It depends on what effort you're using etc. As an example [1] of what codex is capable of, here's hugo (written in golang) ported to TypeScript - and then a TypeScript to Rust transpiler which converts arbitrary TypeScript into Rust.
replyThe TypeScript code which was transpiled into Rust (and is compatible with most hugo templates) runs faster than the original hugo.
[1]: https://github.com/tsoniclang/tsonic-examples/tree/main/rust...
The transpiler is still WIP, but the fact that it can do this says a lot of about how far LLMs have come.
I find all effort levels of sol are the same in terms of amount of hallucinated unnecessary changes. Luna is much better all round on xhigh but my point still stands, every release of these new models is not an upgrade, its re-learning how to work with it.
replyIts like rehiring an employee every few months then training them up. Its honestly tiring and cant stay like this.
Opus has the same problem too…
That's not been my experience. My prompting methods haven't changed much between recent GPT releases. I do put a lot of effort into building tooling and tests around a project, so the LLM output is converging around it.
replyCan you give examples of the tooling and tests?
replyits over engineered problem solver ???? well because its a designed to do that
replyif you want to solve basic problem then use Luna
I mean it added additional changes when it doesnt need to. Its basically hallucinating changes it thinks it needs to make regardless of effort levels i try.
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