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.

I can be frustrating at times. but my experience is the more you try the better you become at knowing what to ask and to expect. But I guess you understand now why some people say vibe coding is a bit overrated: https://www.lycee.ai/blog/why-vibe-coding-is-overrated

"Overrated" is one way to call it.

Giving sharp knives to monkeys would be another.

Why do people keep thinking they're intellectually superior when negatively evaluating something that is OBVIOUSLY working for a very large percentage of people?