Most embedding models are not Matryoshka-trained, so naive dimension truncation tends to destroy them. I tested a simple alternative: fit PCA once on a sample of embeddings, rotate vectors into the PCA basis, and then truncate. The idea is that PCA concentrates signal into leading components, so tru
