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[–] 10 points 3 months ago (1 child)

Issue triage, code exploration, extracting information from disparate sources, first pass code review. There are loads of use cases that it's potentially useful.

For me it's a lot better at extracting the requirements for a CPU feature from a 10,000 page architecture reference manual than I am.

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  • [–] 2 points 3 months ago

    Quite; I just set a (locally hosted) LLM off writing the tickets for implementing all the opcodes in a simple device emulator, based on grovelling through datasheets and documentation. Whether the tickets get implemented by an AI or a human, it's a timesaver having the AI do it, and the tickets will be better written than I would have done.

    Everyone railing against this also overlooks the reality of professional software development: professional software is developed 5% by skilled, trained Software Engineers, and 95% by code monkeys who shotgun copypasta from Stack Overflow until it works. Even if we extremely generously assume that the hardcore "never use AI" Lemmy brigade are in the 5% (and not, more likely the 95% drowning in their own Dunning Kruger,) the "but AIs produce unreadable code and make mistakes" threat isn't putting off anyone who's ever actually had to hire a significantly sized development team.

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