AI-Powered Resume Analyzer & ATS Optimizer
BlackBox Recruiter is an intelligent resume optimization platform that helps job seekers beat Applicant Tracking Systems (ATS) and land more interviews. Powered by Groq's Llama 3.3 70B model, it provides instant AI-driven insights on resume-job compatibility.
- 75% of resumes are rejected by ATS before reaching recruiters
- Job seekers don't know what's missing from their resumes
- Generic advice doesn't address specific job requirements
- Manual analysis takes 15+ minutes per application
🤖 AI-Powered Analysis - Llama 3.3 70B semantic understanding
📊 Match Scoring - 0-100% compatibility rating
✅ Strength Detection - Identifies competitive advantages
🎯 ATS Optimizer - Keyword matching & formatting checks
✍️ Rewrite Suggestions - AI-improved bullet points
⚡ Real-Time - Results in under 3 seconds
🎨 Modern UI - Responsive dark theme
- Node.js 18+
- Groq API key (free at console.groq.com)
# Clone repository
git clone https://github.com/CODEBRAKERBOYY/blackbox-recruiter.git
cd blackbox-recruiter
# Install dependencies
npm install
# Create .env file and add your API key
echo "VITE_GROQ_API_KEY=your_groq_api_key_here" > .env
# Start development server
npm run devOpen http://localhost:5173 in your browser.
- Upload resume (TXT/PDF) or paste text
- Add job description
- Click "Match Analysis"
- Get compatibility score, strengths, gaps, recommendations
- Same input as above
- Click "ATS Optimizer"
- Get ATS score, keyword analysis, formatting issues, rewrite suggestions
Frontend: React 18, Vite, Tailwind CSS, Lucide Icons
AI/LLM: Groq API, Llama 3.3 70B (70 billion parameters)
Deployment: Vercel, GitHub Actions
Version Control: Git, GitHub
blackbox-recruiter/
├── src/
│ ├── services/
│ │ └── aiService.js # Groq API integration & AI logic
│ ├── App.jsx # Main application component
│ ├── index.css # Global styles
│ └── main.jsx # React entry point
├── .env # Environment variables (not in repo)
├── .gitignore # Git ignore rules
├── package.json # Dependencies
├── tailwind.config.js # Tailwind configuration
├── vite.config.js # Vite configuration
└── README.md # This file
User Input → Text Extraction → Prompt Engineering →
Groq API (Llama 3.3 70B) → JSON Parsing → Display Results
Prompt Engineering:
- Structured prompts for consistent JSON outputs
- Temperature tuning: 0.5-0.7 for balanced responses
- Token optimization: 1024-2048 based on analysis type
Error Handling:
- Graceful fallbacks for API failures
- Markdown code block removal
- Input validation
⚡ Response Time: < 3 seconds
🎯 Accuracy: 85%+ semantic matching
💰 Cost: ~$0.001 per analysis (Groq free tier)
📦 Bundle Size: ~450 KB (optimized)
- Push to GitHub
- Go to vercel.com
- Import
blackbox-recruiterrepository - Add environment variable:
VITE_GROQ_API_KEY - Deploy!
Live URL: blackbox-recruiter-f4b4eunb7-aloks-projects-a320deac.vercel.app
Contributions are welcome!
- Fork the repository
- Create feature branch:
git checkout -b feature/amazing-feature - Commit changes:
git commit -m 'Add amazing feature' - Push to branch:
git push origin feature/amazing-feature - Open Pull Request
MIT License - see LICENSE file for details.
CODEBRAKERBOYY
- Groq - Fast LLM inference
- Meta AI - Llama 3.3 model
- Vercel - Deployment platform
- Tailwind CSS - Styling
- React - UI library
If this project helped you, please give it a ⭐!
Built with ❤️ by CODEBRAKERBOYY