The unreasonable effectiveness of an LLM agent loop with tool use

Today I tried "vibe-coding" for the first time using GPT-4o and 4.1. I did it manually - just feeding compilation errors, warnings, and suggestions in a loop via the canvas interface. The file was small, around 150 lines.

It didn't go well. I started with 4o:

- It used a deprecated package.

- After I pointed that out, it didn't update all usages - so I had to fix them manually.

- When I suggested a small logic change, it completely broke the syntax (we're talking "foo() } return )))" kind of broken) and never recovered. I gave it the raw compilation errors over and over again, but it didn't even register the syntax was off - just rewrote random parts of the code instead.

- Then I thought, "maybe 4.1 will be better at coding" (as advertized). But 4.1 refused to use the canvas at all. It just explained what I could change - as in, you go make the edits.

- After some pushing, I got it to use the canvas and return the full code. Except it didn't - it gave me a truncated version of the code with comments like "// omitted for brevity".

That's when I gave up.

Do agents somehow fix this? Because as it stands, the experience feels completely broken. I can't imagine giving this access to bash, sounds way too dangerous.

The default chat interface is the wrong tool for the job.

The LLM needs context.

https://github.com/marv1nnnnn/llm-min.txt

The LLM is a problem solver but not a repository of documentation. Neural networks are not designed for that. They model at a conceptual level. It still needs to look up specific API documentation like human developers.

You could use o3 and ask it to search the web for documentation and read that first, but it's not efficient. The professional LLM coding assistant tools manage the context properly.

Eh, given how much about anything these models know without googling, they are certainly knowledge repositories, designed for it or not. How deep and up-to-date their knowledge of some obscure subject, is another question.

I meant a verbatim exact copy of all documentation they have ever been trained on - which they are not. Neural networks are not designed for that. That's not how they encode information.