Yeah, I think most success stories in AI seem to come from ports, or from projects where a deliberate architecture has been laid by humans who understand it, and AI is sprinkling features on top. Adding features and drivers to a Linux distro seems like one of those places where AI is uniquely well positioned to bring value, because the foundation has already been laid. I wrote a post the other day on how agentic code still needs this kind of architectural foundation to not end up in a tangled mess. It might interest someone: https://ljtn.github.io/epiq/blog/the-soft-fabric-of-agentic-...

anecdotally this has been a huge help for my projects. I'm seeing AI one-shot porting from one library/framework to another with very minimal handholding. It used to be that these decisions needed to be thought through a lot because they were hard to change later on

LLMs are great at translation. That's why language translators were among the first professionals losing their jobs to AI.

It's foundations all the way down. The difference is people have reality as our reinforcement learning environment.