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Comment by jorams | Hacker News Reader

I agree with this. There are cases where it produces good results, but there are also cases where it produces bs, and it's not always obvious. I find it to work fine for cases where I know what I want but could use a starting point, but it often invents or misunderstands all kinds of things.

The most frustrating situations are those where it invents a function that would miraculously do what's necessary, I tell it that function does not exist, it apologizes, shuffles the code around a bit and invents a different function, etc. It's the most annoying kind of debugging there is.

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Another very obvious thing it does when it comes to code is take the most common misconceptions & anti-patterns used within the programming community & repeats them in an environment where there's no-one to comment. People have critiqued Stack Overflow for having so many "wrong" answers with green checkmarks, but at least those threads have surrounding context & discussion.

A case in point: I asked ChatGPT to give me some code for password complexity validation. It gave me perfectly working code that took a password and validated it against X metrics. Obviously the metrics are garbage, but the code works, and what inexperienced developer would be any the wiser. The only way to get ChatGPT to generate something "correct" there would be to tell it algorithmically what you want (e.g. "give me a function measuring information entropy of inputs", etc.) - you could ask it 50 times for a password validator: every one may execute successfully & produce a desired UI output for a web designer, but be effectively nonsense.

> there are also cases where it produces bs, and it's not always obvious

Particularly annoying because I wind up risking not actually saving time because it’s producing subtle bugs that I wouldn’t have written myself.

So, you save yourself the time of thought and research at the risk of going down new and mysterious rabbit holes

For me the trick to avoiding this trap is to limit usage to small areas of code, test frequently, and know its limits. I love using copilot/GPT for boilerplate stuff.

> There are cases where it produces good results, but there are also cases where it produces bs, and it's not always obvious.

Pessimistically, this is the medium term role I see for a lot of devs. Less actual development, more assembly of pieces and being good enough at cleaning up generated code.

If an LLM can get you even 25% there most of the time, that's a massive disruption of this industry.

I mean, especially in webdev we've been heading in that direction for a while now anyway. So much of the job is already just wiring up different npm packages and APIs that someone else has written. I've read substantially similar comments back in the mid 2010s about how people weren't learning the fundamentals and just pulling things like left pad off of a repo. That did cause a disruption in how people coded by abstraction away many of the problems and making the job more about integrating different things together.

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