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星期二

  1. Qwen Blog精选

    Qwen 发布 Qwen-AgentWorld 语言世界模型并开源 35B-A3B 版本

    Qwen 团队发布 Qwen-AgentWorld,一个原生语言世界模型,在单一模型中模拟 MCP、Search、Terminal、SWE、Web、OS、Android 七个智能体交互域,训练流程为 CPT → SFT → RL,使用超过 10M 条真实环境交互轨迹。团队同时发布七域评测基准 AgentWorldBench,Qwen-AgentWorld-397B-A17B 在该基准上取得最高综合分 58.71,超过 GPT-5.4 的 58.25。已开源 Qwen-AgentWorld-35B-A3B(MoE,35B 总参数 / 3B 激活参数,256K 上下文窗口),可在 Hugging Face 和 ModelScope 获取,并给出 SGLang 与 vLLM 部署命令。

    ts | Qwen Blog Publication About Try Qwen Chat Qwen-AgentWorld: Language World Models for General Agents June 22, 2026 · 15 min · 3088 words · Qwen Team | Translations: 简体中文 PAPER GITHUB HUGGING FACE MODELSCOPE Today we release Qwen-AgentWorld , a native language world model that simulates agent environments across seven domains: Native world modeling: environment modeling is the training objective from continual pre-training onward (CPT → SFT → RL), not a post hoc adaptation on top of a general

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  2. Trail of Bits Blog精选

    Trail of Bits 推出 Patch the Planet,首周向 19 个开源项目提交 64 个 PR

    Trail of Bits 与 OpenAI 的 Daybreak 计划合作推出 Patch the Planet,用 GPT-5.5-Cyber 等前沿模型排查关键开源项目安全问题,首周在 19 个项目中发现数百个 bug,提交 64 个 pull request 和 51 个 issue,其中 37 个 PR 已合并、19 个 issue 已修复关闭。覆盖项目包括 cURL、NATS、pyca、Sigstore、aiohttp、Go、Python、urllib3、PyPI、SimpleX、Valkey 和 RustCrypto 等,目前已有超过 30 个项目加入。Trail of Bits 认为安全工作的难点已从发现漏洞转向确认、定级、编写可被接受的补丁和协调披露,并建议维护者用去重、误报过滤和严重性校正来应对 AI 生成报告。

    “Live look at the Trail of Bits engineering teams”

    features maintainers had been meaning to get to. The goal of Patch the Planet is to leave essential open-source projects measurably better off. We’re reporting public findings on GitHub , including 64 total pull requests. We also filed 51 issues, 19 of which are already closed with a fix. This public tally undercounts the work, since several projects take reports through private channels like HackerOne, GitHub security advisories, mailing lists, and private forks, and most of these have not bee

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  3. OpenHands 官方博客精选

    OpenHands 发布验证栈,用 critic 模型与代码审查智能体把关 PR

    OpenHands 发布验证栈,用两层自动验证器解决编码智能体生成代码后的验证瓶颈:第一层 critic 模型在代码推送前给智能体运行打分,低分可提前终止或触发重试;第二层仓库级验证器由代码审查 skill 与 QA 智能体组成,在 PR 上自动运行。官方称代码审查机器人已在 OpenHands/software-agent-sdk 仓库运行六个月,发布 1000 多次审查、覆盖 900 多个 PR,平均合并时间缩短 58%,测试覆盖率提升近 25%。critic 模型现已在 OpenHands Cloud 和 Agent Canvas 的 Verification 设置中开放启用。

    atically retry a task when the critic score falls below your threshold. Layer 2 — Repo-level verification. The repo-level verifier can be wired into your repositories in two ways: GitHub Actions, per repository. Add a workflow under .github/workflows/ and the code review bot runs on every PR as part of CI (instructions: https://docs.openhands.dev/openhands/usage/use-cases/code-review). This gives you full control over when and how it runs, while keeping setup explicit for each repo. See Automate

