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Comment by antirez

7 hours ago

I depends on the situation. In this case the agent worked only using the reference code provided by Flux's Black Forest Labs which is basically just the pipeline implemented as a showcase. The fundamental way for this process to work is that the agent can have a feedback to understand if it is really making progresses, and to debug failures against a reference implementation. But then all the code was implemented with many implementation hints about what I wanted to obtain, and without any reference of other minimal inference libraries or kernels. So I believe this just is the effect of putting together known facts about how Transformers inference works plus an higher level idea of how software should appear to the final user. Btw today somebody took my HNSW implementation for vector sets and translated it to Swift (https://github.com/jkrukowski/swift-hnsw). I'm ok with that, nor I care of this result was obtained with AI or not. However it is nice that the target license is the same, given the implementation is so similar to the C one.

This is pretty great. I’ve gone and hacked your GTE C inference project to Go purely for kicks, but this one I will look at for possible compiler optimizations and building a Mac CLI for scripting…

This repo has Swift wrappers, not a rewrite of hnsw.c, which apparently you weren't the only author of.