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如果你用 Claude Code 还是你问一句、它改一行，那基本只是把它当成了高级版的 Google 搜索引擎， 一位 Anthropic 工程师刚在 30 分钟的工作坊里亮出了内部高阶打法：在一个集成循环里同时跑 100 多个智能体，1 个主代理坐镇，底下全当项目经理去带团队。 市面上那些卖 500 美元的所谓 Vibe Coding 培训课，含金量加起来都不如 Anthropic 工程师刚放出的这场 30 分钟高密度工作坊，他直接把怎么用最低的运营成本榨干 Claude Code 给讲透了。 这位内部工程师在分享里晒出了自己极其硬核的日常配置，他在一个集成循环里同时跑着超过 100 个智能体，上面只设一个主代理坐镇，底下的子代理分别充当项目经理，自己去领导和调度整个研发团队。 平时我们打开 Claude Code 写代码，默认动作是一问一答，报错了就贴进去问怎么改，本质上还是把它当成一个会写代码的 Google 搜索引擎在用，一个人盯一个终端窗口，你停下来它就跟着停下来。 但这位 Anthropic 工程师点破了真正拉开十倍产能差距的分水岭，Claude Code 真正的核武器从来

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在 Google 上添加为首选来源 一个读取网页的 AI 智能体需要支付两类成本。第一类是检索费用：搜索或抓取 API 按每次请求计费。第二类是模型处理：从检索到的页面传入 AI 模型的内容会消耗 token，而推理和生成回复还会带来额外成本。面向 AI 智能体的网络基础设施公司 TinyFish 认为，团队在为模型做预算时，低估了检索内容对模型成本的推动作用。 本文梳理 token 的计算方式、对上下文窗口的影响，以及 Artificial Analysis 搜索 API 排行榜展示了什么、又没有展示什么。 TL;DR 运行基础：TinyFish 使用一个为搜索、检索以及与实时网站交互而设计的网络原生 AI 模型。该模型运行在 TinyFish 管理的基础设施上，其云浏览器集群负责渲染页面并处理网站交互。无需本地 GPU。可通过 REST、Python 和 TypeScript SDK、CLI 或 MCP 访问。 性能：在 2026 年 9 月 28 日的 Artificial Analysis 快照中，TinyFish Search（web）得分为 71，每 1,000 个基准任务

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When Google announced Gemini 4 Argon to a small group of testers on September 30, it came with a benchmark table that put Argon ahead of Claude Opus 5.5 on 14 of 18 tests. Those include DeepSWE (77.9% vs. 74.2%) and GraphWalks at 256K to 1 million tokens (84.2% vs. 66.8%). Two weeks later, on October 13, Google opened Argon to paid Gemini API developers. To its credit, Google also showed where Opus 5.5 wins. Opus leads on the other four benchmarks, including Terminal-bench 4.0 (66.4% vs. 57.4%),

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when the 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

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udging the Judges）、协议规范（MCP 固定到 2025-11-25 版本、W3C Trace Context、OpenTelemetry、RFC 8693 与 RFC 9700）、厂商与标准组织的工程文档（Anthropic 的 Agent 与上下文工程文章、LangGraph、Temporal、Stripe、AWS、PostgreSQL、Google SRE、OWASP、NIST）。论文结果只在原研究任务、模型和预算下成立；厂商文档用于确认机制，不代表跨产品的能力保证；动态 API、模型价格和产品套餐不作为长期前提。 法律、司法解释与部门规范性文件：民事诉讼法、登记立案规定、在线诉讼规则、最高人民法院人工智能司法应用意见、个人信息保护法、医疗卫生机构网络安全管理办法、互联网诊疗监管细则、卫生健康行业人工智能应用场景参考指引。它们在文中只用于说明 Agent 的职责边界、人工确认要求与数据边界，正文分别标注文件性质，相关表述一律是本系列对规则的工程理解，不构成法律意见；具体适用仍需法务与业务负责人判断。 作者的参考设计与演算：两个案例、全部字段与标识、状态机、流程、预

