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QUICKSTART.md
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# Quick Start Guide # ⚡ Quick Start Guide
Get Munich News Daily running in 5 minutes! Get Munich News Daily running in 5 minutes!
## Prerequisites ## 📋 Prerequisites
- **Docker** & **Docker Compose** installed
- **4GB+ RAM** (for AI models)
- *(Optional)* NVIDIA GPU for faster processing
- Docker & Docker Compose installed ## 🚀 Setup Steps
- 4GB+ RAM (for Ollama AI models)
- (Optional) NVIDIA GPU for 5-10x faster AI processing
## Setup
### 1. Configure Environment ### 1. Configure Environment
```bash ```bash
# Copy example environment file
cp backend/.env.example backend/.env cp backend/.env.example backend/.env
# Edit with your settings (required: email configuration)
nano backend/.env nano backend/.env
``` ```
**Required:** Update `SMTP_SERVER`, `EMAIL_USER`, and `EMAIL_PASSWORD`.
**Minimum required settings:** ### 2. Start the System
```env
SMTP_SERVER=smtp.gmail.com
SMTP_PORT=587
EMAIL_USER=your-email@gmail.com
EMAIL_PASSWORD=your-app-password
```
### 2. Start System
```bash ```bash
# Option 1: Auto-detect GPU and start (recommended) # Auto-detects GPU capabilities and starts services
./start-with-gpu.sh ./start-with-gpu.sh
# Option 2: Start without GPU # Watch installation progress (first time model download ~2GB)
docker-compose up -d
# View logs
docker-compose logs -f
# Wait for Ollama model download (first time only, ~2-5 minutes)
docker-compose logs -f ollama-setup docker-compose logs -f ollama-setup
``` ```
**Note:** First startup downloads the phi3:latest AI model (2.2GB). This happens automatically. ### 3. Add News Sources
### 3. Add RSS Feeds
```bash ```bash
mongosh munich_news # Connect to database
docker-compose exec mongodb mongosh munich_news
# Paste this into the mongo shell:
db.rss_feeds.insertMany([ db.rss_feeds.insertMany([
{ {
name: "Süddeutsche Zeitung München", name: "Süddeutsche Zeitung München",
@@ -65,11 +45,9 @@ db.rss_feeds.insertMany([
]) ])
``` ```
### 4. Add Subscribers ### 4. Add Yourself as Subscriber
```bash ```bash
mongosh munich_news # Still in mongo shell:
db.subscribers.insertOne({ db.subscribers.insertOne({
email: "your-email@example.com", email: "your-email@example.com",
active: true, active: true,
@@ -78,90 +56,35 @@ db.subscribers.insertOne({
}) })
``` ```
### 5. Test It ### 5. Verify Installation
```bash ```bash
# Test crawler # 1. Run the crawler manually to fetch news
docker-compose exec crawler python crawler_service.py 5 docker-compose exec crawler python crawler_service.py 5
# Test newsletter # 2. Send a test email to yourself
docker-compose exec sender python sender_service.py test your-email@example.com docker-compose exec sender python sender_service.py test your-email@example.com
``` ```
## What Happens Next? ## 🎮 Dashboard Access
The system will automatically: Once running, access the services:
- **Backend API**: Runs continuously at http://localhost:5001 for tracking and analytics - **Dashboard**: [http://localhost:3000](http://localhost:3000)
- **6:00 AM Berlin time**: Crawl news articles - **API**: [http://localhost:5001](http://localhost:5001)
- **7:00 AM Berlin time**: Send newsletter to subscribers
## View Results ## ⏭️ What's Next?
The system is now fully automated:
1. **6:00 AM**: Crawls news and generates AI summaries.
2. **7:00 AM**: Sends the daily newsletter.
### Useful Commands
```bash ```bash
# Check articles # Stop everything
mongosh munich_news
db.articles.find().sort({ crawled_at: -1 }).limit(5)
# Check logs
docker-compose logs -f crawler
docker-compose logs -f sender
```
## Common Commands
```bash
# Stop system
docker-compose down docker-compose down
# Restart system # View logs for a service
docker-compose restart docker-compose logs -f crawler
# View logs # Update code & rebuild
docker-compose logs -f
# Rebuild after changes
docker-compose up -d --build docker-compose up -d --build
``` ```
## New Features
### GPU Acceleration (5-10x Faster)
Enable GPU support for faster AI processing:
```bash
./check-gpu.sh # Check if GPU is available
./start-with-gpu.sh # Start with GPU support
```
See [docs/GPU_SETUP.md](docs/GPU_SETUP.md) for details.
