "sha": "unknown",
        "default_branch": "main",
        "source_url": "https://raw.githubusercontent.com/continuedev/continue/main/README.md"
      },
      "readme_fetch_status": "ok",
      "url": "https://github.com/continuedev/continue",
      "event_date": "2026-06-25",
      "source": "GitHub Trending TypeScript weekly",
      "language": "typescript",
      "window": "weekly",
      "rank": 12,
      "source_rank": 9,
      "source_scope": "weekly:typescript",
      "previous_rank": 11,
      "rank_delta": 2,
      "trend": "up",
      "importance": "general",
      "description": "continuedev/continue 本周出现在开源榜单 TypeScript weekly #12，本周 +627 stars；当前只能确认榜单动量，正式采用前还要核对 README、许可证、维护状态和 issue 反馈。"
    },
    {
      "name": "tursodatabase/turso",
      "repo": "tursodatabase/turso",
      "readme_cache": {
        "key": "github-readme/tursodatabase/turso/main/unknown",
        "hit": true,
        "repo": "tursodatabase/turso",
        "sha": "unknown",
        "default_branch": "main",
        "source_url": "https://raw.githubusercontent.com/tursodatabase/turso/main/README.md"
      },
      "readme_fetch_status": "ok",
      "url": "https://github.com/tursodatabase/turso",
      "event_date": "2026-06-25",
      "source": "GitHub Trending Rust weekly",
      "language": "rust",
      "window": "weekly",
      "rank": 13,
      "source_rank": 2,
      "source_scope": "weekly:rust",
      "previous_rank": 13,
      "rank_delta": 11,
      "trend": "up",
      "importance": "general",
      "description": "turso 是AI 工程实践相关的开源项目，公开说明提到项目框架、示例代码和可复用工具链等能力，并提供部署说明；评估时核对安装步骤、许可证、示例质量、近期维护和接入边界。 它把相关能力沉淀为代码、示例和集成入口，方便和同类方案做功能与工程成本比较。"
    },
    {
      "name": "Tencent/WeKnora",
      "repo": "Tencent/WeKnora",
      "readme_cache": {
        "key": "github-readme/tencent/weknora/main/unknown",
        "hit": true,
        "repo": "tencent/weknora",
        "sha": "unknown",
        "default_branch": "main",
        "source_url": "https://raw.githubusercontent.com/Tencent/WeKnora/main/README.md"
      },
      "readme_fetch_status": "ok",
      "url": "https://github.com/Tencent/WeKnora",
      "event_date": "2026-06-25",
      "source": "GitHub Trending Go weekly",
      "language": "go",
      "window": "weekly",
      "rank": 14,
      "source_rank": 1,
      "source_scope": "weekly:go",
      "previous_rank": null,
      "rank_delta": null,
      "trend": "new",
      "importance": "general",
      "description": "Tencent/WeKnora 本周出现在开源榜单 Go weekly #14，本周 +748 stars；当前只能确认榜单动量，正式采用前还要核对 README、许可证、维护状态和 issue 反馈。"
    },
    {
      "name": "AutoMQ/automq",
      "repo": "AutoMQ/automq",
      "readme_cache": {
        "key": "github-readme/automq/automq/main/unknown",
        "hit": true,
        "repo": "automq/automq",
        "sha": "unknown",
        "default_branch": "main",
        "source_url": "https://raw.githubusercontent.com/AutoMQ/automq/main/README.md"
      },
      "readme_fetch_status": "ok",
      "url": "https://github.com/AutoMQ/automq",
      "event_date": "2026-06-25",
      "source": "GitHub Trending Java weekly",
      "language": "java",
      "window": "weekly",
      "rank": 15,
      "source_rank": 2,
      "source_scope": "weekly:java",
      "previous_rank": 15,
      "rank_delta": 13,
      "trend": "up",
      "importance": "general",
      "description": "AutoMQ/automq 本周出现在开源榜单 Java weekly #15，本周 +83 stars；当前只能确认榜单动量，正式采用前还要核对 README、许可证、维护状态和 issue 反馈。"
    },
    {
      "name": "interviewstreet/hiring-agent",
      "repo": "interviewstreet/hiring-agent",
      "readme_cache": {
        "key": "github-readme/interviewstreet/hiring-agent/main/unknown",
        "hit": true,
        "repo": "interviewstreet/hiring-agent",
        "sha": "unknown",
        "default_branch": "main",
        "source_url": "https://raw.githubusercontent.com/interviewstreet/hiring-agent/main/README.md"
      },
      "readme_fetch_status": "ok",
      "url": "https://github.com/interviewstreet/hiring-agent",
      "event_date": "2026-06-25",
      "source": "GitHub Trending Python weekly",
      "language": "python",
      "window": "weekly",
      "rank": 16,
      "source_rank": 9,
      "source_scope": "weekly:python",
      "previous_rank": null,
      "rank_delta": null,
      "trend": "new",
      "importance": "general",
      "description": "interviewstreet/hiring-agent 本周出现在开源榜单 Python weekly #16，本周 +902 stars；当前只能确认榜单动量，正式采用前还要核对 README、许可证、维护状态和 issue 反馈。"
    },
    {
      "name": "Kilo-Org/kilocode",
      "repo": "Kilo-Org/kilocode",
      "readme_cache": {
        "key": "github-readme/kilo-org/kilocode/main/unknown",
        "hit": true,
        "repo": "kilo-org/kilocode",
        "sha": "unknown",
        "default_branch": "main",
        "source_url": "https://raw.githubusercontent.com/Kilo-Org/kilocode/main/README.md"
      },
      "readme_fetch_status": "ok",
      "url": "https://github.com/Kilo-Org/kilocode",
      "event_date": "2026-06-25",
      "source": "GitHub Trending TypeScript weekly",
      "language": "typescript",
      "window": "weekly",
      "rank": 17,
      "source_rank": 10,
      "source_scope": "weekly:typescript",
      "previous_rank": 17,
      "rank_delta": 7,
      "trend": "up",
      "importance": "general",
      "description": "Kilo-Org/kilocode 本周出现在开源榜单 TypeScript weekly #17，本周 +3,676 stars；当前只能确认榜单动量，正式采用前还要核对 README、许可证、维护状态和 issue 反馈。"
    },
    {
      "name": "1jehuang/jcode",
      "repo": "1jehuang/jcode",
      "readme_cache": {
        "key": "github-readme/1jehuang/jcode/master/unknown",
        "hit": true,
        "repo": "1jehuang/jcode",
        "sha": "unknown",
        "default_branch": "master",
        "source_url": "https://raw.githubusercontent.com/1jehuang/jcode/master/README.md"
      },
      "readme_fetch_status": "ok",
      "url": "https://github.com/1jehuang/jcode",
      "event_date": "2026-06-25",
      "source": "GitHub Trending Rust weekly",
      "language": "rust",
      "window": "weekly",
      "rank": 18,
      "source_rank": 3,
      "source_scope": "weekly:rust",
      "previous_rank": 4,
      "rank_delta": 1,
      "trend": "up",
      "importance": "general",
      "description": "1jehuang/jcode 本周出现在开源榜单 Rust weekly #18，本周 +638 stars；当前只能确认榜单动量，正式采用前还要核对 README、许可证、维护状态和 issue 反馈。"
    },
    {
      "name": "cilium/cilium",
      "repo": "cilium/cilium",
      "readme_fetch_status": "failed",
      "readme_error": "HTTP 404",
      "url": "https://github.com/cilium/cilium",
      "event_date": "2026-06-25",
      "source": "GitHub Trending Go weekly",
      "language": "go",
      "window": "weekly",
      "rank": 19,
      "source_rank": 2,
      "source_scope": "weekly:go",
      "previous_rank": 14,
      "rank_delta": 12,
      "trend": "up",
      "importance": "general",
      "description": "cilium/cilium 本周出现在开源榜单 Go weekly #19，本周 +72 stars；当前只能确认榜单动量，正式采用前还要核对 README、许可证、维护状态和 issue 反馈。"
    },
    {
      "name": "apache/nifi",
      "repo": "apache/nifi",
      "readme_cache": {
        "key": "github-readme/apache/nifi/main/unknown",
        "hit": true,
        "repo": "apache/nifi",
        "sha": "unknown",
        "default_branch": "main",
        "source_url": "https://raw.githubusercontent.com/apache/nifi/main/README.md"
      },
      "readme_fetch_status": "ok",
      "url": "https://github.com/apache/nifi",
      "event_date": "2026-06-25",
      "source": "GitHub Trending Java weekly",
