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simply describe what you need and the Agent does the work. You can even build different Artifacts in parallel. You can also connect external services (e.g. Linear, Slack, Notion, Google Sheets) and ask the Agent to pull information from them, work across them, and build outputs based on that data. Stand-alone “Design Mode” has been replaced by the always available Design Canvas What it was in Agent 3 Design Mode was a separate tab in the editor. You'd switch into it, ask the Agent to create a d

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o enable automated proofreading and error corrections. Legacy OCR Stack Classical OCR engines - ranging from open-source staples like Tesseract and EasyOCR to enterprise APIs like Google Cloud Vision - face significant architectural constraints when processing complex, unstructured documents. These systems typically rely on a bottom-up approach, identifying characters and words in isolation without understanding the semantic or spatial context of the page. This limitation manifests as a failure

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s (WER) and Character Error Rates (CER) across the supported Indic languages. To establish a performance baseline, we compare our Edge ASR Model with cloud models Saarika v2.6 and Google Cloud STT, while native on-device support for Indian languages continues to develop. Speech Synthesis Speech Synthesis provides reliable text-to-speech directly on device, without requiring an internet connection. It supports 10 Indian languages and 8 multilingual speakers using a single compact model, while mai

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esign are out of beta and on every Claude plan, including Free. Claude Haiku 5.5 Introducing Claude Haiku 5.5: the cheapest, fastest, and most capable small model we’ve ever released. Claude now works with Google Docs, Sheets, and Slides Teams that run on Google Workspace can now bring Claude into their files or work on their files directly from Claude, with our new add-on and Google Docs, Sheets, and Slides connectors (in beta). We’re expanding the Claude Startups program to help founders build

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控制的开放源代码语言模型。 该项目的文档将其描述为“超越代码建议”，能够“使用任何 LLM 进行安装、执行、编辑和测试”。最后那句话——“任何 LLM”——是关键的区别所在。Goose 在设计上就是模型无关的。 如果你有 API 访问权限，你可以将 Goose 连接到 Anthropic 的 Claude 模型。你可以使用 OpenAI 的 GPT-5 或 Google 的 Gemini。你可以通过 Groq 或 OpenRouter 等服务来路由它。或者——这正是有趣之处——你可以使用 Ollama 等工具完全在本地运行它，这些工具让你可以在自己的硬件上下载和执行开源模型。 其实际意义重大。通过本地设置，没有订阅费、没有使用上限、没有速率限制，也不必担心你的代码被发送到外部服务器。你与 AI 的对话永远不会离开你的机器。 “我在飞机上经常使用 Ollama——非常有趣！”Sareen 在一次演示中说道，强调了本地模型如何让开发者摆脱互联网连接的束缚。 Goose 作为命令行工具或桌面应用程序运行，可以自主执行复杂的开发任务。它可以从零开始构建整个项目、编写和执行代码、调试故障、协调跨

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. Agentic coding is the hot thing in AI-assisted development, with the formation of the just-announced Agentic AI Foundation and recent releases from VS Code competitors including Google’s Antigravity and JetBrains Air in preview. Not wanting to be left behind, VS Code now enables multiple background agents to work concurrently, and introduces Agent HQ as a single place to manage them. Oddly, although “VS Code now includes Agent HQ” is touted as the first new feature in the release notes, the te

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assistant that integrates git operations, file manipulation, package managers, testing frameworks, and deployment pipelines. An operations coordinator that connects Slack, GitHub, Google Drive, Jira, company databases, and dozens of MCP servers simultaneously. To build effective agents , they need to work with unlimited tool libraries without stuffing every definition into context upfront. Our blog article on using code execution with MCP discussed how tool results and definitions can sometimes

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n: keep the same user experience, telemetry, and interface vLLM TPU In February 2025, just when vLLM’s V1 integration was first taking shape, a “small but mighty” team composed of Googlers and core vLLM contributors, set themselves a goal of launching a performant TPU backend across a small number of models in time for Cloud Next 2025. They encountered several challenges over the 2 months that followed, namely: vLLM V1 Integration: The team had to integrate into the new V1 code path, requiring a

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back and forth into the ChatGPT window. Then, in 2024, Cognition released their demo video of an autonomous coding agent—an AI that didn't just write code! It could run the code, Google error messages, add debug statements, and keep iterating until it solved the task at hand. The next day, OpenDevin—now OpenHands—was born. This was a massive leap forward in AI-driven development. Agents were able to take on the entire inner loop of software development, making incremental changes, testing them,

