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上周，开发者Gathin在社交平台上发了一组给父母改电视的照片。按他的描述，家里的小米电视连打开Netflix前都要先看一段赞助广告。 于是，他把电视型号交给了编程助手Claude Code，让它通过Wi-Fi接入设备，一口气停用了25个系统组件，再写一个极简桌面——原来杂乱的主页，一下子就变得清爽起来。 他随后把这套prompt、桌面源码和踩坑记录开源到了GitHub，项目命名为CleanYourTV。 短短几天，项目迅速收获了约1.37万颗Star，「天下苦智能电视久矣」了属于是。在Reddit的相关讨论区中，挤满了追问自家机型能否适配的用户，不少人也陆续贴出自己改造后的极简首页。 图｜🔗github.com/Gathin23/cleanyourtv AI「接管」电视的底层逻辑并不神秘，核心依旧是安卓生态里用了十几年的老工具：ADB（Android调试桥）。 只要连上同一局域网、开启电视的调试权限，AI就能顺着终端接口排查正在运行的几百个应用组件。哪些是开机广告，哪些是偷偷上报收视数据的遥测程序，AI逐个筛查并下达命令将其「冻结」，最后再换上一个纯净的开源桌面。 图｜CleanYo

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. Step 2: Deploy the agent to Amazon Bedrock AgentCore Clone the sample repository, set your account and Region in Wordle/agentcore/aws-targets.json, and deploy: git clone https://github.com/aws-samples/sample-multi-turn-rl-wordle cd sample-multi-turn-rl-wordle/Wordle agentcore validate agentcore deploy The CLI builds the agent’s dependencies for the runtime’s Linux ARM64 platform, creates the execution role with AmazonSageMakerJobRuntimeAccess attached, and deploys through AWS CloudFormation. W

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s 上，TinyFish 在编码任务中实现了 79.0% 的完成率，同时提供了最低的中位任务成本（0.050 美元）和 token 占用，凸显了刚性控制循环、针对具体失败的重试以及统一推理优于独立规划器的重要性。 TEXTTABLELAYOUT你的 PDF 解析器真的解析正确了吗？omni parse bench · 对文本、表格和布局进行评分 · 开源在 GitHub 上加星→赞助 什么是智能体搜索架构？ 智能体搜索架构使 AI 智能体能够规划检索、访问来源、评估证据，并在新信息出现时调整其方法。模型提供推理能力，而外围应用则管理状态、工具执行、证据处理、运行时策略和输出验证。这些组件共同使智能体能够处理不完整的结果、可恢复的故障以及不断变化的信息需求。 TinyFish 的架构从 2026 年 2 月的一篇架构预印本中汲取了基础概念，该预印本通过类型化工具接口将推理与执行解耦。该论文确立了必要的系统护栏，包括详细的追踪，以及对 token、执行时间、总体成本和工具调用的严格上限。 生产环境中的搜索运行是如何工作的？ TinyFish 将一次运行拆分为 7 个阶段： 定义任务：将目标

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he full workflow, from uploading data and reviewing the analysis plan to real-time streaming and downloading the finished report. How can I try it? Deep Insight is open sourced on GitHub with a full setup guide, and offers three deployment options spanning development to production. The first is the Self-Hosted version, which runs locally through the CLI. It’s optimized for rapid iteration with full code-level access to every agent, prompt, and workflow. It sets up in about 10 minutes, which mak

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红米等安卓手机上成功引导运行iOS 16，但功能残缺、仅限极客演示，苹果生态护城河尚未被真正突破。 要点 1. 安卓手机运行iOS成为现实 开发者dipa-studio在红米Note 10 Pro上成功引导iOS 16.7.16，能进入桌面、操作设置和触控反馈；随后红米Note 12、Nothing Phone、一加12等设备也出现同类演示，iOS移植项目在GitHub上爆发式增长。 2. AI大幅降低逆向工程门槛 开发者使用Claude、Codex等AI工具辅助编写驱动适配代码，甚至出现专门工具REA（Reverse Engineer Anything），能自动分析硬件接口并生成转接代码，人类几乎沦为“按电源键的机械臂”。 3. 当前版本远无法实用 蜂窝网络、蓝牙、相机、扬声器均不可用，GPU硬件加速未打通导致卡顿，开机等待4分钟，安全机制被削弱（代码签名放宽、密钥服务简化），仅适合尝鲜演示。 4. 项目演进速度加快但前景未定 由于AI辅助的“接力赛”模式（项目仓库中AGENTS.md交接手册），项目进展极快：手势导航、Wi-Fi连接等已被部分分支攻克，但能否真正实现安卓硬件+iOS