      "language": "java",
      "window": "weekly",
      "rank": 20,
      "source_rank": 3,
      "source_scope": "weekly:java",
      "previous_rank": null,
      "rank_delta": null,
      "trend": "new",
      "importance": "general",
      "description": "apache/nifi 本周出现在开源榜单 Java weekly #20，本周 +17 stars；当前只能确认榜单动量，正式采用前还要核对 README、许可证、维护状态和 issue 反馈。"
    }
  ],
  "huggingface_trending": [],
  "model_releases": [],
  "hot_blogs": [
    {
      "title": "Azure披露 agent 与开发者工具能力",
      "editorial_category": "viewpoint_analysis",
      "url": "https://azure.microsoft.com/en-us/blog/from-insight-to-action-the-next-phase-of-agentic-cloud-operations/",
      "publisher": "Azure Blog",
      "author": "Azure Blog",
      "event_date": "2026-06-23",
      "topic": "AI engineering tools",
      "summary": "Azure更新agent 工作流和开发工具能力，重点落在任务编排、上下文、权限控制、工程集成和失败恢复。更有价值的信息是agent 工作流、开发工具入口、权限控制和工程集成，判断这类方案时还要看落地质量取决于权限模型、评估回放、团队流程和可观测性。文章拆解 agent、开发工具或自动化流程里的任务规划、权限、上下文、工具调用和失败恢复。",
      "content_type": "blog",
      "importance": "notable",
      "image_urls": []
    },
    {
      "title": "GitHub Changelog更新模型能力和推理入口变化",
      "editorial_category": "viewpoint_analysis",
      "url": "https://github.blog/changelog/2026-06-24-changes-to-model-selection-for-free-and-student-plans",
      "publisher": "GitHub Changelog",
      "author": "GitHub Changelog",
      "event_date": "2026-06-24",
      "topic": "AI industry",
      "summary": "该开源项目披露模型能力和评估方法更新，重点落在能力边界、评估设置、数据来源、使用场景和限制说明。更有价值的信息是模型能力、评估设置、数据来源和限制说明，判断这类方案时还要看结论仍要依赖可复现评测、真实任务和公开限制。文章拆解 agent、开发工具或自动化流程里的任务规划、权限、上下文、工具调用和失败恢复。",
      "content_type": "blog",
      "importance": "notable",
      "image_urls": []
    },
    {
      "title": "Apple 研究指出 LLM 评审团的相关错误会削弱多评委投票价值",
      "editorial_category": "viewpoint_analysis",
      "url": "https://machinelearning.apple.com/research/correlated-llm-evaluation-panels",
      "publisher": "Apple Machine Learning Research",
      "author": "Apple Machine Learning Research",
      "event_date": "2026-06-23",
      "topic": "research / evaluation",
      "summary": "Apple披露模型能力和评估方法更新，重点落在能力边界、评估设置、数据来源、使用场景和限制说明。更有价值的信息是模型能力、评估设置、数据来源和限制说明，判断这类方案时还要看结论仍要依赖可复现评测、真实任务和公开限制。文章梳理模型评测或研究结论怎样改变能力边界、成本预期和可靠性判断。",
      "content_type": "blog",
      "importance": "notable",
      "image_urls": []
    },
    {
      "title": "NVIDIA披露模型能力和推理入口变化",
      "editorial_category": "viewpoint_analysis",
      "url": "https://developer.nvidia.com/blog/accelerating-bev-pooling-on-nvidia-gpus-for-physical-ai-applications/",
      "publisher": "NVIDIA Developer Blog",
      "author": "NVIDIA Developer Blog",
      "event_date": "2026-06-23",
      "topic": "AI engineering tools",
      "summary": "NVIDIA介绍机器人学习与多模态推理实验，重点落在具身任务规划、训练数据、多模态推理和评估工作流。更有价值的信息是机器人学习实验、多模态推理和任务规划评估，判断这类方案时还要看研究信号仍需要看真实机器人任务、数据规模和评测设置。",
      "content_type": "blog",
      "importance": "notable",
      "image_urls": []
    },
    {
      "title": "AWS更新agent 工作流和开发工具能力",
      "editorial_category": "viewpoint_analysis",
      "url": "https://aws.amazon.com/blogs/machine-learning/huntington-bank-redacting-sensitive-data-from-400m-documents-with-aws/",
      "publisher": "AWS Machine Learning Blog",
      "author": "AWS Machine Learning Blog",
      "event_date": "2026-06-24",
      "topic": "AI industry",
      "summary": "AWS更新agent 工作流和开发工具能力，重点落在任务编排、上下文、权限控制、工程集成和失败恢复。更有价值的信息是agent 工作流、开发工具入口、权限控制和工程集成，判断这类方案时还要看落地质量取决于权限模型、评估回放、团队流程和可观测性。文章梳理一个 AI 产品、平台或工程实践的具体变化，而不是只给观点。",
      "content_type": "blog",
      "importance": "notable",
      "image_urls": []
    },
    {
      "title": "AWS披露 agent 与开发者工具能力",
      "editorial_category": "viewpoint_analysis",
      "url": "https://aws.amazon.com/blogs/machine-learning/build-a-healthcare-appointment-agent-with-amazon-nova-2-sonic/",
      "publisher": "AWS Machine Learning Blog",
      "author": "AWS Machine Learning Blog",
      "event_date": "2026-06-24",
      "topic": "AI engineering tools",
      "summary": "AWS更新agent 工作流和开发工具能力，重点落在任务编排、上下文、权限控制、工程集成和失败恢复。更有价值的信息是agent 工作流、开发工具入口、权限控制和工程集成，判断这类方案时还要看落地质量取决于权限模型、评估回放、团队流程和可观测性。文章拆解 agent、开发工具或自动化流程里的任务规划、权限、上下文、工具调用和失败恢复。",
      "content_type": "blog",
      "importance": "notable",
      "image_urls": []
    },
    {
      "title": "AWS更新AI 产品、平台或工程实践",
      "editorial_category": "viewpoint_analysis",
      "url": "https://aws.amazon.com/blogs/machine-learning/ai-powered-bi-with-snowflake-and-amazon-quick/",
      "publisher": "AWS Machine Learning Blog",
      "author": "AWS Machine Learning Blog",
      "event_date": "2026-06-24",
      "topic": "AI industry",
      "summary": "AWS更新AI 产品、平台或工程实践，重点落在功能变化、使用场景、接入方式、限制条件和后续部署边界。更有价值的信息是功能变化、适用场景、接入方式和限制条件，判断这类方案时还要看公开材料仍需要回到原文核对入口、权限、价格和适用范围。文章梳理一个 AI 产品、平台或工程实践的具体变化，而不是只给观点。",
      "content_type": "blog",
      "importance": "notable",
      "image_urls": []
    },
    {
      "title": "NVIDIA说明安全治理和平台控制更新",
      "editorial_category": "viewpoint_analysis",
      "image_url": "https://cdn-avatars.huggingface.co/v1/production/uploads/65df9200dc3292a8983e5017/Vs5FPVCH-VZBipV3qKTuy.png",
      "image_alt": "nvidia/Nemotron-3.5-Content-Safety",
      "image_source": "html_index",
      "url": "https://huggingface.co/nvidia/Nemotron-3.5-Content-Safety",
      "publisher": "NVIDIA Hugging Face Organization",
      "author": "NVIDIA Hugging Face Organization",
      "event_date": "2026-06-24",
      "topic": "AI industry",
      "summary": "NVIDIA说明安全治理和平台控制更新，重点落在策略检查、风险控制、上线约束、审计记录和组织执行。更有价值的信息是策略检查、风险控制、审计记录和上线约束，判断这类方案时还要看治理效果取决于误判率、日志留存、人工复核和系统接入范围。文章说明安全、治理或平台规则会怎样变成团队需要执行的产品和上线约束。",
      "content_type": "blog",
      "importance": "notable",
      "image_urls": []
    }
  ],
  "chinese_media_dynamics": [
    {
      "title": "跟Claude谈个恋爱怎么了？Nature最新研究：真能给人聊傻了",
      "url": "https://www.qbitai.com/2026/06/438365.html",
      "publisher": "QbitAI",
      "author": "QbitAI",
      "event_date": "2026-06-25",
      "topic": "中文 AI 媒体动态",
      "summary": "跟Claude谈个恋爱怎么了？Nature最新研究：真能给人聊傻了：Claude已经，俨然成为了新一代电子老公。",
      "key_points": [
        "跟Claude谈个恋爱怎么了",
        "Nature最新研究：真能给人聊傻了：Claude已经，俨然成为了新一代电子老公",
        "This is an intermediary/self-media lead; trace it to a primary source before treating it as a reported fact."
      ],
      "importance": "notable",
      "image_urls": []
    },
    {
      "title": "不靠单款爆款吃红利，中国AI应用首现3亿ARR独角兽！腾讯顺为红杉继续加码",
      "url": "https://www.qbitai.com/2026/06/438336.html",
      "publisher": "QbitAI",
      "author": "QbitAI",
      "event_date": "2026-06-25",
      "topic": "中文 AI 媒体动态",
      "summary": "不靠单款爆款吃红利，中国AI应用首现3亿ARR独角兽！腾讯顺为红杉继续加码：中国AI应用公司并没有停留在追赶全球浪潮。",
      "key_points": [
        "不靠单款爆款吃红利，中国AI应用首现3亿ARR独角兽",
        "腾讯顺为红杉继续加码：中国AI应用公司并没有停留在追赶全球浪潮",
        "This is an intermediary/self-media lead; trace it to a primary source before treating it as a reported fact."
      ],
      "importance": "notable",
      "image_urls": []
    },
    {
      "title": "聚焦GW级Token工厂，解码下一代算力底座｜6月30日，深圳",
      "url": "https://www.qbitai.com/2026/06/438297.html",
      "publisher": "QbitAI",
      "author": "QbitAI",
      "event_date": "2026-06-25",
      "topic": "中文 AI 媒体动态",
      "summary": "聚焦GW级Token工厂，解码下一代算力底座｜6月30日，深圳：谁将定义下一代算力基础设施？谁又能在Token时代占据产业制高点？",
      "key_points": [
        "聚焦GW级Token工厂，解码下一代算力底座｜6月30日，深圳：谁将定义下一代算力基础设施",
        "谁又能在Token时代占据产业制高点",
        "This is an intermediary/self-media lead; trace it to a primary source before treating it as a reported fact."
