[show off] i built an ai-powered wheel fitment database using a hybrid search index. learned a lot about spatial data management for micro-saas.

hey guys, i’ve been frustrated for a while by how bloated and ad-heavy existing wheel fitment databases are. if you’ve ever tried to look up a simple bolt pattern or offset, you know the pain: 3 trackers, 5 pop-ups, and a database that looks like it was built in 2005. so i decided to build a "zero-bloat" alternative: https://boltpatternhq.com/ the core challenge here wasn't the AI part—it was the data structure. i needed to map 10,000+ vehicles with PCD, center bore, and offset specs in a way that was instantly searchable but didn’t require a massive backend hit. a few technical details for those curious: architecture: the site is served as a pure static frontend (html/css/js). no backend, no server maintenance. search: i’m using a pre-computed client-side search index (json-based) for the auto-complete. it’s instant, local-first, and keeps the search experience snappier than any backend call. ai integration: this is the fun part. i'm using cloudflare workers ai to run the models directly at the edge. it avoids all the typical "openai wrapper" latency and cost issues. the model is constrained specifically to my structured database, which helps keep the fitment advice precise and prevents it from hallucinating wildly. it’s still a work in progress, but the goal was to create something "utility-first" for car guys who just want the specs without the tracking trash. i'm currently looking for feedback on the ux of the search widget and the load performance. does the search feel snappy enough on your side? would love to hear what you guys think about the tech stack. submitted by /u/SideQuestDev [link] [留言]