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，以及 Claude 生成的一张用来向我展示其工作成果的图片： v12：通过声音实现多人游戏 我刚刚才让它跑通，所以还没有太多时间主动测试它。最新的 Battle Drones 现在支持多人游戏了，只需两台设备彼此处于麦克风可拾音的距离内即可！ 这个功能最初源于我上周的 Chirp Pong 实验。我把 Claude Code 指向那个可用的概念验证： 从 GitHub 克隆 Simonw/tools，并查看最近的分支，找到那个使用网页麦克风通过音频信号在两部手机之间实现多人游戏的示例乒乓球游戏 为 battledrones 构建一个该游戏的版本 Opus 构建了这个 Radio Probe 工具，用来测量两台设备之间能获得多少带宽；基于该工具取得的积极结果，它实现了一套完整的音频调制解调器系统。到目前为止，我已经在笔记本电脑、iPhone 和 iPad 之间试过它，效果似乎非常好！ 为什么用声音来实现这个？因为我喜欢零运行成本的静态 HTML，而且我不想在某处启动一台服务器，然后还要在任何人可能想玩这个游戏的整个期间一直负责让它保持运行。 一些想法 我对游戏开发几乎一无所知。这使它成

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er-harness skill , and connect it to my browser. Ask whether I want local browser recordings enabled; default to no and preserve my existing preference on upgrades. Follow https://github.com/browser-use/browser-harness/blob/main/install.md if setup or connection fails. One-time Chrome step: open chrome://inspect/ remote-debugging and tick "Allow remote debugging for this browser instance". On Chrome 144+ you then click Allow once per connection. On macOS, browser-harness mac-approve clicks it fo

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next step needs judgment, but its results still need checking. The Browser Use Cloud quickstart lets you send a task to a hosted web browser agent. Eligible sign-ups with Google, GitHub, or Microsoft include $1 in one-time free credits. Compare 11 MCP servers for browser automation, including Playwright MCP, Chrome DevTools MCP, Browser Use, Browserbase, and Kernel: which browser each controls, install, license, and cost. Gregor Zunic Guides/Oct 15, 2026 Compare Browser Use vs Firecrawl on use

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gin, CAPTCHA, payment or local file, then you hand it back. It also runs against a self-hosted steel-browser with STEEL LOCAL=true, one session at a time. Version 3.0.0 ships from GitHub releases and is not on npm. Controls: Steel cloud Chrome, or self-hosted steel-browser License: MIT Cost: $0.08 to $0.10 per browser-hour; free tier with $30 one-time credits, then $250 per month (pricing) claude mcp add steel -e STEEL API KEY=your-steel-api-key -- npx -y github:steel-dev/steel-mcp-server 10. Hy

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a work. A separate Claude-generated Linux page-cache fix recently ran into a race condition during review. Inside the hw-harness toolkit Schaller has since published hw-harness on GitHub. He notes that the released version isn’t the exact tooling he used for the scanner and camera projects. Instead, the setup runs the manufacturer’s Windows driver in a KVM virtual machine, with the physical device passed through to the guest. Claude operates the Windows session remotely while usbmon captures tra

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turn it into a permanent regression safety net: CI/CD pull request gates with headless CLI: Execute tools like agent-eval headlessly in your build pipeline (such as Cloud Build or GitHub Actions). On every pull request, it runs your golden dataset and enforces two merge rules: A global pass threshold: The suite must meet a minimum pass bar (for example, 95% boolean success). Zero regressions on core evaluation metrics: Even if the overall score holds steady, the build fails if any previously pas

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our first AA-AgentPerf-Local results, an RTX Spark laptop completes our local agent workload on Qwen3.8 27B in 18.7 minutes.Laptops & workstations leaderboardAA-AgentPerf-Local on GitHubThis article shows AA-AgentPerf-Local results as of October 15, 2026. Its charts update live, so they may differ from the figures in the text. For the latest results, see the laptops & workstations leaderboard. AA-AgentPerf-Local replays recorded agent sessions on local hardware AA-AgentPerf-Local is our open-sou

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ason, which is how you clear a false positive. If a later commit fixes the issue, the finding clears on its own. You can set up the same thing with the open-source security review GitHub Action or the /security-review command. Setup is covered in automated security reviews in Claude Code. 4. Automated checks that block The usual: lint, type checks, tests on every platform, and a build. (We wrote about scaling CI for this volume here.) A typical CLI pull request may run dozens of checks before an

