Semantic search alone wasn't good enough. Here's how I improved search on kentcdodds.com through three rounds of iteration with Cursor and GPT-5.4, each time learning something that the previous design missed.

Kent C. Dodds is a well-known software engineer, educator, and open-source advocate renowned for his expertise in JavaScript and React. Through his blog, courses, and conference talks, Kent shares insights, best practices, and practical tips on front-end development, testing, and software engineering. Developers can learn from Kent's extensive experience and deep understanding of the JavaScript ecosystem, gaining  insights into building modern web applications with React and other technologies.

Kent C. Dodds

A developer iterates through three rounds of building hybrid search (semantic + lexical) for their personal site. Pure vector search failed to surface exact-match results like library names and API identifiers. The solution combines Cloudflare Vectorize for semantic search with SQLite FTS5/D1 for lexical BM25 search, fused via Reciprocal Rank Fusion. A critical production bug emerged when synchronous SQLite writes blocked the entire Node.js server during foreground requests. The fix moved all search logic into a dedicated Cloudflare Worker with its own D1 database, then a third round unified both semantic and lexical search into a single worker. Key lessons: keep index maintenance off the request path, avoid split architectures when possible, and AI agents make iterative redesigns cheap enough to actually do.

Implementing Hybrid Semantic + Lexical Search