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use Gadgets, an open-source project that lets hobbyists build their own AI hardware. It includes firmware for ESP32 boards and a Linux SDK to connect homemade devices to Meta's AI agent Muse . The code is Apache 2.0 licensed, with a Discord community discussing the project. The open-source approach likely serves a second purpose, too. By watching what the community builds, Meta can learn which form factors people actually want, since nobody has settled on what AI hardware should look like. Apple

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n't sandboxed, so Anthropic warns users to only install them from trusted sources. They work in the CLI, the desktop app, and partly in the VS Code extension. Organizations can control which Mods are allowed to load. The first official Mod plugin is called "You Should Know." It spins up a separate agent that watches Claude's output and sends a "Heads up" message when it spots important information users might have missed. You can turn it on with /plugin enable cc-plugin-you-should-know@builtin .

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figuration of DGX Spark — from Acer, ASUS, Dell, Gigabyte, HP and MSI — its GB10-powered desktop AI system. It gives developers a way to start with one system for local models and agents, then cluster two 64GB units for 128GB of memory across the cluster and more compute when workloads grow. The direct message is: Run open models and always-on agents on your own desk, then cluster DGX Spark system as the work grows – instead of on a metered API. The timing is not accidental. Agents burn tokens c

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Before public release, it processed nearly a trillion tokens per day internally. That traffic came from RL rollouts, synthetic data generation, evaluations and long-running coding agents. What is Prime Inference? Prime Inference is the serving layer of Prime Intellect’s open training stack. The company already ships post-training tools such as prime-rl, verifiers and sandboxes. Serving closes that loop: deployed models generate production traces that can feed back into training. Prime reports it

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IBM has made a self-hosted deployment option for IBM Bob generally available. Bob is IBM’s agentic software development platform. It covers the full lifecycle: understanding code, planning work, executing changes and validating results. The new option lets enterprises run Bob on premises, in private or sovereign clouds, and in air-gapped networks. Is it deployable today? Yes. The self-hosted option is generally available to enterprise customers. You must source, license and host a supported mode

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Meta now lets you make your own Muse gadgets that feature the company’s new AI agent with code that the company open sourced . The company suggests projects like loading Muse on a color E Ink display to show reminders, adding it to an HDMI stick so you can display Muse on a big screen, or putting Muse on a small touchscreen device to make what looks kind of like a DIY Muse Charm . “Muse gadgets are open source devices you build yourself,” Meta says. “Program an off-the-shelf ESP32 board or set u

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eats OpenAI’s Privacy Filter on all 5 public benchmarks. also released with the PII-TRACE benchmark: 13k conversations in 13 languages •WANDR: benchmark for wide and deep research agents. 500 tasks needing 170k source-backed records •Numbat: agent detection and response for laptops and workstations. 52 rules, single go binary for macOS, linux and windows •Bumblebee: read-only supply chain scanner for dev machines. covers packages, MCP configs, and editor and browser extensions. a lot more open s

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WITH ALGOLIA 🔍 From search box to workflow copilot The Rundown: Search used to be a list of links. Now it’s a list of actions aligned with remote MCP servers built for tool-using agents. These agents require fast, intelligent retrieval and ranking to understand user intent, find relevant data, and execute. Failing to build systems that help agents move faster slows down your entire organization. In this white paper, you’ll learn: How Algolia’s retrieval and ranking layer powers enterprise inten

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Local AI is becoming more useful by the token. As AI agents move from experiments into everyday development, increasingly capable open models are shrinking to fit on more devices, giving builders more to run locally. Coming this month, NVIDIA DGX Spark will be available with 64GB of unified memory from top manufacturer partners — Acer, ASUS, Dell, Gigabyte, HP and MSI — giving developers, researchers and AI enthusiasts a new configuration with DGX OS and the NVIDIA AI software stack ready to use

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のHooksにClaude Code内で動作するTypeScript関数を登録することで、セッション内で状態を保持できる。このため、イベントの前後、あるいはイベントを置き換えて処理を実行でき、ボタンや入力フォーム、ペインなどのUIの追加/変更やプロンプトの書き換え、ツールのブロックや再試行、権限要求の自動承認/拒否が可能。 またClaude Code自体にもAGENTS.mdのサポートやサイドバーに表示される/diffペインなど、modとして構築されている機能がある。これらのソースコードはGitHubで公開されているので、mod作成のサンプルとして確認できる。 modはユーザー自身で作成できるほか、Claude Codeに作成を依頼することも可能。プロンプトから作成したmodは、動作を見ながら調整することもできる。なお、この方法で作成したmodはそのセッション限りの一時的なもので、継続して使うには、フォルダーを別の場所へコピーし、通常のプラグインとしてインストールする必要がある。modの詳しい作り方はチュートリアルを参照。 公開されているmodのプラグインはClaudeディレクトリから

