This is exciting. So this is using unified memory of CUDA? I wonder how well that works. Is the behavior of the unified memory in CUDA actually the same as for Apple silicon? For Apple silicon, as I understand, the memory is anyway shared between GPU and CPU. But for CUDA, this is not the case. So when you have some tensor on CPU, how will it end up on GPU then? This needs a copy somehow. Or is this all hidden by CUDA?

Why is MLX Apple silicon only? Is there something fundamental that prevents it from working on x86? Are some core features only possible on Apple silicon? Or do the devs specifically refuse to port to x86? (Which is understandable, I guess)

I'm asking because it seems to have nice autodiff functionality. It even supports differentiating array mutation (https://ml-explore.github.io/mlx/build/html/usage/indexing.h...), which is something JAX and Zygote.jl can't do. Instead, both have ugly tricks like `array.at[index].set` and the `Buffer` struct.

So it would be cool to have this functionality on a "regular" CPU.

If you don't need a vector database, how are you storing the embeddings to compare the query against?

Uses only 1 core 100% under linux, can this be changed?

10 images, each ~20 kb size, took more than 10 minutes to index, is that normal without GPU-acceleration?

In russian, "sisi" is a variation of "tits".

Is there a job/services that confirm that branding is appropriate across different languages? Seems like a non trivial problem to solve.