      ],
      "importance": "notable",
      "image_urls": []
    },
    {
      "title": "风暖鸟声碎，日高花影重：我的昆明与腾冲行记",
      "url": "https://sspai.com/post/111349",
      "publisher": "SSPAI",
      "author": "SSPAI",
      "event_date": "2026-06-25",
      "topic": "中文 AI 媒体动态",
      "summary": "风暖鸟声碎，日高花影重：我的昆明与腾冲行记：云南气候实在是太好了，昆明海拔将近两千，每次出门都有一种凉爽感包裹全身。而腾冲太适合旅居了，既有中高端的酒店可供享受奢靡之风，也有历史文化供我们反思来时的路，更好的去面对未来，还有拿上衣服就可以随地大小泡的温泉，美哉！ 查看全文。This is an intermediary/self-media lead; t。",
      "key_points": [
        "风暖鸟声碎，日高花影重：我的昆明与腾冲行记：云南气候实在是太好了，昆明海拔将近两千，每次出门都有一种凉爽感包裹全身",
        "而腾冲太适合旅居了，既有中高端的酒店可供享受奢靡之风，也有历史文化供我们反思来时的路，更好的去面对未来，还有拿上衣服就可以随地大小泡的温泉，美哉",
        "查看全文"
      ],
      "importance": "notable",
      "image_urls": []
    },
    {
      "title": "派早报：豆包推出专业版、GTA VI 开启预售等",
      "url": "https://sspai.com/post/111476",
      "publisher": "SSPAI",
      "author": "SSPAI",
      "event_date": "2026-06-25",
      "topic": "中文 AI 媒体动态",
      "summary": "派早报：豆包推出专业版、GTA VI 开启预售等：OpenAI 发布 AI 推理芯片 Jalapeño、Goodram 推出 SD 存储卡 PRO S6B0 等。 查看全文。",
      "key_points": [
        "派早报：豆包推出专业版、GTA VI 开启预售等：OpenAI 发布 AI 推理芯片 Jalapeño、Goodram 推出 SD 存储卡 PRO S6B0 等",
        "查看全文",
        "This is an intermediary/self-media lead; trace it to a primary source before treating it as"
      ],
      "importance": "notable",
      "image_urls": []
    }
  ],
  "daily_tracking": [
    {
      "id": "openrouter-rankings",
      "name": "OpenRouter",
      "url": "https://openrouter.ai/rankings",
      "event_date": "2026-06-25",
      "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 4.97T tokens；#2 MiMo-V2.5 4.36T tokens；#3 MiniMax M3 3.75T tokens；GLM 5.2 周变化 523%。",
      "summary": "OpenRouter 公开榜单显示，本周调用热度第一是 DeepSeek V4 Flash（deepseek，4.97T tokens，周变化 10%）。 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 523%，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）：4.97T tokens，周变化 10%",
          "trend": "up"
        },
        {
          "label": "#2",
          "value": "MiMo-V2.5（xiaomi）：4.36T tokens，周变化 18%",
          "trend": "up"
        },
        {
          "label": "#3",
          "value": "MiniMax M3（minimax）：3.75T tokens，周变化 19%",
          "trend": "up"
        },
        {
          "label": "#4",
          "value": "Hy3 preview（tencent）：3.44T tokens，周变化 7%",
          "trend": "up"
        },
        {
          "label": "#5",
          "value": "Owl Alpha（openrouter）：2.92T tokens，周变化 19%",
          "trend": "up"
        },
        {
          "label": "#6",
          "value": "Claude Opus 4.7（anthropic）：2.54T tokens，周变化 11%",
          "trend": "up"
        },
        {
          "label": "#7",
          "value": "DeepSeek V4 Pro（deepseek）：2.19T tokens，周变化 0%",
          "trend": "same"
        },
        {
          "label": "#8",
          "value": "Claude Opus 4.8（anthropic）：1.87T tokens，周变化 37%",
          "trend": "up"
        },
        {
          "label": "#9",
          "value": "GLM 5.2（z-ai）：1.8T tokens，周变化 523%",
          "trend": "up"
        },
        {
          "label": "#10",
          "value": "Claude Sonnet 4.6（anthropic）：1.49T tokens，周变化 17%",
          "trend": "up"
        }
      ],
      "snapshot": {
        "type": "openrouter_rankings_public_page",
        "collection_method": "public_page_playwright",
        "snapshot_status": "complete",
        "snapshot_as_of": "2026-06-25T08:21:29.470Z",
        "source_url": "https://openrouter.ai/rankings",
        "top_entries": [
          {
            "rank": 1,
            "model": "DeepSeek V4 Flash",
            "provider": "deepseek",
            "tokens": "4.97T tokens",
            "change": "10%"
          },
          {
            "rank": 2,
            "model": "MiMo-V2.5",
            "provider": "xiaomi",
            "tokens": "4.36T tokens",
            "change": "18%"
          },
          {
            "rank": 3,
            "model": "MiniMax M3",
            "provider": "minimax",
            "tokens": "3.75T tokens",
            "change": "19%"
          },
          {
            "rank": 4,
            "model": "Hy3 preview",
            "provider": "tencent",
            "tokens": "3.44T tokens",
            "change": "7%"
          },
          {
            "rank": 5,
            "model": "Owl Alpha",
            "provider": "openrouter",
            "tokens": "2.92T tokens",
            "change": "19%"
          },
          {
            "rank": 6,
            "model": "Claude Opus 4.7",
            "provider": "anthropic",
            "tokens": "2.54T tokens",
            "change": "11%"
          },
          {
            "rank": 7,
            "model": "DeepSeek V4 Pro",
            "provider": "deepseek",
            "tokens": "2.19T tokens",
            "change": "0%"
          },
          {
            "rank": 8,
            "model": "Claude Opus 4.8",
            "provider": "anthropic",
            "tokens": "1.87T tokens",
            "change": "37%"
          },
          {
            "rank": 9,
            "model": "GLM 5.2",
            "provider": "z-ai",
            "tokens": "1.8T tokens",
            "change": "523%"
          },
          {
            "rank": 10,
            "model": "Claude Sonnet 4.6",
            "provider": "anthropic",
            "tokens": "1.49T tokens",
            "change": "17%"
          }
        ],
        "official_component_snapshot": {
          "source": "official_dom",
          "component_kind": "openrouter_rankings",
          "source_url": "https://openrouter.ai/rankings",
          "captured_at": "2026-06-25T08:21:29.470Z",
          "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>4.97T tokens</td><td>10%</td></tr>\n        <tr><td>#2</td><td>MiMo-V2.5</td><td>xiaomi</td><td>4.36T tokens</td><td>18%</td></tr>\n        <tr><td>#3</td><td>MiniMax M3</td><td>minimax</td><td>3.75T tokens</td><td>19%</td></tr>\n        <tr><td>#4</td><td>Hy3 preview</td><td>tencent</td><td>3.44T tokens</td><td>7%</td></tr>\n        <tr><td>#5</td><td>Owl Alpha</td><td>openrouter</td><td>2.92T tokens</td><td>19%</td></tr>\n        <tr><td>#6</td><td>Claude Opus 4.7</td><td>anthropic</td><td>2.54T tokens</td><td>11%</td></tr>\n        <tr><td>#7</td><td>DeepSeek V4 Pro</td><td>deepseek</td><td>2.19T tokens</td><td>0%</td></tr>\n        <tr><td>#8</td><td>Claude Opus 4.8</td><td>anthropic</td><td>1.87T tokens</td><td>37%</td></tr>\n        <tr><td>#9</td><td>GLM 5.2</td><td>z-ai</td><td>1.8T tokens</td><td>523%</td></tr>\n        <tr><td>#10</td><td>Claude Sonnet 4.6</td><td>anthropic</td><td>1.49T tokens</td><td>17%</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:fcf92c555b37c8ecaa37e4127fcab1600853fb8d7cd4cbc9d4a000fe59a334dc",
          "css_hash": "sha256:e5df3dc0e07de42f5c2ca4021bbf59258d47d1ef0fa569d55d98b09876618e43"
        },
        "history_entries": []
      },
      "tracking_component_snapshot": {
        "source": "OpenRouter",
        "component_kind": "openrouter_rankings",
        "source_url": "https://openrouter.ai/rankings",
        "collected_at": "2026-06-25T08:21:29.470Z",
        "selector_version": "openrouter-rankings-v1",
        "raw_dom_hash": "sha256:45b0f3a5e57a4d04471d3d17e49b8be23b3ea31961b66b235213902a19cb6ae1",
        "data_hash": "sha256:9ebd0fa95d74ad7d3c50675d5c69b527167fc824d18bd0b3a5448c597273e9a0",
        "tabs": [
          {
            "id": "top-models",
            "label": "Top Models",
            "view": "stacked_bar",
            "fallback_reason": ""
          },
          {
            "id": "leaderboard",
            "label": "LLM Leaderboard",
            "view": "leaderboard",
            "fallback_reason": ""
          }
        ],
        "series": [
          {
            "id": "openrouter-top-models-weekly-usage",
            "tab_id": "top-models",
            "label": "Weekly usage across OpenRouter",
            "chart": "stacked_bar",
            "rows": [
              {
                "rank": 1,
                "model": "DeepSeek V4 Flash",
                "provider": "deepseek",
                "value": 4970000000000,
                "value_label": "4.97T tokens",
                "change": "10%"
              },
              {
                "rank": 2,
                "model": "MiMo-V2.5",
                "provider": "xiaomi",
                "value": 4360000000000.0005,
                "value_label": "4.36T tokens",
                "change": "18%"
              },
              {
                "rank": 3,
                "model": "MiniMax M3",
                "provider": "minimax",
                "value": 3750000000000,
                "value_label": "3.75T tokens",
                "change": "19%"
              },
              {
                "rank": 4,
                "model": "Hy3 preview",
                "provider": "tencent",
                "value": 3440000000000,
                "value_label": "3.44T tokens",
                "change": "7%"
              },
              {
                "rank": 5,
                "model": "Owl Alpha",
                "provider": "openrouter",
                "value": 2920000000000,
                "value_label": "2.92T tokens",
                "change": "19%"
              },
              {
                "rank": 6,
                "model": "Claude Opus 4.7",
                "provider": "anthropic",
                "value": 2540000000000,
                "value_label": "2.54T tokens",
                "change": "11%"
              },
              {
                "rank": 7,
                "model": "DeepSeek V4 Pro",
                "provider": "deepseek",
                "value": 2190000000000,
                "value_label": "2.19T tokens",
                "change": "0%"
              },
              {
                "rank": 8,
                "model": "Claude Opus 4.8",
                "provider": "anthropic",
                "value": 1870000000000,
                "value_label": "1.87T tokens",
                "change": "37%"
              },
              {
                "rank": 9,
                "model": "GLM 5.2",
                "provider": "z-ai",
                "value": 1800000000000,
                "value_label": "1.8T tokens",
                "change": "523%"
              },
              {
                "rank": 10,
                "model": "Claude Sonnet 4.6",
