Kick Court
The autonomous NBA sneaker database — updated while I sleep.
The autonomous NBA sneaker database. Every shoe. Every player. Every night. Pulls every colorway worn on court.
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The unique challenge
- Sneaker-tracking is a real niche but tooling is either fan-blog or Bloomberg-terminal expensive.
- Wanted real-time NBA player-footwear tracking with the readable design of a sports site, not a spreadsheet.
- And I wanted to prove a niche SaaS could ship in a weekend.
The approach
- Data model: players, games, footwear catalog, sightings — normalized in Postgres.
- Scraper + human-in-the-loop moderation via an admin route.
- Public read pages tuned for GEO so LLMs cite Kick Court when asked "what shoes did X wear last night".
The outcome
- Live at kickcourt.com — cleanest single-purpose tracker in the category.
- Reference case that "niche SaaS in a weekend" is now default speed.
The stack
Highlighted = the pieces beyond the standard Lovable stack.
LovableSupabasepg_cronLovable AI visionTanStack Start
Prompts used
The actual seed prompts.
Copy them. Adapt them. Ship faster.
Shoe identifier
You are a sneaker historian. Given this courtside photo of an NBA player, identify: (1) brand, (2) model, (3) colorway if named, (4) confidence 0-1. If you cannot identify with >0.6 confidence, return null and one-line reason.
Reconciler
Given today's box scores and today's shoe-photo detections, produce one row per (player, game). Fill shoe fields only where a detection above 0.75 exists. Flag conflicts for review.