DevArena

An AI-Powered Competitive Programming Analytics Platform

DevArena is a comprehensive analytics dashboard and developer portfolio tracker. It aggregates data from multiple platforms—LeetCode, Codeforces, and GitHub—transforming raw API responses and statistics into actionable visualizations and performance metrics.

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Key Features

• Unified Developer Stats
  Consolidate your competitive programming rating, total solved counts, streak data, and global rankings across platforms.
• Growth & Rating Analytics
  Interactive charts tracking your contest rating changes, monthly submissions, and growth trajectory.
• Dynamic Topic & Skill Profiling
  Visualizes category strengths (Arrays, DP, Graphs, Math, Trees, Strings) and GitHub skill highlights using dynamic radar charts.
• Automated Sync Service
  Single-click manual sync that pulls live statistics, difficulty breakdowns, and recently solved problems with rate-limit protection.
• Secure Auth & Session Handling
  JWT-based secure authentication featuring access/refresh token flows, cookie storage, and Google OAuth integration.
• Glassmorphic UI
  A beautifully crafted, responsive Next.js dark-themed workspace dashboard utilizing Framer Motion and Recharts.

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Tech Stack

Frontend

• Framework: Next.js 16 (App Router), React 19
• Styling: Tailwind CSS 4, Framer Motion
• Data Visualization: Recharts
• Language: TypeScript

Backend

• Framework: Node.js, Express
• Authentication: JWT, Google OAuth
• Database: MongoDB Atlas, Mongoose
• Caching & Queueing: Redis, BullMQ
• Media: Cloudinary

AI & RAG Microservice

• Framework: Python, FastAPI, Uvicorn
• Supported Providers: Google Gemini, Groq (LPUs), OpenAI ChatGPT, Anthropic Claude
• RAG Architecture: In-Memory Vector Search, Curated DSA Patterns & Problem Corpus
• Features: Socratic AI Coach, 3-Tier Progressive Hints, Adaptive Study Planner, Contest Review

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Architecture Overview

Project Dev/
├── ai-service/           # FastAPI Python AI & RAG Microservice
│   ├── app/
│   │   ├── api/          # Coach, Hints, Planner, Review endpoints
│   │   ├── data/         # DSA patterns & problem corpus
│   │   └── rag/          # Vector store, embeddings, retriever, chains
│   ├── requirements.txt  # Python dependencies
│   └── run.py            # Startup runner
├── backend/              # Express Node.js backend server
│   ├── src/
│   │   ├── controllers/  # Route controller handlers
│   │   ├── models/       # Mongoose schemas
│   │   ├── routes/       # API endpoint routes
│   │   ├── services/     # RAG client, caching, achievements
│   │   ├── middlewares/  # Authentication & upload middlewares
│   │   └── utils/        # Platform API fetchers & keep-alive pinger
│   └── package.json
├── frontend/             # Next.js frontend web app
│   ├── src/
│   │   ├── app/          # Next.js App Router (authenticated & dashboard routes)
│   │   ├── components/   # Reusable UI widgets & graphs
│   │   ├── context/      # AuthContext state management
│   │   └── lib/          # Custom hooks and utilities
│   └── package.json
├── PROGRESS.md           # High-level feature roadmap & status
├── PLAN.md               # Original development outline
└── README.md             # Global project documentation

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Getting Started

Prerequisites

• Node.js (v18+)
• MongoDB (Local or Atlas URI)
• Git

Installation

1. Clone the repository

   git clone https://github.com/Tiyas04/project-dev.git
   cd project-dev

2. Configure the Backend

   cd backend
   npm install

   Create a .env file in backend/:

   PORT=5000
   MONGODB_URI=your_mongodb_connection_uri
   CORS_ORIGIN=http://localhost:3000
   ACCESS_TOKEN_SECRET=your_jwt_access_secret
   ACCESS_TOKEN_EXPIRY=7d
   REFRESH_TOKEN_SECRET=your_jwt_refresh_secret
   REFRESH_TOKEN_EXPIRY=30d
   SERVER_URL=http://localhost:5000
   GOOGLE_CLIENT_ID=your_google_oauth_client_id
   CLOUDINARY_CLOUD_NAME=your_cloudinary_cloud_name
   CLOUDINARY_API_KEY=your_cloudinary_api_key
   CLOUDINARY_API_SECRET=your_cloudinary_api_secret
   GITHUB_TOKEN=your_github_personal_access_token

3. Configure the Frontend

   cd ../frontend
   npm install

   Create a .env file in frontend/:

   NEXT_PUBLIC_API_URL=http://localhost:5000/api/v1
   NEXT_PUBLIC_GOOGLE_CLIENT_ID=your_google_oauth_client_id

4. Configure the AI & RAG Microservice

   cd ../ai-service
   python -m venv .venv
   # Windows:
   .venv\Scripts\Activate.ps1
   # Linux/macOS:
   source .venv/bin/activate
   pip install -r requirements.txt

   Create a .env file in ai-service/:

   PORT=8000
   HOST=0.0.0.0
   GEMINI_API_KEY=your_gemini_api_key
   INTERNAL_AI_SECRET=devarena_rag_secret_key_2026
   DEFAULT_MODEL=gemini-3.8-flash

Running the Application

Start all three services concurrently with a single command from the project root:

npm run dev

This uses concurrently to launch:

• Backend API: http://localhost:5000 (prefixed with [BACKEND])
• Frontend Web App: http://localhost:3000 (prefixed with [FRONTEND])
• AI & RAG Microservice: http://localhost:8000 (prefixed with [AI_RAG])

Alternatively, you can run them individually:

# Run only backend
npm run dev:backend

# Run only frontend
npm run dev:frontend

# Run only AI microservice
npm run dev:ai

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License

Distributed under the MIT License. See LICENSE for more details.