MESH - Developer Teammate Matching Platform

MESH is a collaborative platform that helps developers discover compatible teammates based on skills, interests, and experience levels.

Features

Core Features

• User Authentication - Oauth to take in verified users
• Developer Profiles - Comprehensive developer information
• Smart Matching - Compatibility algorithm to find the best match
• Like System - Like profiles you're interested in
• Mutual Matching - Create matches when both users like each other
• In-App Chat - Real-time messaging between matched developers

Additional Features

• Permission-based access control
• Admin dashboard for management
• Detailed profile information (GitHub, Twitter, Portfolio)
• Skill-based filtering and matching
• Compatibility scoring system

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Tech Stack

Backend

• Framework: Django 4.2.7
• API: Django REST Framework
• Authentication: JWT (djangorestframework-simplejwt)
• Database: PostgreSQL
• CORS: django-cors-headers
• User Management: djoser

Frontend

• Framework: React 18
• Build Tool: Vite
• Styling: Tailwind CSS

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Quick Start

Prerequisites

• Python 3.8+
• Node.js 16+
• npm or yarn

Backend Setup

# Navigate to backend directory
cd backend

# Create virtual environment
python -m venv venv

# Activate virtual environment
# On Windows:
venv\Scripts\activate
# On macOS/Linux:
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Run migrations
python manage.py migrate

# Create superuser
python manage.py createsuperuser

# Load sample data (optional)
python manage.py populate_profiles

# Start server
python manage.py runserver

Backend will run on: http://localhost:8000

Frontend Setup

# Navigate to frontend directory
cd frontend

# Install dependencies
npm install

# Create .env file
echo 'VITE_API_URL=http://localhost:8000/api' > .env

# Start development server
npm run dev

Frontend will run on: http://localhost:5173 (Vite) or http://localhost:3000 (Create React App)

🗄️ Database Models

UserProfile

• Extends Django User model
• Stores developer-specific information
• Fields: age, bio, github_url, x_url, portfolio_url, skills, looking_for, etc.

Like

• Represents when user1 likes user2
• Unique constraint: (liker, liked)

Match

• Represents mutual like between two users
• Stores compatibility score
• Unique constraint: (user1, user2)

ChatMessage

• Stores messages between matched users
• Tracks read status

MatchSuggestion

• Caches top suggestions for each user
• One-to-One relationship with UserProfile

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Matching Algorithm

The compatibility score is calculated as:

Score = (Overlap × 0.25) + (Complementary × 0.35) + 
        (TypeCompat × 0.15) + (Availability × 0.15) + 
        (Experience × 0.10)

Where:
- Overlap: Percentage of shared skills
- Complementary: Skills alignment (25-50%)
- TypeCompat: Developer type preference match
- Availability: Can they work together?
- Experience: Similar experience levels?

Score Range: 0-100 (higher is better)

Example:

• User A (React, Node.js, 4 years) looking for Backend
• User B (Python, FastAPI, 5 years) looking for Frontend
• Compatibility Score: ~78%

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Environment Variables

Backend (.env or settings.py)

DEBUG=True
SECRET_KEY=your-secret-key
ALLOWED_HOSTS=localhost,127.0.0.1
CORS_ALLOWED_ORIGINS=http://localhost:3000,http://localhost:5173

# Google OAuth (optional)
GOOGLE_CLIENT_ID=your-client-id
GOOGLE_CLIENT_SECRET=your-client-secret

Frontend (.env)

VITE_API_URL=http://localhost:8000/api
VITE_JWT_KEY=access_token

Security Considerations

• JWT token-based authentication
• CORS validation
• Permission-based access control
• SQL injection protection (ORM)
• CSRF protection enabled

Admin Dashboard

Access at: http://localhost:8000/admin/

Manage:

• User accounts
• Developer profiles
• Likes and matches
• Chat messages
• User verification status

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Built with ❤️ for CC internal Hack