> indicates a lack of professionalism

Appropriately, because OP is describing a hobby project. Perhaps you could pay them for a version without cat pictures.

That's apt.

One of the first thing you learn in CS 101 is "computers are impeccable at math and logic but have zero common sense, and can easily understand megabytes of code but not two sentences of instructions in plain English."

LLMs break that old fundamental assumption. How people can claim that it's not a ground-shattering breakthrough is beyond me.

I love this, thank you

"Why didn't you do that earlier?"

unzipped Ruby 3.4.7 into the appropriate place (third-party) in the repo and explained what i wanted (it used the Lua and Python port for reference)

first it built the Cosmo Make tooling integration and then we (ha "we" !) started iterating and iterating compiling Ruby with the Cosmo compiler … every time we hit some snag Claude Code would figure it out

I would have completed it sooner but I kept hitting the 5 hourly session token limits on my Pro account

https://github.com/igravious/cosmoruby

Its a lie, or fake.

here you go, still early days, rough round the edges :)

https://github.com/igravious/cosmoruby

I do that too, when I code.

In my experience that “blink of an eye” has turned out to be a single moment when the LLM misses a key point or begins to fixate on an incorrect focus. After that, it’s nearly impossible to recover and the model acts in noticeably divergent ways from the prior behavior.

That single point is where the model commits fully to the previous misunderstanding. Once it crosses that line, subsequent responses compound the error.

The solution to "nonexistent APIs" is to use a coding agent (Claude Code etc) that has access to tooling that lets it exercise the code it's writing.

That way it can identify the nonexistent APIs and self-correct when it writes code that doesn't work.

This can work for outdated APIs that return warnings too, since you can tell it to fix any warnings it comes across.

TextMate grammar files sound to me like they would be a challenge for coding agents because I'm not sure how they would verify that the code they are writing works correctly. ChatGPT just told me about vscode-tmgrammar-test https://www.npmjs.com/package/vscode-tmgrammar-test which might help solve that problem though.

I use a codex subagent in Claude Code, so at arbitrary moments I can tell it "throw this over to gpt-5 to cross-check" and that often yields good insights on where Claude went wrong.

Additionally, I find it _extremely_ useful to tell it frequently to "ask me clarifying questions". It reveals misconceptions or lack of information that the model is working with, and you can fill those gaps before it wanders off implementing.

> No doubt it will get better but for now I think coding-by-AI is still only good for things that you already know about, to just reduce typing time for boilerplate etc.; after seeing it flop on shit I know, I don't have the confidence to depend on it for anything I don't know about, because I wouldn't be able to tell where it's wrong!

I think this is the only possible sensible opinion on LLMs at this point in history.

Yeah, LLMs are absolutely terrible for GDscript and anything gamedev related really. It's mostly because games are typically not open source.

Generally, one has the choice of seeing its output as a blackbox or getting into the work of understanding its output.

Okay show the code.

Claude Code sure does love to make CLIs.