This was possible before, though

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.

I have a set of prompts that are essentially “audit the current code changes for logic errors” (plus linting and testing, including double checking test conditions) and I run them using GPT-5.x-Codex on Claude generated code.

It’s surprising how much even Opus 4.5 still trips itself up with things like off-by-one or logic boundaries, so another model (preferably with a fresh session) can be a very effective peer reviewer.

So my checks are typically lint->test->other model->me, and relatively few things get to me in simple code. Contrived logic or maths, though, it needs to be all me.

Very cool!

Yep, a constantly updated spec is the key. Wrote about this here:

https://lukebechtel.com/blog/vibe-speccing

I've also found it's helpful to have it keep an "experiment log" at the bottom of the original spec, or in another document, which it must update whenever things take "a surprising turn"

Salvatore - this is cool. I am a fan of using Steve Yegge's beads for this - it generally cuts the markdown file cruft significantly.

Did you run any benchmarking? I'm curious if python's stack is faster or slower than a pure C vibe coded inference tool.

The reference code shows how to setup the inference pipeline. It does not implement 99% of what the C code does. That is, the inference kernels, the transformer and so forth.