### Send Newsletter to All Subscribers
```bash
# Send newsletter to all active subscribers
curl -X POST http://localhost:5001/api/admin/send-newsletter \
-H "Content-Type: application/json" \
-d '{"max_articles": 10}'
```
### Security Features
- ✅ Only Backend API exposed (port 5001)
- ✅ MongoDB internal-only (secure)
- ✅ Ollama internal-only (secure)
- ✅ All services communicate via internal Docker network
## Need Help?
- **Documentation Index**: [docs/INDEX.md](docs/INDEX.md)
- **GPU Setup**: [docs/GPU_SETUP.md](docs/GPU_SETUP.md)
- **API Reference**: [docs/ADMIN_API.md](docs/ADMIN_API.md)
- **Security Guide**: [docs/SECURITY_NOTES.md](docs/SECURITY_NOTES.md)
- **Full Documentation**: [README.md](README.md)
## Next Steps
1. ✅ **Enable GPU acceleration** - [docs/GPU_SETUP.md](docs/GPU_SETUP.md)
2. Set up tracking API (optional)
3. Customize newsletter template
4. Add more RSS feeds
5. Monitor engagement metrics
6. Review security settings - [docs/SECURITY_NOTES.md](docs/SECURITY_NOTES.md)
That's it! Your automated news system is running. 🎉

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README.md
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# Munich News Daily - Automated Newsletter System # Munich News Daily - Automated Newsletter System
A fully automated news aggregation and newsletter system that crawls Munich news sources, generates AI summaries, and sends daily newsletters with engagement tracking. A fully automated news aggregation system that crawls Munich news sources, generates AI-powered summaries, tracks local transport disruptions, and delivers personalized daily newsletters.
![Munich News Daily](https://via.placeholder.com/800x400?text=Munich+News+Daily+Dashboard)
## ✨ Key Features ## ✨ Key Features
- **🤖 AI-Powered Clustering** - Automatically detects duplicate stories from different sources - **🤖 AI-Powered Clustering** - Smartly detects duplicate stories and groups related articles using ChromaDB vector search.
- **📰 Neutral Summaries** - Combines multiple perspectives into balanced coverage - **📝 Neutral Summaries** - Generates balanced, multi-perspective summaries using local LLMs (Ollama).
- **🎯 Smart Prioritization** - Shows most important stories first (multi-source coverage) - **🚇 Transport Updates** - Real-time tracking of Munich public transport (MVG) disruptions options.
- **🎨 Personalized Newsletters** - AI-powered content recommendations based on user interests - **🎯 Smart Prioritization** - Ranks stories based on relevance and user preferences.
- **📊 Engagement Tracking** - Open rates, click tracking, and analytics - **🎨 Personalized Newsletters** - diverse content delivery system.
- **⚡ GPU Acceleration** - 5-10x faster AI processing with GPU support - **📊 Engagement Analytics** - Detailed tracking of open rates, click-throughs, and user interests.
- **🔒 GDPR Compliant** - Privacy-first with data retention controls - **⚡ GPU Acceleration** - Integrated support for NVIDIA GPUs for faster AI processing.
- **🔒 Privacy First** - GDPR-compliant with automatic data retention policies and anonymization.
**🚀 NEW:** GPU acceleration support for 5-10x faster AI processing! See [docs/GPU_SETUP.md](docs/GPU_SETUP.md)
## 🚀 Quick Start ## 🚀 Quick Start
For a detailed 5-minute setup guide, see [QUICKSTART.md](QUICKSTART.md).
```bash ```bash
# 1. Configure environment # 1. Configure environment
cp backend/.env.example backend/.env cp backend/.env.example backend/.env
# Edit backend/.env with your email settings # Edit backend/.env with your email settings
# 2. Start everything # 2. Start everything (Auto-detects GPU)
docker-compose up -d ./start-with-gpu.sh
# 3. View logs # Questions?
docker-compose logs -f # See logs: docker-compose logs -f
``` ```
That's it! The system will automatically: The system will automatically:
- **Frontend**: Web interface and admin dashboard (http://localhost:3000) 1. **6:00 AM**: Crawl news & transport updates.
