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ION Cannon

A high-powered content collection and analysis system that uses multiple Large Language Models (LLMs) to collect, validate, and summarize content from various sources. It helps you stay up-to-date with the latest information by automatically filtering and processing content based on your interests.

Features

• Multi-source Content Collection

  • RSS feeds from major tech and security blogs
  • Reddit channel monitoring

• Intelligent Content Processing

  • Keyword-based content filtering
  • Date-based filtering (excludes content older than 10 days)
  • Multi-LLM validation system for relevance checking
  • Automated content summarization
  • Configurable output formats

Prerequisites

1. Install and run Ollama locally:

# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh

# Start Ollama service
ollama serve

# In a new terminal, pull required models
ollama pull mistral

Installation

1. Install uv (if not already installed):

curl -LsSf https://astral.sh/uv/install.sh | sh

2. Clone the repository:

git clone https://github.com/shaidar/ion-cannon.git
cd ion_cannon

3. Create and activate a virtual environment:

uv venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

4. Install the package:

uv pip install -e ".[dev]"

Configuration

The system is highly configurable through the settings file. You can customize:

1. Content Sources

   • RSS_FEEDS: List of RSS feed URLs to monitor
   • REDDIT_CHANNELS: Reddit channels to monitor

2. Reddit API Setup

   1. Go to https://www.reddit.com/prefs/apps
   2. Click "create another app..." at the bottom
   3. Select "script" as the application type
   4. Fill in:
      • name: ion-cannon (or any name you prefer)
      • description: Content collection tool
      • redirect uri: http://localhost:8080
   5. Click "create app"
   6. Note your:
      • client_id (under your app name)
      • client_secret
   7. Add credentials to your settings:

   # filepath: ion_cannon/config/settings.py
   REDDIT_CLIENT_ID = "your_client_id"
   REDDIT_CLIENT_SECRET = "your_client_secret"
   REDDIT_USER_AGENT = "ion-cannon:v1.0.0 (by /u/your_username)"

3. Content Filtering

   • KEYWORDS: List of keywords to filter content (matches both title and content)
   • Default age filter of 10 days (configurable in code)

4. LLM Settings

   • Configure multiple LLMs for content validation
   • Set up dedicated LLMs for summarization

Example settings:

# filepath: ion_cannon/config/settings.py
RSS_FEEDS = [
    "https://www.schneier.com/blog/atom.xml",
    "https://krebsonsecurity.com/feed/"
]

KEYWORDS = [
    "security",
    "artificial intelligence",
    "machine learning"
]

Usage

Basic usage:

# Collect and process content with default settings
ion-cannon collect

# Use multiple LLMs for better validation
ion-cannon collect --multi-llm

# Save processed content to a specific directory
ion-cannon collect --output ./my-reports

# Show verbose output during processing
ion-cannon collect --verbose

List configured sources:

# Show basic source configuration
ion-cannon sources

# Show detailed source information
ion-cannon sources --verbose

Development

Set up the development environment:

# Install development dependencies
uv pip install -e ".[dev]"

# Run tests
pytest

# Run linter
ruff check .

# Run formatter
ruff format .

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the GNU General Public License v3 - see the LICENSE file for details.