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-...
replyLLMs are great at translation. That's why language translators were among the first professionals losing their jobs to AI.
replyI'm sure they are shit at translating poetry and artistic prose.
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replyanecdotally 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
replyIt's foundations all the way down. The difference is people have reality as our reinforcement learning environment.
replyThat's a really great point, and you can often start with documenting current behavior with tests and then carefully going through to figure out what bugs you've just enshrined in test cases…
reply“Ideal use case” such a wild take. The “10%” (assuming you mean all the issues pointed out in the article) are the things that take the most time and effort to do (or fix now), which you can’t do without understanding and fixing the “90%” of “translation”.
replyYou simply pointed out that challenges remain in what the other poster said is otherwise the ideal use case, which was his point. What is the ideal use case in your opinion, since you disagree?
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