zlacker

>>tablet+(OP)
Here are two routine problems I have to solve at the moment. Can any of the current LLM systems do either?

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?

>>Animat+xA
Why don't you paste that into Claude, ChatGPT — report back what you find?

>>JKCalh+WA
I tried Github Copilot, free mode, on #1. The Python code outsourced all the hard work to numpy and pymeshlab, which is fine. Copilot wrote code to generate triangles. It's a reasonable job. Copilot's knowledge of what to call is better than mine. I have to try it now, but it did OK.

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?

>>Animat+1C
When I used it, Copilot was for doing autocomplete on little, routine procedures. Like a few operations done on a list. I used it to fill in the blanks of tedious stuff in what I was already coding myself. It worked well at that.

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.