I’m building a browser-based platform that lets travelers instantly spot nearby street-food gems. The core idea is simple: open the site, grant location permission, and a Google Map appears dotted with local stalls. Each pin expands into a card showing dish names, mouth-watering photos, live “open / closed” status pulled from vendor inputs, distance from the visitor’s current position, community ratings, and written reviews. Visitors sign up with a quick email-and-password flow so they can leave feedback and bookmark favourites. As engagement grows, I also want a lightweight recommendation module—an AI prompt suggesting, for example, “If you loved pad thai, try hoy tod at the stall two blocks away.” A basic collaborative-filtering or GPT-powered suggestion is fine for the first release; we can refine later. I already have styling references and a colour palette; what I need now is full-stack execution: responsive front-end, secure back-end, database schema for vendors and reviews, and Google Maps API integration with geofencing for distance calculations. Real-time opening hours can be handled via a simple vendor dashboard or an admin panel where owners update status, which then propagates to the map without refresh. Deliverables: • Responsive website (desktop & mobile) hosted on my domain • Email/password authentication with password reset flow • Google Maps-based interface showing stalls within a user-defined radius • CRUD dashboard for vendors to update hours, menu items, and images • User module for ratings, reviews, and favourites • AI recommendation microservice (initial MVP level) • Documentation: setup steps, environment variables, and API keys Code should be clean, commented, and pushed to a private Git repo. Once the MVP is stable, I’m open to iterative feature sprints, but this brief covers the first public launch.