                "provider": "anthropic",
                "value": 1490000000000,
                "value_label": "1.49T tokens",
                "change": "17%"
              }
            ],
            "fallback_reason": ""
          },
          {
            "id": "openrouter-llm-leaderboard",
            "tab_id": "leaderboard",
            "label": "LLM Leaderboard",
            "chart": "leaderboard",
            "rows": [
              {
                "rank": 1,
                "model": "DeepSeek V4 Flash",
                "provider": "deepseek",
                "value": 4970000000000,
                "value_label": "4.97T tokens",
                "change": "10%"
              },
              {
                "rank": 2,
                "model": "MiMo-V2.5",
                "provider": "xiaomi",
                "value": 4360000000000.0005,
                "value_label": "4.36T tokens",
                "change": "18%"
              },
              {
                "rank": 3,
                "model": "MiniMax M3",
                "provider": "minimax",
                "value": 3750000000000,
                "value_label": "3.75T tokens",
                "change": "19%"
              },
              {
                "rank": 4,
                "model": "Hy3 preview",
                "provider": "tencent",
                "value": 3440000000000,
                "value_label": "3.44T tokens",
                "change": "7%"
              },
              {
                "rank": 5,
                "model": "Owl Alpha",
                "provider": "openrouter",
                "value": 2920000000000,
                "value_label": "2.92T tokens",
                "change": "19%"
              },
              {
                "rank": 6,
                "model": "Claude Opus 4.7",
                "provider": "anthropic",
                "value": 2540000000000,
                "value_label": "2.54T tokens",
                "change": "11%"
              },
              {
                "rank": 7,
                "model": "DeepSeek V4 Pro",
                "provider": "deepseek",
                "value": 2190000000000,
                "value_label": "2.19T tokens",
                "change": "0%"
              },
              {
                "rank": 8,
                "model": "Claude Opus 4.8",
                "provider": "anthropic",
                "value": 1870000000000,
                "value_label": "1.87T tokens",
                "change": "37%"
              },
              {
                "rank": 9,
                "model": "GLM 5.2",
                "provider": "z-ai",
                "value": 1800000000000,
                "value_label": "1.8T tokens",
                "change": "523%"
              },
              {
                "rank": 10,
                "model": "Claude Sonnet 4.6",
                "provider": "anthropic",
                "value": 1490000000000,
                "value_label": "1.49T tokens",
                "change": "17%"
              }
            ],
            "fallback_reason": ""
          }
        ],
        "rows": [
          {
            "rank": 1,
            "model": "DeepSeek V4 Flash",
            "provider": "deepseek",
            "value": 4970000000000,
            "value_label": "4.97T tokens",
            "change": "10%"
          },
          {
            "rank": 2,
            "model": "MiMo-V2.5",
            "provider": "xiaomi",
            "value": 4360000000000.0005,
            "value_label": "4.36T tokens",
            "change": "18%"
          },
          {
            "rank": 3,
            "model": "MiniMax M3",
            "provider": "minimax",
            "value": 3750000000000,
            "value_label": "3.75T tokens",
            "change": "19%"
          },
          {
            "rank": 4,
            "model": "Hy3 preview",
            "provider": "tencent",
            "value": 3440000000000,
            "value_label": "3.44T tokens",
            "change": "7%"
          },
          {
            "rank": 5,
            "model": "Owl Alpha",
            "provider": "openrouter",
            "value": 2920000000000,
            "value_label": "2.92T tokens",
            "change": "19%"
          },
          {
            "rank": 6,
            "model": "Claude Opus 4.7",
            "provider": "anthropic",
            "value": 2540000000000,
            "value_label": "2.54T tokens",
            "change": "11%"
          },
          {
            "rank": 7,
            "model": "DeepSeek V4 Pro",
            "provider": "deepseek",
            "value": 2190000000000,
            "value_label": "2.19T tokens",
            "change": "0%"
          },
          {
            "rank": 8,
            "model": "Claude Opus 4.8",
            "provider": "anthropic",
            "value": 1870000000000,
            "value_label": "1.87T tokens",
            "change": "37%"
          },
          {
            "rank": 9,
            "model": "GLM 5.2",
            "provider": "z-ai",
            "value": 1800000000000,
            "value_label": "1.8T tokens",
            "change": "523%"
          },
          {
            "rank": 10,
            "model": "Claude Sonnet 4.6",
            "provider": "anthropic",
            "value": 1490000000000,
            "value_label": "1.49T tokens",
            "change": "17%"
          }
        ],
        "previous_snapshot": null,
        "diff": {
          "summary": "No previous component snapshot was available for comparison.",
          "changed_rows": [],
          "new_entries": [
            "DeepSeek V4 Flash",
            "MiMo-V2.5",
            "MiniMax M3",
            "Hy3 preview",
            "Owl Alpha",
            "Claude Opus 4.7",
            "DeepSeek V4 Pro",
            "Claude Opus 4.8",
            "GLM 5.2",
            "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": "2026-06-25T08:21:29.470Z",
          "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>4.97T tokens</td><td>10%</td></tr>\n        <tr><td>#2</td><td>MiMo-V2.5</td><td>xiaomi</td><td>4.36T tokens</td><td>18%</td></tr>\n        <tr><td>#3</td><td>MiniMax M3</td><td>minimax</td><td>3.75T tokens</td><td>19%</td></tr>\n        <tr><td>#4</td><td>Hy3 preview</td><td>tencent</td><td>3.44T tokens</td><td>7%</td></tr>\n        <tr><td>#5</td><td>Owl Alpha</td><td>openrouter</td><td>2.92T tokens</td><td>19%</td></tr>\n        <tr><td>#6</td><td>Claude Opus 4.7</td><td>anthropic</td><td>2.54T tokens</td><td>11%</td></tr>\n        <tr><td>#7</td><td>DeepSeek V4 Pro</td><td>deepseek</td><td>2.19T tokens</td><td>0%</td></tr>\n        <tr><td>#8</td><td>Claude Opus 4.8</td><td>anthropic</td><td>1.87T tokens</td><td>37%</td></tr>\n        <tr><td>#9</td><td>GLM 5.2</td><td>z-ai</td><td>1.8T tokens</td><td>523%</td></tr>\n        <tr><td>#10</td><td>Claude Sonnet 4.6</td><td>anthropic</td><td>1.49T tokens</td><td>17%</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:fcf92c555b37c8ecaa37e4127fcab1600853fb8d7cd4cbc9d4a000fe59a334dc",
          "css_hash": "sha256:e5df3dc0e07de42f5c2ca4021bbf59258d47d1ef0fa569d55d98b09876618e43"
        },
        "public_trace": {
          "source_url": "https://openrouter.ai/rankings",
          "collected_at": "2026-06-25T08:21:29.470Z",
          "selector_version": "openrouter-rankings-v1",
          "data_hash": "sha256:9ebd0fa95d74ad7d3c50675d5c69b527167fc824d18bd0b3a5448c597273e9a0",
          "top_rows": [
            {
              "rank": 1,
              "model": "DeepSeek V4 Flash",
              "provider": "deepseek",
              "value_label": "4.97T tokens",
              "change": "10%"
            },
            {
              "rank": 2,
              "model": "MiMo-V2.5",
              "provider": "xiaomi",
              "value_label": "4.36T tokens",
              "change": "18%"
            },
            {
              "rank": 3,
              "model": "MiniMax M3",
              "provider": "minimax",
              "value_label": "3.75T tokens",
              "change": "19%"
            },
            {
              "rank": 4,
              "model": "Hy3 preview",
              "provider": "tencent",
              "value_label": "3.44T tokens",
              "change": "7%"
            },
            {
              "rank": 5,
              "model": "Owl Alpha",
              "provider": "openrouter",
              "value_label": "2.92T tokens",
              "change": "19%"
            },
            {
              "rank": 6,
              "model": "Claude Opus 4.7",
              "provider": "anthropic",
              "value_label": "2.54T tokens",
              "change": "11%"
            },
            {
              "rank": 7,
              "model": "DeepSeek V4 Pro",
              "provider": "deepseek",
              "value_label": "2.19T tokens",
              "change": "0%"
            },
            {
              "rank": 8,
              "model": "Claude Opus 4.8",
              "provider": "anthropic",
              "value_label": "1.87T tokens",
              "change": "37%"
            },
            {
              "rank": 9,
              "model": "GLM 5.2",
              "provider": "z-ai",
              "value_label": "1.8T tokens",
              "change": "523%"
            },
            {
              "rank": 10,
              "model": "Claude Sonnet 4.6",
              "provider": "anthropic",
              "value_label": "1.49T tokens",
              "change": "17%"
            }
          ],
          "diff": {
            "summary": "No previous component snapshot was available for comparison.",
            "changed_rows": [],
            "new_entries": [
              "DeepSeek V4 Flash",
              "MiMo-V2.5",
              "MiniMax M3",
              "Hy3 preview",
              "Owl Alpha",
              "Claude Opus 4.7",
              "DeepSeek V4 Pro",
              "Claude Opus 4.8",
              "GLM 5.2",
              "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": "2026-06-25",
      "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、openai 2、alibaba 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、openai 2、alibaba 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": "GPT-5.5 (high)（openai）：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": "Claude Sonnet 4.6 (max)（anthropic）：47 分",
