搜索“Anthropic”

installation easier and putting a new wrapper on the interface while leaving most of the security to users is a recipe for more trouble with the popular agent harness MOST POPULAR Anthropic asks users to stop being mean to Claude; '123456' password linked to Denmark data breach affecting 8.8M people; Intel optimizes IPv6 networking into nothingness, say users; When you just need to build a tiny desktop app: TinyJS; No shame in the Domain Name game: DNS is doomed, literally OpenClaw has unveiled

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tion set to ramp up through 2027. Why it matters: Google's in-house TPUs have been a good example of what a lab gains from owning its silicon, and the club is filling up fast with Anthropic, Amazon, and Microsoft also planning their own designs. If Jalapeño's numbers hold, OAI gets cheaper, faster tokens and a tighter, personalized design loop for each model after. The Rundown: Physical AI is the next frontier, but complex real-world variables and multimodal experimentation make building embodie

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ss Small business Scientists Scientists Scientists Developer docs Developer docs Developer docs Developer blog Developer blog Developer blog Console Console Console Engineering at Anthropic Engineering at Anthropic Engineering at Anthropic Overview Overview Overview Marketplace Marketplace Marketplace Claude on AWS Claude on AWS Claude on AWS Blog Blog Blog Claude partner network Claude partner network Claude partner network Customer stories Customer stories Customer stories Powered by Claude Po

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e a specific engine 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 s

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areer Cisco, Nvidia team up to build fatter, faster pluggable If you thought 1.6 Tbps was fast, just wait, Cisco's USFP targets 12.8 Tbps in a single optical pluggable TOP STORIES Anthropic asks users to stop being mean to Claude; '123456' password linked to Denmark data breach affecting 8.8M people; Microsoft and Anthropic play invoice tennis with startup's $17,600 Claude bill; Starlink's plan to avoid orbital near-misses: Ephemeris sharing; Shai-Hulud worm makes jump to AI infrastructure with

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Microsoft Foundry is a platform for building and operating agentic AI applications. Foundry starts with the widest model selection on any cloud — models from Microsoft, OpenAI, Anthropic, Meta, Mistral, DeepSeek, Hugging Face, and others, spanning frontier, open-source, and custom weights — all accessible through a single endpoint and a single set of SDKs in Python, C , JavaScript, and Java. On top of those models sits the Foundry Agent Service : multi-agent orchestration with built-in memory, k

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model and sidekick dynamically makes this system much more robust; It avoids costly cache misses when routing between models. We've previously explored a "Smart Friend" tool, and Anthropic released a similar "Advisor" tool. The core of both these ideas is to give one model a tool to query another model for helpful advice. The catch? Upon every call to the other model, the context for the task is not shared in a way that is cached, and you pay a very expensive price. In the sidekick setup, both

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used only where it should be used, such as understanding and generating code, rather than being wasted on flow control. Our implementation is compatible with the core semantics of Anthropic Dynamic Workflow, and extends it in several ways. The workflow() primitive allows scripts to call other scripts, so orchestration logic can be organized into reusable and composable building blocks. The result of each agent() call is synchronously written to disk, allowing the process to recover from logs aft

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areer Cisco, Nvidia team up to build fatter, faster pluggable If you thought 1.6 Tbps was fast, just wait, Cisco's USFP targets 12.8 Tbps in a single optical pluggable TOP STORIES Anthropic asks users to stop being mean to Claude; '123456' password linked to Denmark data breach affecting 8.8M people; Microsoft and Anthropic play invoice tennis with startup's $17,600 Claude bill; Starlink's plan to avoid orbital near-misses: Ephemeris sharing; Shai-Hulud worm makes jump to AI infrastructure with

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re a helpful new way to extend Claude Code. I encourage you to think of them as a starting point to explore new ways to use Claude to help accomplish your tasks. There is still much to discover in how to use them best. Let me know what you find. For principles on what belongs in a harness in the first place, see our three harness design patterns for building with Claude. This article was written by Thariq Shihipar and Sid Bidasaria, members of technical staff at Anthropic working on Claude Code.

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eep background tasks free of pollution from new messages. This echoes the design principles for long-running tasks, status updates, and human-in-the-loop in Google's A2A protocol. Anthropic's Managed Agents blog argues that “session is not the same as a model context window”, and that long tasks need a recoverable session log as the external context object. Industry consensus is forming: the underlying logic of IM async execution is that when a task spans multiple message rounds, multiple tools,

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areer Cisco, Nvidia team up to build fatter, faster pluggable If you thought 1.6 Tbps was fast, just wait, Cisco's USFP targets 12.8 Tbps in a single optical pluggable TOP STORIES Anthropic asks users to stop being mean to Claude; '123456' password linked to Denmark data breach affecting 8.8M people; Microsoft and Anthropic play invoice tennis with startup's $17,600 Claude bill; Starlink's plan to avoid orbital near-misses: Ephemeris sharing; Shai-Hulud worm makes jump to AI infrastructure with

