Discover trending GitHub repos before they go mainstream.
6 data sources · smart scoring · anti-spam · daily email digest

Website · Quick Start · Features · Development

GitHub Discovery automatically collects signals from 5 data sources every day, uses a smart scoring system (100 points) to filter the most promising projects, and delivers them to you via email and web.

The problem it solves: GitHub Trending shows you what's popular today. GitHub Discovery shows you what's about to be popular — repos with unusual growth patterns, community picks from Hacker News and Reddit, and early-stage projects gaining traction.

• Star fraud detection: 1000+ stars in 1 day with age < 1 day → flagged
• Batch fraud detection: Same owner with multiple repos growing at once → flagged
• Content quality: No description or no README → penalty
• Cross-day dedup: 7-day window, no duplicate recommendations

• Vote on every recommendation (👍/👎)
• Feedback integrated into scoring algorithm
• localStorage persistence

• Daily curated repos delivered to your inbox
• Dark mode support (Apple Mail / iOS)
• Powered by Resend API

• Modern, professional web interface
• Filter by date and language
• Real-time scoring display

Click the Fork button in the top right corner.

Go to Settings → Secrets and variables → Actions and add:

Go to Actions and click I understand my workflows, go ahead and enable them.

Go to Actions → Daily Discovery → Run workflow to trigger a test run.

• GitHub Pages: Visit https://<your-username>.github.io/github-discovery/
• Email: Subscribers receive daily digests

github-discovery/
├── scripts/
│   ├── sources.py           # 5 data source collectors
│   ├── scorer.py            # Scoring algorithm
│   ├── quality.py           # Code quality detection
│   ├── dedup.py             # Cross-day deduplication (7-day window)
│   ├── feedback.py          # User feedback system
│   ├── fraud_detection.py   # Batch fraud detection
│   ├── verify_scoring.py    # Scoring verification / backtesting
│   ├── main.py              # Entry point
│   └── config.py            # Configuration
├── tests/                   # 117 unit tests
├── docs/index.html          # GitHub Pages
├── .github/workflows/       # Daily automation
├── subscribers.txt          # Email subscriber list
└── config.yaml              # Runtime configuration

git clone https://github.com/alloevil/github-discovery.git
cd github-discovery
python scripts/main.py

pip install pytest
python -m pytest tests/ -v

1. Add a new fetch_xxx() function in scripts/sources.py
2. Call it in fetch_all()
3. Add tests in tests/test_sources.py
4. Submit a PR

Scoring logic is in scripts/scorer.py. Weights can be adjusted in config.py:

SCORING_WEIGHTS = {
    "acceleration": 40,
    "quality": 30,
    "antispam": 30,
}

Run backtesting to verify whether high-scored repos actually took off:

python scripts/verify_scoring.py --days 30

Contributions are welcome! Please follow these steps:

1. Fork this repo
2. Create a feature branch: git checkout -b feature/your-feature
3. Commit your changes: git commit -m 'feat: add your feature'
4. Push the branch: git push origin feature/your-feature
5. Submit a Pull Request

• 📡 Add new data sources
• 🎯 Optimize scoring algorithm
• 🐛 Fix bugs
• 📖 Improve documentation
• ✅ Add tests

This project is licensed under the MIT License.

• GitHub API
• Hacker News API
• Reddit API
• OSSInsight — AI/ML repository trends and analytics
• Resend

⭐ If you find this useful, please give it a star!