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.

"It used a deprecated package"

That's because models have training cut-off dates. It's important to take those into account when working with them: https://simonwillison.net/2025/Mar/11/using-llms-for-code/#a...

I've switched to o4-mini-high via ChatGPT as my default model for a lot of code because it can use its search function to lookup the latest documentation.

You can tell it "look up the most recent version of library X and use that" and it will often work!

I even used it for a frustrating upgrade recently - I pasted in some previous code and prompted this:

This code needs to be upgraded to the new recommended JavaScript library from Google. Figure out what that is and then look up enough documentation to port this code to it.

It did exactly what I asked: https://simonwillison.net/2025/Apr/21/ai-assisted-search/#la...

> That's because models have training cut-off dates.

Which is precisely the issue with the idea of LLMs completely replacing human engineers. It doesn't understand this context unless a human tells it to understand that context.