While I’ve had tremendous success with Golang projects and Typescript Web Apps, when I tried to use Metal Mesh Shaders in January, both Codex and Claude both had issues getting it right.

That sort of GPU code has a lot of concepts and machinery, it’s not just a syntax to express, and everything has to be just right or you will get a blank screen. I also use them differently than most examples; I use it for data viz (turning data into meshes) and most samples are about level of detail. So a double whammy.

But once I pointed either LLM at my own previous work — the code from months of my prior personal exploration and battles for understanding, then they both worked much better. Not great, but we could make progress.

I also needed to make more mini-harnesses / scaffolds for it to work through; in other words isolating its focus, kind of like test-driven development.

My impression is that it always comes down to how well what you’re trying to do pattern-matches the training set.

" or if it just can't solve everything yet."

Obviously it cannot. But if you give the AI enough hints, clear spec, clear documentation and remove all distracting information, it can solve most problems.

It works somewhat well with trivial things. That's where most of these success stories are coming from.

Most of the folks are building CRUD apps with AI and that works fine.

What you're doing is more specialized and these models are useless there. It's not intelligence.

Another NFT/Crypto era is upon us so no you're not holding it wrong.