Chong Han

[NVIDIA @nvidia]

AI compute is going to orbit. 🚀

@SpaceX’s Starmind AI1 satellite compute payload is powered by NVIDIA Vera Rubin NVL72, bringing AI factory compute closer to the stars.

The next chapter of AI infrastructure boldly goes where no AI compute has gone before.

[Elon Musk @elonmusk]

This is exactly right. Source code is on the verge of becoming like assembly.

The next step is getting rid of “source code” entirely and just making an efficient binary directly with AI.

[OpenAI @OpenAI]

GPT-Live can listen while it speaks.

To make that feel natural at ChatGPT scale, we rebuilt the voice stack from client to model.

This new architecture keeps audio flowing continuously, so deeper reasoning and tool use don't interrupt the conversation.

[OpenAI @OpenAI]

An internal version of our next major model produced 10 new results on long-standing open problems in mathematics and theoretical computer science, using roughly $2,000 worth of tokens at GPT-5.6 Sol API rates.

[Chong Han @chonghan90]

Not about the model. I am looking forward for the automation it is presenting.

[Andrej Karpathy @karpathy]

We're starting to leave the territory where you'd test an LLM by e.g. "create an svg of pelican on a bicycle". As one idea to generalize it, I was interested what Opus 5 would do if I gave it the first paragraph of the Lord of the Rings, a 1M token budget (~$10) and asked for three js render of it. Opus went off for ~2 hours and wrote 5500 lines of code that (procedurally) rendered the story. It's kind of janky but fun. But it's a bit mindboggling that the LLM has to place and orchestrate various polygon assets in (x,y,z) coordinates and write code that animates it all, and that it even does anything at all.

I also like this kind of examples because no one in their right mind would ever spend the time to write something this custom but LLMs have all the stamina and patience in the world, so it's an example where we go from "no one would ever do this" to "sure, why not, it's ~free". There might be a lot more. But I'm excited about creating hyper custom worlds that you can imagine dropping players into, e.g. here to participate in the LoTR story as a spectator NPC, or one of the characters, or etc. Something like an ephemeral GTA of X on demand.

Last thought is that the domain of worlds/games exposes a weakness in LLMs: they can't easily audit their work because they aren't able to efficiently and natively perceive videos or play games within them. Here, Opus 5 had to very slowly and painstakingly take screenshots at different points, and it messed up a few times and created a bunch of jank. An example of raw capability (multimodal, gameplay) that I think is still quite lacking.

[Noam Brown @polynoamial]

An internal version of Astra, @OpenAI’s next major model family, solved 10 major open problems in mathematics, quantum complexity, and theoretical computer science.

We believe it will be a major step for scientific reasoning. https://t.co/iP6cyheZ7i

[Bilgehan Avser @bavser]

Introducing Applied Electrodynamics.

What would the world look like if we could see beyond visible light, across the entire electromagnetic spectrum?

The way waves interact with matter, at every wavelength, encodes far more than just shape and color. It reveals internal structure, temperature, chemical composition, and more: a whole layer of the physical world that's invisible to us today.

That's what we're building at Applied Electrodynamics: the infrastructure to fully perceive the physical world.

WaveSight is the first piece of the puzzle. It brings radio frequencies into human perception, revealing the internal structure of objects behind opaque layers. Designed for construction crews, industrial inspectors, and security personnel.

See through walls: https://t.co/Fae9wad4oW

[OpenAI @OpenAI]

After deployment, we applied GPT-5.6 Sol to advance the frontier of efficiency by making itself more efficient to run.

The results:
- 20% lower serving costs from production GPU kernel improvements.
- 15%+ better token-generation efficiency from improved speculative decoding.

[NVIDIA @nvidia]

AI security advances when the industry builds in the open, together.

We're introducing the Open Secure AI Alliance with industry leaders to develop new techniques and tools to safeguard software and agents.

By sharing models, tooling and research in the open, we can broaden the community of defenders.

Learn more about the founding members’ contributions: https://t.co/A16oqxs5Ty

[Andrej Karpathy @karpathy]

One pattern I find useful for working with LLMs is a nice long ramble session. Sometimes the LLM needs more bits to understand what you're trying to achieve, but you're too lazy to type them. In these cases I like to lean back, switch to /voice and just ramble for like 10 minutes, total mess, anything goes, full stream of consciousness. Sometimes I declare it up top, something like "switching to speech recognition sorry for any typos...". Sometimes I turn it into a small interview of a few turns. But I find that the LLMs are somehow very good at reconstructing long incoherent rambles and often their echo of your own tangle of thoughts comes out quite a bit cleaner than what you started with. The result is that you improve the mind meld and have to correct things less from that point on.

[Carlos E. Perez @IntuitMachine]

https://t.co/CHEk211f7Y

[Chong Han @chonghan90]

I still learning prompt. 🤯

[Peter Steinberger 🦞 @steipete]

Are we still talking loops or did we shift to graphs yet?

[Cerebras @cerebras]

https://t.co/FvKy1StDpQ

[Chong Han @chonghan90]

Open model and start to compete frontier.

[Chong Han @chonghan90]

@elonmusk It should an international body apply to country that creating or using AI.

[Chong Han @chonghan90]

Good read.

[Demis Hassabis @demishassabis]

https://t.co/PTeDiv1b6L

[Chong Han @chonghan90]

@adityaag What Chinese say: “有舍才有得”.

[Michael Truell @mntruell]

https://t.co/jZKOk8RAsV

[Chong Han @chonghan90]

@elonmusk @grok fly me to the moon.