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Comment by gwoolhurme | Hacker News Reader

That has been a bit of my experience with LLMs in general. If you know what you are doing before hand it's not too much of a boost, in fact it can be a bit frustrating to wrestle with it to do what you want when you can do it faster. I can see where it is amazing tooling though. Especially for a new project where you don't really have much of an idea or need a working demo.

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But if you don’t know what you are doing, you have to worry about its hallucinations slipping pass you.

Really good point too, though for the most part I like to think if I audit the code well enough it will work? That said the other day I used it for some string manipulation based on powers of 10, and the code looked correct as per my audit of it. However in reality when I ran the code it was really bad. You might say that I my self would produce buggy code the first time around, that is true... but I also find the LLM equally frustrating because for me (and only me, a sample size of 1 calm down people)? I find I can iterate faster over an idea by hand vs using an LLM for a lot of things.

eh, there is always somebody who inevitably frets on about these hallucinations, but I find that most of the people who are super worried about these aren't actually using the LLMs the way that they're intended.

For example, I needed a GitHub action that would automatically check out build and deploy a node project to a VPS. I also needed it to pipe out the secrets into a local environment variable (.env) and copy that over to the VPS as well.

GPT4 got it in one try. Its this kind of DevOps boring mundane crap that GPT excels at eliminating. Even though I had very little familiarity with the syntax for GH action yaml, I was able to read it and reason out that it was correct.

Could I have sifted through the documentation, or waited through 50 stack overflow pages? Sure, but this was a hell of a lot more convenient.

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