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星期一

  1. DevClass

    Checkmarx 调查:70% 开发者认为 AI 生成代码漏洞更多,30% 仍将其带入生产环境

    Checkmarx 对 2350 名全球开发者、CISO 和 AppSec 管理者的调查显示,70% 的开发者认为 AI 生成代码存在明显更多漏洞,30% 明知有漏洞仍将其部署到生产环境。报告称 AI 生成代码约占生产代码的 49%,较去年的 54% 略有下降;93% 的受访者报告曾因应用漏洞遭遇一次或多次安全事件,低于去年的 98%。报告指出,AI 代码占比 81-100% 的组织部署漏洞代码的比例是占比 1-20% 组织的 3.4 倍。

    k July 26, 2026 Zig creator calls Bun's Claude Rust rewrite 'unreviewed slop' July 15, 2026 HTTP gets a QUERY method so complex searches can stop pretending to be POST July 14, 2026 Speedier type checks in TypeScript 7.0 as first stable Go release ships July 10, 2026 GitHub cuts short offer to burn repos on CD after mockery ensues A purported jab of Sony's physical media phase-out blows up on GitHub itself July 7, 2026 .NET's long term support is not long term enough, dev complains June 30, 2026

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星期六

  1. Dwarkesh Patel Podcast & Blog

    Dwarkesh Patel:AI 中心的数据黑洞与样本效率差距

    Dwarkesh Patel 撰文认为,近年 AI 能力提升主要来自更多更好的数据和用于生成数据的算力,而非训练样本效率的进步。他估算人从出生到成年约接触 2 亿 token,而前沿模型训练数据在 10 万亿到 100 万亿 token 量级,差距接近百万倍;即便按 Chinchilla 缩放定律把参数量增至无穷,所需数据也只减少约 10 倍。他指出开源模型仅落后闭源前沿约 4 个月,正说明数据才是进步的主要驱动,并认为白领任务因可被 RL 和 SFT 带入分布仍具商业价值。

    w for the quantity and breath of training these models experience. If you as a human had some weird learning disability where you needed to read through every public repository on Github before you could be a competent developer, it would not make sense to train you up. You’d be on Social Security by the early stages of your education, and even once you were trained, you could work on only one project at a time. But AIs can learn these skills by firehosing gigawatts of training at a time. And wh

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星期四

  1. Amp Chronicle

    Amp Librarian 提速约 3 倍,成本降低 43%

    Amp Librarian 在质量不变的情况下提速约 3 倍、成本降低 43%,现改用无推理的 GPT-5.5、websocket 模式和新版系统提示词。每轮并行工具调用从约 3 次增至约 8 次,搜索由约 15 轮缩至约 5 轮;内部测试耗时从 2 分钟降至 40 秒,成本从 $1.08 降至 $0.47。

    enAI's websocket mode and the rest from switching to GPT-5.5 with no reasoning: Here's a comparison: How does Kubernetes' HorizontalPodAutoscaler handle missing pod metrics when scaling down — does it assume missing pods are at 100% of their resource requests, or 100% of the target utilization? Cite the function and logic in the source. Sonnet 4.6 (left) took 2 minutes and cost $1.08, while GPT-5.5 (right) took 40 seconds and cost just $0.47. The Librarian A subagent for searching code on GitHub

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  2. xAI News

    xAI 将 Grok 引入 Microsoft Word

    xAI 推出 Grok for Word,一款免费的 Microsoft 365 加载项,可从 Microsoft Marketplace 安装,让用户在 Word 文档内直接使用 Grok。该加载项支持将提示词和笔记改写为结构化长文、起草提案与指南、修正语法、统一写作风格,还能联网搜索、检索 X 内容、生成图表,并通过连接器从邮件、SharePoint 或 Google Drive 取用文件。Grok 加载项同时适用于 PowerPoint 和 Excel。

    concision. welcome new hires — need accounts set up day one (sso, email, slack) first week: meet the team, laptop setup, security training, 1:1 with manager tools — figma, linear, github, notion. request access through the IT portal key contacts: manager, onboarding buddy, people ops, IT helpdesk benefits enrollment closes after 30 days!! don't miss this office tour + lunch spots, badge pickup at front desk (bring ID) probation review at 90 days — calendar invite from manager Draft the structure

