Dan Zheng

We've trained our best coding model yet, and it's also incredibly token-efficient. Give Grok 4.5 a try!

My favorite thing about this model is how light/fast it feels for the level of capability. We focused on extracting as much intelligence-per-latency as we could. Many more bigruns to run and RL recipes to scale.

Grok 4.5 is our most useful model yet! It’s Opus-class in capabilities while being fast and cheap. Also very excited about the models we have on the factory line with @SpaceXAI and @cursor_ai teams. Please try Grok 4.5 on your most challenging problems and share critical feedback: we aim to rapidly improve!

Announcing Grok 4.5, our first model trained specifically for coding and agents. It was trained with Cursor and offers frontier intelligence at leading speeds and cost efficiency. x.ai/news/grok-4-5

@Guodzh Thank you for your leadership Guodong, best of luck!!

Last day at xAI. Wild journey past three years but excited about next chapter. Thanks all for the love and support yesterday. So many friends made along the way and I will miss you all!

@jiayi_pirate It was great working together Jiayi, best of luck!!

Today is my last day at xAI. It's been an intense, memorable year building in the frontier — I'm grateful to have worked with and learned from such talented and supportive colleagues. The journey itself is the reward. So long, and thanks for all the fish! 🐬

@Yuhu_ai_ It was a pleasure working with you Tony, best of luck and thank you!

I resigned from xAI today. This company - and the family we became - will stay with me forever. I will deeply miss the people, the warrooms, and all those battles we have fought together. It's time for my next chapter. It is an era with full possibilities: a small team armed with AIs can move mountains and redefine what's possible. Thank you to the entire xAI family. Onward. 🚀 And to Elon @elonmusk - thank you for believing in the mission and for the ride of a lifetime.

Official PyTorch code is released! github.com/apple/ml-tract

Happy to announce that we open sourced our implementation at github.com/apple/ml-tract I am looking forward to see applications of it! Feel free to reach out if you have questions. If you find issues in the code, we welcome issues on the repository.

New paper TRACT - Faster diffusion model sampling - Single-step diffusion SotA for CIFAR10 and ImageNet64 with L2 loss without architecture changes - Up to 2.4x FID improvement arxiv.org/abs/2303.04248