          "trend": "unknown"
        },
        {
          "label": "#9",
          "value": "Gemini 3.1 Pro Preview（google）：46 分",
          "trend": "unknown"
        },
        {
          "label": "#10",
          "value": "Qwen3.7 Max（alibaba）：46 分",
          "trend": "unknown"
        }
      ],
      "snapshot": {
        "type": "artificial_analysis_intelligence_index_public_page",
        "collection_method": "public_page_playwright",
        "snapshot_status": "complete",
        "snapshot_as_of": "2026-06-25T08:21:29.470Z",
        "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": "GPT-5.5 (high)",
            "provider": "openai",
            "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": "Claude Sonnet 4.6 (max)",
            "provider": "anthropic",
            "tokens": "47 分",
            "change": "AA Index"
          },
          {
            "rank": 9,
            "model": "Gemini 3.1 Pro Preview",
            "provider": "google",
            "tokens": "46 分",
            "change": "AA Index"
          },
          {
            "rank": 10,
            "model": "Qwen3.7 Max",
            "provider": "alibaba",
            "tokens": "46 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": "GPT-5.5 (high)",
                "provider": "openai",
                "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": "Claude Sonnet 4.6 (max)",
                "provider": "anthropic",
                "value": 47,
                "value_label": "47 分",
                "change": "AA Index",
                "metric": "Score"
              },
              {
                "rank": 9,
                "model": "Gemini 3.1 Pro Preview",
                "provider": "google",
                "value": 46,
                "value_label": "46 分",
                "change": "AA Index",
                "metric": "Score"
              },
              {
                "rank": 10,
                "model": "Qwen3.7 Max",
                "provider": "alibaba",
                "value": 46,
                "value_label": "46 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": "2026-06-25T08:21:29.470Z",
          "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>GPT-5.5 (high)</td><td>openai</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>Claude Sonnet 4.6 (max)</td><td>anthropic</td><td>47 分</td><td>AA Index</td></tr>\n        <tr><td>#9</td><td>Gemini 3.1 Pro Preview</td><td>google</td><td>46 分</td><td>AA Index</td></tr>\n        <tr><td>#10</td><td>Qwen3.7 Max</td><td>alibaba</td><td>46 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": "sha256:2a6211eed7acea0dbf188caca37b12ac5937ac03c08119c42ccd2e94f897d66e",
          "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": "2026-06-25T08:21:29.470Z",
        "selector_version": "artificial-analysis-index-v1",
        "raw_dom_hash": "sha256:caa7d33c2116c5495c0ff87bb3af671e33346e063be109cb1e5beccdf50e227a",
        "data_hash": "sha256:c0f66bded6ca2a1e6361d355c46babc9e49a66ec72240230883f603b87777009",
        "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": "GPT-5.5 (high)",
                "provider": "openai",
                "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": "Claude Sonnet 4.6 (max)",
                "provider": "anthropic",
                "value": 47,
                "value_label": "47 分",
                "change": "AA Index",
                "metric": "Score"
              },
              {
                "rank": 9,
                "model": "Gemini 3.1 Pro Preview",
                "provider": "google",
                "value": 46,
                "value_label": "46 分",
                "change": "AA Index",
                "metric": "Score"
              },
              {
                "rank": 10,
                "model": "Qwen3.7 Max",
                "provider": "alibaba",
                "value": 46,
                "value_label": "46 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": "GPT-5.5 (high)",
            "provider": "openai",
            "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": "Claude Sonnet 4.6 (max)",
            "provider": "anthropic",
            "value": 47,
            "value_label": "47 分",
            "change": "AA Index",
            "metric": "AA Index"
          },
          {
            "rank": 9,
            "model": "Gemini 3.1 Pro Preview",
            "provider": "google",
            "value": 46,
            "value_label": "46 分",
            "change": "AA Index",
            "metric": "AA Index"
          },
          {
            "rank": 10,
            "model": "Qwen3.7 Max",
            "provider": "alibaba",
            "value": 46,
            "value_label": "46 分",
            "change": "AA Index",
            "metric": "AA Index"
          }
        ],
        "previous_snapshot": null,
        "diff": {
          "summary": "No previous component snapshot was available for comparison.",
          "changed_rows": [],
          "new_entries": [
            "Claude Fable 5 (with fallback)",
            "Claude Opus 4.8 (max)",
            "GPT-5.5 (xhigh)",
            "Claude Opus 4.7 (max)",
            "GPT-5.5 (high)",
            "GLM-5.2 (max)",
            "Gemini 3.5 Flash",
            "Claude Sonnet 4.6 (max)",
            "Gemini 3.1 Pro Preview",
            "Qwen3.7 Max"
          ]
        },
        "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": "2026-06-25T08:21:29.470Z",
          "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>GPT-5.5 (high)</td><td>openai</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>Claude Sonnet 4.6 (max)</td><td>anthropic</td><td>47 分</td><td>AA Index</td></tr>\n        <tr><td>#9</td><td>Gemini 3.1 Pro Preview</td><td>google</td><td>46 分</td><td>AA Index</td></tr>\n        <tr><td>#10</td><td>Qwen3.7 Max</td><td>alibaba</td><td>46 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": "sha256:2a6211eed7acea0dbf188caca37b12ac5937ac03c08119c42ccd2e94f897d66e",
          "css_hash": "sha256:ca6c14bf5033437bf403050f8a1267f835b68aab80f54905dfa8ecc653808bbf"
        },
        "public_trace": {
          "source_url": "https://artificialanalysis.ai/evaluations/artificial-analysis-intelligence-index",
          "collected_at": "2026-06-25T08:21:29.470Z",
          "selector_version": "artificial-analysis-index-v1",
          "data_hash": "sha256:c0f66bded6ca2a1e6361d355c46babc9e49a66ec72240230883f603b87777009",
          "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": "GPT-5.5 (high)",
              "provider": "openai",
              "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": "Claude Sonnet 4.6 (max)",
              "provider": "anthropic",
              "value_label": "47 分",
              "change": "AA Index"
            },
            {
              "rank": 9,
              "model": "Gemini 3.1 Pro Preview",
              "provider": "google",
              "value_label": "46 分",
              "change": "AA Index"
            },
            {
              "rank": 10,
              "model": "Qwen3.7 Max",
              "provider": "alibaba",
              "value_label": "46 分",
              "change": "AA Index"
            }
          ],
          "diff": {
            "summary": "No previous component snapshot was available for comparison.",
            "changed_rows": [],
            "new_entries": [
              "Claude Fable 5 (with fallback)",
              "Claude Opus 4.8 (max)",
              "GPT-5.5 (xhigh)",
              "Claude Opus 4.7 (max)",
              "GPT-5.5 (high)",
              "GLM-5.2 (max)",
              "Gemini 3.5 Flash",
              "Claude Sonnet 4.6 (max)",
              "Gemini 3.1 Pro Preview",
              "Qwen3.7 Max"
            ]
          },
          "cache_status": "live",
          "fallback_reason": ""
        }
      }
    },
    {
      "id": "swe-bench-pro-public",
      "name": "SWE-bench Pro",
      "url": "https://labs.scale.com/leaderboard/swe_bench_pro_public",
      "event_date": "2026-06-25",
      "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 gpt-5.4 (xHigh)* 59.10±3.56%，#2 Muse Spark* 55.00±3.60%，#3 claude-opus-4-6 (thinking)* 51.90±3.61%；新进榜条目包括 Muse Spark*。",
      "summary": "Scale Labs 公开榜单显示，SWE-bench Pro Public Dataset 当前第一是 gpt-5.4 (xHigh)*（openai，Resolve Rate 59.10±3.56%）。 前三名为 gpt-5.4 (xHigh)* 59.10±3.56%、Muse Spark* 55.00±3.60%、claude-opus-4-6 (thinking)* 51.90±3.61%，Top 10 供应商分布为 anthropic 4、openai 3、google 2、scale 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 gpt-5.4 (xHigh)* 的 Resolve Rate 为 59.10±3.56%，需要看它是否依赖特定 agent scaffold 或成本上限。",
        "新进榜条目：Muse Spark*（scale）。",
        "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 10",
          "trend": "same"
        },
        {
          "label": "供应商分布",
          "value": "anthropic 4、openai 3、google 2、scale 1",
          "trend": "unknown"
        },
        {
          "label": "#1",
          "value": "gpt-5.4 (xHigh)*（openai）：Resolve Rate 59.10±3.56%",
          "trend": "unknown"
        },
        {
          "label": "#2",
          "value": "Muse Spark*（scale）：Resolve Rate 55.00±3.60%",
          "trend": "new"
        },
        {
          "label": "#3",
          "value": "claude-opus-4-6 (thinking)*（anthropic）：Resolve Rate 51.90±3.61%",
          "trend": "unknown"
        },
        {
          "label": "#4",
          "value": "gemini-3.1-pro (thinking)*（google）：Resolve Rate 46.10±3.60%",
          "trend": "unknown"
        },
        {
          "label": "#5",
          "value": "claude-opus-4-5-20251101（anthropic）：Resolve Rate 45.89±3.60%",
          "trend": "unknown"
        },
        {
          "label": "#6",
          "value": "claude-4-5-Sonnet（anthropic）：Resolve Rate 43.60±3.60%",
          "trend": "unknown"
        },
        {
          "label": "#7",
          "value": "gemini-3-pro-preview（google）：Resolve Rate 43.30±3.60%",
          "trend": "unknown"
        },
        {
          "label": "#8",
          "value": "claude-4-Sonnet（anthropic）：Resolve Rate 42.70±3.59%",
          "trend": "unknown"
        },
        {
          "label": "#9",
          "value": "gpt-5-2025-08-07 (High)（openai）：Resolve Rate 41.78±3.49%",
          "trend": "unknown"
        },