- **Backend API**: Runs continuously for tracking and analytics (http://localhost:5001) 2. **6:30 AM**: Generate AI summaries & clusters.
- **6:00 AM Berlin time**: Crawl news articles and generate summaries 3. **7:00 AM**: Send personalized newsletters.
- **7:00 AM Berlin time**: Send newsletter to all subscribers
### Access Points ## 📋 System Architecture
- **Newsletter Page**: http://localhost:3000 The system is built as a set of microservices orchestrated by Docker Compose.
- **Admin Dashboard**: http://localhost:3000/admin.html
- **Backend API**: http://localhost:5001
📖 **New to the project?** See [QUICKSTART.md](QUICKSTART.md) for a detailed 5-minute setup guide. ```mermaid
graph TD
User[Subscribers] -->|Email| Sender[Newsletter Sender]
User -->|Web| Frontend[React Frontend]
Frontend -->|API| Backend[Backend API]
🚀 **GPU Acceleration:** Enable 5-10x faster AI processing with [GPU Setup Guide](docs/GPU_SETUP.md) subgraph "Core Services"
Crawler[News Crawler]
Transport[Transport Crawler]
Sender
Backend
end
## 📋 System Overview subgraph "Data & AI"
Mongo[(MongoDB)]
Redis[(Redis)]
Chroma[(ChromaDB)]
Ollama[Ollama AI]
end
``` Crawler -->|Save| Mongo
6:00 AM → News Crawler Crawler -->|Embeddings| Chroma
↓ Crawler -->|Summarize| Ollama
Fetches articles from RSS feeds
Extracts full content Transport -->|Save| Mongo
Generates AI summaries
Saves to MongoDB Sender -->|Read| Mongo
↓ Sender -->|Track| Backend
7:00 AM → Newsletter Sender
↓ Backend -->|Read/Write| Mongo
Waits for crawler to finish Backend -->|Cache| Redis
Fetches today's articles
Generates newsletter with tracking
Sends to all subscribers
↓
✅ Done! Repeat tomorrow
``` ```
## 🏗️ Architecture ### Core Components
### Components | Service | Description | Port |
|---------|-------------|------|
| **Frontend** | React-based user dashboard and admin interface. | 3000 |
| **Backend API** | Flask API for tracking, analytics, and management. | 5001 |
| **News Crawler** | Fetches RSS feeds, extracts content, and runs AI clustering. | - |
| **Transport Crawler** | Monitors MVG (Munich Transport) for delays and disruptions. | - |
| **Newsletter Sender** | Manages subscribers, generates templates, and sends emails. | - |
| **Ollama** | Local LLM runner for on-premise AI (Phi-3, Llama3, etc.). | - |
| **ChromaDB** | Vector database for semantic search and article clustering. | - |
- **Ollama**: AI service for summarization and translation (internal only, GPU-accelerated) ## 📂 Project Structure
- **MongoDB**: Data storage (articles, subscribers, tracking) (internal only)
- **Backend API**: Flask API for tracking and analytics (port 5001 - only exposed service)
- **News Crawler**: Automated RSS feed crawler with AI summarization (internal only)
- **Newsletter Sender**: Automated email sender with tracking (internal only)
- **Frontend**: React dashboard (optional)
### Technology Stack ```text
munich-news/
├── backend/ # Flask API for tracking & analytics
├── frontend/ # React dashboard & admin UI
├── news_crawler/ # RSS fetcher & AI summarizer service
├── news_sender/ # Email generation & dispatch service
├── transport_crawler/ # MVG transport disruption monitor
├── docker-compose.yml # Main service orchestration
└── docs/ # Detailed documentation
```
- Python 3.11 ## 🛠️ Installation & Setup
- MongoDB 7.0
- Ollama (phi3:latest model for AI)
- Docker & Docker Compose
- Flask (API)
- Schedule (automation)
- Jinja2 (email templates)
## 📦 Installation 1. **Clone the repository**
```bash
git clone https://github.com/yourusername/munich-news.git
cd munich-news
```
### Prerequisites 2. **Environment Configuration**
```bash
cp backend/.env.example backend/.env
nano backend/.env
```
*Critical settings:* `SMTP_SERVER`, `EMAIL_USER`, `EMAIL_PASSWORD`.
