CTO Signal Scanner

A tool for scanning and analyzing technology blog posts to identify relevant content for CTOs and technology leaders.

Features

• Scans multiple technology blogs and news sources
• Uses AI to analyze and rate content relevance
• Generates PDF reports with summaries and ratings
• Web interface for viewing and managing scans
• Configurable rating system (1-10 scale)
• Support for both OpenAI and local Ollama models

Rating System

Articles are rated on a scale of 1-10 based on their relevance to CTOs and technology leaders:

• 1-3: Low relevance
• 4-6: Medium relevance
• 7-10: High relevance

Installation

1. Clone the repository:

git clone https://github.com/yourusername/cto_signal_scanner.git
cd cto_signal_scanner

2. Create and activate a virtual environment:

python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt
pip install -e .

4. Create a .env file with your configuration:

OPENAI_API_KEY=your_api_key_here
GPT_MODEL=gpt-3.5-turbo

Usage

Web Interface

1. Start the web server:

python run_web.py

2. Open your browser and navigate to http://localhost:5000

Command Line

1. Run a scan:

python run_scan.py

2. Generate a report:

python run_report.py

Configuration

Environment Variables

• OPENAI_API_KEY: Your OpenAI API key
• GPT_MODEL: The GPT model to use (default: gpt-3.5-turbo)
• USE_OLLAMA: Set to 'true' to use local Ollama model
• OLLAMA_BASE_URL: URL for Ollama API (default: http://localhost:11434/v1)
• OLLAMA_MODEL: Model to use with Ollama (default: qwen2:7b)

Blog Sources

Edit cto_signal_scanner/config/blog_sources.py to add or modify blog sources.

Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.