https://video.ut0pia.org/videos/watch/28c6d955-e833-4ed3-8479-6b0a58542b5c By default, Claude Code wastes one in every three file reads. Add windowed grep and that drops to one in five. Add semantic search on top and it drops to one in eight, with file precision climbing from 65% to 87%. Kuba Rogut from Turbopuffer ran a 50-task benchmark against ContextBench to measure not whether the agent solved the problem but whether it found the right files, lines, and symbols along the way. The benchmark tested three conditions: raw Claude Code, windowed reads capped at 50 lines, and windowed reads plus a semantic search tool backed by Turbopuffer. Semantic search won on behavior adjacent tasks where files share no keywords. Grep won on import tracing where the keyword is right there. Cursor's production numbers show a 24% relative improvement in answer accuracy from semantic retrieval, plus a 2.6% increase in code retention in large codebases. Kuba's explanation for why his gains were smaller: Cursor's model knows when and why to call semantic search. Claude Code just has it as another tool in the list. Speaker info: https://ca.linkedin.com/in/kubarogut, https://rogutkuba.com/ Thu, 04 Jun 2026 07:44:33 GMT https://validator.w3.org/feed/docs/rss2.html PeerTube - https://video.ut0pia.org https://video.ut0pia.org/lazy-static/avatars/0287a09a-aae7-4840-9843-b416426e7046.webp https://video.ut0pia.org/videos/watch/28c6d955-e833-4ed3-8479-6b0a58542b5c All rights reserved, unless otherwise specified in the terms specified at https://video.ut0pia.org/about and potential licenses granted by each content's rightholder.