On #2, it set up the problem, but bailed on the gap filling part with
# (This is a complex step; see note at bottom.)
That's not surprising, since I didn't tell it how to solve the problem. Can any of the premium systems do #2?I did Qwen3-256B (a free model, but you'd need a host for something that large, probably. I used Kagi) and Claude Code.
Curious how these look to you.
However, if it took creativity or real analysis, I had to throw the big models at it with a carefully-written prompt. You want to use their recent, best, big models. I used to include precise descriptions of data structures and function API's, too, which helped a lot. From there, tweak the description until you find pieces that get the result.
Another thing. It will eventually synthesize code that's close but not quite it. I found that asking it to make small changes, one at a time, would help for a while. Ex: "Modify that code to do X" or "...X in part Y." It eventually started hallucinating in loops. I always had to fix some amount of what it generated but it still saved time.
I hope that helps.
I like the Python code which outsourced the hard stuff to existing libraries. The odds of that working are higher.
Can you tell it to use the "glam" crate for the vectors, instead of writing out things like vector length the long way?
(We now need standardized low-level types more than ever, so the LLMs will use them.)
https://gist.github.com/cpsquonk/348009eb7c83a7d499ff5ae70d7...