Haitang Hu

Running models @openai

[Weijie Su @weijie444]

Announcing that GPT-6 Astra has pushed the prime gap to 186, with Lean formalization!

I was 9 when I first heard the twin prime conjecture. Its elegance and Yitang Zhang’s legendary story have always stuck with me. A truly surreal night, being the first to see our model make progress, pushing 246 all the way down to 186, on a problem I’ve revered since I was a kid.

For me, it felt like witnessing a new era of intelligence being born, made possible by everyone at @OpenAI!

[Haitang Hu @hthu2017]

@_aidan_clark_ Low key known

[Mark Chen @markchen90]

Very excited to welcome @NoamShazeer to OpenAI as our new lead for architecture research! His work on transformers, MoE, and efficient decoding have shaped modern AI.

He’s extremely AGI-pilled and is super thoughtful about making it all go well. Welcome, Noam!

[Noam Shazeer @NoamShazeer]

I’m excited to share that I’ll be joining OpenAI and look forward to working with the exceptional team there.

It was a difficult decision to move on. I’m incredibly proud of the amazing team at Google and everything we’ve built together. It has been an honor and a pleasure to work with all of you.

[Haitang Hu @hthu2017]

@hyhieu226 Codex is that good :)

[Igor Babuschkin @ibab]

A common mistake that AI companies make nowadays is to not give their engineers enough time and mental calm to do their best work. Constant deadlines, pressure and distractions from daily AI news are poison for writing good code and systems that scale well. That’s why most AI APIs and products have reliability issues.

A good company culture that mixes excellence with focus and enough rest leads to faster and better results. The best example of how to do it well is the early Google culture from 1998 which resulted in one of the largest scale and most reliable services on the web in just a few short years. Founders should copy some of the strategies that Larry and Sergey used. They are still underrated IMO despite their huge reputation.

[Adam Paszke @apaszke]

Curious how to write SOTA performance Blackwell matmul kernels using MGPU? We just published a short step-by-step tutorial: https://t.co/XRVX34juEz

At each step, we show exactly what (small) changes are necessary to refine the kernel and the final kernel is just under 150 lines.

[Liam Fedus @LiamFedus]

Today, @ekindogus and I are excited to introduce @periodiclabs.

Our goal is to create an AI scientist.

Science works by conjecturing how the world might be, running experiments, and learning from the results.

Intelligence is necessary, but not sufficient. New knowledge is created when ideas are found to be consistent with reality. And so, at Periodic, we are building AI scientists and the autonomous laboratories for them to operate.

Until now, scientific AI advances have come from models trained on the internet. But despite its vastness — it’s still finite (estimates are ~10T text tokens where one English word may be 1-2 tokens). And in recent years the best frontier AI models have fully exhausted it.

Researchers seek better use of this data, but as any scientist knows: though re-reading a textbook may give new insights, they eventually need to try their idea to see if it holds.

Autonomous labs are central to our strategy. They provide huge amounts of high-quality data (each experiment can produce GBs of data!) that exists nowhere else. They generate valuable negative results which are seldom published. But most importantly, they give our AI scientists the tools to act.
We’re starting in the physical sciences.

Technological progress is limited by our ability to design the physical world.

We’re starting here because experiments have high signal-to-noise and are (relatively) fast, physical simulations effectively model many systems, but more broadly, physics is a verifiable environment. AI has progressed fastest in domains with data and verifiable results - for example, in math and code. Here, nature is the RL environment.

One of our goals is to discover superconductors that work at higher temperatures than today's materials. Significant advances could help us create next-generation transportation and build power grids with minimal losses. But this is just one example — if we can automate materials design, we have the potential to accelerate Moore’s Law, space travel, and nuclear fusion.

We’re also working to deploy our solutions with industry. As an example, we're helping a semiconductor manufacturer that is facing issues with heat dissipation on their chips. We’re training custom agents for their engineers and researchers to make sense of their experimental data in order to iterate faster.