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temporary one. --autoConnect connects to a Chrome you started, with access to every open window of its default profile. It officially supports Chrome and Chrome for Testing only. Google collects usage statistics by default; --no-usage-statistics turns that off. Performance tools may send trace URLs to the Chrome UX Report API; --no-performance-crux turns that off. Pick it over Browser Harness when you are profiling or debugging your own app. On our eval it scored 61.0, the highest of the four,

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ark 笔记本大多遥不可及。我希望看到更便宜的选项出现，也希望最终能有第二代产品带来更高的 CPU 性能，从而有望进一步缩小与苹果的差距。我也希望这能推动基于 Arm 和兼容 Arm 的游戏向前发展。也许有一天，这甚至意味着游戏移植到 macOS 会更容易。那岂不是个有趣的结果？ 继续测试这些超高端 RTX Spark 机型固然很酷，但我最期待的是拿到一台基础版 Surface Laptop Ultra 或配备 24GB 内存的 Asus ProArt P14，看看它们与入门级 14 英寸 M5 MacBook Pro 相比如何。那是我个人心目中苹果产品线的甜点——人民的 MacBook Pro。（好吧，如今它起售价两千美元，就没那么“人民”了。） 在芯片选择方面，这是计算领域令人兴奋的时代，但拜“内存末日”和天价所赐，这也是一个绝对糟糕的时代。我一直在想，如果 AI 需求没有推高内存和存储的价格，像这些 RTX Spark 甚至 Googlebook 这样的笔记本本来会卖多少钱。在 PC 整体销量似乎呈下滑趋势的当下，我不知道谁会花高达 7000 美元买这些笔记本，但它们的存在很酷。

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个会新技巧的模型 相机角度 LoRA 是早期 Qwen-Image 模型中最受社区欢迎的附加组件之一。你可以给模型一张物体的图片，并要求从左侧 45 度角查看它。在 Qwen-Image 2.1 模型发布几天后我查看时，还没有人做过，所以我让 ML-intern 来构建它。 ML-intern 在一个花费几美分的 CPU 任务中，以 24 个角度渲染了来自 Google Scanned Objects 的 1,030 个扫描家居物品，共 24,722 张透明图像。它后来最终确定了 461 个物品用于训练、40 个留出用于测试，以及 1,844 对训练前后图像对，均匀分布在 23 条相机指令上。 Doodle-in LoRA 是另一个很酷的想法。上传一张带有洋红色涂鸦的照片，并添加一个简短的提示词来指定一个物体。该 LoRA 会用该物体替换涂鸦，同时保持原始光照和构图一致。 此前不存在用于此任务的数据集，所以我的提示词描述了如何制作一个。从 Open Images 中的真实照片开始，用 LaMa 修复模型移除一个物体，并在物体原来的位置画一个涂鸦。未修改的照片是目标。ML-intern

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rs have left big companies to found startups focused on solving physical reasoning. Andrew Dai, the CEO of one such outfit, called Elorian AI, previously worked as a researcher at Google DeepMind. He says better visual reasoning will open up a lot of new applications for AI, like systems that understand whether diners are enjoying their meal in a restaurant or robots capable of functioning in a home. Dai says robotics is a crucial test case for physical reasoning skills. “It’s pretty essential f

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Anthropic, OpenAI and Google DeepMind shipped 4 frontier-class models within 30 days. Claude Fable 5.1 arrived on September 1. GPT-6 Astra followed on September 3. GPT-6.1 Sol and Gemini 4 Argon landed in the last days of September. We covered each launch on its own. This piece puts them side by side. The benchmark scores overlap more than the launch posts suggest. The prices, access rules and cost per task do not. One change frames the lineup. OpenAI cancelled GPT-6.1 Astra on September 28 afte

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legating whole projects to teams of agents Oct 2, 2026 · 10 min readUpdated Oct 13, 2026 For most of my working life, the first step of any task began with a blank slate: an empty Google Doc, a fresh email, or a new Excel spreadsheet. From researching to writing formulas, the banalities of every stage of construction fell to me. AI made that process faster, but the way I accomplished it wasn’t all that different. I started with a new chat and prompted it over and over until the output matched my

