Hacker News Reader

> the clean modeled structure we want to see is for humans to maintain and extend, but the AI doesn't need this.

This does not match my experience. I do a lot of AI-assisted coding at this point, and what I've seen is that when the AI is asked to extend or modify existing code, it does a much better job on clean, well-structured and well-abstracted code.

I think the reason is simple, and tracks for humans as well: well-structured code is simply easier to understand and reason about, and takes a smaller amount of working-set memory. Even as LLMs get better with coding, I expect that they would converge on the same conclusion, namely that good structure + good abstractions make for code that is more efficient to work with.

Yeah I have had claude take over multiple internal (human written) projects that were in a dire state and spent a week just completely refactoring them and adding exhaustive tests before doing any new features. It's worth starting from a clean slate.

I keep hearing the assertion that you can’t make high quality, maintainable code with LLMs. The last two years using AI have shown me exactly the opposite.

I think it’s all about the structure you use to work in and how you use the model. We are shipping better, more human friendly code, with less bugs, then we ever did before and doing it at 1/10 the cost before LLMs.

But we are definitely not vibe coding, and the key seems to be devs with years of experience managing teams, managing the LLM instead. Basically you create the same kind of formal specifications, conventions, and documentation that you would develop for a project with two or three teams, then use that to keep the project on the rails recursively looping back through the docs as you go along. I’ve only had to back out of a couple of issues over the last year, and even though that cost a couple of hours, it was still extremely cheap.

Meanwhile we are shipping at 4x speed with 1/4 the labor, and the code is better than it was because the “overhead” of writing maintainable, self documented code has inverted into the secret ingredient to shipping bug free code at unprecedented speed.

If you just explain the standards to which you want the code written, use a strict style guide, have a separate process that ensures test coverage (not in the same context) you can get example quality code all the way through. Turns out that’s also in the training data.

Many of us recognize that the days of nearly-free tokens is quickly drawing to a close, and at some point humans may very well have to dig their keyboards out of cold storage and return once again to the code mines.

OAI and Anthropic need to generate cash flows from operations - once they go public that’s it. Any future funding for reinvestment has to come from internal funds beyond existing raising + IPO.

So yeah, it’s imminent. Let’s see how demand shifts in response in the future.

June 1st for all the folks taking advantage of copilot. It was an astounding deal and a lot of people were “abusing” it.

The reason why you will never get software engineers (in companies) to accept the man behind the curtain is liability. If a human software engineer is still responsible for what happens when the AI developed code has a catastrophic bug or security vulnerability, then the only way for the human to know if there is a problem is to be able to read through the code or run it through some <insert advanced formal verification tool here> that guarantees zero issues.

I think we eventually end up at the tool approach via vendors providing the tools to other companies, but it still feels like there's a long road ahead to get there.

> but the AI doesn't need this

That's not true. The LLM performance will degrade as the codebase gets messier as well. You get to a point where every fix breaks something else and you can't really make forward progress.

Yes, you might be able to get a bit further with a messy codebase just because the LLM won't complain and will just grind through fixing things, but eventually it will just start disabling failing tests instead of actually fixing things.

The token cost to fix might surpass what a human would cost to just do it.

> Why was it a maintenance dead end?

LLMs have a limit to how deep they can understand and refactor architectural issues.

That limit is far, far lower than a human's.

>This just might be the new reality, and we need to stop looking behind the curtain and accept what the wizard presents us.

This is how societies become shittier. People who are ostensibly responsible for doing their jobs not giving a damn about quality.

Sometimes I think the main value in AI-maintained code being “high quality” is when the structure can enforce invariants. If invalid states aren’t representable, then the AI can’t easily add bugs in the future.

Of course that just leads to: what’s the best way to achieve that goal? Through elegant code or adding lots of tests? Which is a debate from long before LLMs existed.