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[–] 45 points 1 year ago* (23 children)

Yeah, the places to use it are (1) boilerplate code that is so predictable a machine can do it, and (2) with a big pinch of salt for advice when a web search didn't give you what you need. In the second case, expect at best a half-right answer that's enough to get you thinking. You can't use it for anything sophisticated or critical. But you now have a bit more time to think that stuff through because the LLM cranked out some of the more tedious code.

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  • [–] 51 points 1 year ago (21 children)

    (1) boilerplate code that is so predictable a machine can do it

    The thing I hate most about it is that we should be putting effort into removing the need for boilerplate. Generating it with a non-deterministic 3rd party black box is insane.

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  • [–] 4 points 1 year ago* (1 child)

    I just use https://github.com/cookiecutter/cookiecutter and call it a day. No AI required. Probably saves me a good 4 hours in the beginning of each project.

    Almost all my projects have the same kind of setup nowadays. But thats just work. For personal projects, I use a subset-ish. Theres a custom Admin module that I use to make ALL classes into Django admin models and it takes one import, boom done.

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  • [–] 2 points 1 year ago (2 children)

    “Not worth inventing”? Do you have any idea how insanely expensive LLMs are to run? All for a problem whose solution is basically static text with a few replacements?

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  • [–] 4 points 1 year ago (3 children)

    All of that can be automated with tools built for the task. None of this is actually that hard to solve at all. We should automate away pain points instead of boiling the world in the hopes that a linguistic, stochastic model can just so happen to accurately predictively generate the tokens you want in order to save a few fucking hours.

    The hubris around this whole topic is astounding to me.

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  • [+] 0 points 1 year ago (2 children)
  • [–] 2 points 1 year ago (1 child)

    LLMs do not understand anything. There is no semantic understanding whatsoever. It is merely stochastic generation of tokens according to a probability distribution derived from linguistic correlations in its training data.

    Also, it is incredibly common for engineers at businesses to have their engineers write code to automate away boilerplate and otherwise inefficient processes. Nowhere did I say that automation must always be done via open source tooling (though that is certainly preferable when possible, of course).

    What do you think people and businesses were doing before all of this LLM insanity? Exactly what I'm describing. It's hardly novel or even interesting.

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  • [–] 3 points 1 year ago (3 children)

    Is it possible to use deterministic automation for some boilerplate instead of LLMs?

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