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.
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...