If you’ve ever waited 12 seconds for a git clone of a 5GB monorepo behind a corporate firewall, you know the cost of poor Git server performance: $47k annual productivity loss for a 50-person engineering team, per our 2024 internal benchmark. For 15 years, I’ve tuned Git infrastructure for teams from 4-person startups to 10k+ engineer orgs, and the debate between lightweight Gitea and feature-heav
Traditional search engines match keywords. If you search for "dog shelters around Gurgaon" and the indexed page says "animal shelters near Delhi," you get no results. The words do not overlap. Semantic search fixes this by converting text into vectors. Similar ideas end up close together in vector space, even when the words differ. An embedding model takes a word or sentence and produces a high-di
The first time I implemented Vamana from the DiskANN paper, my approximate nearest neighbor index was slower than brute force. On tiny test fixtures, brute force took 0.27 ms per query. My Vamana implementation took 22.98 ms. That sounds absurd. ANN exists to skip work. The problem was not the algorithm. It was how I mapped the paper's abstractions to actual data structures. The DiskANN pseudocode