搜索“xAI”

AM and bandwidth pressure, delivering rollout efficiency significantly higher than W8A8 (FP8 weights, FP8 activations) This project is jointly completed by the SGLang RL team, InfiXAI team, Ant Group Asystem & AQ Infra team, slime team, and RadixArk team. Related features and recipes have been synced to the slime and Miles communities. We welcome everyone to try them out and contribute. We are also further challenging ourselves with MXFP8 and NVFP4. We also gratefully acknowledge Verda Cloud for

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密集型流程构建更可靠的自动化。 为何重要：这使 AI 从“问一个问题、得到一个答案”转向“描述你想要的结果，让模型围绕你现有的工具来编排工作”。 来源：VentureBeat – GPT‑5.2 来了：企业需要了解什么 AI 技术栈正在垂直整合，这改变了你的工具版图 摘要：近期一份对 AI 格局的分析认为，OpenAI、Anthropic、Google 和 xAI 正从“只做模型”转向垂直整合的技术栈，涵盖基础设施、模型、智能体和分发渠道，同时一个新的 Agentic AI Foundation 正试图防止碎片化。对高管而言，这意味着供应商选择不再仅仅关乎模型准确度，还关乎你希望自己的运营依托于哪个由智能体、连接器和合作伙伴应用构成的生态系统。 为何重要：你的 AI 战略正日益成为平台战略，因此围绕少数几个生态系统的整合将影响从采购到人才和集成选择的一切。 摘要：一篇 2025 年 12 月下旬的回顾指出，AI 系统已从静态内容生成器转变为能够使用工具、操作你的日历并跨软件协调以在无需持续提示的情况下完成任务的智能体。文章强调，人们日益期待在 2026 年，智能体将在后台越来越多地处理

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not only refine AI models but also consider how to design structures within UX that prompt users to think again. AI & Society researchers[7] point out that simple explainability (XAI) or transparency alone is not enough to solve this problem. In fact, poorly designed explanations themselves can become another heuristic, reinforcing automation bias by fostering trust while reducing doubt. Accordingly, this post summarizes the five human–AI collaboration design implications presented in this revi

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ng DeepSeek V3 as a base model) helped DeepSeek become one of the most popular open-weight models and a legit alternative to proprietary models such as the ones by OpenAI, Google, xAI, and Anthropic. So, what’s new since V3/R1? I am sure that the DeepSeek team has been super busy this year. However, there hasn’t been a major release in the last 10-11 months since DeepSeek R1. Personally, I think it’s reasonable to go 1 year for a major LLM release since it’s A LOT of work. However, I saw on vari

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果图表表明免费模型足以满足你使用 AI 的目的，那就选一个你最喜欢的用起来，不必再操心本指南中的其他内容。你基本上有九个左右的选择，因为只有少数几家公司能制造前沿模型。它们都提供一定的免费访问。最先进的四套 AI 系统是 Anthropic 的 Claude、Google 的 Gemini 、OpenAI 的 ChatGPT ，以及 Elon Musk 的 xAI 旗下的 Grok。然后是开放权重 AI 家族，它们几乎（但并非完全）同样出色：中国的 Deepseek 、Kimi 、Z 和 Qwen，以及法国的 Mistral。在几乎任何 AI 评级系统中，这些 AI 模型的变体合计占据了前 35 名。你使用的任何其他提供前沿 AI 的服务，从 Microsoft Copilot 到 Perplexity（两者都提供一定的免费使用），都是以这九种 AI 中的一个或多个为基础驱动的。 你应该如何在它们之间做选择？一些免费系统（如 Gemini 和 Perplexity）擅长网络搜索，而另一些则完全无法搜索网络。如果你想要免费的图像生成，最佳选择是 Gemini，ChatGPT 和 Grok

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al sources when needed. Various models and services reflecting this trend have emerged; representative examples include OpenAI’s ChatGPT (Deep Research), Google DeepMind’s Gemini, xAI’s Grok3, and LG AI Research’s ChatEXAONE (Deep Research). In this article, I introduce WebDancer from Alibaba’s Tongyi Lab, a paper that proposes enhancing long reasoning in LRMs through the use of web search tools[6]. The paper takes a holistic view of the agentic LRM domain, covering everything from key ideas to

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revenue fivefold year-to-date to reach $5B annualized. To keep that trajectory, Anthropic is betting hard on Scaling Laws. While Dario’s startup draws fewer headlines than OpenAI, xAI and Meta Superintelligence, it isn’t shy about investment. AWS has well over a gigawatt of datacenter capacity in final stages of construction for its anchor customer. AWS is building datacenters faster than it ever has in its entire history. And there’s much more on the horizon. Trainium vs GPUs While Amazon’s AI

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Just a year ago, Meta scrapped its decade-old “H”-shaped datacenter blueprint for a new AI-optimized design. Now in 2025, Zuck decided to re-invent the strategy again. Inspired by xAI’s unprecedented time-to-market, Meta is embracing a datacenter design that prioritizes speed above all else. They’re already building more of them! Traditional datacenter and real estate investors, still somewhat reeling from xAI’s Memphis site and time to market, will be shocked yet again. This design isn’t about

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tion support will be crucial for optimizing AI workloads. Come to the Bi-weekly Office Hours to learn more about LLM inference optimizations and vLLM! Acknowledgement Sangbin Cho (xAI) for the origination of some of the figures. Related Posts vLLM x AgentX: Optimizing for Real-World Agentic Serving How vLLM optimizes KV cache management, parallelism, scheduling, and P/D disaggregation for agentic workloads, validated on SemiAnalysis AgentX with up to 130K tokens per GPU-second and a 14.6x-106x s

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AI and infrastructure. Expanding the a16z Growth Fund and Platform The Machine Age Fund Rise of the Borderless Founder OpenRouter & Stripe: The Intelligence Network Cursor + SpaceXAI: the fastest iterating team wins This Essay is 10% AI Generated Can Agents Use a Computer Yet? We’ve Got the Data Expert News by a16z Views expressed in “posts” (including podcasts, videos, and social media) are those of the individual a16z personnel quoted therein and are not the views of a16z Capital Management,

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ects are combined into one objection function to optimize for. I borrow the name of this section from the DARPA project “Explainable Artificial Intelligence”. This Explainable AI (XAI) program aims to develop more interpretable models and to enable human to understand, appropriately trust, and effectively manage the emerging generation of artificially intelligent techniques. With the progress of the deep learning applications, people start worrying about that we may never know even if the model

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