Author has mentioned many times that the llama.cpp maintainers don't want code that's prevalently written by AI with no human revision. If anyone wants to try and get the support upstreamed into that project, they're quite free to do that: the code is MIT licensed.

Also Antirez has been able to use GPT to iterate on the code and performance. He/they (others contributed to DS4) has a set of result files to ensure that correctness is maintained, and benchmarks to verify performance, and the LLM is able to iterate within that framework. Having a small, focussed codebase helps here.

Antirez explained the dev process when he posted a pure C implementation of the Flux 2 Klein image gen model, at https://news.ycombinator.com/item?id=46670279