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mall group of teams to get the model into production. That work shaped how GPT-Live runs on the platform. Now we're opening it up to learn from more of you. What GPT-Live is Voice agents have historically run on a modular pipeline: a speech-to-text model transcribes the call, an LLM reasons over the text, and a voice model speaks the response. GPT-Live is a speech-to-speech model. It listens to the caller's audio and generates its own speech, all in one model. On Vapi, GPT-Live runs as two roles

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bilities of the NVIDIA Blackwell architecture, Ultrafast offers up to 8x faster token generation than the Astra Standard mode. For developers, faster generation can shorten coding agents’ edit-test-debug cycles, reduce the time spent generating responses between tool calls and make interactive applications feel more responsive. A faster response matters most when it’s repeated across a workflow: an agent writes code, uses a tool, checks the result and decides what to do next. Ultrafast brings As

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r use cases around shopping. OpenAI already had to pivot from one of its earlier ideas in this space, an instant checkout feature that ended up not performing well. More recently, agentic AI startup Instinct began pushing product recommendations to users, but some felt that the proactive recommendations were an overreach, more akin to ads than helpful suggestions. OpenAI said its new shopping features leverage the newly launched ChatGPT Images 2.5 model , which the company claims produces more n

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nging modular sections and blocks. Deeper customizations, however, would require editing code or bringing in a developer. Now, with Canvas, merchants simply chat with Shopify’s AI agent, Sidekick , to create their site. As the AI makes changes, Canvas displays the results in real time, allowing merchants to see how the store looks overall as it comes together or zoom in to focus on certain details. Notably, this isn’t a static preview, but the real code behind their store being rendered, accordi

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ent applications on Clusters We’re proud of the cutting-edge research and production applications built on top of multi-node clusters. Decagon trained a better customer experience agent Decagon builds AI agents for customer experience. In partnership with Modal, Decagon fine-tuned open models with up to a trillion parameters using Miles on Modal Clusters, tailoring them to the demands of customer interactions with a focus on response quality and inference efficiency. As part of this collaboratio

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w Ultrafast mode for GPT‑5.6 Sol running on Cerebras, at up to 750 output tokens per second. This speed changes how we use AI:we can now stay in the flow, and collaborate with the agent in realtime. The most important detail is what we did not change. GPT‑5.6 Sol on Cerebras is not a smaller model, distilled, or quantized to lower precision. It uses the same model architecture, weights, precision, context configuration, and reasoning settings as GPT-5.6 Sol on the standard OpenAI endpoint. It re

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4 31B is now running at over 1,800 tokens per second on Cerebras Inference. This multimodal model unlocks an entirely new class of applications, from computer use to image-driven agentic workflows. As the leader in fast inference, Cerebras has set benchmarks across numerous open-weight models including Kimi, GLM, GPT-OSS, and Qwen. Gemma 4 is the first Google DeepMind model we have brought to the platform, and the first to let developers feed images—screenshots, documents, charts, UI states—int

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has set benchmarks across numerous open-weight models including GLM-4.7, GPT-OSS-120B, and Qwen 3, while delivering dramatic speedups to customers such as OpenAI and Cognition on agentic coding models. K2.6 is one of the most frequently requested models, and we are excited to bring it to customers. It is the first one trillion parameter open-weight model we have served, achieving performance approaching 1,000 tokens per second as measured by Artificial Analysis. The result: agentic coding shift

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May 01 2026 Powered by Cerebras Inference, Cognition's SWE-1.6 and the SWE-grep family deliver frontier-level coding performance up to 5x faster than on GPU, with a smoother agent experience that keeps developers in flow while they explore codebases, ship features, and debug complex systems. " Working with Cerebras lets us treat speed as a first-class design parameter. When your agent runs at 1000 tokens per second, you have the opportunity to optimize all parts of the agent together, including

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ise customers. September 30, 2026 CoreWeave has unveiled CoreWeave Forge, an integrated software platform for organizations to develop, run, and continuously improve AI models and agents. Announced today (Sept. 30) during the company’s Fully Connected event in San Francisco, Forge extends the AI cloud provider’s strategy beyond GPU infrastructure to the software layer enterprises need to develop, operate, and refine AI applications. “We have moved from a focus on being the best provider for AI-c

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