I have my best successes by keeping things constrained to method-level generation. Most of the things I dump into ChatGPT look like this:

  public static double ScoreItem(Span<byte> candidate, Span<byte> target)
  {
     //TODO: Return the normalized Levenshtein distance between the 2 byte sequences.
     //... any additional edge cases here ...
  }

> I work on a large product with two decades of accumulated legacy, maybe that's the problem.

I'm in a similar situation, and for the first time ever I'm actually considering if a rewrite to microservices would make sense, with a microservice being something small enough an AI could actually deal with - and maybe even build largely on its own.

I've tried it extensively, and have the same experience as you. AI is also incredibly stubborn when it wants to go down a path I reject. It constantly tries to do it anyway and will slip things in.

I've tried vibe coding and usually end up with something subtly or horribly broken, with excessive levels of complexity. Once it digs itself a hole, it's very difficult to extricate it even with explicit instruction.

It's architecture dependent. A fairly functional modular monolith with good documentation can be accessible to LLMs at the million line scale, but a coupled monolith or poorly instrumented microservices can drive agents into the ground at 100k.

I think your intuition matches mine. When I try to apply Claude Code to a large code base, it spends a long time looking through the code and then it suggests something incorrect or unhelpful. It's rarely worth the trouble.

When I give AI a smaller or more focused project, it's magical. I've been using Claude Code to write code for ESP32 projects and it's really impressive. OTOH, it failed to tell me about a standard device driver I could be using instead of a community device driver I found. I think any human who works on ESP-IDF projects would have pointed that out.

AI's failings are always a little weird.

> as long as actual complexity is low.

You can start there. Does it ever stay that way?

> I work on a large product with two decades of accumulated legacy

Survey says: No.

Are you using it only on massive codebases? It's much better with smaller codebases where it can put most of the code in context.

Another good use case is to use it for knowledge searching within a codebase. I find that to be incredibly useful without much context "engineering"

> I work on a large product with two decades of accumulated legacy, maybe that's the problem

Definitely. I've found Claude at least isn't so good at working in large existing projects, but great at greenfielding.

Most of my use these days is having it write specific functions and tests for them, which in fairness, saves me a ton of time.

You need to realize when you’re being marketed to and filter out the nonsense.

Now I use agentic coding a lot with maybe 80-90% success rate.

I’m on greenfield projects (my startup) and maintaining strict Md files with architecture decisions and examples helps a lot.

I barely write code anymore, and mostly code review and maintain the documentation.

In existing codebases pre-ai I think it’s near impossible because I’ve never worked anywhere that maintained documentation. It was always a chore.

Yes, unfortunately those who jumped on the microservices hype train over the past 15 years or so are now getting the benefits of Claude Code, since their entire codebases fits into the context window of Sonnet/Opus and can be "understood" by the LLM to generate useful code.

This is not the case for most monoliths, unless they are structured into LLM-friendly components that resemble patterns the models have seen millions of times in their training data, such as React components.

Honestly, if you've ever looked at a claude.md file, it seems like absolute madness. I feel like I'm reading affirmations from AA.

Can you prove it in a blog and post it here that you do better code snippets than AI. If you claim "what kind of codebase", you should be able to use some codebase from github to prove it?

Agreed. It's a new programming paradigm that will put more pressure on API and framework design, to protect vibe developers from themselves.

I disagree. I think we're testing it, and we haven't seen the worst of it yet.

And I think it's less about non-deterministic code (the code is actually still deterministic) but more about this new-fangled tool out there that finally allows non-coders to generate something that looks like it works. And in many cases it does.

Like a movie set. Viewed from the right angle it looks just right. Peek behind the curtain and it's all wood, thinly painted, and it's usually easier to rebuild from scratch than to add a layer on top.

Code has always been nondetermistic. Which engineer wrote it? What was their past experience? This just feels like we are accepting subpar quality because we have no good way to ensure the code we generate is reasonable that wont mayyyybe rm-rf our server as a fun easter egg.

i've seen a lot of startups that use AI to QA human work. how about the idea of use humans to QA AI work? a lot of interesting things might follow

And with any luck, they don't vibe code their tests that ultimately just return true;

indeed, i see verification debt outweighing tradition tech debt very very soon...

I can automatically generate suites of plausible tests using Claude Code.

If you can make as a rule "no AI for tests", then you can simply make the rule "no AI" or just learn to cope with it.

> And realistically in a year, there won't be any people who can keep up.

I've heard the same claim every year since GPT-3.

It's still just as irrational as it was then.

> And realistically in a year, there won't be any people who can keep up.

Bold claim. They said the same thing at the start of this year.

im hoping this can introduce a framework to help people visualize the problem and figure out a way to close that gap. image generation is something every one can verify, but code generation is perhaps not. but if we can make verifying code as effortless as verifying images (not saying it's possible), then our productivity can enter the next level...

> I don't know what the point of "super intelligent" AI is if we aren't going to even listen to it

Because you asked the wrong question. The most likely question would be "How do I make a quadrillion dollars and humiliate my super rich peers?".

But realistically, it gave you an answer according to its capacity. A real super intelligent AI, and I mean oh-god-we-are-but-insects-in-its-shadow super intelligence, would give you a roadmap and blueprint, and it would take account for our deep-rooted human flaws, so no one reading it seriously could dismiss it as superficial. in fact, anyone world elite reading it would see it as a chance to humiliate their world elite peers and get all the glory for themselves.

You know how adults can fool little children to do what they don't want to? We would be the toddlers in that scenario. I hope this hypothetical AI has humans in high regard, because that would be the only thing saving us from ourselves.

Did you expect some answer that decried world peace as impossible ? It's just repeating what people say [0] when asked the same question. That's all that a large language model can do (other than putting it to rhyme or 'in the style of Charles Dickens').

[0] https://newint.org/features/2018/09/18/10-steps-world-peace

If you are looking for a vision of general AI that confirms a Hobbsian worldview, you might enjoy Lars Doucet's short story, _Four Magic Words_.

[1] https://www.fortressofdoors.com/four-magic-words/

> So I don't know what the point of "superintelligent" AI is if we aren't going to even listen to it

I would kind of feel sorry for a super-intelligent AI having to deal with humans who have their fingers on on/off switch. It would be a very frustrating existence.

I dunno, many people have that weird, unfounded trust in what AI says, more than in actual human experts it seems

"How to be in good health? Sleep, eat well, exercise." However, knowledge ≠ application.

I don't believe that this is going to happen, but the primary arguments revolving around a "super intelligent" ai involve removing the need for us to listen to it.

A super intelligent ai would have agency, and when incentives are not aligned would be adversarial.

In the caricature scenario, we'd ask, "super ai, how to achieve world peace?" It would answer the same way, but then solve it in a non-human centric approach: reducing humanities autonomy over the world.

Fixed: anthropogenic climate change resolved, inequality and discrimination reduced (by reducing population by 90%, and putting the rest in virtual reality)