1. Input is an 256x256 pixel elevation map stored as a greyscale .png file, and a minimum and maximum elevation. A pixel value of 0 corresponds to the minimum elevation, and a pixel value of 255 corresponds to the maximum elevation. Read in the .png file and the elevation limits. Then construct a 256x256 floating point array of heights. From that array, construct a triangle mesh with X and Y dimensions 0..255. Perform a mesh reduction operation on the triangle mesh to reduce the number of triangles. Mesh reduction must not generate holes in the mesh. From the reduced mesh, generate a glTF file where the UV parameters run from 0.0 to 1.0 along the X and Y axes.
2. Given four glTF files constructed as above, corresponding to four quadrants of a larger square, construct a single 511x511 mesh which combines all four input meshes to cover a larger area. Because the input meshes are 0..255, not 0..256, there will be gaps where the four quadrants meet. Fill those gaps with reasonable triangles. Perform a mesh reduction as above. From the reduced mesh, generate a glTF file where the UV parameters run from 0.0 to 1.0 along the X and Y axes.
Rust code is preferred; Python code is acceptable.
So, what service should I sign up for?
On #2, it set up the problem, but bailed on the gap filling part with
# (This is a complex step; see note at bottom.)
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...
Edit: took a while but here is the code generated by DeepSeek: https://gist.github.com/omarabid/b30195011abf570fde2a084cd11... If you have an example file I could give it a test run.
Github Copilot seems to be able to do it. Mostly because it knows how to call Meshlab. The documentation for Meshlab is hard to search, and having an LLM find the correct function is itself helpful.
Also, it looks like quadric decimation has improved since the last time I looked at it. That algorithm, if pushed too hard, will generate meshes with holes. Now there are options ("preservetopology") which supposedly prevent that. I last looked at that a few years ago, and the only mesh decimator which handled that correctly was the one inside Unreal Engine Editor.
Now I have to test all this stuff, of course.
LLMs seem to be good at finding obscure library functions from a description of the function needed, something where where keyword searches aren't too helpful. Previously, searching Stack Overflow helped, but nobody is posting there any more. Good reference manuals are almost historical artifacts now.
Supposedly, you know something about this problem. If you use an LLM to craft a solution, that's probably fine since you'd know how well you did.
I know nothing about this problem. I could use an LLM to delude myself into thinking that I do. Should my boss be very happy about that? They probably shouldn't