What's your way of showing you care? In Seoul, it can sound like "Have you eaten?" In Moscow, like "Put your hat on, it's minus twenty out." In Tbilisi, like a toast to the guests. We built Simba 3.5 to connect people in 48 languages, spoken like a local. Out today. Sound on.

AI agents need fast tool calls and security controls they can’t rewrite. NVIDIA OpenShell Reference Architecture combines the compute to run agents at scale with runtime controls that govern their access to tools and data. See how the hardware and software work together: nvda.ws/4BiJBfY

Mercury 3 is live: the most capable diffusion LLM released to date. It outperforms GPT-6 Luna and Claude Haiku 5.5 across a range of benchmarks while running at over 1,000 tokens per second. More than 40x the size of Mercury 1, 3x more intelligent across standard benchmarks, and just as fast, with a 1M-token context window. Available today on the Inception API, @baseten, and @OpenRouter .

Scaling past a trillion parameters requires inference engineers to solve 3 issues: - Width: Routing between hundreds of sparse experts - Depth: Mixing global and local linear attention - Length: Processing 1M-token sequences I asked Ali and Bryce to draw it up. They delivered.

AI-generated fake documents grew 500% last year, and humans can no longer tell the difference That's why we’re launching Extend Verify, the first document fraud detection API built for agents Verify catches the fastest growing types of document fraud, including: - fully AI-generated documents - localized AI edits (down to a single modified character) - patched text, overlays, and in-painting from Photoshop, PDF editors, and coding agents - digital tampering after a document was signed - and many more In our internal evals, Verify detected over 96% of AI-generated and tampered documents. Verify is available today: extend.ai/verify/request-acc…

public benchmarks rarely tell you how a model does in your repo and the full story. so how do you decide which model is optimal for each engineering task? next week, molly and I will walk through the framework we use with development teams to evaluate open models for coding agents, powering the fastest growing ai applications in the world. we'll cover which models to shortlist, how to run a successful pilot without changing your harness, and how to measure cost per successful task on your own merged PRs. we hope you can join us! baseten.co/resources/webinar…

Fast is better than slow. We've got two new PhoneLLM models optimized for conversational voice AI. PhoneLLM Mini (30B) — When you need the lowest possible latency (sub-100ms TTFAT), smallest model size, and lowest cost. Good multi-turn conversation behavior and tool-calling accuracy in a model that can run >100 concurrent agents on a B200 and fits on an RTX 5090. PhoneLLM Max (120B) — A "microthinking" model for complex conversational tasks. Sub-200ms TTFAT. SOTA task completion performance at 1/10th the latency and cost of models with similar capabilities. Both of these models are full-weights fine-tunes of @NVIDIAAI Nemotron models. Mini is based on Nemotron 3 Nano. Max is based on the brand new Nemotron 3.5 Super VL. Both of these models are available as open weights on HuggingFace, and from four inference partners: AWS, Baseten, Cloudflare, and Modal.