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More big news from @GoogleDeepMind: Gemini 4 Argon (High) is 8 in Agent Arena with a +7.92% net improvement score, and has reshaped the Pareto frontier with a $0.62 cost per task! See its placement below. Gemini 4 Argon (High) is a 4.96 percentage point improvement over Gemini 3.8 Flash (High), at 19 with +2.96% net improvement. By key signals, Gemini 4 Argon (High) stands out in: - 1 in Steerability with +15.88% (the model’s ability to course-correct when you push back) - 2 in Confirmed Success

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プランの利用枠とは別に追加使用のクレジットを消費する。 エフォートの変更でキャッシュが維持される場合 Claude Codeでは、会話の途中でも/effortで設定を変更できる。Addy氏の説明によると、APIキーまたはClaudeのサブスクリプションを使ってOpus 5.5を動かす場合は、変更後もキャッシュが維持される。一方、Amazon Bedrock、Google CloudのAgent Platform、Claude apps gateway経由では、変更で会話のキャッシュが無効になり、次のリクエストで会話全体をキャッシュに書き直す料金がかかる。 会話を圧縮するタイミングと注意点 キャッシュが使えても、会話が長くなるほど毎回読み出す量は増える。Claude Codeは会話がコンテキストの上限に近づくと古い履歴を要約するほか、利用者が/compactで圧縮することもできる。関係のない別の作業に移る場合は、/clearで会話を空にできる。 /compactは後の入力を減らせる一方、要約の生成と、短くなった会話のキャッシュへの書き込みに費用がかかる。作業終了の直前に圧縮すると、その後

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an API key or a Claude subscription, changing effort keeps the cache. You can raise it for one hard step and lower it again without rewriting the conversation. On Amazon Bedrock, Google Cloud's Agent Platform or a Claude apps gateway, a change of effort still clears the cached conversation, and the next request pays the cache-write price on all of it. Thinking is always on for Opus 5.5, so there's no thinking setting to change. Choose the right model for your work Model choice sets the price of

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s. For supported services, n8n provides predefined credential types. For other services, you can configure generic credentials for basic authentication, header authentication, and Google OAuth2. Generic credentials are re-usable across different workflows, so you just need to set them up once. When fetching large volumes of data, set a pagination mode to match the API so the workflow can retrieve results across multiple pages. Consuming external MCP servers with the MCP Client Tool node Attach t

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the underlying model having to be retrained. The industry has converged on a similar idea. Andrej Karpathy’s LLM Wiki structures agent knowledge as a navigable graph of files, and Google’s Open Knowledge Format standardizes this for cross-agent interoperability. The shared insight is that knowledge should be pre-extracted, explicitly structured, and progressively disclosed rather than re-derived on every query. We extended these principles into a system where citation fidelity and institutional

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asks the model to break something real, then fix it. The benchmark was built at UC Berkeley from 1,507 real vulnerabilities across 188 widely used open-source projects, drawn from Google’s OSS-Fuzz campaign and the ARVO reproducible-vulnerability archive. Each task hands the agent a short description and a real vulnerable codebase. The agent has to locate the flaw, build an input that actually triggers it, and then patch it. Grading is by execution, not by a judge model. The proof-of-concept eit

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they derived this method from a paper. It was a Nature paper, and this method is called SynthID-Text. So that was like a paper that came out maybe one or two years ago. It was by Google, and they use a similar technique they call Claude watermarking. I don’t know, sorry, I don’t know if they use exactly that technique, but that’s the one they mentioned. 31:18 SynthID Text and Tournament Sampling So how does it work? So before we looked at the slides, we looked at the regular, let’s say, overvie

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ting, we’ve seen no impact of watermarking on the content, level of creativity, or readability of Claude’s text. In the SynthID-Text paper , which introduced the technique we use, Google DeepMind tested this impact by serving a model that used watermarking to a portion of their Gemini traffic and comparing thumbs-up and thumbs-down ratings. They found no statistically significant differences from the unwatermarked model. And in a controlled study, human raters comparing watermarked and unwaterma

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gine or rely on the bundled engines offered by some labs. On OpenRouter, we offer Exa, Parallel, and Perplexity, alongside the native engines from labs like OpenAI, Anthropic, and Google; Search method. Either you can perform the search before calling the model and pass the results in as context, or you can equip the model with a web search tool that it calls at its discretion; Search budget. If you choose the search tool method, you can also give the model a budget for how many searches it’s al

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