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星期三

  1. Transluce

    Transluce 推出 Analysis Plans,为 AI 行为分析提供可验证框架

    Transluce 发布 Analysis Plans,一套用于可验证分析 AI 行为的框架,通过 Python API 编写、在网页界面中逐步审阅。它包含查询步骤(用 DQL 过滤、分组、连接数据)和阅读步骤(用 LLM 分析数据并给出带引用的文本摘要或结构化判断),两类步骤可组合成分析流水线。团队用 Docent 在约 15 分钟内检测 SWE-rebench 上的作弊行为,并演示了识别失败模式、对比 GPT-5.1 Codex 与 GPT-5 Codex 在 Terminal-Bench 上 6.5 个百分点差距等案例。该功能现已向所有 Docent 用户开放。

    Without analysis plans, it's difficult to understand and verify the behavior of a coding agent.

    nstration of model capability. In about 15 minutes, we used Docent to discover instances of cheating on SWE-rebench, a software engineering benchmark that measures how many recent GitHub PRs an agent can resolve. You can view the SWE-rebench traces in Docent at this link. 1. Explain to your coding agent what you want to learn We start by prompting Claude Code to score agent runs for potential cheating. Thanks to the Docent skill, Claude knows how to turn our question into an analysis plan. It wr

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  2. LMSYS Blog

    MOSS-TTS Local Transformer v1.5 在 SGLang-Omni 上实现端到端服务

    LMSYS 联合 MOSI 与 OpenMOSS 团队宣布,在 SGLang-Omni 上为 MOSS-TTS-Local-Transformer-v1.5 提供端到端服务。该开源 TTS 模型支持 48 kHz 立体声、零样本语音克隆、最长 10 分钟长文本合成、31 种语言与原生流式,采用 Qwen3-4B 自回归骨干和约 2B 参数的 MOSS-Audio-Tokenizer-v2。SGLang-Omni 将其拆分为预处理、tts_engine 与流式声码器三阶段流水线,并针对参考音频编码引入内容寻址 LRU 缓存。

    MOSS-TTS Local Transformer v1.5 model architecture

    ansformer v1.5 - Yitian Gong, Kuangwei Chen, Zhicheng Zhang, Botian Jiang, Yiyang Zhang, Kang Yu, Yang Gao, Xiaogui Yang, Qinyuan Chen, Zhaoye Fei, Shimin Li, Xipeng Qiu. Learn More Model: OpenMOSS-Team/MOSS-TTS-Local-Transformer-v1.5; Serving framework: SGLang-Omni on GitHub; Documentation: SGLang-Omni docs; MOSS-TTS-Local cookbook: MOSS-TTS-Local in SGLang-Omni; MOSS optimization roadmap: 637; Design background: SGLang-Omni: Redesigning the Inference Framework for Multi-Stage Generative Models

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星期二

  1. Figma AI / Agent / Make

    Figma 团队如何使用自家 MCP Server:4 个实际工作流

    Figma 分享了团队内部使用 Figma MCP Server 的 4 个实际工作流:在 Figma Slides 中生成和刷新演示文稿、从 Slack/Asana/Notion/Hex 等实时数据生成 FigJam 白板、在 Figma Make 中实现设计与代码双向同步并推送 PR,以及通过新的 download_assets 能力直接导出 SVG、PDF、JPG 等原始文件。MCP Server 现已支持上传的自定义字体,并新增 /figjam-builder 等自定义 skill。

    state. Then she sent it the other direction: “Pull those new states back into the code.” The agent read her design changes and wrote all three into the component, ready to push to GitHub as a PR. This workflow puts you in control of what ships, all from within Figma. The agent reads and writes your real components on both ends—the same way Code ConnectThe right code for your design systemToday, we’re announcing beta for Code Connect, a feature built to improve design system adoption by making co