- Docker & Docker Compose 3. **Start the System**
- 4GB+ RAM (for Ollama AI models) ```bash
- (Optional) NVIDIA GPU for 5-10x faster AI processing # Recommended: Helper script (handles GPU & Model setup)
./start-with-gpu.sh
### Setup # Alternative: Standard Docker Compose
docker-compose up -d
```
1. **Clone the repository** 4. **Initial Setup (First Run)**
```bash * The system needs to download the AI model (approx. 2GB).
git clone <repository-url> * Watch progress: `docker-compose logs -f ollama-setup`
cd munich-news
```
2. **Configure environment**
```bash
cp backend/.env.example backend/.env
# Edit backend/.env with your settings
```
3. **Configure Ollama (AI features)**
```bash
# Option 1: Use integrated Docker Compose Ollama (recommended)
./configure-ollama.sh
# Select option 1
# Option 2: Use external Ollama server
# Install from https://ollama.ai/download
# Then run: ollama pull phi3:latest
```
4. **Start the system**
```bash
# Auto-detect GPU and start (recommended)
./start-with-gpu.sh
# Or start manually
docker-compose up -d
# First time: Wait for Ollama model download (2-5 minutes)
docker-compose logs -f ollama-setup
```
📖 **For detailed Ollama setup & GPU acceleration:** See [docs/OLLAMA_SETUP.md](docs/OLLAMA_SETUP.md)
💡 **To change AI model:** Edit `OLLAMA_MODEL` in `.env`, then run `./pull-ollama-model.sh`. See [docs/CHANGING_AI_MODEL.md](docs/CHANGING_AI_MODEL.md)
## ⚙️ Configuration ## ⚙️ Configuration
Edit `backend/.env`: Key configuration options in `backend/.env`:
```env | Category | Variable | Description |
# MongoDB |----------|----------|-------------|
MONGODB_URI=mongodb://localhost:27017/ | **Email** | `SMTP_SERVER` | SMTP Server (e.g., smtp.gmail.com) |
| | `EMAIL_USER` | Your sending email address |
| **AI** | `OLLAMA_MODEL` | Model to use (default: phi3:latest) |
| **Schedule** | `CRAWLER_TIME` | Time to start crawling (e.g., "06:00") |
| | `SENDER_TIME` | Time to send emails (e.g., "07:00") |
# Email (SMTP) ## 📊 Usage & Monitoring
SMTP_SERVER=smtp.gmail.com
SMTP_PORT=587
EMAIL_USER=your-email@gmail.com
EMAIL_PASSWORD=your-app-password
# Newsletter ### Access Points
NEWSLETTER_MAX_ARTICLES=10 * **Web Dashboard**: [http://localhost:3000](http://localhost:3000) (or configured domain)
NEWSLETTER_HOURS_LOOKBACK=24 * **API**: [http://localhost:5001](http://localhost:5001)
# Tracking ### Useful Commands
TRACKING_ENABLED=true
TRACKING_API_URL=http://localhost:5001
TRACKING_DATA_RETENTION_DAYS=90
# Ollama (AI Summarization) **View Logs**
OLLAMA_ENABLED=true ```bash
OLLAMA_BASE_URL=http://127.0.0.1:11434 docker-compose logs -f [service_name]
OLLAMA_MODEL=phi3:latest # e.g., docker-compose logs -f crawler
``` ```
## 📊 Usage **Manual Trigger**
### View Logs
```bash ```bash
# All services # Run News Crawler immediately
docker-compose logs -f
# Specific service
docker-compose logs -f crawler
docker-compose logs -f sender
docker-compose logs -f mongodb
```
### Manual Operations
```bash
# Run crawler manually
docker-compose exec crawler python crawler_service.py 10 docker-compose exec crawler python crawler_service.py 10
# Send test newsletter # Run Transport Crawler immediately
docker-compose exec sender python sender_service.py test your-email@example.com docker-compose exec transport-crawler python transport_service.py
# Preview newsletter # Send Test Newsletter
docker-compose exec sender python sender_service.py preview docker-compose exec sender python sender_service.py test user@example.com
``` ```
### Database Access **Database Access**
```bash ```bash
# Connect to MongoDB # Connect to MongoDB
docker-compose exec mongodb mongosh munich_news docker-compose exec mongodb mongosh munich_news
# View articles
db.articles.find().sort({ crawled_at: -1 }).limit(5).pretty()
# View subscribers
db.subscribers.find({ active: true }).pretty()
# View tracking data
db.newsletter_sends.find().sort({ created_at: -1 }).limit(10).pretty()
