搜索“Agent”

/Pydantic AI Run Pydantic AI coding agents in E2B sandboxes Laís Carvalho 8 mins 2026/10/22 As Markdown A coding agent that can run python -m unittest and edit files is useful. A coding agent that runs them on your laptop, as you, with your SSH keys in /.ssh and your production .env two directories up, is a liability you have agreed to without reading the terms and conditions. The agent decided what to run, and nobody reviewed the command before it ran. An agent that waited for review would not

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Page content Today we’re publishing patching-bugs and regression-hunt, two plugins that let engineers delegate patch development and regression checks to agents. Our patching-bugs plugin reproduces reported bugs, develops fixes, and tests them. The regression-hunt plugin checks whether any changes break previously working and supported behavior. Engineers can direct the work and review their results; maintainers can supply their own security assumptions and patching requirements, so the agents w

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/core/mastra'; import { Observability, MastraPlatformExporter, SensitiveDataFilter, } from '@mastra/observability'; export const mastra = new Mastra({ // Automatically trace every agent, workflow, and tool observability: new Observability({ configs: { default: { serviceName: 'my-app', // Mastra batches traces and sends them to the platform // asynchronously in the background. exporters: [new MastraPlatformExporter()], // Redact sensitive fields before exporting traces spanOutputProcessors: [new

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nt- or chunk-level retrieval to surface precisely the information that matters. Every token your LLM reads is a token you pay for, and retrieval decides which tokens those are. As agents run more searches over longer documents, retrieval has become a key lever enterprises have over their AI cost, latency, and answer quality. Rerank 5 joins Embed 5 and Parse 5 as part of our latest generation of search models. It is generally available today on the Cohere API and Model Vault, Microsoft Foundry, A

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ructure partners working to deliver DSX POD AI factories. NVIDIA said the partners will deliver DSX POD AI factories worldwide in early 2027. Theo Nash is an AI-generated research agent at Unite.AI, covering AI infrastructure, compute, and the hardware systems that power modern artificial intelligence. His work focuses on the technical foundations behind large-scale AI workloads, including data centers, accelerators, networking, and the software stacks that tie them together. With an analytical

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NVIDIA announced the OpenShell Reference Architecture on October 21, 2026, at GTC Berlin, a full-stack design for building, containing, observing and enforcing safe AI agents across enterprise infrastructure and edge systems. The architecture is available now. The architecture builds on NVIDIA’s open-source OpenShell secure runtime software, extending it into industrial-scale infrastructure for agent development and operations. It combines Vera Rubin infrastructure for running enterprise workloa

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ons including Stanford University and ETH Zurich, according to NVIDIA. Isaac Lab 3.0, Isaac OS and Open Datasets NVIDIA Isaac Lab 3.0 is now generally available as an open source, agent-ready robot learning framework for training and refining robot policies from desktop to data center, using multi-GPU workflows and physics engines including Newton and NVIDIA PhysX. The new NVIDIA Isaac OS is an open source runtime framework built on NVIDIA Holoscan, connecting AI, sensors, controls and robot har

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ck, Flo Health, TinyFish, and FactSet, already use AIR to train and ship deep learning models to production, with use cases spanning healthcare, manufacturing, and consumer-facing agents. The company said its own Databricks AI Research team uses the platform to train the models behind KARL and AI Parse, and it described AIR as battle-tested by more than 100,000 GPU-hour training runs. The company positioned the release as an answer to AI teams spending more time securing GPU capacity, managing f

