搜索“RAG”

accepts your insurance. Copilot Health connects to real‑time US provider directories so users can search for clinicians by specialty, location, languages spoken and insurance coverage. A truly helpful health companion needs to do more than provide general answers – it needs to draw on your health history and goals. Copilot Health gives you a dedicated space to bring all your personal health data together into a comprehensive profile including your: Activity levels, sleep patterns, vital signs,

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Introducing Groundsource: Turning news reports into data with Gemini March 12, 2026 Today, we’re introducing Groundsource, a new scalable methodology that leverages Gemini to transform unstructured global news into actionable, historical data. Our first, open-access Groundsource dataset for urban flash floods comprises 2.6 million records, paving the way for more accurate, life-saving forecasts. Natural disasters pose a continuous threat to global populations and economies. Every year, they affe

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, the Agent has focused on building apps. But real work is broader than that. You don’t just ship apps, you ship outcomes. And a huge amount of build time gets burned on workflow fragmentation: your app lives in one place, your deck in another, your data somewhere else, your launch assets scattered across tools, and every handoff costs time and context. Agent 4 is designed to allow you to seamlessly think, ideate, build, and iterate. With Agent 4, you can build:Web and mobile apps; Slide decks;

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stems that need both efficiency and capability. The 1M-token context is built for long-horizon agent work: agents can keep full conversation history and plan state in context, and RAG pipelines can supply large document sets in one shot. That reduces fragmentation and goal drift in multi-step workflows. Together, this makes Super a strong choice for orchestrating and running many agents on a single node—from code generation and debugging to research summarization, alert triage, and document anal

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th sparse outcomes, then train the critic For the small subset of segments where we can align production signals, we attach sparse outcome proxies—primarily code survival ( 4% coverage) and PR merge ( 6% coverage). We then train a small semi-supervised, multi-task critic that learns to predict rubric features (dense) and success from outcome proxies (sparse), turning previously unlabeled segments into usable training data. Does it work? Key evaluation takeaways We structure the evaluation into t

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and quickly route the request across 21 tools and 6 servers, all while running locally What it feels like: sub-second tool selection On this laptop configuration, LFM2-24B-A2B averaged 385 ms per tool-selection response while fitting in 14.5 GB of memory in this setup. That means no data leaves the device during inference, no outbound API call, no vendor subprocessor, no egress event to audit. It reached 80% accuracy on single-step tool selection across 100 prompts. It enables a simple, high-le

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de review bots, data ingestion tools, and more. The patterns in this codebase—conversation management, task execution, real-time updates—apply to any application that wants to leverage AI agents for software tasks. What's Next Vulnerability Fixer is a working application, but it's also a starting point. The codebase is MIT licensed, and we encourage teams to fork it and adapt it to their specific needs. Maybe you want to integrate with your existing security tooling, add support for specific vul

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web search is now built into the Anthropic compatibility layer. When a model needs current information, Ollama handles the search and returns results directly without any additional configuration. Subagents can leverage web search to research topics in parallel and come back with actionable results. Recommended cloud models minimax-m2.5:cloud; glm-5:cloud; kimi-k2.5:cloud Learn more ollama launch for more integrations; Claude Code with Ollama for basic setup; Web search API for standalone usage

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r gets free credits to upload documents, try Agent Mode, and experience Akshar’s capabilities end-to-end. If you’re an individual or organization interested in using Akshar in production, we encourage you to join our waitlist from within the platform. You can also try Akshar via our Document Intelligence APIs and experience Sarvam Vision on our playground. Curious what else we're building? Explore our APIs and start creating. Curious what else we're building? Explore our APIs and start creating.