        {
          "label": "#10",
          "value": "gpt-5.2-codex（openai）：Resolve Rate 41.04±3.57%",
          "trend": "unknown"
        }
      ],
      "snapshot": {
        "type": "swe_bench_pro_public_page",
        "collection_method": "public_page_static",
        "snapshot_status": "complete",
        "snapshot_as_of": "2026-06-25T08:21:29.470Z",
        "source_url": "https://labs.scale.com/leaderboard/swe_bench_pro_public",
        "top_entries": [
          {
            "rank": 1,
            "model": "gpt-5.4 (xHigh)*",
            "provider": "openai",
            "tokens": "59.10±3.56%",
            "change": "Resolve Rate"
          },
          {
            "rank": 2,
            "model": "Muse Spark*",
            "provider": "scale",
            "tokens": "55.00±3.60%",
            "change": "new"
          },
          {
            "rank": 3,
            "model": "claude-opus-4-6 (thinking)*",
            "provider": "anthropic",
            "tokens": "51.90±3.61%",
            "change": "Resolve Rate"
          },
          {
            "rank": 4,
            "model": "gemini-3.1-pro (thinking)*",
            "provider": "google",
            "tokens": "46.10±3.60%",
            "change": "Resolve Rate"
          },
          {
            "rank": 5,
            "model": "claude-opus-4-5-20251101",
            "provider": "anthropic",
            "tokens": "45.89±3.60%",
            "change": "Resolve Rate"
          },
          {
            "rank": 6,
            "model": "claude-4-5-Sonnet",
            "provider": "anthropic",
            "tokens": "43.60±3.60%",
            "change": "Resolve Rate"
          },
          {
            "rank": 7,
            "model": "gemini-3-pro-preview",
            "provider": "google",
            "tokens": "43.30±3.60%",
            "change": "Resolve Rate"
          },
          {
            "rank": 8,
            "model": "claude-4-Sonnet",
            "provider": "anthropic",
            "tokens": "42.70±3.59%",
            "change": "Resolve Rate"
          },
          {
            "rank": 9,
            "model": "gpt-5-2025-08-07 (High)",
            "provider": "openai",
            "tokens": "41.78±3.49%",
            "change": "Resolve Rate"
          },
          {
            "rank": 10,
            "model": "gpt-5.2-codex",
            "provider": "openai",
            "tokens": "41.04±3.57%",
            "change": "Resolve Rate"
          }
        ],
        "official_component_snapshot": {
          "source": "official_dom",
          "component_kind": "swe_bench_pro",
          "source_url": "https://labs.scale.com/leaderboard/swe_bench_pro_public",
          "captured_at": "2026-06-25T08:21:29.470Z",
          "selector_version": "swe-bench-pro-v1",
          "source_selector": "[data-swe-bench-pro-leaderboard]",
          "sanitized_html": "<section class=\"swe-bench-pro-card\" data-swe-bench-pro-leaderboard>\n        <header>\n          <p>SWE-Bench Pro (Public Dataset)</p>\n          <h2>Performance Comparison</h2>\n          <span>Primary metric: Resolve Rate</span>\n        </header>\n        <table>\n          <thead><tr><th>Rank</th><th>Model / Agent</th><th>Provider</th><th>Resolve Rate</th><th>Change</th></tr></thead>\n          <tbody>\n        <tr>\n          <td>#1</td>\n          <td>gpt-5.4 (xHigh)*</td>\n          <td>openai</td>\n          <td>59.10±3.56%</td>\n          <td>Resolve Rate</td>\n        </tr>\n        <tr>\n          <td>#2</td>\n          <td>Muse Spark*</td>\n          <td>scale</td>\n          <td>55.00±3.60%</td>\n          <td>new</td>\n        </tr>\n        <tr>\n          <td>#3</td>\n          <td>claude-opus-4-6 (thinking)*</td>\n          <td>anthropic</td>\n          <td>51.90±3.61%</td>\n          <td>Resolve Rate</td>\n        </tr>\n        <tr>\n          <td>#4</td>\n          <td>gemini-3.1-pro (thinking)*</td>\n          <td>google</td>\n          <td>46.10±3.60%</td>\n          <td>Resolve Rate</td>\n        </tr>\n        <tr>\n          <td>#5</td>\n          <td>claude-opus-4-5-20251101</td>\n          <td>anthropic</td>\n          <td>45.89±3.60%</td>\n          <td>Resolve Rate</td>\n        </tr>\n        <tr>\n          <td>#6</td>\n          <td>claude-4-5-Sonnet</td>\n          <td>anthropic</td>\n          <td>43.60±3.60%</td>\n          <td>Resolve Rate</td>\n        </tr>\n        <tr>\n          <td>#7</td>\n          <td>gemini-3-pro-preview</td>\n          <td>google</td>\n          <td>43.30±3.60%</td>\n          <td>Resolve Rate</td>\n        </tr>\n        <tr>\n          <td>#8</td>\n          <td>claude-4-Sonnet</td>\n          <td>anthropic</td>\n          <td>42.70±3.59%</td>\n          <td>Resolve Rate</td>\n        </tr>\n        <tr>\n          <td>#9</td>\n          <td>gpt-5-2025-08-07 (High)</td>\n          <td>openai</td>\n          <td>41.78±3.49%</td>\n          <td>Resolve Rate</td>\n        </tr>\n        <tr>\n          <td>#10</td>\n          <td>gpt-5.2-codex</td>\n          <td>openai</td>\n          <td>41.04±3.57%</td>\n          <td>Resolve Rate</td>\n        </tr></tbody>\n        </table>\n      </section>",
          "sanitized_css": ".swe-bench-pro-card { border: 1px solid currentColor; padding: 16px; }\n      .swe-bench-pro-card table { width: 100%; border-collapse: collapse; }\n      .swe-bench-pro-card th, .swe-bench-pro-card td { padding: 8px; border-top: 1px solid currentColor; text-align: left; }",
          "dom_hash": "sha256:cc4506cfb6f43dc3ea7331ad2e9ab545ed056a3aab2309bf4201d14baa1ce040",
          "css_hash": "sha256:bbb18aff9c5bd0f35808108d31a64c2b62e0adcab81b24181da21bcd9a31c42d"
        },
        "history_entries": []
      },
      "tracking_component_snapshot": {
        "source": "Scale Labs SWE-Bench Pro",
        "component_kind": "swe_bench_pro",
        "source_url": "https://labs.scale.com/leaderboard/swe_bench_pro_public",
        "collected_at": "2026-06-25T08:21:29.470Z",
        "selector_version": "swe-bench-pro-v1",
        "raw_dom_hash": "sha256:2d5ebf88219cf9ca3b8537c2a99a9339988bd6f4b8cb73e80b37c9c1e786a9a9",
        "data_hash": "sha256:f9d92bad51d79847d104988b266c973b76b03d4fcdd9eeccd4c2cb2e3a4b3cf4",
        "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": "gpt-5.4 (xHigh)*",
                "provider": "openai",
                "value": 59.1,
                "value_label": "59.10±3.56%",
                "change": "Resolve Rate",
                "metric": "Resolve Rate"
              },
              {
                "rank": 2,
                "model": "Muse Spark*",
                "provider": "scale",
                "value": 55,
                "value_label": "55.00±3.60%",
                "change": "new",
                "metric": "Resolve Rate"
              },
              {
                "rank": 3,
                "model": "claude-opus-4-6 (thinking)*",
                "provider": "anthropic",
                "value": 51.9,
                "value_label": "51.90±3.61%",
                "change": "Resolve Rate",
                "metric": "Resolve Rate"
              },
              {
                "rank": 4,
                "model": "gemini-3.1-pro (thinking)*",
                "provider": "google",
                "value": 46.1,
                "value_label": "46.10±3.60%",
                "change": "Resolve Rate",
                "metric": "Resolve Rate"
              },
              {
                "rank": 5,
                "model": "claude-opus-4-5-20251101",
                "provider": "anthropic",
                "value": 45.89,
                "value_label": "45.89±3.60%",
                "change": "Resolve Rate",
                "metric": "Resolve Rate"
              },
              {
                "rank": 6,
                "model": "claude-4-5-Sonnet",
                "provider": "anthropic",
                "value": 43.6,
                "value_label": "43.60±3.60%",
                "change": "Resolve Rate",
                "metric": "Resolve Rate"
              },
              {
                "rank": 7,
                "model": "gemini-3-pro-preview",
                "provider": "google",
                "value": 43.3,
                "value_label": "43.30±3.60%",
                "change": "Resolve Rate",
                "metric": "Resolve Rate"
              },
              {
                "rank": 8,
                "model": "claude-4-Sonnet",
                "provider": "anthropic",
                "value": 42.7,
                "value_label": "42.70±3.59%",
                "change": "Resolve Rate",
                "metric": "Resolve Rate"
              },
              {
                "rank": 9,
                "model": "gpt-5-2025-08-07 (High)",
                "provider": "openai",
                "value": 41.78,
                "value_label": "41.78±3.49%",
                "change": "Resolve Rate",
                "metric": "Resolve Rate"
              },
              {
                "rank": 10,
                "model": "gpt-5.2-codex",
                "provider": "openai",
                "value": 41.04,
                "value_label": "41.04±3.57%",
                "change": "Resolve Rate",
                "metric": "Resolve Rate"
              }
            ],
            "fallback_reason": ""
          }
        ],
        "rows": [
          {
            "rank": 1,
            "model": "gpt-5.4 (xHigh)*",
            "provider": "openai",
            "value": 59.1,
            "value_label": "59.10±3.56%",
            "change": "Resolve Rate",
            "metric": "Resolve Rate"
          },
          {
            "rank": 2,
            "model": "Muse Spark*",
            "provider": "scale",
            "value": 55,
            "value_label": "55.00±3.60%",
            "change": "new",
            "metric": "Resolve Rate"
          },
          {
            "rank": 3,
            "model": "claude-opus-4-6 (thinking)*",
            "provider": "anthropic",
            "value": 51.9,
            "value_label": "51.90±3.61%",
            "change": "Resolve Rate",
            "metric": "Resolve Rate"
          },
          {
            "rank": 4,
            "model": "gemini-3.1-pro (thinking)*",
            "provider": "google",
            "value": 46.1,
            "value_label": "46.10±3.60%",
