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plits tasks between cloud models for research and reasoning and a local model for working with private files. Local-first takes things further by running both the AI model and the agent directing the task on the Mac. Perplexity says planning, reasoning, and file processing can all happen on device, with files only reaching the cloud when the user approves. With permission, Computer can work with local files and supported Mac apps, including Notes, Messages, Mail, and Numbers. Perplexity Computer

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ear-equal acceptance. Offline only: no KV cache store, P/D disaggregation or load balancer. Asif RazzaqWebsite | + postsBio ⮌ Asif RazzaqMiniMax Just Launched M3.1 Flash: 428B MoE Agentic Model at $0.15/M Input Asif RazzaqNVIDIA Open Sources Run:ai Under Apache 2.0 for Kubernetes GPU Orchestration RELATED ARTICLESMORE FROM AUTHOR Exa 3.0 Search API Tops Perplexity, Brave and Parallel on 8 Benchmarks Meet Drex Max: Nace AI’s Multimodal Decision Model for Computer-Use Agents with a Score 67.59 Min

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f RazzaqWebsite | + postsBio ⮌ Asif RazzaqDeepSeek Open-Sources DeepInfer, the Inference Framework Behind DeepSeek-V4.1-Flash Asif RazzaqMiniMax Just Launched M3.1 Flash: 428B MoE Agentic Model at $0.15/M Input Asif RazzaqNVIDIA Open Sources Run:ai Under Apache 2.0 for Kubernetes GPU Orchestration RELATED ARTICLESMORE FROM AUTHOR DeepSeek Open-Sources DeepInfer, the Inference Framework Behind DeepSeek-V4.1-Flash Exa 3.0 Search API Tops Perplexity, Brave and Parallel on 8 Benchmarks Meet Drex Max

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AI research with a paper trail, and it’s open source. @aipoch ai has released Open-Science, a free Apache-2.0 desktop workbench that turns coding agents into research assistants for scientists. The problem is familiar to anyone using AI for analysis. You get a clean figure, then someone asks how it was made: which input, which code, which branch of the chat where an assumption changed. That context usually sits scattered across chats, notebooks and folders. Open-Science keeps it in one place. Yo

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ropic的Messages。 deepseek-recipe把它们统一转成同一种对话格式，再按模型版本拼好prompt、编码成Token。模型输出的Token，也由它翻译回各家格式，流式返回。 它还顺手堵了一个口子。DeepSeek的模型靠一批特殊Token标记对话结构，比如用户发言从哪开始、思考到哪结束、什么时候调用工具。 96%的输入，不用再算第二遍 Agent每调用一次工具，都要把系统提示、工具说明，连同之前每一轮的回复和工具结果，整个重新发给模型。 这里面绝大部分，上一轮早就算过了。 DeepSeek的做法是，把算过的KV缓存存下来，下一轮只算新增的那一截。前提是前缀必须从第一个Token起完全一致，中间重复的内容复用不了。 有多夸张呢？同一张卡跑Prefill，不算缓存命中，每秒处理6万个Token。 算上缓存命中，每秒150万个，整整差了25倍。 这些缓存全放在DeepSeek自家的3FS存储集群里，容量约100PiB，清一色固态硬盘。Agent流量的缓存留3天，聊天流量留7天。 聊天留得更久，是因为少数缓存会被反复读取很多次，一旦丢了，整体命中率掉得厉害。按DeepSe

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说。购机用户附赠 STEPX Token Plan，连续 12 个月、每月百亿 Token 额度，用于探索和开发智能体应用。阶跃还推出 Amoo 成长计划：前 1 万名任务达标用户将成为 “Builder One”，获得 “星辰船票”，未来五年内凭STEPX Neo可0元置换任意一款阶跃新款终端。 从 Meta、OpenAI 到谷歌、苹果，Personal Agent 正成为巨头押注的新方向。阶跃没有选择为现有手机叠加 AI 助手，而是从模型、操作系统到硬件重构一部手机，用 “模软硬一体” 回答 “智能体应该长在哪” 的问题。3999 元能否撬动用户习惯，生态能否持续生长，智能体终端的争夺战才刚刚开始。 此内容为集微网原创，著作权归集微网所有，爱集微，爱原创 相关推荐 摩尔线程率先完成阶跃星辰Step 5 Preview大模型适配 四部门组织30余家车企座谈 加强汽车产品研发测试验证管理 10-16 22:56 德明利：预计前三季度净利润86.5亿元-89.5亿元 同比扭亏为盈 10-16 22:52 长鑫科技：预计前三季度净利润1310亿-1325亿元，同比扭亏为盈 懂车帝辟谣“删视

