I won't say so. From my experience the key to success is the ability to split big tasks into smaller ones and help the model with solutions when it's stuck.

Reproducible environments (Nix) help a lot, yes, same for sound testing strategies. But the ability to plan is the key.

I had to throw away a couple days worth of work because the code it built to pass the tests wasn't able to do the actual thing it was designed for and the only workaround was to go back and build it correctly while, ironically, still keeping the same tests.

You kind of have to keep it on a short leash but it'll get there in the end... hopefully.

> is this possible with those?

You can always write to stdin/read from stdout even if there is no SDK available I guess. Or create your own agent on top of an LLM provider.

Or you can just switch the models in a regular conversation and tell one to review everything up until now, optionally telling it to get a git diff of all the unstaged changes.

In the end I've just reanimated the original parser generator and progressed to full Java 7 syntactically (-att5 branch), but there are some major obstacles with bytecode.

"AI DID EVERYTHING IN A DAY"

"How do you know it works?"

"... it just looks like it does"

Like when I ask AIs to port sed to java, and it writes test cases ... running sed on a CLI and doesn't implement the full lang spec no matter how much prompting I give it.