

He doesn’t really specify use cases beyond Sashiko (I believe that’s the project they’re discussing), which, if I understand correctly, uses an LLM to review kernel patches using a pre-set list of prompts. The output of those prompts presumably gets added to the PR for anyone who cares to read it.
These outputs sometimes have mistakes in them, so the maintainers must read them with a critical eye, but I think Linus is arguing here that it’s sufficiently… “good enough” that including them is helpful to the maintainers, that it catches enough real stuff without wasting too much time on fake stuff. There are always tradeoffs when adding a tool to the stack, and sufficiently rare misfires are probably worth the times it catches real problems that humans miss.













I think if we, as a community, really put our heads together, we could figure out how to define and make useful user-respecting tools that incorporate LLMs. There are a lot of hard problems, like the massive power consumption, and the ethical use of data, that I don’t really know how to solve.
I think a start would be to focus on making smaller, lower-complexity models that are built for purpose, rather than trying to make a jillion-parameter jack-of-all-trades trades model. I think it would also make sense to focus initially on areas where there are already large corpuses of freely available text, like all the writings in the public domain. But I don’t really have a good idea of what these tools would be used for, exactly, which is where I’m stuck.