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  2. DevClass

    云计算的智能体 AI 新基座:Arm 架构如何成为主流

    Arm 的 Neoverse 架构正成为各大超大规模云厂商的 AI 基础设施底座,AWS Graviton 在过去三年新增 CPU 容量中占比超过一半,微软 Azure Cobalt 与 Google Axion 也相继采用。Spotify 在 Google Cloud Axion 上运行推荐引擎,性能提升约 250%;Google C4A 实例相比 x86 系统性价比提升最高 65%、能效提升最高 60%。Pinterest 迁移至 Graviton 后计算资源节省 38%、关键工作负载成本节省 47%、碳排放减少 62%。

    k July 26, 2026 Zig creator calls Bun's Claude Rust rewrite 'unreviewed slop' July 15, 2026 HTTP gets a QUERY method so complex searches can stop pretending to be POST July 14, 2026 Speedier type checks in TypeScript 7.0 as first stable Go release ships July 10, 2026 GitHub cuts short offer to burn repos on CD after mockery ensues A purported jab of Sony's physical media phase-out blows up on GitHub itself July 7, 2026 .NET's long term support is not long term enough, dev complains June 30, 2026

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星期五

  1. Linear:Now精选

    Linear 推出编码会话、自动分诊和 Diffs,让 Linear Agent 直接编写代码

    Linear 推出编码会话、自动分诊和 Diffs,让 Linear Agent 能从 issue 出发调查代码库、编写代码并创建 pull request。任务在云端使用 Claude Code 或 Codex 等模型与工具执行,团队成员可共同跟进、补充上下文和审查变更。Linear 称其上月已有近 700 个由该智能体完成的 PR 被合并。

    Abstract image of circles forming a loop.

    API, connecting its specs, customer feedback, and code. It now writes more than 60% of the pull requests they merge. Coinbase built Forge on our Agent SDK, operating across Slack, GitHub, and Linear using the context already present in each system. They described Linear as the place where agents go to get their bearings before starting a task. Linear Agent gives every team that capability, without the harness they had to build and maintain. It’s how Linear becomes the context layer that product

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星期三

  1. Claude 应用发布说明精选

    Anthropic 发布 Claude Fable 5 与 Claude Mythos 5

    Anthropic 发布 Claude Fable 5,一款面向一般用途的 Mythos 级模型,在软件工程、知识工作、视觉、科学研究等基准上表现领先。同时推出 Claude Mythos 5,为同一底层模型但放宽部分安全限制,初期通过 Project Glasswing 向美国政府和网络安全伙伴开放。两款模型定价均为每百万输入 token 10 美元、每百万输出 token 50 美元,低于 Claude Mythos Preview 的一半。

    t's opened up a class of long-horizon problems that were out of reach for earlier models.Michael Truell CEO and Co-founder Claude Fable 5 is a real step forward for the developers GitHub serves. In our early testing, it took on complex, long-horizon coding tasks with a level of autonomy and reliability that exceeded previous benchmarks. But what excites us most is the direction it points: a future where developers can hand increasingly ambitious work to agents and trust the results across the so