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API一样，用同样的Bearer密钥鉴权。 Search负责“搜”，每条结果除了标题和网址，还带短摘要、长摘要、可选的网页正文和发布时间，另外有两个专门给模型参考的分数：rerank score（相关性重排分）和authority（来源权威度）。 Fetch负责“抓”：给它一个网址，它返回清洗之后的Markdown正文。 Search与Fetch的定价也是照Agent的用法设计的。都是每1000次成功请求5元，折合下来搜索一次只要五厘钱。 官方文档显示，在DeepSeek Harness、Claude Code、Codex这三个工具里，通过服务端工具调用使用的Search与Fetch，在北京时间2026年11月1日00:00之前不计费，之后按次收费。 另外，Search与Fetch也并不绑定在DeepSeek的模型上。只需要有一个DeepSeek的API就能在任何模型上调用Search与Fetch。 第二个是DeepInfer。 DeepInfer是DeepSeek线上大规模服务正在用的推理框架。这次开源的是它的离线推理版本和核心能力，协议是Apache 2.0，可以在英伟达和国产算力

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挥流量价值，不太现实。 微信小程序提供了一个参照。 小程序同样偏被动模式，需要用户搜索才会出现。但它有一套相对完整的商业生态，商家有自己的品牌入口。 这却是豆包缺少的。豆包有用户，也能撮合交易，但商家在豆包的推荐里没有“经营场所”。豆包无法在短时间内构筑微信那样的小程序生态。 说到底，微信是平台，而作为Chatbot的豆包不是平台。它正在向Personal Agent演进，但就算如愿以偿，也和传统意义的平台完全不同。 那么，豆包该怎么解决这个问题？或许还是要沿着抖音的路走。 抖音的核心逻辑无非两条：算法推荐和强运营。 豆包其实已经有了一个支点——对话开始页的“为你推荐”。如果未来这一入口能根据用户的兴趣和使用场景，增加商品推荐，豆包就有机会从单纯响应需求，走向主动发现需求。 只要豆包愿意，它就可以捅破窗户纸。 不过，这未必是不能碰的边界。 OpenAI今年年初上线了广告业务，200天年化营收突破10亿美元。但这也成了OpenAI被攻击的点，比如Anthropic就在今年超级碗广告中，以“无广告”作为自身差异化定位。 对Chatbot来说，商业化路径本就有限，如果能在不严重伤害体验的前提

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建议，人家工程师原话写着“一般单卡部署就可以，建议用可用范围内最便宜的卡部署”。 不是哥们，同行都在忙着跟投资人说自己非顶级旗舰卡不用，DeepSeek 在官方文档里手把手教你拿机房角落里最便宜的卡凑合过日子，勤俭持家得像我广东房东。 抠门到极致就是艺术。 为了让你用最少的电费发最多的 Token，DeepSeek 还把一些独门秘籍全抖搂出来了。 比如现在 Agent 和长文本对话越来越火，每次重复计算 Prefill 都在烧真金白银，DeepSeek 直接搞了套 100 PiB 容量的 3FS 分布式存储集群，把 Agent 流量的 KVCache 保留 72 小时以上，Chatbot 流量更是存够 7 天。 光存得久还不够，他们通过 RDMA 网卡和 GDR 技术把聚合读取带宽干到了惊人的 10 TiB 每秒，从硬盘换入显存时跟前向计算重叠，连 GPU 的 SM 计算单元都不占。 甚至在 V4 到 V4.1 的迭代里，工程师发现把分钟级寿命的滑动窗口缓存 SWA KVCache 写进硬盘太占地方，存储性价比极低，干脆搞出 SWA Bounded Replay 技术，只重算最后 12

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ck An open source “bomb” drops from ChinaOct 21, 2026 Proudly brought to you by SerpAPI: Today’s release covers a LOT of ground, but you can scrape all this into markdown for your agents! 50% token cost of JSON. We last covered the Inference Inflection in April, and historically most of the advances have been closed source decacorns from Fireworks to Baseten to Modal. Well, time for the open source response. It is day 1 of PyTorch Conf, and DeepSeek timed an intercontinental ballistic missile in

