How I turned hundreds of thousands of "dumb" SVG icons into a semantic search engine in 7 languages under 20ms (using LLM and Meilisearch)

Every frontend and full-stack developer knows this pain: You're building a UI, you need an icon for "settings", and you type settings into the library's search bar. The result? 0 results. Why? Because the library author named that icon heroicons-outline-cog. Searching for icons without semantics is like looking for a life partner and the search engine offers you an e-shop with a lifetime warranty on refrigerators. It frustrated me so much that I decided to build ycon.cc ? a tool that aggregates hundreds of open-source libraries and actually understands what you're looking for. In this article, I'll show you the technical background of how I enriched a massive icon dataset with semantics using AI and how I forced the whole thing to run under 20 milliseconds thanks to Meilisearch. 1. The Problem: Great data, zero context When designing the architecture, I didn't want to reinvent the wheel and write my own scrapers for every icon library (Tabler, Heroicons, Material Design, etc.). Instead