Storefront NYC

CATEGORIES

Go Deeper

Every city has empty storefronts — the ones with "For Lease" signs you walk past wondering what could go there. Storefront NYC lets you find out. You start with $350K and a map of Brooklyn. Every storefront on it is real — a real address, a real building, sitting empty right now. Tap one and you'll see what the neighborhood actually looks like: who lives there, what they earn, how they get to work, what's already nearby, what the rent would actually cost. Each location is graded A+ through D− so you can spot the gems and avoid the traps. Pick a storefront. Open a café, a bar, a boutique, a gym — ten types to choose from. Then run it. Set your pricing and quality to match what the neighborhood actually wants (a premium café in a budget neighborhood won't last long). Hire staff. Buy upgrades. Launch marketing. Read your Jelp reviews. Watch AI competitors open shops around you and try to steal your customers. Every month brings something new — a health inspection, a viral TikTok moment, a pipe burst, a block party. The game is designed to feel like SimCity on real streets — mobile-first, the kind of thing you pick up for 10 minutes while waiting for the train. Under the hood, six real-world data sources power everything: city vacancy records, property data, Census demographics, subway ridership, nearby business mapping, and commercial rents. The scoring engine uses research-backed demand modeling so each neighborhood behaves differently — not randomly, but based on what the data says people there actually want. The game design and algorithms were a collaboration — the demand modeling, scoring systems, and balance tuning came from marketing science research and a lot of iteration, with Claude as the building partner throughout. The soundtrack was made with Suno. It's a complete, playable game built with AI.

Stack Used

AI: Claude (Anthropic) — primary coding agent across dozens of sessions. Suno — 12-track lo-fi chiptune soundtrack. Data pipeline (6 real-world APIs): NYC Open Data — vacant storefront locations and vacancy status NYC PLUTO — property records (zoning, building size, year built) US Census ACS — neighborhood demographics (income, education, commute patterns, density) MTA — subway stations and ridership for foot traffic scoring Google Places — nearby business mapping for competition analysis Commercial rent data — aggregated from public listing sites for rent estimation Frontend: React 19, Vite 7, MapLibre GL JS Deployment: Vercel, no backend — runs entirely in-browser (native app on the roadmap)