There is not a single power plant left in Ukraine that has not been damaged by Russian attacks. Not one. But we still generate electricity. Thanks to our people. We have kept our system running thanks to physical protection of the facilities – and thanks to everyone who helps us with air defense. Just as important, Ukraine still has power because of our people – thousands of workers who serve millions. When I see our energy workers, our repair crews, rescue teams – I see something that is often missing in politics: true dedication – the ability to work at one hundred percent, to act immediately – in a real emergency. Not in a month, not in a year or two, but they save lives here, they save lives now. All these years. Many politicians could learn – and must learn – from ordinary rescuers, from ordinary repair crews, from ordinary electricians – how to act immediately.

GPT-5.3-Codex-Spark is now in research preview. You can just build things—faster.

GPT-5.2 Thinking evals

Wild NeurIPS moment: @darkproger from @OpenAI told me he uses our open-source macapptree as his go-to tool for parsing macOS accessibility 🤯 Made my day!

I am excited to be organizing the 8th scaling workshop at @NeurIPSConf this year! Dec 5-6 | 5-8pm PT | Hard Rock Hotel San Diego Co-organized by @cerebras, @Mila_Quebec, and @mbzuai Register: luma.com/gy51vuqd

periodic ❤️ open-source! for example, we’ve been collaborating with the @PyTorch team to build the highest MFU gpt-oss training implementation (includes thinky sinky flexattn) here’s a few SFT runs of gpt-oss-20b & 120b, where i get ~24% MFU for 20b and ~8% for 120b

Heads-up for developers trying gpt-oss: performance and correctness can vary a bit across providers and runtimes right now due to implementation differences. We’re working with inference providers to make sure gpt-oss performs at its best everywhere, and we’d love your feedback!

👀 we care a lot about correctness, ran many evals and stared at many tensors to compare them. numerics of vLLM on hopper should be solid and verified! if you run into any correctness issue on vLLM, we would love to know and debug them!