MindMesh 🧠

AI-Powered Learning Community Platform

MindMesh is a Next.js application that connects learners with study partners using AI-driven matchmaking. Join communities, set learning goals, and find accountability partners who share your interests and learning objectives.

✨ Features

🤝 AI Matchmaking

• Intelligent Partner Matching: AI analyzes your learning goals and matches you with compatible study partners
• Community-Based Matching: Find partners within specific learning communities
• Direct Messaging: Connect with anyone through user search and direct match requests

👥 Communities

• Join Learning Communities: Browse and join communities focused on different topics (Full Stack, Leadership, AI/ML, DevOps, etc.)
• Create Communities: Start your own learning community
• Community Goals: Set and track learning goals within each community

💬 Real-Time Chat

• One-on-One Messaging: Chat with your matched study partners
• Polling-Based Updates: Currently uses polling for real-time message updates
• Match Management: Accept or decline match requests

Note: Chat currently uses polling for updates. WebSockets and group chat functionality will be implemented in future releases.

📊 Dashboard

• Learning Stats: Track your active matches, pending requests, and learning goals
• Quick Access: Easy navigation to communities and conversations

🔐 Authentication

• Powered by Clerk for secure user authentication
• Support for multiple sign-in methods

🛠️ Tech Stack

Frontend

• Framework: Next.js 16 (App Router)
• UI Library: React 19
• Styling: Tailwind CSS 4
• Components: shadcn/ui + Radix UI
• State Management: TanStack Query
• Forms: React Hook Form + Zod

Backend

• API: Hono (lightweight web framework)
• Database: PostgreSQL with Drizzle ORM
• Authentication: Clerk
• AI: Vercel AI SDK with Google Gemini

Development

• Runtime: Bun
• Language: TypeScript
• Linting: ESLint

🚀 Getting Started

Prerequisites

• Bun installed
• PostgreSQL database
• Clerk account for authentication
• Google AI API key (for AI matchmaking)

Installation

1. Clone the repository

   git clone <your-repo-url>
   cd mind-mess

2. Install dependencies

   bun install

3. Set up environment variables

   Create a .env.local file in the root directory:

   # Database
   DATABASE_URL=your_postgresql_connection_string
   
   # Clerk Authentication
   NEXT_PUBLIC_CLERK_PUBLISHABLE_KEY=your_clerk_publishable_key
   CLERK_SECRET_KEY=your_clerk_secret_key
   NEXT_PUBLIC_CLERK_SIGN_IN_URL=/sign-in
   NEXT_PUBLIC_CLERK_SIGN_UP_URL=/sign-up
   
   # Google AI (for matchmaking)
   GOOGLE_GENERATIVE_AI_API_KEY=your_google_ai_key

4. Set up the database

   # Generate migration files
   bun run db:generate
   
   # Run migrations
   bun run db:migrate
   
   # (Optional) Seed with sample data
   bun run db:seed

5. Run the development server

   bun run dev

6. Open your browser

   Navigate to http://localhost:3000

📝 Available Scripts

bun run dev          # Start development server
bun run build        # Build for production
bun run start        # Start production server
bun run lint         # Run ESLint

# Database commands
bun run db:generate  # Generate Drizzle migrations
bun run db:migrate   # Run migrations
bun run db:push      # Push schema changes
bun run db:studio    # Open Drizzle Studio
bun run db:seed      # Seed database with sample data

📁 Project Structure

mind-mess/
├── app/                    # Next.js app directory
│   ├── (main)/            # Main app routes
│   │   ├── chat/          # Chat pages
│   │   ├── communities/   # Community pages
│   │   └── dashboard/     # Dashboard
│   ├── api/               # API routes (Hono)
│   └── server/            # Server-side logic
├── components/            # React components
│   ├── ui/               # shadcn/ui components
│   └── communities/      # Community-specific components
├── database/             # Database schema and migrations
├── hooks/                # Custom React hooks
├── lib/                  # Utility functions
└── public/               # Static assets

🎯 Key Features Explained

AI Matchmaking Algorithm

The AI matchmaking system uses Google's Gemini model to:

1. Analyze user learning goals and interests
2. Compare compatibility between users
3. Generate match recommendations based on:
   • Learning objectives alignment
   • Skill level compatibility
   • Community overlap
   • Availability preferences

Subscription Tiers

• Free Tier:

  • Join 1 community
  • Set 1 learning goal
  • 2 AI match requests

• Pro Tier:

  • Unlimited communities
  • Unlimited learning goals
  • Unlimited AI matching

🔮 Roadmap

• WebSockets Integration: Replace polling with real-time WebSocket connections
• Group Chats: Multi-user chat rooms within communities
• Video Calls: Integrated video chat for study sessions
• Calendar Integration: Schedule study sessions
• Progress Tracking: Detailed learning progress analytics
• Mobile App: Native iOS and Android applications

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📄 License

This project is private and proprietary.

🙏 Acknowledgments

• Built with Next.js
• UI components from shadcn/ui
• Icons from Lucide
• AI powered by Google Gemini

---

Made with ❤️ for learners everywhere