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Computer Science
Google-scale search feels like a triumph of server farms and fiber. It isn't. The real trick — refusing to ever read a document at query time — was invented on machines with less memory than a phone photo.
A license plate that vector search couldn't find became the industry's favorite parable for hybrid search. The part of the story usually left out: the 'no-tuning' fusion method everyone defaults to has a documented accuracy discount nobody checks for.
Ask a vector database for your ten nearest matches and it will comply with total confidence — even though 'nearest' is a promise it was engineered to break. The story of how approximation became AI's memory is stranger than the marketing admits.