``` ```
## 🔧 Management ## 🌐 Production Deployment (Traefik)
### Add RSS Feeds This project is configured to work with **Traefik** as a reverse proxy.
The `docker-compose.yml` includes labels for:
- `news.dongho.kim` (Frontend)
- `news-api.dongho.kim` (Backend)
```bash To use this locally, add these to your `/etc/hosts`:
mongosh munich_news ```text
127.0.0.1 news.dongho.kim news-api.dongho.kim
db.rss_feeds.insertOne({
name: "Source Name",
url: "https://example.com/rss",
active: true
})
``` ```
### Add Subscribers For production, ensure your Traefik proxy network is named `proxy` or update the `docker-compose.yml` accordingly.
```bash
mongosh munich_news
db.subscribers.insertOne({
email: "user@example.com",
active: true,
tracking_enabled: true,
subscribed_at: new Date()
})
```
### View Analytics
```bash
# Newsletter metrics
curl http://localhost:5001/api/analytics/newsletter/2024-01-15
# Article performance
curl http://localhost:5001/api/analytics/article/https://example.com/article
# Subscriber activity
curl http://localhost:5001/api/analytics/subscriber/user@example.com
```
## ⏰ Schedule Configuration
### Change Crawler Time (default: 6:00 AM)
Edit `news_crawler/scheduled_crawler.py`:
```python
schedule.every().day.at("06:00").do(run_crawler) # Change time
```
### Change Sender Time (default: 7:00 AM)
Edit `news_sender/scheduled_sender.py`:
```python
schedule.every().day.at("07:00").do(run_sender) # Change time
```
After changes:
```bash
docker-compose up -d --build
```
## 📈 Monitoring
### Container Status
```bash
docker-compose ps
```
### Check Next Scheduled Runs
```bash
# Crawler
docker-compose logs crawler | grep "Next scheduled run"
# Sender
docker-compose logs sender | grep "Next scheduled run"
```
### Engagement Metrics
```bash
mongosh munich_news
// Open rate
var sent = db.newsletter_sends.countDocuments({ newsletter_id: "2024-01-15" })
var opened = db.newsletter_sends.countDocuments({ newsletter_id: "2024-01-15", opened: true })
print("Open Rate: " + ((opened / sent) * 100).toFixed(2) + "%")
// Click rate
var clicks = db.link_clicks.countDocuments({ newsletter_id: "2024-01-15" })
print("Click Rate: " + ((clicks / sent) * 100).toFixed(2) + "%")
```
## 🐛 Troubleshooting
### Crawler Not Finding Articles
```bash
# Check RSS feeds
mongosh munich_news --eval "db.rss_feeds.find({ active: true })"
# Test manually
docker-compose exec crawler python crawler_service.py 5
```
### Newsletter Not Sending
```bash
# Check email config
docker-compose exec sender python -c "from sender_service import Config; print(Config.SMTP_SERVER)"
# Test email
docker-compose exec sender python sender_service.py test your-email@example.com
```
### Containers Not Starting
```bash
# Check logs
docker-compose logs
# Rebuild
docker-compose up -d --build
# Reset everything
docker-compose down -v
docker-compose up -d
```
## 🔐 Privacy & Compliance
### GDPR Features
- **Data Retention**: Automatic anonymization after 90 days
- **Opt-Out**: Subscribers can disable tracking
- **Data Deletion**: Full data removal on request
- **Transparency**: Privacy notice in all emails
### Privacy Endpoints
```bash
# Delete subscriber data
curl -X DELETE http://localhost:5001/api/tracking/subscriber/user@example.com
# Anonymize old data
curl -X POST http://localhost:5001/api/tracking/anonymize
# Opt out of tracking
curl -X POST http://localhost:5001/api/tracking/subscriber/user@example.com/opt-out
```
