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[–] 25 points 1 month ago (1 child)

I understand the spirit of what he means, but I still don't know how an LLM can be used efficiently as a precise tool like this. If I can describe a problem narrowly enough to guide an LLM to give a useful output, I've already solved it myself and may as well just type it out.

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  • [–] 6 points 1 month ago* (last edited 1 month ago)

    Exactly same thinking. I can usually type it out faster than AI (when taking into account latency, context building, prompt writing, prompt fixing, AI hallucination review) if I know what exactly needs to be done.

    When you're out of your depth, it shines in providing beautifully confident and a botanical garden of a code piece that wil most likely be broken and break in new and exciting ways. Great for incompetent sycophantically-challenged managers that forgot how to code, or never actually had any experience.

    Large-scale code transformations, taking into account the bigger picture of the repository are most likely hallucination free (not generative, just transformative, as per the actual LLM model) and a very exciting use-case.

    Also it's currently a very nice pragmatic tool for checking for any mistakes, because it can connect the larger context of the repository quickly to the diff. Terrible if done by a manager without understanding of copilot and workflow, but a great tool if done through claude and cli as a pre-commit step with just quick checklists. Makes plenty of mistakes, but allows to catch your own big mistakes nicely quickly.

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