            "change": "Resolve Rate",
            "metric": "Resolve Rate"
          },
          {
            "rank": 5,
            "model": "claude-opus-4-5-20251101",
            "provider": "anthropic",
            "value": 45.89,
            "value_label": "45.89±3.60%",
            "change": "Resolve Rate",
            "metric": "Resolve Rate"
          },
          {
            "rank": 6,
            "model": "claude-4-5-Sonnet",
            "provider": "anthropic",
            "value": 43.6,
            "value_label": "43.60±3.60%",
            "change": "Resolve Rate",
            "metric": "Resolve Rate"
          },
          {
            "rank": 7,
            "model": "gemini-3-pro-preview",
            "provider": "google",
            "value": 43.3,
            "value_label": "43.30±3.60%",
            "change": "Resolve Rate",
            "metric": "Resolve Rate"
          },
          {
            "rank": 8,
            "model": "claude-4-Sonnet",
            "provider": "anthropic",
            "value": 42.7,
            "value_label": "42.70±3.59%",
            "change": "Resolve Rate",
            "metric": "Resolve Rate"
          },
          {
            "rank": 9,
            "model": "gpt-5-2025-08-07 (High)",
            "provider": "openai",
            "value": 41.78,
            "value_label": "41.78±3.49%",
            "change": "Resolve Rate",
            "metric": "Resolve Rate"
          },
          {
            "rank": 10,
            "model": "gpt-5.2-codex",
            "provider": "openai",
            "value": 41.04,
            "value_label": "41.04±3.57%",
            "change": "Resolve Rate",
            "metric": "Resolve Rate"
          }
        ],
        "previous_snapshot": null,
        "diff": {
          "summary": "No previous component snapshot was available for comparison.",
          "changed_rows": [],
          "new_entries": [
            "gpt-5.4 (xHigh)*",
            "Muse Spark*",
            "claude-opus-4-6 (thinking)*",
            "gemini-3.1-pro (thinking)*",
            "claude-opus-4-5-20251101",
            "claude-4-5-Sonnet",
            "gemini-3-pro-preview",
            "claude-4-Sonnet",
            "gpt-5-2025-08-07 (High)",
            "gpt-5.2-codex"
          ]
        },
        "cache_path": "",
        "fallback_reason": "",
        "official_component_snapshot": {
          "source": "official_dom",
          "component_kind": "swe_bench_pro",
          "source_url": "https://labs.scale.com/leaderboard/swe_bench_pro_public",
          "captured_at": "2026-06-25T08:21:29.470Z",
          "selector_version": "swe-bench-pro-v1",
          "source_selector": "[data-swe-bench-pro-leaderboard]",
          "sanitized_html": "<section class=\"swe-bench-pro-card\" data-swe-bench-pro-leaderboard>\n        <header>\n          <p>SWE-Bench Pro (Public Dataset)</p>\n          <h2>Performance Comparison</h2>\n          <span>Primary metric: Resolve Rate</span>\n        </header>\n        <table>\n          <thead><tr><th>Rank</th><th>Model / Agent</th><th>Provider</th><th>Resolve Rate</th><th>Change</th></tr></thead>\n          <tbody>\n        <tr>\n          <td>#1</td>\n          <td>gpt-5.4 (xHigh)*</td>\n          <td>openai</td>\n          <td>59.10±3.56%</td>\n          <td>Resolve Rate</td>\n        </tr>\n        <tr>\n          <td>#2</td>\n          <td>Muse Spark*</td>\n          <td>scale</td>\n          <td>55.00±3.60%</td>\n          <td>new</td>\n        </tr>\n        <tr>\n          <td>#3</td>\n          <td>claude-opus-4-6 (thinking)*</td>\n          <td>anthropic</td>\n          <td>51.90±3.61%</td>\n          <td>Resolve Rate</td>\n        </tr>\n        <tr>\n          <td>#4</td>\n          <td>gemini-3.1-pro (thinking)*</td>\n          <td>google</td>\n          <td>46.10±3.60%</td>\n          <td>Resolve Rate</td>\n        </tr>\n        <tr>\n          <td>#5</td>\n          <td>claude-opus-4-5-20251101</td>\n          <td>anthropic</td>\n          <td>45.89±3.60%</td>\n          <td>Resolve Rate</td>\n        </tr>\n        <tr>\n          <td>#6</td>\n          <td>claude-4-5-Sonnet</td>\n          <td>anthropic</td>\n          <td>43.60±3.60%</td>\n          <td>Resolve Rate</td>\n        </tr>\n        <tr>\n          <td>#7</td>\n          <td>gemini-3-pro-preview</td>\n          <td>google</td>\n          <td>43.30±3.60%</td>\n          <td>Resolve Rate</td>\n        </tr>\n        <tr>\n          <td>#8</td>\n          <td>claude-4-Sonnet</td>\n          <td>anthropic</td>\n          <td>42.70±3.59%</td>\n          <td>Resolve Rate</td>\n        </tr>\n        <tr>\n          <td>#9</td>\n          <td>gpt-5-2025-08-07 (High)</td>\n          <td>openai</td>\n          <td>41.78±3.49%</td>\n          <td>Resolve Rate</td>\n        </tr>\n        <tr>\n          <td>#10</td>\n          <td>gpt-5.2-codex</td>\n          <td>openai</td>\n          <td>41.04±3.57%</td>\n          <td>Resolve Rate</td>\n        </tr></tbody>\n        </table>\n      </section>",
          "sanitized_css": ".swe-bench-pro-card { border: 1px solid currentColor; padding: 16px; }\n      .swe-bench-pro-card table { width: 100%; border-collapse: collapse; }\n      .swe-bench-pro-card th, .swe-bench-pro-card td { padding: 8px; border-top: 1px solid currentColor; text-align: left; }",
          "dom_hash": "sha256:cc4506cfb6f43dc3ea7331ad2e9ab545ed056a3aab2309bf4201d14baa1ce040",
          "css_hash": "sha256:bbb18aff9c5bd0f35808108d31a64c2b62e0adcab81b24181da21bcd9a31c42d"
        },
        "public_trace": {
          "source_url": "https://labs.scale.com/leaderboard/swe_bench_pro_public",
          "collected_at": "2026-06-25T08:21:29.470Z",
          "selector_version": "swe-bench-pro-v1",
          "data_hash": "sha256:f9d92bad51d79847d104988b266c973b76b03d4fcdd9eeccd4c2cb2e3a4b3cf4",
          "top_rows": [
            {
              "rank": 1,
              "model": "gpt-5.4 (xHigh)*",
              "provider": "openai",
              "value_label": "59.10±3.56%",
              "change": "Resolve Rate"
            },
            {
              "rank": 2,
              "model": "Muse Spark*",
              "provider": "scale",
              "value_label": "55.00±3.60%",
              "change": "new"
            },
            {
              "rank": 3,
              "model": "claude-opus-4-6 (thinking)*",
              "provider": "anthropic",
              "value_label": "51.90±3.61%",
              "change": "Resolve Rate"
            },
            {
              "rank": 4,
              "model": "gemini-3.1-pro (thinking)*",
              "provider": "google",
              "value_label": "46.10±3.60%",
              "change": "Resolve Rate"
            },
            {
              "rank": 5,
              "model": "claude-opus-4-5-20251101",
              "provider": "anthropic",
              "value_label": "45.89±3.60%",
              "change": "Resolve Rate"
            },
            {
              "rank": 6,
              "model": "claude-4-5-Sonnet",
              "provider": "anthropic",
              "value_label": "43.60±3.60%",
              "change": "Resolve Rate"
            },
            {
              "rank": 7,
              "model": "gemini-3-pro-preview",
              "provider": "google",
              "value_label": "43.30±3.60%",
              "change": "Resolve Rate"
            },
            {
              "rank": 8,
              "model": "claude-4-Sonnet",
              "provider": "anthropic",
              "value_label": "42.70±3.59%",
              "change": "Resolve Rate"
            },
            {
              "rank": 9,
              "model": "gpt-5-2025-08-07 (High)",
              "provider": "openai",
              "value_label": "41.78±3.49%",
              "change": "Resolve Rate"
            },
            {
              "rank": 10,
              "model": "gpt-5.2-codex",
              "provider": "openai",
              "value_label": "41.04±3.57%",
              "change": "Resolve Rate"
            }
          ],
          "diff": {
            "summary": "No previous component snapshot was available for comparison.",
            "changed_rows": [],
            "new_entries": [
              "gpt-5.4 (xHigh)*",
              "Muse Spark*",
              "claude-opus-4-6 (thinking)*",
              "gemini-3.1-pro (thinking)*",
              "claude-opus-4-5-20251101",
              "claude-4-5-Sonnet",
              "gemini-3-pro-preview",
              "claude-4-Sonnet",
              "gpt-5-2025-08-07 (High)",
              "gpt-5.2-codex"
            ]
          },
          "cache_status": "live",
          "fallback_reason": ""
        }
      }
    }
  ],
  "projects": [
    {
      "name": "calesthio/OpenMontage",
      "editorial_category": "open_source",
      "domains": [
        "agent"
      ],
      "url": "https://github.com/calesthio/OpenMontage",
      "event_date": "2026-06-25",
      "source": "GitHub Trending weekly",
      "signal": "trending",
      "importance": "notable"
    },
    {
      "name": "DeusData/codebase-memory-mcp",
      "editorial_category": "open_source",
      "domains": [
        "agent"
      ],
      "url": "https://github.com/DeusData/codebase-memory-mcp",
      "event_date": "2026-06-25",
      "source": "GitHub Trending weekly",
      "signal": "trending",
      "importance": "notable"
    },
    {
      "name": "google-research/timesfm",
      "editorial_category": "open_source",
      "domains": [
        "AI tooling"
      ],
      "url": "https://github.com/google-research/timesfm",
      "event_date": "2026-06-25",
      "source": "GitHub Trending weekly",
      "signal": "trending",
      "importance": "notable"
    },
    {
      "name": "n0-computer/iroh",
      "editorial_category": "open_source",
      "domains": [
        "AI tooling"
      ],
      "url": "https://github.com/n0-computer/iroh",
      "event_date": "2026-06-25",
      "source": "GitHub Trending weekly",
      "signal": "trending",
      "importance": "notable"
    },
    {
      "name": "koala73/worldmonitor",
      "editorial_category": "open_source",
      "domains": [
        "AI tooling"
      ],
      "url": "https://github.com/koala73/worldmonitor",
      "event_date": "2026-06-25",