## 📚 Documentation
### Getting Started
- **[QUICKSTART.md](QUICKSTART.md)** - 5-minute setup guide
- **[CONTRIBUTING.md](CONTRIBUTING.md)** - Contribution guidelines
### Core Features
- **[docs/AI_NEWS_AGGREGATION.md](docs/AI_NEWS_AGGREGATION.md)** - AI-powered clustering & neutral summaries
- **[docs/PERSONALIZATION.md](docs/PERSONALIZATION.md)** - Personalized newsletter system
- **[docs/PERSONALIZATION_COMPLETE.md](docs/PERSONALIZATION_COMPLETE.md)** - Personalization implementation guide
- **[docs/FEATURES.md](docs/FEATURES.md)** - Complete feature list
- **[docs/API.md](docs/API.md)** - API endpoints reference
### Technical Documentation
- **[docs/ARCHITECTURE.md](docs/ARCHITECTURE.md)** - System architecture
- **[docs/SETUP.md](docs/SETUP.md)** - Detailed setup guide
- **[docs/OLLAMA_SETUP.md](docs/OLLAMA_SETUP.md)** - AI/Ollama configuration
- **[docs/GPU_SETUP.md](docs/GPU_SETUP.md)** - GPU acceleration setup
- **[docs/DEPLOYMENT.md](docs/DEPLOYMENT.md)** - Production deployment
- **[docs/SECURITY.md](docs/SECURITY.md)** - Security best practices
- **[docs/REFERENCE.md](docs/REFERENCE.md)** - Complete reference
- **[docs/DEPLOYMENT.md](docs/DEPLOYMENT.md)** - Deployment guide
- **[docs/API.md](docs/API.md)** - API reference
- **[docs/DATABASE_SCHEMA.md](docs/DATABASE_SCHEMA.md)** - Database structure
- **[docs/BACKEND_STRUCTURE.md](docs/BACKEND_STRUCTURE.md)** - Backend organization
### Component Documentation
- **[docs/CRAWLER_HOW_IT_WORKS.md](docs/CRAWLER_HOW_IT_WORKS.md)** - Crawler internals
- **[docs/EXTRACTION_STRATEGIES.md](docs/EXTRACTION_STRATEGIES.md)** - Content extraction
- **[docs/RSS_URL_EXTRACTION.md](docs/RSS_URL_EXTRACTION.md)** - RSS parsing
## 🧪 Testing
All test files are organized in the `tests/` directory:
```bash
# Run crawler tests
docker-compose exec crawler python tests/crawler/test_crawler.py
# Run sender tests
docker-compose exec sender python tests/sender/test_tracking_integration.py
# Run backend tests
docker-compose exec backend python tests/backend/test_tracking.py
# Test personalization system (all 4 phases)
docker exec munich-news-local-backend python test_personalization_system.py
```
## 🚀 Production Deployment
### Environment Setup
1. Update `backend/.env` with production values
2. Set strong MongoDB password
3. Use HTTPS for tracking URLs
4. Configure proper SMTP server
### Security
```bash
# Use production compose file
docker-compose -f docker-compose.prod.yml up -d
# Set MongoDB password
export MONGO_PASSWORD=your-secure-password
```
### Monitoring
- Set up log rotation
- Configure health checks
- Set up alerts for failures
- Monitor database size
## 📚 Documentation
Complete documentation available in the [docs/](docs/) directory:
- **[Documentation Index](docs/INDEX.md)** - Complete documentation guide
- **[GPU Setup](docs/GPU_SETUP.md)** - 5-10x faster with GPU acceleration
- **[Admin API](docs/ADMIN_API.md)** - API endpoints reference
- **[Security Guide](docs/SECURITY_NOTES.md)** - Security best practices
- **[System Architecture](docs/SYSTEM_ARCHITECTURE.md)** - Technical overview
## 📝 License
[Your License Here]
## 🤝 Contributing ## 🤝 Contributing
Contributions welcome! Please read [CONTRIBUTING.md](CONTRIBUTING.md) first. We welcome contributions! Please check [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.
## 📧 Support ## 📄 License
For issues or questions, please open a GitHub issue. MIT License - see [LICENSE](LICENSE) for details.
---
**Built with ❤️ for Munich News Daily**