Our founding team co-created ChatGPT, DeepMind’s GNoME, OpenAI’s Operator (now Agent), the neural attention mechanism, MatterGen; have scaled autonomous physics labs; and have contributed to some of the most important materials discoveries of the last decade. We’ve come together to scale up and reimagine how science is done.

We’re fortunate to be backed by investors who share our vision, including @a16z who led our $300M round, as well as @Felicis, DST Global, NVentures (NVIDIA’s venture capital arm), @Accel and individuals including @JeffBezos , @eladgil , @ericschmidt, and @JeffDean. Their support will help us grow our team, scale our labs, and develop the first generation of AI scientists.

[Haitang Hu @hthu2017]

I said this and I'll say it again. Noam is GOAT.

[Raj Dabre @prajdabre]

My wildest brag is that during my internship in Google in 2016, I showed some improvements in Google Translate for some low resource languages and Noam actually replied to my results thread calling my work "awesome".

I think that was my peak.

Fwiw, at that point of time, I had no idea who Noam was. I thought he was some rando.

[Haitang Hu @hthu2017]

@zhuohan123 Nice work!

[Sam Altman @sama]

gpt-oss is a big deal; it is a state-of-the-art open-weights reasoning model, with strong real-world performance comparable to o4-mini, that you can run locally on your own computer (or phone with the smaller size). We believe this is the best and most usable open model in the world.

We're excited to make this model, the result of billions of dollars of research, available to the world to get AI into the hands of the most people possible. We believe far more good than bad will come from it; for example, gpt-oss-120b performs about as well as o3 on challenging health issues. We have worked hard to mitigate the most serious safety issues, especially around biosecurity. gpt-oss models perform comparably to our frontier models on internal safety benchmarks.

We believe in individual empowerment. Although we believe most people will want to use a convenient service like ChatGPT, people should be able to directly control and modify their own AI when they need to, and the privacy benefits are obvious.

As part of this, we are quite hopeful that this release will enable new kinds of research and the creation of new kinds of products. We expect a meaningful uptick in the rate of innovation in our field, and for many more people to do important work than were able to before.

OpenAI’s mission is to ensure AGI that benefits all of humanity. To that end, we are excited for the world to be building on an open AI stack created in the United States, based on democratic values, available for free to all and for wide benefit.

[OpenAI @OpenAI]

Our open models are here.
Both of them.

https://t.co/9tFxefOXcg

[Haitang Hu @hthu2017]

@JeffDean @jeal0uspengu1n At least my Sanjay number is 1 :)

[Yi Shen @shen72_steven]

A new voice model made for free-tier users have been launched by my team. Please try it and let me know if you like it.

[Haitang Hu @hthu2017]

@_xjdr Why not tensorflow lol

[Haitang Hu @hthu2017]

@shen72_steven Pure sadness 😭

[Haitang Hu @hthu2017]

@selini0 No you can't. Noam is goat

[Haitang Hu @hthu2017]

@giffmana Lol same except I have 7 years of dota1.

[Haitang Hu @hthu2017]

@_arohan_ This is not an opinion it is a fact

[Jiahui Yu @jiahuiyu]

“Thinking with Images” has been one of our core bets in Perception since the earliest o-series launch. We quietly shipped o1 vision as a glimpse—and now o3 and o4-mini bring it to life with real polish. Huge shoutout to our amazing team members, especially:
- @mckbrando, for relentlessly improving infra & ML to lay the foundation (his o3/o4-mini livestreams are the best I’ve seen)
- @ZhangZhshuai, for pioneering our next-gen perception architecture
- @jilin_14, for baking in the strongest perception priors
- @bowenc0221, for initiating and showing early signs of life in thinking with images
- Jamie Kiros, for jumping in wherever work needed to get done
- @dmed256 & @hthu2017, for heroic infra efforts
-the Perception team, and everyone else at OpenAI who made it happen.

Multimodal is critical to OpenAI's path to AGI, and join us to push the next frontiers!