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Comment by raddan | Hacker News Reader

I don’t know if a blanket answer is possible. I had the experience yesterday of asking for a simplification of a working (a computational geometry problem, to a first approximation) algorithm that I wrote. ChatGPT responded with what looked like a rather clever simplification that seemed to rely on some number theory hack I did not understand, so I asked it to explain it to me. It proceeded to demonstrate to itself that it was actually wrong, then it came up with two alternative algorithms that it also concluded were wrong, before deciding that my own algorithm was best. Then it proceeded to rewrite my program using the original flawed algorithm.

I later worked out a simpler version myself, on paper. It was kind of a waste of time. I tend not to ask for solutions from whole cloth anymore. It’s much better at giving me small in-context examples of API use, or finding handy functions in libraries, or pointing out corner cases.

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I think there's two different cases here that need to be treated carefully when working with AI:

1. Using a well know but complex algorithm that I don't remember fully. AI will know it and integrate it into my existing code faster (often much, much faster) than I could, and then I can review and confirm it's correct

2. Developing a new algorithm or at least novel application of an existing one, or using a complex algorithm in an unusual way. The AI will need a lot of guidance here, and often I'll regret asking it in the first place.

I haven't used Claude Code, however every time I've criticized AI in the past, there's always someone who will say "this tool released in the last month totally fixes everything!"... And so far they haven't been correct. But the tools are getting better, so maybe this time it's true.

$200 a month is a big ask though, completely out of reach for most people on earth (students, hobbyists, people from developing countries where it's close to a monthly wage) so I hope it doesn't become normalized.

> I haven't used Claude Code, however every time I've criticized AI in the past, there's always someone who will say "this tool released in the last month totally fixes everything!"... And so far they haven't been correct. But the tools are getting better, so maybe this time it's true.

The cascading error problem means this will probably never be true. Because LLMs are fundamentally guess the next token based on the previous tokens, whenever it gets a single token wrong - future tokens become even more likely to be wrong which snowballs to absurdity.

Extreme hallucination issues can probably eventually be resolved by giving it access to a compiler and, where appropriate, you could also probably feed it test cases, but I don't think the cascading errors will ever be able to be resolved. The best case scenario will eventually it being able to say 'I don't know how to achieve this.' Of course then you ruin the mystique of LLMs which think they can solve any problem.

It obviously can be resolved, otherwise we wouldn't be able to self-correct our own selves. When is unknown, but not the if.

We aren't LLMs, obviously.

We can sometimes correct ourselves. With training, in specific circumstances.

The same insight (given enough time, a coding agent will make a mistake) is true for even the best human programmers, and I don’t see any mechanism that would make an LLM different.

The reason you will basically never just recommend e.g. somebody use a completely nonexistent function is because you're not just guessing what the answer to something should be. Rather you have a knowledge base which you believe to be correct and are constantly evolving and drawing from it.

LLMs do not function like this at all. Rather all they have is a series of weights to help predict the next token given the prior tokens. Cascading errors is a lot like a math problem. If you make a mistake somewhere along when solving a lengthy problem then your further calculations will also continue to be more and more wrong. The same is true of an LLM when executing its prediction algorithm.

This is why an LLM does give you a wrong answer it's usually just an exercise in frustration trying to get it to correct itself, and you'd be better of just creating a completely new context.

You really can’t compare free "check my algorithm" ChatGPT with $200/month "generate a working product" Claude Code.

I’m not saying Claude Code is perfect or is the panacea but those are really different products with orders of magnitude of difference in capabilities.

Claude 4? Or is Claude Code really so much better than say Aider also using Claude 4?

The scaffolding and system prompting around Claude 4 is really, really good. More importantly it’s advanced a lot in the last two months. I would definitely not make assumptions that things are equal without testing.

It's both Claude 4 Opus and the secret sauce that Claude Code has for UX (as well as Claude.md files for project/system rules and context) that is the killer I think. The describe, build, test cycle is very tight and produces consistently high quality results.

Aider feels a little clunky in comparison, which is understandable for a free product.

Yes. The tooling harness of Claude Code is really good, and Claude 4 is well-optimized for it. The combination is very powerful.

I think it’s also very nice that CC uses fancy search and replace for it’s edit actions. No waiting hours for the editor to scan over a completely regenerated file.

That's pretty much impossible comparison to make. Workflow between two is very different, aider has way more toggles. I can tell you that Aider using sonnet-4 started Node.js library in otherwise rust project given the same prompt as claud code that did finish the task.

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