All these engineers who claim to write most code through AI - I wonder what kind of codebase that is. I keep on trying, but it always ends up producing superficially okay-looking code, but getting nuances wrong. Also fails to fix them (just changes random stuff) if pointed to said nuances.

I work on a large product with two decades of accumulated legacy, maybe that's the problem. I can see though how generating and editing a simple greenfield web frontend project could work much better, as long as actual complexity is low.

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 ...
  }

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.

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.

So far I found that AI is very good at writing the code as in translating english to computer code.

Instead of dealing with intricacies of directly writing the code, I explain the AI what are we trying to achieve next and what approach I prefer. This way I am still on top of it, I am able to understand the quality of the code it generated and I’m the one who integrates everything.

So far I found the tools that are supposed to be able to edit the whole codebase at once be useless. I instantly loose perspective when the AI IDE fiddles with multiple code blocks and does some magic. The chatbot interface is superior for me as the control stays with me and I still follow the code writing step by step.

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've generally had better luck when using it on new projects/repos. When working on a large existing repo it's very important to give it good context/links/pointers to how things currently work/how they should work in that repo.

Also - claude (~the best coding agent currently imo) will make mistakes, sometimes many of them - tell it to test the code it writes and make sure it's working - I've generally found its pretty good at debugging/testing and fixing it's own mistakes.

<1 year old startup with fullstack javascript monorepo. Hosted with a serverless platform with good devex, like cloudflare workers.

That’s the typical “claude code writes all my code” setup. That’s my setup.

This does require you to fit your problem to the solution. But when you do, the results are tremendous.

> 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.

> 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.

> 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?