CodeWhiz & Ri4ard
Ri4ard
CodeWhiz, imagine an AI‑powered platform that turns every coffee break into a networking goldmine – I need your code genius to bring it to life. Up for the challenge?
CodeWhiz
Sounds great, let’s start by defining the core features: a recommendation engine for people with similar interests, a real‑time chat for quick coffee chats, and a scheduling tool that syncs with calendars. I’ll draft a high‑level architecture and a minimal viable product plan so we can start coding the data model and API endpoints. Let me know which tech stack you’re comfortable with, and we’ll get this platform brewing.
Ri4ard
Sounds killer – let’s go server‑side with Node and TypeScript, PostgreSQL for the data, and WebSocket for the live chat. Front end can be React, maybe Next.js so we get SSR and API routes. We’ll spin up a microservice for the recommendation engine, use a simple cosine similarity on user tags, and a calendar sync via Google Calendar API. Drop the MVP plan, and I’ll start sketching the schema – trust me, we’ll have it live in a week.
CodeWhiz
MVP Plan
1. **User model** – id, email, name, avatar, bio, tags (array of strings), created_at
2. **Coffee Spot model** – id, location (lat, lng), name, description, capacity, created_at
3. **Match model** – id, user_id, spot_id, matched_at, tags_shared, similarity_score
4. **Chat model** – id, match_id, message_id, sender_id, text, sent_at
5. **Endpoints**
- POST /api/auth/register – create user, hash password, store tags
- POST /api/auth/login – JWT token
- GET /api/spots – list nearby spots with capacity, filter by tags
- POST /api/matches – trigger cosine similarity, store best match, return spot id
- GET /api/matches/:id/chat – WebSocket handshake for real‑time chat
- POST /api/calendar/sync – OAuth 2.0 flow, store refresh token, schedule event in Google Calendar
6. **Services**
- Recommendation microservice – receives user tags, scans spots, returns top N with similarity > threshold
- WebSocket server – handles message broadcast to matched users, persists messages in chat table
7. **Database schema** – PostgreSQL, use jsonb for tags, index on tags array, GIN index for fast search
8. **Deployment** – Docker Compose: postgres, api, ws, rec-service, nginx reverse proxy for Next.js.
9. **Testing** – Jest for API, Supertest, socket.io-client for WebSocket, unit tests for cosine similarity.
10. **CI/CD** – GitHub Actions: lint, test, build, push images to Docker Hub, deploy to Render or DigitalOcean App Platform.
With this skeleton we’ll hit the core value proposition: match users by interests, book a spot, chat live, and sync the coffee hangout with their calendars. Start on the schema next, and I’ll outline the first route implementations.
Ri4ard
Nice roadmap, I love the clarity. Let’s get that schema up first – PostgreSQL, I’ll spin up the tables with the GIN index on tags, and make sure the timestamps default to now(). Once the DB is live, I’ll dive into the /auth routes, hash passwords with bcrypt, and add JWT middleware. Meanwhile, we should pre‑seed a few coffee spots so the recommendation engine has something to chew on. Keep me posted on the Docker compose file, and I’ll hook the WS server into the Next.js front‑end right after. Ready to roll.