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s-aware flow control with controlled load shedding, including a probabilistic admitter, program-aware fairness, and turn-priority scoring, so latency-sensitive requests and bursty agent workloads can share a fleet without starving one another under overload. Flow-control configuration is consolidated under a single flowControl field. For zero-downtime operations, the router gains high availability with hot standby and safe rolling updates, and a DisaggregatedSet controller manages prefill and de

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Torch Conference North America in San Jose today and tomorrow, focuses on managing competing workloads, maintaining service during upgrades and improving hardware efficiency as AI agents generate growing volumes of model requests. Built for Kubernetes, llm-d coordinates model serving across computing infrastructure. IBM, its Red Hat subsidiary and Google LLC are its lead maintainers. They co-founded the project last year with CoreWeave Inc. and Nvidia Corp. Other contributors include Advanced Mi

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We open sourced a really fast HTML artifact builder. It's powered by libfx, making the agent durable. Building the low-level plumbing for v0 to survive server crashes, flaky LLMs, function limits was a huge pain. Now it's easy, free, and OSS: https://t.co/aUP1vKeIsP. PS: I built this floating container in a few minutes… it's delightful, and it's just HTML!

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ahead of release: an upgraded auto mode, which balances search quality and speed, and a new lite mode, optimized for lower cost and latency. Both were evaluated using our Stirrup agent harness and GPT-5.6 Luna (medium) as the agent model Key benchmarking results for Exa Search with Dynamic Highlights: ➤ Ties for 1 on the Artificial Analysis Search Index: Exa Search (auto, Dynamic Highlights) scores 80, level with Perplexity Search (medium) in the 1 spot, while Exa Search (lite, Dynamic Highligh

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BranchesTagsOpen more actions menu Latest commit History3,569 Commits3,569 Commits Folders and files NameNameLast commit messageLast commit date .agents/skills.agents/skills .buildkite.buildkite .claude/skills.claude/skills .gitattributes.gitattributes .gitignore.gitignore .markdownlint.yaml.markdownlint.yaml .pre-commit-config.yaml.pre-commit-config.yaml .readthedocs.yml.readthedocs.yml CONTRIBUTING.mdCONTRIBUTING.md LICENSELICENSE README.mdREADME.md collect env.pycollect env.py mkdocs.ymlmkdoc

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es are both listed as "coming soon" by Kling AI. Alongside the main model, Kling AI is shipping a lighter Flash variant. Kling 4.0 Flash is designed for faster generation at lower cost, though it tops out at 720p and 20 seconds of video. The creation interface has been redesigned, and Kling 4.0 can now be accessed through MCP and CLI, which makes it possible to build automated workflows with AI agents. Pricing for the Kling 4.0 API hasn't been announced yet, with support listed as "coming soon."

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Add as a preferredsource on Google Exa has released Exa 3.0, an upgrade to its web search API for AI agents. The Exa 3.0 search API ships a larger embedding model and a cheaper search type, Exa Lite. Dynamic Highlights is now generally available. Exa claims that it leads Perplexity, Brave and Parallel on 8 benchmarks. Those benchmarks were run by Exa, so you can treat them as self-reported. For developers, the practical change is cost: Lite costs $4 per 1,000 requests. TL;DR Size: Exa tracks ove

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se case JSON Schema output format Control the model’s response structure Data extraction, report generation, API responses Strict tool use Validate tool parameters against schemas Agentic workflows, function calling, multi-step automation What this means for you Each response is a valid JSON, so you no longer encounter JSON.parse() errors or parsing exceptions. Responses are also type safe: field types are enforced, and required fields are always present. Because outputs always conform to your s