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large（421M）和 Qwen3 级别的解码器，对长上下文、复杂语义的理解远超当年。 升级二：概率校准。 这是最关键的。当年 BERT 分类器的 softmax 概率是"伪概率"，训练目标把它推向 0 或 1，你拿到的数字没有概率意义。Jev 专门解决了这个问题——后面的训练章节会详细讲。 四、从两个开源项目还原 Jev 的技术路线 - https://github.com/jaredpalmer/kev - https://github.com/NandhaKishorM/laya 我们从零开始讲：输入怎么拼 → 模型怎么跑 → 概率怎么读出来 → 多题怎么并发。架构和推理先讲，训练最后说。 Laya：BERT 复古，但复古得很精妙 一句话概述：Bert基座，每个选项前插一个[MASK]，最后去这些 [MASK]的隐藏向量上logit打分 输入是怎么拼的？ 每个选项前面各顶着一个 [MASK]。这个设计的巧妙之处在于：MASK 的位置在 Encoder 里能双向 attend 所有 token——它同时"看"到了问题、自己选项的描述、其他选项的描述，以及完整的 state。所以

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IDE. The CORS requirement likely comes from Electron. After the initial verification, all requests come from Cursor’s servers. You can find the entire codebase for “CursorZero” on GitHub. It finally worked! Finally: Observability for Cursor We could finally see everything coming in and out of Cursor — including its prompts. For example, here’s what we saw after asking Cursor “how do I get nginx to get the key from an environment variable in my .env?” This example paints a picture of what prompt

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IDE. The CORS requirement likely comes from Electron. After the initial verification, all requests come from Cursor’s servers. You can find the entire codebase for “CursorZero” on GitHub. It finally worked! Finally: Observability for Cursor We could finally see everything coming in and out of Cursor — including its prompts. For example, here’s what we saw after asking Cursor “how do I get nginx to get the key from an environment variable in my .env?” This example paints a picture of what prompt

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ropics/claude-code-playground. SHARING YOUR MOD A mod is a Claude Code plugin, so you share it the same way as any other plugin, and there's nothing new to learn. Put the mod in a GitHub repo with a marketplace file and that repo becomes your marketplace. Anyone can install from it, and you can update it with a normal push. Installing takes three commands in Claude Code: The mod starts when you reload. If it doesn't show up, restart Claude Code. A mod is code that runs inside Claude Code on your

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k to each other (Step 4). Finally, Step 5 shows how any one team can ship a new version of its agent without disturbing the others. The complete sample is in the AgentCore samples GitHub repository. Prerequisites Before you begin, make sure you have: An AWS account, with credentials for a principal that can create infrastructure. This sample creates AWS Identity and Access Management (IAM) roles, an S3 bucket, ECR repositories, and an AgentCore capacity provider and runtimes; AWS Command Line In

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of these. Mercury 2 optimizes for all three. We evaluated Mercury 2 on PinchBench, the open-source benchmark built on top of OpenClaw — the fastest-growing open-source project in GitHub history, with 250K+ stars in under 60 days. Here's what we found. The result Mercury 2 sits in the upper-left of the chart: high task success rate and the fastest execution time in its performance class. 78% success rate — matching or exceeding GPT-5 Mini (75%), Gemini 2.5 Flash (71%), DeepSeek Chat (72%), and G

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の利用状況の分析も行える。 Build plugins for Claude - Anthropic プラグインには、外部ツールやデータにつなぐMCPコネクター、作業手順などをまとめたAgent Skills、またはその両方を含められる。申請ポータルでは、単体のMCPコネクターならリモートMCPサーバーを指定し、MCPサーバーとスキルをまとめたプラグインならGitHubリポジトリを提出する。 申請時には自動検証と安全性スキャンが行われ、開発者は審査の進み具合や修正の提案を確認できる。承認後は自分で公開時期を決め、公開後には製品別・バージョン別のインストール数、掲載ページの閲覧数、どの検索から閲覧につながったかを把握できる。 今後数週間で、ClaudeとClaude Codeでプラグインを探す仕組みも統一する予定。既存のスキルやMCPコネクターは引き続きディレクトリに掲載され、掲載済みのスキル、コネクター、プラグインを変更する必要はないとしている。 SlackのClaude Tagが個人コネクターに対応 Anthropicは9月24日、SlackのチャンネルにClaudeを参加させるCl

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