Introducing OlmoEarth 🌍, state-of-the-art AI foundation models paired with ready-to-use open infrastructure to turn Earth data into clear, up-to-date insights within hours—not years.

OpenAI is absolutely hunting and grabbing its employees from the top universities: 1. Stanford — 422 2. UC Berkeley — 316 3. MIT — 230 4. Carnegie Mellon — 198 5. Harvard — 144 6. UCLA — 106 7. USC — 83 8. Columbia — 82 9. NYU — 81 10. Cornell — 79

🤔 Can AI optimize the systems it runs on? 🚀 Introducing FlashInfer-Bench, a workflow that makes AI systems self-improving with agents: - Standardized signature for LLM serving kernels - Implement kernels with your preferred language - Benchmark them against real-world serving workloads - Fastest kernels get day-0 integrated into production First-class integration with FlashInfer, SGLang (@lmsysorg ), and vLLM (@vllm_project ) at launch🙌 Blog post: flashinfer.ai/2025/10/21/fla… Leaderboard: bench.flashinfer.ai/

This is not a particularly good take and is indicative of a fundamental misunderstanding of what a top-tier technical college education is suppose to offer. Preparing to understand modern AI as a Harvard or Stanford undergrad is not about learning "prompt engineering", vibe coding, or building Slop Domain-Specific Wrapper Agent #1000, all of which can be picked up in a few days if not hours. To the contrary, the best way for a smart 18-22 year-old to understand AI is to develop a very solid intuition for undergraduate and graduate level probability, linear algebra, and classical ML. If you actually know how foundational RL topics like Q-learning work, you are 95% of the way there, and if you can't even learn that from Harvard or Stanford then this is probably a skill issue on your end. In @boazbaraktcs's excellent ML theory seminar in 2021, I don't think I wrote more than 200 lines of code cumulatively in the entire semester yet I learned an immense amount and credit that class for sparking my interest in modern AI. A year ago I couldn't coherently tell you what a transformer was, but it doesn't matter, because when you develop proper quantitative foundations in college you can figure it out in a couple of weeks. None of this stuff is really that complicated, people just like to pretend that it is.

✨ We were honored to deliver some of the very first NVIDIA DGX Sparks to AI Pioneer @ylecun and AI Researcher, @soumithchintala, from @Meta and @NYUniversity in NYC. “Every PhD student in AI should have one of these,” said Yann. We couldn’t agree more. We are anticipating great things from their visionary AI research. Learn more: nvda.ws/42EGKOM #SparkSomethingBig 💫

Fun tensor-puzzle in the wild in the recent anthropic blog post. Can anyone do it in 1 line? anthropic.com/engineering/a-…

there was ~1 guy at openai responsible for inference CUDA kernels let’s call him Bob, people would refer to his attention kernel as “the Bob kernel” it executed probably trillions of times a day on hundreds of thousands of GPUs one singular guy

Join NVIDIA at #PyTorch2025 📣 Discover how @PyTorch and NVIDIA accelerate research, computing, #datascience & #AI innovation. Connect live with experts, watch technical sessions, and see how we’re driving the future of AI. Register now 👉 nvda.ws/4n9Zg9H