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— and they've earned that spot at Zapier. Skills especially have spread like lightning, letting our experts create systems that turn individual productivity into company-wide leverage." - Anna Marie Clifton, Director of Product, AI & Agents at Zapier” ““Deloitte is making this significant investment in Anthropic’s AI platform because our approach to responsible AI is very aligned, and together we can reshape how enterprises operate over the next decade. Claude continues to be a leading choice f

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stays the same. Each node is Markovian: it depends only on the current state, not on how you got there. The difference between "we don't test agentic workflows" and "100% test coverage." Unit testing for agents. Not "run the whole pipeline and squint at the output." Actual unit testing. Full observability is an emergent property, not a feature. Every state transition is a persisted record. One correlation ID traces through the entire execution tree. This enables distributed tracing: audit trails

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effective in image-heavy scenarios (e.g., multi-image inputs), where the visual encoding process is the primary computational bottleneck. For instance, in these scenarios, we leverage EPD to significantly reduce request TTFT under load—achieving approximately 6–8× lower latency compared to the colocation approach at 1 QPS. Conversely, for image-light scenarios with few images, EPD may be less efficient or even counterproductive. This is because the additional network latency incurred by transmi

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s) of the key outcomes and decisions from this transcript. Detailed summary Provide a detailed summary of the transcript, covering all major topics, discussions, and outcomes in paragraph form. Action items List the specific action items that were assigned during this meeting. Include who is responsible for each item when mentioned. Key decisions List the key decisions that were made during this meeting. Focus on concrete decisions and outcomes. Participants List the participants mentioned in th

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ng Conversations Collaborate on Projects 0:00 / 0:00 0:00 / 0:00 Share conversations securely with your teammates. Links are only accessible to the people you share them with. Leverage your company knowledge with Grok Grok has the ability to access your company knowledge and tools directly with Apps . Starting with Google Drive, your team is guaranteed: Permission-awareness by design. Grok respects your existing Google Drive permissions. If a file isn't shared with you in Drive, you won't see it

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Contents TL;DR We are excited to introduce the design and implementation of the Diffusion Large Language Model (dLLM) framework within SGLang. By leveraging the existing Chunked-Prefill mechanism, our system achieves: Seamless integration: Built into the SGLang ecosystem without core architectural changes; Inherited performance: The framework benefits from the existing inference optimization; Maximum flexibility: Full flexibility for users to define and customize diffusion decoding algorithms Ba

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Amp has a new agent and it specializes in code review. In the VS Code extension, you can use this agent by going to the review panel. Start by dragging the selection of changes you want to review. Sometimes, you want to review a single commit; other times you want to review all outstanding changes on your branch: Amp will pre-scan the diff and recommend an order in which to review the files. It will also provide a summary of the changes in each file and the changeset overall: This addresses a ke

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ng support to their entire editor lineup, including IntelliJ IDEA, PyCharm, WebStorm, and more; Neovim: Through community plugins like CodeCompanion.nvim, neovim users can now leverage ACP to bring AI agents into their workflow; Obsidian: The knowledge management and note-taking app Obsidian supports ACP, enabling AI-assisted writing and content generation workflows You can find the full list of ACP-compatible clients and agents at agentclientprotocol.com/overview/clients. OpenHands provides bui

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s, including Chinese, English, German, Italian, Portuguese, Spanish, Japanese, Korean, French, and Russian. On the MiniMax TTS multilingual test set, the model achieves a lower average word error rate (WER) than MiniMax, ElevenLabs, and GPT-4o-Audio-Preview. It also supports dialect synthesis for more dialects, including Mandarin, Hokkien, Wu, Cantonese, Sichuanese, Beijing, Nanjing, Tianjin, and Shaanxi dialects, authentically reproducing local accents and linguistic nuances. More Natural and H

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just 1KB of results. The efficiency gains are substantial: Token savings : By keeping intermediate results out of Claude's context, PTC dramatically reduces token consumption. Average usage dropped from 43,588 to 27,297 tokens, a 37% reduction on complex research tasks. Reduced latency : Each API round-trip requires model inference (hundreds of milliseconds to seconds). When Claude orchestrates 20+ tool calls in a single code block, you eliminate 19+ inference passes. The API handles tool execu

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the latest Qwen DeepResearch How does inspiration die? It usually doesn’t die from “not being good enough”, but from being “too much trouble”. When a thought flashes, it’s still fragile and unverified. After a brief moment of excitement, our brains immediately begin to assess the “cost”: “How many hours will I need to spend researching to verify this?” “The quality of information online is patchy. Where should I even start, and who should I trust?” “This problem is too complex. Is it really wor

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