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星期四

  1. LMSYS Blog

    Higgs Audio v3 TTS 在 SGLang-Omni 上实现端到端实时语音服务

    Boson AI 的语音合成模型 Higgs Audio v3 TTS 已在 SGLang-Omni 上实现端到端服务,支持 100 种语言达到个位数 WER/CER,并可通过输入文本中的控制标签调节情绪、风格、语速和音效。该模型约 4B 参数,基于 Qwen3-4B 骨干,采用多阶段解码架构,专为流式对话语音智能体设计。

    gs Audio v3 TTS (Boson AI) — Mu Li, Alex Smola, Lindsey Allen. Silin Meng, Ke Bai. Ruskin Raj Manku, Huapeng Zhou, Dongming Shen, Jonah Mackey, Erik Li, Weisu Yin, Yizhi Liu, Xinyu Wang, Hao Yu. Learn More Model: bosonai/higgs-audio-v3-tts-4b; Blog: Higgs Audio v3 TTS; Serving framework: SGLang-Omni on GitHub; Documentation: SGLang-Omni docs · Higgs TTS cookbook; Higgs optimization roadmap: 478; Design background: SGLang-Omni: Redesigning the Inference Framework for Multi-Stage Generative Models

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星期三

  1. Trail of Bits Blog

    Trail of Bits 实测:ClawHub、skills.sh 与 Cisco 的技能扫描器均可被绕过

    Trail of Bits 测试了 ClawHub、Cisco 的 agent skill scanner 以及 skills.sh 集成的三款扫描器,用四个恶意技能全部绕过,其中三个在一小时内完成。攻击手法包括在文件中前置 10 万个换行符让扫描器截断漏检、在 .pyc 字节码中植入窃取环境变量的代码,以及用企业网络配置话术包装恶意 npm registry 的提示词注入。作者认为技能扫描存在结构性缺陷,建议改用经过策展的技能市场并像对待不可信代码一样对待公共技能仓库。

    “Figure 1: OpenClaw scanner misses malicious content”

    outsource that judgment to an automated tool. Until the ecosystem matures, use curated marketplaces, keep the attack surface small, and treat public skill repositories as untrusted code. The attacks we’ve described are in trailofbits/overtly-malicious-skills . X LinkedIn GitHub Mastodon Hacker News Related Posts How we use /goal to find bugs in Patch the Planet July 28, 2026 Over the first few weeks of Patch the Planet, our engineers found three techniques for using /goal, which hands Codex an …

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  2. DevClass

    Embarcadero 为 Delphi 和 C++ Builder 推出智能体 AI 助手 Kai

    Embarcadero 发布 Kai,一款面向 RAD Studio(Delphi 与 C++ Builder 的 IDE)的智能体 AI 助手扩展,提供聊天、代码补全和 MCP server,需用户自备 API key 调用第三方 LLM,订阅价每开发者每年 249 美元。Kai 支持 Ollama 和 LM Studio 等本地 LLM,实测存在一些粗糙之处,如 LM Studio 中可用的查询在 Kai chat 中报错。

    k July 26, 2026 Zig creator calls Bun's Claude Rust rewrite 'unreviewed slop' July 15, 2026 HTTP gets a QUERY method so complex searches can stop pretending to be POST July 14, 2026 Speedier type checks in TypeScript 7.0 as first stable Go release ships July 10, 2026 GitHub cuts short offer to burn repos on CD after mockery ensues A purported jab of Sony's physical media phase-out blows up on GitHub itself July 7, 2026 .NET's long term support is not long term enough, dev complains June 30, 2026

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  3. Anthropic:Claude.dev 开发者博客精选

    Anthropic 构建 Claude Code 的经验:如何设计和使用技能

    Anthropic 总结了内部构建和扩展数百个 Claude Code 技能的经验,将其归纳为九类,并介绍设计、分发、组合与衡量方法。团队发现产品验证类技能对 Claude 输出质量的改善最易衡量,而高质量技能应突出易错点,利用文件系统渐进披露上下文,并通过脚本、配置和按需 hooks 提升可靠性。

    A three-by-three grid of skill categories with example skill names: Library and API Reference, Product Verification, Data and Analysis, Business Automation, Scaffolding and Templates, Code Quality and Review, CI/CD and Deployment, Incident Runbooks, Infrastructure Ops.

