We have work to do as we move to a more open developer ecosystem. Today we shipped an XBOX Godot sample to GitHub, giving developers a simple starting point for building games with the GDK and other title services. developer.microsoft.com/en-u…

"This is a protectionist tale as old as time. And the justifications are just as tired: It's about quality! It's about attribution! It's about workers! Spare me. It's about you, your insecurities, and your privileges." world.hey.com/dhh/let-the-ag…

Introducing NVIDIA DGX Station for Windows, the world's most powerful deskside AI supercomputer with Windows powered by NVIDIA GB300. ✅ Run frontier AI models with up to 1 trillion parameters locally ✅ Build and run secure AI agents on Windows with NVIDIA OpenShell ✅ Built by @ASUS, @Dell, @GIGABYTE, @HP, @msigaming, and @Supermicro #NVIDIAGTC nvda.ws/3RIkjpc

Experience immersive graphics, smooth gameplay, and lightning-fast response, all while gaming stays cool on smartphones powered by Snapdragon | India's Most Trusted Chipset Brand*

New blackboard lecture w @ericjang11 He walks through how to build AlphaGo from scratch, but with modern AI tools. Sometimes you understand the future better by stepping backward. AlphaGo is still the cleanest worked example of the primitives of intelligence: search, learning from experience, and self-play. You have to go back to 2017 to get insight into how the more general AIs of the future might learn. Once he explained how AlphaGo works, it gave us the context to have a discussion about how RL works in LLMs and how it could work better – naive policy gradient RL has to figure out which of the 100k tokens in your trajectory actually got you the right answer, while AlphaGo’s MCTS suggests a strictly better action every single move, giving you a training target that sidesteps the credit assignment problem. The way humans learn is surely closer to the second. Eric also kickstarted an Autoresearch loop on his project. And it was very interesting to discuss which parts of AI research LLMs can already automate pretty well (implementing and running experiments, optimizing hyperparameters) and which they still struggle with (choosing the right question to investigate next, escaping research dead ends). Informative to all the recent discussion about when we should expect an intelligence explosion, and what it would look like from the inside. Timestamps: 0:00:00 – Basics of Go 0:08:06 – Monte Carlo Tree Search 0:31:53 – What the neural network does 1:00:22 – Self-play 1:25:27 – Alternative RL approaches 1:45:36 – Why doesn’t MCTS work for LLMs 2:00:58 – Off-policy training 2:11:51 – RL is even more information inefficient than you thought 2:22:05 – Automated AI researchers

When looking at deep learning profiles, one of the most obvious tells between a mediocre and great candidate is whether they list PyTorch or JAX.

The Jensen Huang episode. 0:00:00 – Is Nvidia’s biggest moat its grip on scarce supply chains? 0:16:25 – Will TPUs break Nvidia’s hold on AI compute? 0:41:06 – Why doesn’t Nvidia become a hyperscaler? 0:57:36 – Should we be selling AI chips to China? 1:35:06 – Why doesn’t Nvidia make multiple different chip architectures? Look up Dwarkesh Podcast on YouTube, Apple Podcasts, Spotify, etc. Enjoy!

@karpathy's post about LiteLLM supply chain attack post genuinely scared me. A routine pip install being able to exfiltrate secrets and CI/CD creds was enough to make me build secure-packages. It’s a supply chain security gate for packages, starting with PyPI: - point it at requirements.txt - fetch package source - review it - cache approved versions by hash - diff new releases against the last approved version - block risky updates in CI/CD github.com/kunaldeo/secure-p…

🙌 Andrej Karpathy’s lab has received the first DGX Station GB300 -- a Dell Pro Max with GB300. 💚 We can't wait to see what you’ll create @karpathy! 🔗 blogs.nvidia.com/blog/gtc-20… @DellTech

📢 Open-sourcing the Sarvam 30B and 105B models! Trained from scratch with all data, model research and inference optimisation done in-house, these models punch above their weight in most global benchmarks plus excel in Indian languages. Get the weights at Hugging Face and AIKosh. Thanks to the good folks at SGLang for day 0 support, vLLM support coming soon. Links, benchmark scores, examples, and more in our blog - sarvam.ai/blogs/sarvam-30b-1…