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guのモデルは、私たちのICLR 2026採択論文（Trinity およびConductor）をベースとしており、さらなる性能向上とユーザー体験の向上に向けて手法を改良しています。 こうした適応的なオーケストレーションによって、Sakana Fuguは既存のベンチマーク上でも高い性能を発揮します。以下は結果の一部です。 各ベンチマークタスクごとのスコア：＊はAnthropic独自の検証用フレームワークを使用した自己申告スコア。SWEPro の評価には mini-swe-agent のスキャフォールドを使用。Anthropic が公表している Opus の最大思考モードのスコアについては、当社での評価試行中に頻繁にタイムアウトが発生したため、Anthropic 公式の報告値を採用。 Sakana Fuguの使い方 Sakana FuguはAPIで利用できます。OpenAI形式のエンドポイントとの互換性があり、いまGPT、Gemini、ClaudeなどのAPIをお使いの方は、既存のワークフローをほとんど変えずにそのまま導入いただけます。いつものインターフェースの背後で、Sakana Fug

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tokens to make a difference, and token consumption is only going up from here. Along with this, OpenAI now offers subscription plans with even more aggressive discounts than what Anthropic offered before. You can pay OpenAI $20/month to get (seemingly) $1000+/month in tokens. This gives you a lot more "free" usage than Amp's ad-supported free tier and is a better choice if cost is your top concern. What about the $10 daily free usage of Amp? For now, most who have it will keep getting it, witho

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r your needs, clone the repository: Then open http://localhost:3001 and configure your API keys: OpenHands Cloud API Key - Get one from OpenHands Cloud; LLM API Key - Your OpenAI, Anthropic, or OpenHands LLM key; GitHub Token - Create one at github.com/settings/tokens with repo scope All credentials are stored locally in your browser and never sent to external servers except for the services they authenticate with. A Reference Implementation Vulnerability Fixer serves two purposes beyond just fi

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nt to figure it out; that’s closer to the spirit. How do I use GLM-5 with Claude Code? Claude Code is not as open as other frontend agent frameworks. It is tightly integrated with Anthropic’s proprietary backend offerings. However, you can use a LiteLLM gateway to proxy between a Claude Code client and an OpenAI API-compatible endpoint like ours. We demonstrate how to deploy a such a proxy on Modal here. How do I use GLM-5 with the Vercel AI SDK? The Vercel AI SDK is a key library for JavaScript

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图片来源：VentureBeat 使用 Midjourney 制作 人工智能编码革命伴随着一个代价：它很昂贵。 Claude Code 是 Anthropic 推出的基于终端的 AI 智能体，能够自主编写、调试和部署代码，它激发了全球软件开发者的想象力。但其定价——根据使用量每月从 20 美元到 200 美元不等——却在它本意服务的程序员群体中引发了日益高涨的反抗。 如今，一个免费的替代方案正在获得关注。Goose 是由 Block（这家金融科技公司原名 Square）开发的开源 AI 智能体，提供与 Claude Code 几乎相同的功能，但完全在用户本地机器上运行。没有订阅费。没有云依赖。没有每五小时重置一次的速率限制。 “你的数据始终留在你身边，就是这样，”软件工程师 Parth Sareen 在最近的一次直播中演示该工具时说道。这句话道出了其核心吸引力：Goose 让开发者完全掌控其 AI 驱动的工作流程，包括离线工作的能力——甚至在飞机上。 该项目人气暴涨。Goose 目前在代码共享平台 GitHub 上拥有超过 26,100 颗星，自发布以来有 362 名贡献者和 102

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nteresting paradigm and a new axis for model development, especially for simpler agent architectures or systems built around subagent delegation. It's clearly a new direction from Anthropic: likely pointing toward a future where models are more context-aware and where this becomes the way multiple agents communicate with each other. The RL training hasn't fully progressed to the point where this is reliable yet, but we'll be tracking how it evolves. Testing to create feedback loops Sonnet 4.5 is

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roader principle of task-aware compute allocation, where every inference token must contribute meaningful value—not just be consumed. Similar ideas are appearing in other systems: Anthropic Claude 3.7/4: differentiates “fast thinking” and “slow thinking” pathways; Google Gemini 2.5: offers explicit thinking budgets, allowing enterprises to cap reasoning depth; Alibaba Qwen3: supports instruction-driven switching between reasoning and non-reasoning modes; DeepSeek v3.1: merges conversational and

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sources to advance open-source AI through our platform and later on also more easily with their own hardware. Paradigm Shift for Distributed Training. As Jack Clark, co-founder of Anthropic, highlighted, no model has yet been efficiently trained at the scale of 10B parameters across globally distributed workers. Our initial OpenDiLoCo run broke through the 1B parameter barrier, and with INTELLECT-1, we are reaching a new level of scale for low-communication training. ‍ DiLoCo enables the trainin

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