11–20 of 291 posts
Maybe I’m getting really old, and I know we live in changing times but the fact this works at all is insane, as in black magic insane. I assume I’ll get used to it, but my mental model of what’s possible hasn’t been fully rewired yet.
> GPT-6 Astra and GPT-5.6 Sol are absolutely insane Perhaps before Astra was nerfed. It is now the dumbest model right after Gemini. Sol still holds up.
This is super great. The biggest pain point of Asahi Linux is how it doesn't have GPU acceleration on M3 and newer, especially now that M6 is out! However, Asahi Linux has a strictly no-AI policy [1]. So this great work can't be upstreamed. I expect to see a bunch of AI-assisted forks that get things working smoothly on newer hardware to dominate as most people just care about getting stuff working, while only a hand…
It's extremely impressive that they were able to make a working driver so quickly. I think this is one of the best use cases for LLMs. You don't need someone to spend years reverse engineering undocumented hardware anymore. It will interesting to see how good the driver the LLMs came up with is, and whether it can be upstreamed into the Linux kernel.
It's extremely impressive that they were able to make a working driver so quickly. I think this is one of the best use cases for LLMs. You don't need someone to spend years reverse engineering undocumented hardware anymore. It will interesting to see how good the driver the LLMs came up with is, and whether it can be upstreamed into the Linux kernel.
I mean you can get good stuff out of it if you know what you are doing and guide it, but if you just say “here’s a regression suite. Write an implementation that passes” you will get something that works for a while but ages quickly and will need to be thrown away.
It's extremely impressive that they were able to make a working driver so quickly. I think this is one of the best use cases for LLMs. You don't need someone to spend years reverse engineering undocumented hardware anymore. It will interesting to see how good the driver the LLMs came up with is, and whether it can be upstreamed into the Linux kernel.
Might be one of the only use cases where LLMs are necessary to succeed. Can't imagine the pain of reverse engineering a technology of this scale and "secrecy".
I am not by any means a copyright lawyer, so can anyone enlighten me on if it is a good idea for upstream projects to accept such kind of code from an ex-Apple employee, especially since Apple is currently suing OpenAI.
- During my time at Apple I never saw any of the macOS source code, at all, even for userspace components. I had not even heard of things like SPTM.
- I have not worked there since June 2025
I don't believe that there's any risk due to my former Apple employment. As another example, WINE does not ban all former Microsoft employees, they just ban anyone who has ever looked at the Windows source code. If I felt there was even a chance that my employment at Apple may have exposed me to relevant internal secrets, I would refrain from contributing to community projects.
There's also ongoing issues around Codex for this sort of thing, given Apple is suing OpenAI over stolen trade secrets[2]. And nobody knows whether training data in other models is similarly tainted.
[1]: https://codyho.dev/documents/cody_ho_resume.pdf
[2]: https://www.theguardian.com/technology/2026/jul/10/apple-sue...