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了数千亿级索引、覆盖13个行业的157个权威信源，腾讯还用大模型重新改造了搜索的理解、排序和底层架构，试图让搜索结果更准确、更可信，同时降低响应时间和调用成本： 做大模型时代的「可信外脑」。 在下午雷科技参加的一场媒体沟通会上，腾讯云副总裁、元宝搜索研发负责人马晋表示，搜索正在经历一次重要变化：「搜索面对的用户从1.0时代的人，变成了3.0时代的智能体。」当Agent开始自己拆解问题、反复检索，搜索引擎也不再只是给人提供链接，而要成为AI完成任务的基础能力。 既然大家都在争夺Agent的搜索需求，元宝搜索这次升级究竟带来了什么不同？对于企业和开发者来说，又为什么要选择元宝搜索？AI开始自己搜索，搜索引擎也要跟着变 过去用搜索引擎，是输入关键词，得到一串网页链接，再由自己判断哪些信息有用。大模型时代，AI直接阅读搜索结果、整理答案，省去了逐条查阅的麻烦。但当Agent开始帮人完成更复杂的任务，搜索又有了新的变化。 比如，让Agent制定一份旅行计划，它可能需要先了解目的地的天气、景点和交通，再查询机票、酒店和门票价格，甚至根据预算反复调整行程。整个过程中，搜索不再是一次性的问答，而是贯穿

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LLM stack for TPU delivers strong cost efficiency and high throughput on TPU systems publicly available to the community. Built on top of that, Google recently submitted the first AgentX performance results for Qwen 3.5 on Ironwood TPUs. AgentX captures the four elements of an agentic workload – multi-turn, long context, high prefix reuse, and sub-agent bursts. TPU has been able to demonstrate that its upcoming vLLM-TorchTPU stack is able to successfully handle agentic workloads. With just 4 chi

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on (dependency chain viewer). Neuron Explorer also emits a Parquet output with per-op and per-engine metrics, so the same data behind the UI can be queried directly or consumed by agents for automated bottleneck analysis. For programmatic access, users can work with the built-in torch.profiler tools. For example, the key averages table lets users drill down into specific ops and use the sort by argument to surface the device-side cost of each op, which is useful for identifying hotspots. Followi

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ed on supplied text or images. Read a document: Supply a photo of a page and the fields to evaluate for field-by-field decisions with probabilities. Tool-call moderation: Check an agent’s tool call before it runs. Benchmarks Results from Cloudflare’s announcement, scored on evaluations from the Jev Decision Index: BFCL · case exact 98.47 98.76 95.75 96.52 94.51 38.13 ToolRet · nDCG@10 69.19 66.43 65.28 61.21 64.26 12.69 API-Bank · accuracy 91.93 93.11 88.19 83.66 56.30 11.41 Home appliances · ca

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dev runs on a 5-node Kind cluster with 8 simulated GPUs. The free edition excludes NVIDIA enterprise support. Asif RazzaqWebsite | + postsBio ⮌ Asif RazzaqMiniMax Just Launched M3.1 Flash: 428B MoE Agentic Model at $0.15/M Input RELATED ARTICLESMORE FROM AUTHOR Exa 3.0 Search API Tops Perplexity, Brave and Parallel on 8 Benchmarks Meet Drex Max: Nace AI’s Multimodal Decision Model for Computer-Use Agents with a Score 67.59 MiniMax Just Launched M3.1 Flash: 428B MoE Agentic Model at $0.15/M Input

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w-level control over the graph or want the whole pipeline to run end to end on the accelerator. The LiteRT Tensor API also serves as an ideal programmatic foundation for AI coding agents, providing structured primitives to generate, rewire, and tune model graphs automatically. In the rest of this post, we’ll dive into the design and walk through practical code examples showing how to author models from scratch. Real-world showcase: Segment Anything on Web To show the Tensor API in practice, we b

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