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Comment by pdntspa | Hacker News Reader

I have been deploying useful code from LLMs right and left over the last several months. They are a significant force accelerator for programmers if you know how to prompt them well.

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We’ll see if this is a good idea when we start having millions of lines of LLM-written legacy code. My experience maintaining such code so far has been very bad: accidentally quadratic algorithms; subtly wrong code that looks right; and un-idiomatic use of programming language features.

ah i see so you're saying that LLM-written code is already showing signs of being a maintenance nightmare, and that's a reason to be skeptical about its adoption. But isn't that just a classic case of 'we've always done it this way' thinking?

legacy code is a problem regardless of who wrote it. Humans have been writing suboptimal, hard-to-maintain code for decades. At least with LLMs, we have the opportunity to design and implement better coding standards and review processes from the start.

let's be real, most of the code written by humans is not exactly a paragon of elegance and maintainability either. I've seen my fair share of 'accidentally quadratic algorithms' and 'subtly wrong code that looks right' written by humans. At least with LLMs, we can identify and address these issues more systematically.

As for 'un-idiomatic use of programming language features', isn't that just a matter of training the LLM on a more diverse set of coding styles and idioms? It's not like humans have a monopoly on good coding practices.

So, instead of throwing up our hands, why not try to address these issues head-on and see if we can create a better future for software development?

Maybe it will work out, but I think we’ll regret this experiment because it’s the wrong sort of “force accelerator”: writing tons of code that should be abstracted rather than just dumped out literally has always caused the worst messes I’ve seen.

Yes, same way that the image model outputs have already permeated the blogosphere and pushed out some artists, the other models will all bury us under a pile of auto-generated code.

We will yearn for the pre-GPT years at some point, like we yearn for the internet of the late 90s/early 2000s. Not for a while though. We're going through the early phases of GPT today, so it hasn't been taken over by the traditional power players yet.

When the tool is statistical word vomit based, it will never move beyond cool bar trick levels.

LLMs will allow us to write code faster and create applications and systems faster.

Which is how we ended up here, which I guess is tolerable, where a webpage with a bit of styling and a table uses up 200MB of RAM.

Honestly the code it's been giving me has been fairly cromulent. I don't believe in premature optimization and it is perfect for getting features out quick and then I mold it to what it needs to be.

In a way it's not surprising that people are getting vastly different results out of LLMs. People have different skill levels when it comes to using even Google. An LLM has a vastly bigger input space.

same...but have you considered the broader implications of relying on LLMs to generate code? It's not just about being a 'force accelerator' for individual programmers, but also about the potential impact on the industry as a whole.

If LLMs can generate high-quality code with minimal human input, what does that mean for the wages and job security of programmers? Will companies start to rely more heavily on AI-generated code, and less on human developers? It's not hard to imagine a future where LLMs are used to drive down programming costs, and human developers are relegated to maintenance and debugging work.

I'm not saying that's necessarily a bad thing, but it's definitely something that needs to be considered. As someone who's enthusiastic about the potential of code gen this O1 reasoning capability is going to make big changes.

do you think you'll be willing to take a pay cut when your employer realizes they can get similar results from a machine in a few seconds?

As a society we're not solving for programmer salaries but for general welfare which is basically code for "cheaper goods and services".

My boss is holding a figurative gun to my head to use this stuff. His performance targets necessitate the use of it. It is what it is.

Yeah, but this, in itself, is triggered by a hype wave. These come and go. So we can't really judge the long term impact from inside the wave.

Your job won't be taken by AI, it will be taken by someone wielding AI.

What's a sample prompt that you've used? Every time I've tried to use one for programming, they invent APIs that don't exist (but sound like they might) or fail to produce something that does what it says it does.

Have you tried Claude 3.5 Sonnet?

Use Python or JS. The models definitely don't seem to perform as well on less hyper prevalent languages.

Even then it is hit and miss. If you are doing something that is also copy/paste-able out of a StackOverflow comment, you're apt to be fine, but as soon as you are doing anything slightly less common... Good luck.

Yeah, fair. It's good for short snippets and ways of approaching the problem but not great at execution.

It's like infinitely tailored blog posts, for me at least.

True. It can be good at giving you pointers towards approaching the problem, even if the result is flawed, for slightly less common problems. But as you slide even father towards esotericism, there is no hope. It won't even get you in the right direction. Unfortunately – as that is where it would be most useful.

No matter the prompt, there's a significant difference between how it handles common problems in popular languages (python, JS) versus esoteric algorithms in niche languages or tools.

I had a funny one a while back (granted this was probably ChatGPT 3.5) where I was trying to figure out what payload would get AWS CloudFormation to fix an authentication problem between 2 services and ChatGPT confidently proposed adding some OAuth querystring parameters to the AWS API endpoint.

I just ask it for what I want in very specific detail, stating the language and frameworks in use. I keep the ideas self-contained -- for example if I need something for the frontend I will ask it to make me a webcomponent. Asking it to not make assumptions and ask questions on ambiguities is also very helpful.

It tends to fall apart on bigger asks with larger context. Breaking your task into discrete subtasks works well.

I think that's just the same as using an autocomplete efficiently, though. I tend to like them for Search, but not for anything i have to "prompt correctly" because i feel like i can type fast enough that i'm not too worried about auto-completing.

With that said i'm not one of those "It's just a parrot!" people. It is, definitely just a parrot atm.. however i'm not convinced we're not parrots as well. Notably i'm not convinced that that complexity won't be sufficient to walk talk and act like intelligence. I'm not convinced that intelligence is different than complexity. I'm not an expert though, so this is just some dudes stupid opinion.

I suspect if LLMs can prove to have duck-intelligence (ie duck typing but for intelligence) then it'll only be achieved in volumes much larger than we imagine. We'll continue to refine and reduce how much volume is necessary, but nevertheless i expect complexity to be the real barrier.

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