I only got the clustering working this morning, so aside from playing around with it a bit I've not had any results that have convinced me it's a tool I should throw at lots of different problems.

I mainly like it as another example of the kind of things you can use embeddings for.

My implementation is very naive - it's just this:

    sklearn.cluster.MiniBatchKMeans(n_clusters=n, n_init="auto")

I imagine there are all kinds of improvements that could be made to this kind of thing.

I'd love to understand if there's a good way to automatically pick an interesting number of clusters, as opposed to picking a number at the start.

https://github.com/simonw/llm-cluster/blob/main/llm_cluster....