Regarding the latter, are you able to obtain good results from an LLM? With graphics programming work, I find LLMs only help in cases where the task would take me 5 mins or so.

Absolutely.

Interesting. Do you happen to have a public example?

Of what specifically?

A CUDA kernel, compiler optimization, or anything of similar complexity

Feel free to look, but don't bother replying if you have something negative to say, don't feel like having my day ruined by negativity.

https://github.com/rdaum/mica/tree/main/crates/relation-kern...

https://github.com/rdaum/mica/tree/main/crates/vm

https://github.com/rdaum/pagebox/blob/main/crates/wal/src/wa...

Thanks for sharing. Looks interesting. Was all the code AI generated, or is it a mix?

that stuff in particular -- AI generated with heavy heavy prompting and up front design work and post-implementation testing

I have CUDA work here somewhere too but I have the repository private right now

Very cool. Thanks for sharing.

Here's what is key: never rely fully on the "intelligence" of the model. Build a workflow and/or tooling that allows for empirical improvement. e.g. for performance tuning I have a benchmarking framework and a container/server that contains the differential results available via MCP for the agent to observe as it works. Using /goal and a clear destination you want to get to, it will literally grind for hours and use that help. Doesn't help with architectural stuff, but it does help with producing efficient code.