AI

Foundations underneath the tools: representations, training objectives, and the math of meaning.

01
Representations & Embeddings
From distributional semantics to contrastively trained embedding models.
- 1.1
  Vector embeddings and semantic chunking, under the hood
  How an embedding model turns text into a vector, how it learns similarity, how vectors are searched at scale, and how semantic chunking decides what gets a vector.
  Oct 22, 2026