Lovable BBQ

Context

Lovable BBQ is a mobile-friendly discovery and check-in app for finding barbecue restaurants, tracking visits, earning badges, and competing on a leaderboard.

It is intentionally simpler than the enterprise systems, but it shows product craft in a different register: location, mobile UX, third-party APIs, and lightweight game mechanics.

Problem

Generic map search is broad. A BBQ-focused app needs to filter noisy place results, make nearby discovery fast, and turn visits into something users want to track.

The hard part is not finding a map API. It is making the product feel direct enough to use while standing outside a restaurant.

What Shipped

• Location detection and map centering.
• Search by current location, ZIP, city/state, country/city, or restaurant name.
• Google Places searches for BBQ, barbecue, and smokehouse-style results.
• BBQ-specific name filtering over broad Places matches.
• Restaurant detail drawer with check-in flow.
• Distance-gated check-ins within 150 meters.
• Supabase-backed check-ins, visited markers, dashboard stats, badges, and global leaderboard.

Architecture

The app uses Next.js, Supabase, Google Maps JavaScript API, and Google Places API. The interface is map-first and mobile-first, with restaurant detail and check-in interactions layered over the map.

Check-ins are stored with user, place, location, timestamp, and notes metadata.

Key Decisions

• Start with a narrow vertical. BBQ-specific discovery gives the app a clearer identity than a generic restaurant finder.
• Filter broad Places results. Google Places can match too widely, so the app adds BBQ-specific name filtering.
• Make check-ins physical. The 150-meter distance rule keeps the visit mechanic tied to real-world presence.
• Use badges and leaderboards lightly. The game loop adds motivation without turning the product into a heavy social network.

AI / Workflow Layer

This is not an AI-first system. Its value in OperatorLab is as product evidence: focused scope, fast user loop, location constraints, and a small social/game layer.

It is useful precisely because it shows range beyond AI demos.

Sample Artifact

A good session flow is short:

1. Open the app near a city or current location.
2. Search for BBQ.
3. Inspect a place card.
4. Check in when physically nearby.
5. See visited state, badge progress, and leaderboard movement.

That loop is intentionally small enough to understand immediately.

Constraints

• Browser geolocation can fail or be denied.
• Google Places can return overly broad matches.
• Check-ins need distance checks and duplicate prevention.
• Mobile layout has to prioritize the map without burying actions.

Tradeoffs

• A narrow BBQ focus reduces generic utility, but it makes the product memorable.
• Distance-gated check-ins add trust, but they depend on permission and device accuracy.
• Google APIs accelerate discovery, but they require filtering and careful quota awareness.

What I Would Improve Next

• Add richer place detail capture after check-in.
• Add route planning for BBQ road trips.
• Add regional lists or trails that make the discovery layer more editorial.

What It Proves

Lovable BBQ shows practical consumer product execution: geospatial UX, external APIs, auth-backed user state, simple gamification, and a tight mobile-first loop.