HOW CURSOR INDEXES CODEBASES FAST

Summary from the TLDR Web Dev newsletter

Cursor, an AI IDE, uses Merkle trees to efficiently index codebases. The process involves chunking code into semantic pieces, creating a Merkle tree of hashes, and synchronizing it with Cursor's server to generate and store embeddings in a vector database. Merkle trees allow Cursor to quickly identify and update only the changed files, reducing bandwidth and processing time.