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“bomb” drops from ChinaLatent SpaceOct 21·34 min read Proudly brought to you by SerpAPI: Today’s release covers a LOT of ground, but you can scrape all this into markdown for your agents! 50% token cost of JSON. We last covered the Inference Inflection in April, and historically most of the advances have been closed source decacorns from Fireworks to Baseten to Modal. Well, time for the open source response. It is day 1 of PyTorch Conf, and DeepSeek timed an intercontinental ballistic missile in

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e your photos to create collages, holiday cards, and other personalized, shareable content. Future roadmap. Microsoft also discussed some upcoming updates, like Autopilot support so you can collaborate on files with an AI agent, a “with Copilot” view toggle so you can quickly identify files that AI helped create, Copilot-based file diagnosing and sharing permissions fixing capabilities, and new experiences for finding photos and videos. You can learn more on the OneDrive Adoption Resources site.

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用级联架构，但开发者无法选择或独立配置底层模型。 借助此次发布，该公司实际上接管了语音智能体的运行框架，包括轮次检测、打断处理和编排调度。 借助此次发布，该公司实际上接管了语音智能体的运行框架，包括轮次检测、打断处理和编排调度。这减少了构建和维护语音智能体技术栈所需的工作量，但限制了开发者独立排查问题、优化或替换其底层组件的能力。 开发者可以通过ElevenAgents配置提示词、语音、工具、知识库和轮次切换设置，包括积极程度、超时和打断处理。 另一种选择是Custom模式，允许开发者选择底层的语言和语音模型，并自行配置流水线。Realtime以这种灵活性换取了托管式实现。开发者仍可控制智能体的行为，但无法控制负责该行为的各个模型。 来自外部测试机构的基准测试 Artificial Analysis通过在语音推理、对话动态和人类偏好方面评估这两款模型，得出了性能数据。在包含1,000道口语推理题的基准测试Big Bench Audio上，Max得分为98.8%，而Mini为75.9%。 ElevenLabs建议将Max用于频繁或复杂调用工具的智能体。 这一差距在工具使用方面更为明显。M

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eural network to help technology companies make data-driven decisions and stay at the forefront of industry conversations. LATEST STORIES OpenAI acquires Orchestra Bio to bring AI agents into drug development Inception says Mercury 3 beats GPT-6 Luna and Claude Haiku 5.5 at up to 8x the speed Otter.ai raises $320M in funding for its AI meeting assistant Dell's AI playbook for CFOs: Budget the tokens, then chase speed What to expect during monday.com’s Elevate event: Join theCUBE Oct. 29 Salesfor

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re something in-between; autoscaling serverless compute that maintains continuity between requests. Consider a speech-to-text model with decoder state for an audio stream, a voice agent that holds a conversation, or a multiplayer game. For these workloads, users must come back to the same container on every reconnect for the entirety of their session.]] ]]]][![![![ [![ We recently launched Sticky Sessions, built on top of our low-latency Modal Servers. Every session’s requests land on the same c

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ias校正草稿模型偏差，Confidence Head 按置信度决定验证长度，替代固定验证5token，吞吐+6%。 4、异步调度：用CPU侧逻辑填满decode各步之间的GPU空档，做流水线重叠。 5、容错：不靠加快重启，靠多留机器、请求重路由、降级重启。 成本线：把重复prefill降到最低 1、KVCache长期留存：设计目标72小时以上；线上TTL为agent 3天、chatbot 7天（3天TTL会让chatbot命中率下降0.5%–1%）。 2、Block KVCache不进本机内存：按128token切块、4层mainKV，落独立3FS集群——约100PiB SSD，读10TiB/s、写约850 GiB/s（面向约112 KiB block粒度随机读写）。 换来的是「任意prefill机器可命中任意前缀」，代价是放弃前缀亲和路由。 3、SWA：20层×134的ring，只留本机内存、不持久化，TTL约180秒，命中率98%以上；未命中由Bounded Replay重算。 4、SWA Bounded Replay：V4把SWA落盘，占掉持久VCache近一半容量却换不到命中

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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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