It's All Matrices — So Why Do We Need an Embedding Model?
Computers only know numbers, so text must become vectors first. But one-hot is a vector too, and an embedding lookup is mechanically just one matrix multiply — so what's actually special about an embedding model? This post takes it all the way down with a mechanism / training-objective / product frame: from the distributional hypothesis and word2vec negative sampling, through self-attention, to SBERT prying open the anisotropic cone with contrastive learning — showing that what's special isn't the matrix, but a loss that places every row where geometric distance equals semantic distance, then landing it back on the bi-encoder's role in RAG retrieval.
Why I Built This Site (and How It Works)
The story behind this site — a home for my résumé, projects, and a running log of what I'm learning — plus a quick look at the stack, the data model, and why I chose this architecture.