.@river_ai_inc has a new vision for personal AI. We co-led River’s $1.1B funding round because we believe in @ibab's ambition to build open-weight models that individuals can train and improve continuously. Watch @htaneja in conversation with Igor. Chapters 02:22 — Co-Founding xAI 04:36 — An AI Experience You Own 05:59 — Betting Early on Open Weights 07:47 — How Do You Make Money on Free Models? 09:25 — Why the US Needs Its Own Open Models 11:27 — Enterprises Want to Control Their AI 13:36 — What Is Continual Learning? 16:56 — An API That Updates the Weights 19:17 — A Personal AI for Everyone 20:46 — The 20-Minute Training Run

Chips are the foundation of every AI experience. That's why understanding the hardware behind the software has never mattered more. When you ask a chatbot a question or generate an image, there's a physical chip somewhere doing the heavy lifting. For most of computing history, that was a CPU—the "brain" of a computer—great for general tasks needed to run software and operating systems. AI is more complex: for workloads called training and inference, AI needs to perform trillions of calculations in parallel. That's where AI accelerators come in. Purpose-built accelerators can deliver significantly better performance and efficiency than general-purpose chips. @awscloud Trainium chips are an example—purpose-built for AI training and inference. New chips for a new era. ⬇️

To coincide with the #A250#, Microsoft unveiled an AI experience that lets visitors have a conversation with U.S. President Theodore Roosevelt at the new @TRPresLibrary. And who better to put our avatar to the test than American historian @DorisKGoodwin? The result was a conversation across time. Powered by @Microsoft technology and built on Roosevelt’s writings, speeches, letters, and historical records, this experience shows how technology can make archives more accessible, scholarship more dynamic, and history more engaging for a new generation.

Qwen3.8-27B on 1500$ oh hardware. This is the realistic local AI experience for most people, for tons of reasons including complexity, costs, and over abundance of configs. Still, usable. GLM-4 was about 30 tok/s at peak The future is bright. Goodnight friends.

Ever notice how background noise disappears on a video call? 🎧 Behind that simple AI experience, multiple workloads like noise suppression, echo cancellation, and real-time transcription are running at once. Keeping every interaction responsive means moving data fast. As AI becomes part of everyday moments, memory bandwidth plays an increasingly important role in delivering the speed and responsiveness we rely on.