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ontribute 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 reasoning flows within a dual-mode single model The trend is clear: future inference

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ents worth revisiting, all based on your own data, and all editable. It’s a simple way to turn memory from passive storage into active signal. Download Le Chat on the App Store or Google Play to try memory on mobile. We’re continuing to improve how memory works: Trimming noise, speeding up recall, and making it easier to organize long-term. You’ll see updates soon that let you sort memories into categories, instantly forget something, and get clearer visibility into what memory was used and when

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nectors and Memories are available to all Le Chat users. Try out the new features at chat.mistral.ai , or by downloading the Le Chat mobile by Mistral AI app from the App Store or Google Play Store , for free; no credit card needed. Reach out to us to learn how Le Chat Enterprise can transform your mission-critical work. Webinar Join our webinar on September 9 to dive into Le Chat’s new MCP capabilities with the Mistral team. Learn key insights, ask your questions, and prepare to build cutting-e

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line mode and the ability to integrate ChatGPT, Claude, or Coursor directly within Notion Zoom’s Custom AI Companion lets you bring your own data and indexes into a work assistant Google’s Gemini Live adds visual guidance, useful for on-the-job how-to and documentation capture Adobe launches Acrobat Studio to turn PDFs into AI-powered workspaces Notion adds offline mode and AI integrations Summary: Notion’s August 19 update introduced full offline functionality, addressing a top enterprise reque

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g large documents, Ollama’s context length can be increased in the settings. Note: this will require more memory. Multimodal support Building on Ollama’s new multimodal engine, images can be sent to models that support them, such as Google DeepMind’s Gemma 3 model. Documentation writing Code files can be processed by models for understanding. Download Ollama’s new app today on macOS and Windows. For pure CLI versions of Ollama, standalone downloads are available on Ollama’s GitHub releases page.

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June 3, 2025 Three months ago, Stanford’s Hazy Research lab introduced Minions (ICML 2025), an open-source research project that connects local Ollama models (such as Google’s gemma3:4b) to frontier models in the cloud (such as GPT-4o). In Minions, the raw context stays local and can only be accessed by the local LLM. The frontier model orchestrates local LLMs and aggregates their outputs. By sending fewer tokens to the cloud, the protocol reduces cloud costs by 5x-30x while achieving 98% of fro

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ut the runtime of operations on GPUs to analyze bottlenecking factors during inference workloads on a single node. Much of the notation is borrowed from How To Scale Your Model by Google DeepMind. If you are already familiar with traditional inference math, skip to Distributed Inference. Accelerator Math Whenever we run an operation on an accelerator, there are two things that take time: Computation: How long it takes to compute all the floating-point operations (FLOPs) required for the operatio

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s As part of this mission, we actively collaborate with industry leaders to drive open, cloud-native solutions for LLM serving. "ByteDance has been a phenomenal partner in helping Google drive standardization of LLM serving in Kubernetes through Working Group Serving and contributing to the Gateway API Inference Extension. We are excited to continue collaborating on shared components that will enable AIBrix and large scale inference platforms" - Clayton Coleman, Distinguished Engineer and Infere

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mented the feature across multiple files and wrote tests – we were very impressed! There was a merge conflict which we manually resolved because Devin tends to struggle with that. Google A user of the Go Github client wished it propagated response objects even on HTTP errors. These small chores are annoying for human engineers because testing is often more effort than the fix itself. Devin required a few iterations to get it right and we manually cleaned up a few stray edits using the VS Code ex

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ce serving engine for large language models. vLLM has support for more than 40 types of open-source LLMs, a diverse set of hardware platforms (Nvidia GPU, AMD GPU, AWS Inferentia, Google TPU, Intel CPU, GPU, Gaudi, …) and all kinds of inference optimizations. Learn more about vLLM here. For the new Llama 3.1 series, vLLM can run the models with a full 128K context window. In order to support a large context window, vLLM automatically enables chunked prefill. Chunked prefill not only keeps memory

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ency Insights: Our ablation studies provide valuable insights into the algorithm's scalability and compute efficiency, paving the way for future improvements DiLoCo Recent work by Google DeepMind has introduced an approach that enables the training of language models on islands of devices that are poorly connected. This method allows for data parallel training on these different islands, requiring synchronization of pseudo gradients only every 500 steps. DiLoCo introduces an inner-outer optimiza

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