#xAI

Update on my experience Grok Build. The team at @xai made an update, while I was sleeping, and I can already see the difference. Yesterday, I couldn't get Grok Build to run for more than a minute or two. I literally wake up this morning, see that the team made an update, and now everything I'm doing in Grok Build is running, and running, and running, until it finishes. I'm pretty sure nobody at xAI slept last night, and the end result is Grok Build is 10x better than it was a day ago. What happens over the next week is going to be wild. Now I'd say the beta has gone from a 6/10 to an 8/10, in a freakin' day 😅

你现在可以在 @NousResearch Hermes Agent 中使用你的 @grok 订阅。 http://x.ai/news/grok-hermes

The latest 𝕏 algorithm has been published to GitHub https://github.com/xai-org/x-algorithm

Grok Build is amazing. The early beta just dropped for SuperGrok Heavy users and the first real feedback from developers is overwhelmingly positive. People are saying it already feels 10x ahead of other coding agents. It handles full agentic workflows natively, runs multiple agents in parallel, does live refactoring, and has a surprisingly polished terminal UI with both vim mode and mouse support. It’s fast, manages huge context cleanly, and actually feels like you’re working with a real autonomous coding partner instead of just getting suggestions. This is the kind of serious high quality tool xAI keeps shipping. If the beta keeps this momentum, Grok Build is going to be a real great tool for power users. Try it out right now at https://x.ai/cli if you have SuperGrok Heavy subscription.

Grok Build is a fully interactive CLI, which means you can actually use your mouse to click. No flickers. Especially useful as I find myself running 5+ agents at a time and jumping between plans.

An early beta of Grok Build, an agentic CLI for coding, building apps, and automating workflows is now available for SuperGrok Heavy subscribers. Through this early beta, we will improve the model and product based on your feedback. Try it at http://x.ai/cli

Announcing agentic performance benchmarking for Speech to Speech models on Artificial Analysis. We use 𝜏-Voice to measure tool calling and customer interaction voice agent capabilities in realistic customer service scenarios Even the strongest Speech to Speech (S2S) models today resolve only about half of realistic customer service scenarios end-to-end - a meaningful gap relative to frontier text-based agents on the same tasks. Voice channels introduce significant complexity: challenging accents, background noise, and packet loss, all while requiring fast responses, consistency across long multi-turn conversations, and reliable tool use. Performance also varies considerably by audio condition: in clean audio some models perform notably better, but realistic conditions continue to pose a challenge. Conversation duration also varies meaningfully across models, with implications for both customer experience and operational cost. About 𝜏-Voice: Our Agentic Performance benchmark is based on 𝜏-Voice (Ray, Dhandhania, Barres & Narasimhan, 2026), which extends 𝜏²-bench into the voice modality to evaluate S2S models on realistic customer service tasks. It measures multi-turn instruction following, support of a simulated customer through a complete interaction, and tool use against simulated customer service systems. The simulated user combines an LLM-driven decision model with realistic audio synthesis: diverse accents, background noise, and packet loss modelled on real network conditions. This complements our Big Bench Audio benchmark measuring intelligence and Conversational Dynamics (Full Duplex Bench subset) benchmark measuring conversational naturalness. Scores are the average of three independent pass@1 trials. We evaluate under realistic audio conditions using the 𝜏²-bench base task split across three domains: ➤ Airline (50 scenarios): e.g., changing a flight, rebooking under policy constraints ➤ Retail (114 scenarios): e.g., disputing a charge, processing a return ➤ Telecom (114 scenarios): e.g., resolving a billing issue, troubleshooting a service problem Task success is determined by deterministic checks against expected actions and final database state, consistent with the 𝜏²-bench evaluator. Key results: xAI's Grok Voice Think Fast 1.0 is the clear leader at 52.1%, averaging 5.6 minutes per conversation, the second-longest overall. OpenAI's GPT-Realtime-2 (High) (39.8%, 3.0 min) and GPT-Realtime-1.5 (38.8%, 4.8 min) follow, with Gemini 3.1 Flash Live Preview - High close behind at 37.7% (3.8 min). Speech to Speech is a fast evolving modality and we expect movement in rankings as we continue to add new models with these capabilities, and model robustness improves. Congratulations @xAI @elonmusk! See below for further detail ⬇️

Grok Voice Think Fast 1.0 ranks #1 on the Artificial Analysis τ-Voice benchmark for real-world agentic customer service resolution Absolutely outperforming GPT-Realtime-2 (High) and Gemini 3.1 Flash by a huge margin That's a massive 12%+ lead over OpenAI's best model that just released a few days ago Grok is running real-time background reasoning without the latency penalty, which is why it is already handling live Starlink phone operations autonomously at scale

Everything is Computer, but Computer isn't Everything!

让 Grok 获取你的邮件、改进你的幻灯片、整理你的日历或组织你的 Notion。 今天就在 iOS、Android 和 http://grok.com 上的所有套餐中为 Grok 添加你的连接器。