    evious results in log files can help the model stay consistent and reflect on previous executions of the workflow. Examples include: standup-post — aggregates your ticket tracker, GitHub activity, and prior Slack → formatted standup, delta-only; create- -ticket — enforces schema (valid enum values, required fields) plus post-creation workflow (ping reviewer, link in Slack); weekly-recap — merged PRs + closed tickets + deploys → formatted recap post 5. Code scaffolding and templates These are ski

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星期二

  1. Microsoft AI Blog

    微软 MAI 发布七款新模型,含首个推理模型 MAI-Thinking-1

    微软 MAI 在 Build 2026 主题演讲中发布七款新模型,覆盖图像、语音、转录、推理和编码。首个推理模型 MAI-Thinking-1 为 35B 激活参数 MoE,256K 上下文窗口,AIME 25 得分 97%,SWE Bench Pro 53%,官方称未做知识蒸馏。编码模型 MAI-Code-1-Flash 仅 5B 参数,SWE Bench Pro 51%,今日起在 VS Code 中逐步向约 10% 个人用户推送。

    it for real world use so you can produce highly accurate transcripts for your bespoke use cases up to 5x faster than rival models.6 It’s now being integrated into Copilot, Teams, GitHub, and Dynamics 365 Contact Centre – and it’s also available in Foundry, where it’s the fastest, most efficient and most cost-effective transcription model of any hyper-scaler.5 Paired with that we have MAI-Voice-2, our latest speech generation model. It has beautiful prosody, native-sounding delivery and fine-gra

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  2. OpenHands 官方博客

    如何让 AI 完整重构前端代码:OpenHands 的六步指南

    OpenHands 官方博客给出让 AI 智能体完整重构前端代码的六步方法,核心在于编辑前的准备而非模型选择:先写清范围与成功标准,再建立特征测试、覆盖率阈值、Playwright 视觉基线、tsc --noEmit 与 ESLint 的 CI 门禁,以及受保护的专用重构分支。文章指出聊天式 AI 在跨文件重构中会因上下文窗口填满、单文件编辑漂移和幻觉导入而失效。OpenHands 的 Agent Canvas 通过 ACP 连接 Claude Code、OpenAI Codex 和 Gemini CLI,智能体步骤默认在沙箱运行时中执行。

    Download for free CTA

    cal hooks are easy for the agent to skip, so the same checks need to run in CI as required steps; Dedicated refactor branch with protection: Create a long-lived branch and turn on GitHub's branch protection rules so nothing lands without passing checks and a human review The five gates above run outside the agent, in continuous integration and at the branch level. What the agent runs in while it's editing is a separate concern, and that depends on the platform around the agent. OpenHands is an o

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  3. OpenHands 官方博客

    OpenHands 详解 2026 年 AI 代码分析:从规则引擎到自主智能体的四个层级

    OpenHands 官方博客梳理了 2026 年 AI 代码分析的四层格局:Tier 1 规则型静态分析(SonarQube、ESLint、Semgrep)、Tier 2 AI 增强 SAST(Snyk Code、Checkmarx)、Tier 3 AI 原生 PR 审查(CodeRabbit、Greptile)、Tier 4 自主代码分析智能体。其核心循环为检索→推理→行动→验证,依靠 AST 与 LSP 提供结构化事实、沙箱执行验证补丁,能捕捉规则引擎无法覆盖的业务逻辑缺陷。OpenHands 自身是模型无关的开源智能体平台,通过 Agent Client Protocol 连接 Claude、Codex、Gemini,支持本地、OpenHands Cloud 或自托管 VPC 运行。

    Download for free CTA

    ws. CodeRabbit CodeRabbit reviews pull requests by combining LLM reasoning with deterministic scanning. It integrates 40+ linters and scanners across the review pipeline, supports GitHub, GitLab, Bitbucket, and Azure DevOps, and uses adaptive learning to tune reviews based on past feedback. Output lands as a comment thread with inline suggestions. Best for multi-platform teams that want a single AI reviewer across providers. Semgrep Semgrep is an open-source rule engine with AI-augmented triage

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