      "source": "GitHub Trending weekly",
      "signal": "trending",
      "importance": "notable"
    },
    {
      "name": "asgeirtj/system_prompts_leaks",
      "editorial_category": "open_source",
      "domains": [
        "agent"
      ],
      "url": "https://github.com/asgeirtj/system_prompts_leaks",
      "event_date": "2026-06-25",
      "source": "GitHub Trending weekly",
      "signal": "trending",
      "importance": "notable"
    },
    {
      "name": "Panniantong/Agent-Reach",
      "editorial_category": "open_source",
      "domains": [
        "agent"
      ],
      "url": "https://github.com/Panniantong/Agent-Reach",
      "event_date": "2026-06-25",
      "source": "GitHub Trending weekly",
      "signal": "trending",
      "importance": "notable"
    },
    {
      "name": "penpot/penpot",
      "editorial_category": "open_source",
      "domains": [
        "agent"
      ],
      "url": "https://github.com/penpot/penpot",
      "event_date": "2026-06-25",
      "source": "GitHub Trending weekly",
      "signal": "trending",
      "importance": "notable"
    },
    {
      "name": "withastro/flue",
      "editorial_category": "open_source",
      "domains": [
        "agent"
      ],
      "url": "https://github.com/withastro/flue",
      "event_date": "2026-06-25",
      "source": "GitHub Trending weekly",
      "signal": "trending",
      "importance": "notable"
    },
    {
      "name": "jamiepine/voicebox",
      "editorial_category": "open_source",
      "domains": [
        "agent"
      ],
      "url": "https://github.com/jamiepine/voicebox",
      "event_date": "2026-06-25",
      "source": "GitHub Trending weekly",
      "signal": "trending",
      "importance": "notable"
    }
  ],
  "builder_observations": [
    {
      "author": "Aaron Levie",
      "handle": "levie",
      "editorial_category": "x_discussion",
      "content": "Aaron Levie 提醒，Claude 通过 Slack 接入时不只是单人一对一聊天，而是团队中任何人都能共享调用的协作式 agent；这种模式已经出现在部分 coding agent 中，推广到通用知识工作会继续推动 agent 成为团队工作流里的独立协作者。",
      "original_text": "There are some subtleties in this launch that are very important in practice. This isn’t just you interacting with Claude in a 1:1 format via Slack. In this case, Claude acts as a coworker that any user can tap into in a shared way. We’ve already seen some agentic coding systems start to adopt this pattern (as well as OpenClaw and Hermes), and doing it for general purpose knowledge work continues to push the idea forward. As a result, what this means is that this agentic coworker needs its own ...",
      "translation": "Aaron Levie 提醒，Claude 通过 Slack 接入时不只是单人一对一聊天，而是团队中任何人都能共享调用的协作式 agent；这种模式已经出现在部分 coding agent 中，推广到通用知识工作会继续推动 agent 成为团队工作流里的独立协作者。",
      "avatar_url": "https://unavatar.io/x/levie",
      "url": "https://x.com/levie/status/2069975251476422664",
      "role": "builder",
      "event_date": "2026-06-25",
      "source": "follow-builders X feed",
      "importance": "notable",
      "image_urls": []
    },
    {
      "author": "Peter Yang",
      "handle": "petergyang",
      "editorial_category": "x_discussion",
      "content": "Peter Yang 说 Claude Design 接收他的移动应用 repo 后，几乎完整复刻了应用屏幕；但只过一个提示就开始提醒节省 token，说明设计生成效果之外，上下文和成本消耗仍会影响产品体验。",
      "original_text": "Claude Design is pretty great. I gave it a repo for a mobile app I'm building and it reproduced the screens perfectly. Except after one prompt it's telling me to save tokens already 😅 https://t.co/k6hQ53zmFN",
      "translation": "Peter Yang 说 Claude Design 接收他的移动应用 repo 后，几乎完整复刻了应用屏幕；但只过一个提示就开始提醒节省 token，说明设计生成效果之外，上下文和成本消耗仍会影响产品体验。",
      "avatar_url": "https://unavatar.io/x/petergyang",
      "url": "https://x.com/petergyang/status/2069992268963135897",
      "role": "builder",
      "event_date": "2026-06-25",
      "source": "follow-builders X feed",
      "importance": "notable",
      "image_urls": []
    },
    {
      "author": "Google Labs",
      "handle": "GoogleLabs",
      "editorial_category": "x_discussion",
      "content": "Google Labs 表示 Project Genie 获得 Cannes Lions 的 AI Craft Grand Prix，并感谢 Labs 社区一路参与；这是一条项目认可度和社区信号，不应被解读成单独的模型能力评测。",
      "original_text": "We are honored to share that Project Genie has won the Cannes Lions Grand Prix for AI Craft! 🏆 To our awesome Labs community, thank you for being on this journey with us! https://t.co/FN5nx19g68",
      "translation": "Google Labs 表示 Project Genie 获得 Cannes Lions 的 AI Craft Grand Prix，并感谢 Labs 社区一路参与；这是一条项目认可度和社区信号，不应被解读成单独的模型能力评测。",
      "avatar_url": "https://unavatar.io/x/GoogleLabs",
      "url": "https://x.com/GoogleLabs/status/2069827839826809042",
      "role": "builder",
      "event_date": "2026-06-24",
      "source": "follow-builders X feed",
      "importance": "notable",
      "image_urls": []
    },
    {
      "author": "Swyx",
      "handle": "swyx",
      "editorial_category": "x_discussion",
      "content": "Swyx 推荐一期信息密度很高的播客，话题包括 Databricks 为何胜过 Snowflake、为什么团队都在做 metaharness、Neon 数据库的价值、LTAP 与 HTAP、MosaicML 和 DBRX 的后续，以及大公司如何保持研究和创业文化。",
      "original_text": "LOTS of alpha in this pod: - Why Databricks beat Snowflake (! a straight answer!) - Why everyone is building a metaharness now - Why the @neondatabase made so much sense (so much @nikitabase glazing its not even funny) - How LTAP solves the HTAP dream I discussed with @ankrgyl in our @braintrust pod - What happened to @MosaicML + DBRX - How to maintain research/startup culture in a $175B megacorp - What's more important knowledge/experience in the race to the agent cloud: databases, operating s...",
      "translation": "Swyx 推荐一期信息密度很高的播客，话题包括 Databricks 为何胜过 Snowflake、为什么团队都在做 metaharness、Neon 数据库的价值、LTAP 与 HTAP、MosaicML 和 DBRX 的后续，以及大公司如何保持研究和创业文化。",
      "avatar_url": "https://unavatar.io/x/swyx",
      "url": "https://x.com/swyx/status/2069864073202905501",
      "role": "builder",
      "event_date": "2026-06-24",
      "source": "follow-builders X feed",
      "importance": "notable",
      "image_urls": []
    },
    {
      "author": "Swyx",
      "handle": "swyx",
      "editorial_category": "x_discussion",
      "content": "Swyx 给准备工程和研究演讲的人提建议：幻灯片里 AI 生成 SVG 通常比 AI 图片更有用，AI 图片要少用；演讲最好聚焦一个尖锐观点、配上具体例子，并把代码放到屏幕上。",
      "original_text": "lots of folks prepping talks next week (congrats!). Some thoughts from RLing on thousands of hours of engineer- and researcher- focused talks: - AI generated svgs > AI generated imgs. MAXIMUM 4 ai slop images in your slides, I don't care how pretty your mom thinks they are (exception ofc if your talk is ABOUT imagegen) - Be pointy. Better to have 1 message with 5 surprising applications, than 5 messages with no concrete examples. - Put code on screen. Engineers like to see code. Especially if t...",
      "translation": "Swyx 给准备工程和研究演讲的人提建议：幻灯片里 AI 生成 SVG 通常比 AI 图片更有用，AI 图片要少用；演讲最好聚焦一个尖锐观点、配上具体例子，并把代码放到屏幕上。",
      "avatar_url": "https://unavatar.io/x/swyx",
      "url": "https://x.com/swyx/status/2069964772003770673",
      "role": "builder",
      "event_date": "2026-06-25",
      "source": "follow-builders X feed",
      "importance": "notable",
      "image_urls": []
    },
    {
      "author": "Guillermo Rauch",
      "handle": "rauchg",
      "editorial_category": "x_discussion",
      "content": "Guillermo Rauch 判断 AI 会带来一轮前所未有的创业潮，从一人公司、中小企业复兴，到新一代大型公司都会受益；他的核心判断是 AI 会成为这些业务的基础层。",
      "original_text": "AI will bring forth an unprecedented surge in entrepreneurship. From 'solopreneurs,' to the revitalization of the small &amp; medium business segment, to the emergence of the largest companies of our times… providing the foundation of it all.",
      "translation": "Guillermo Rauch 判断 AI 会带来一轮前所未有的创业潮，从一人公司、中小企业复兴，到新一代大型公司都会受益；他的核心判断是 AI 会成为这些业务的基础层。",
      "avatar_url": "https://unavatar.io/x/rauchg",
      "url": "https://x.com/rauchg/status/2070001110866354345",
      "role": "builder",
      "event_date": "2026-06-25",
      "source": "follow-builders X feed",
      "importance": "notable",
      "image_urls": []
    },
    {
      "author": "Guillermo Rauch",
      "handle": "rauchg",
      "editorial_category": "x_discussion",
      "content": "Guillermo Rauch 称 Vercel AI Gateway 在恢复 token 和 uptime 方面的数据非常惊人；这是一条关于 AI 网关可靠性和成本收益的线索，仍需要结合官方数据核验。",
      "original_text": "The data of tokens and uptime recovered by @vercel AI Gateway is truly astonishing https://t.co/kKzZWtdELa",
      "translation": "Guillermo Rauch 称 Vercel AI Gateway 在恢复 token 和 uptime 方面的数据非常惊人；这是一条关于 AI 网关可靠性和成本收益的线索，仍需要结合官方数据核验。",
      "avatar_url": "https://unavatar.io/x/rauchg",
      "url": "https://x.com/rauchg/status/2069819652365242765",
      "role": "builder",
      "event_date": "2026-06-24",
      "source": "follow-builders X feed",
      "importance": "notable",
      "image_urls": []
    },
    {
      "author": "Ryo Lu",
      "handle": "ryolu_",
      "editorial_category": "x_discussion",
      "content": "Ryo Lu 用“在 Notion 里用 Cursor、在 Cursor 里用 Notion”概括两个产品入口互相嵌入的工作流，关注点是知识库和代码编辑器之间的双向协作。",
      "original_text": "use cursor in notion use notion in cursor https://t.co/3q36oyzwu0",
      "translation": "Ryo Lu 用“在 Notion 里用 Cursor、在 Cursor 里用 Notion”概括两个产品入口互相嵌入的工作流，关注点是知识库和代码编辑器之间的双向协作。",
      "avatar_url": "https://unavatar.io/x/ryolu_",
      "url": "https://x.com/ryolu_/status/2069830172354986418",
      "role": "builder",
      "event_date": "2026-06-24",
      "source": "follow-builders X feed",
      "importance": "notable",
      "image_urls": []
    }
  ],
  "official_org_updates": [],
  "evidence_assets": [],
  "generated_at": "2026-06-25T08:28:18.774Z",
  "report_status": "normal",
  "canonical_url": "https://jasonxzwen.github.io/ai-daily-cn/reports/2026/06/2026-06-25.html",
  "html_path": "reports/2026/06/2026-06-25.html",
  "quality_status": {
    "status": "degraded",
    "public_note": "Some discovery coverage is degraded; this report may be incomplete.",
    "affected_sections": [
      "huggingface_trending"
    ],
    "degraded_events": [
      {
        "section": "huggingface_trending",
        "message": "huggingface_trending coverage is degraded and should be disclosed in the public report.",
        "severity": "degraded"
      },
      {
        "section": "community_leads",
        "message": "Non-primary sources in viewpoint, product, Builder, or community sections must disclose source level and verification/risk notes.",
        "severity": "degraded"
      }
    ]
  }
}
