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How Cursor Indexes Codebases Fast - by Engineer's Codex

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Cursor, the popular AI IDE that recently announced they hit $300M ARR, uses Merkle trees to index code fast. This post goes over exactly how. Before diving into Cursor's implementation, let's first understand what a Merkle tree is. A Merkle tree is a tree structure in which every "leaf" node is labeled with the cryptographic hash of a data block, and every non-leaf node is labeled with the cryptographic hash of the labels of its child nodes. This creates a hierarchical structure where changes at any level can be efficiently detected by comparing hash values. Think of them as a fingerprinting system for data: Each piece of data (like a file) gets its own unique fingerprint (hash) Pairs of fingerprints are combined and given a new fingerprint This process continues until you have just one master fingerprint (the root hash) The root hash summarizes all data contained in the individual pieces, serving as a cryptographic commitment to the entire dataset. The beauty of this approach is that

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