After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI? I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev • 20-200x faster • 40-400x cheaper (w/ output tokens free) • Frontier composable intelligence optimized for decisions AFAICT the shortest path to AI-based economic revolution

Moving AI agents from prototype to production faster than ever? Don't let rapid iteration hide critical runtime behavior. Tune in to What's New Wednesdays: Vibe-code, trace, and deploy your agents to explore: • Production agent observability workflows in W&B Weave • Evaluating and self-improving agent loops at scale • Real-world takeaways from CoreWeave Hacks Register here: utm.io/ur8ga

This is GPT-6 Astra. Anything you can do on a computer, Astra can do for you. Fast.

DeepSeek-V4-Pro-0813 is live on CoreWeave Serverless Inference. 1.6T parameters. 1M context. Built for long-horizon agent work, and priced for it: cache reads run $0.044/M for all the context your agent re-sends every step. Start here: utm.io/usz4F

WeaveHacks is now @CoreWeave Hacks! Our latest agentic hackathon is September 12 and 13 in SF. Build agent loops that catch their own mistakes. $20k+ in prizes, a robot dog for Best Loop Design, and F1 tickets in play. Apply here: lu.ma/coreweavehacks

Resume terminal sessions in desktop app!

Introducing PhoneLLM, an open model for voice agents. GPT 5.6 Terra performance on typical voice agent tasks at 1/3 the latency and 1/18 the cost. For voice agents, we need models that are both very low latency and very good at tool calling and instruction following. There's a trade-off here, and we often have to compromise on either latency or capability when building voice agents. With PhoneLLM (and the training and data stack that made this model possible) we're fixing this problem. For the last couple of years, most of the effort in frontier model development has gone towards leveraging test-time compute. Which is awesome! Models of all shapes and sizes are available that perform really, really well ... if you have "thinking" turned on for your model. But if you need your agent to respond at voice conversation speed, you can't use thinking models. PhoneLLM is a full-weights fine-tune of NVIDIA Nemotron Nano 30B. We trained on a wide range of real-world telephone and customer support use cases. The training focused on taking the excellent Nano 30B base capabilities and teaching the model to do typical voice agent tasks with thinking disabled. The results are really good: accurate tool calling and concise, on-topic responses in long conversations. And fast: TTFAT measured server-side is <100ms if you run PhoneLLM on a lightly loaded B200. :-) But seriously, when we characterize model latency, we do it with full, end-to-end, batched request simulations using real Pipecat voice agent pipelines. You can serve more than 80 concurrent agents on a single B200 with P95 end-to-end TTFAT <600ms. Including network overhead. That's an LLM cost-per-minute around $0.0025. (1/4 of a cent.) At a latency lower than any third-party API offers today. More details about this model, including weights on @huggingface, how to spin it up with one click on Modal, and a starter project repo you can clone, are in the thread ...