MindMirror

Video-based Agentic AI integration with IoT and Dashboard

MindMirror is a sophisticated Java-based system that combines video processing, agentic AI, IoT device integration, and a real-time dashboard for intelligent automation and monitoring.

Features

• Video Processing: Real-time video capture and analysis
• Agentic AI: Intelligent autonomous agents for decision-making and task automation
• IoT Integration: Seamless connectivity with IoT devices for smart home and industrial applications
• Live Dashboard: Interactive web-based dashboard for monitoring and control
• Docker Support: Containerized deployment for easy scaling and cloud integration

Technology Stack

• Language: Java (98.6%)
• Containerization: Docker (1.4%)

Project Structure

MindMirror/
├── src/                    # Java source code
├── Dockerfile              # Docker container configuration
└── README.md              # This file

Getting Started

Prerequisites

• Java 11 or higher
• Docker (optional, for containerized deployment)
• Maven or Gradle (depending on your build configuration)

Installation

1. Clone the repository:

git clone https://github.com/SleepyStack/MindMirror.git
cd MindMirror

2. Build the project:

# Using Maven
mvn clean package

# Using Gradle
gradle build

Running Locally

# Run the application
java -jar target/mindmirror.jar

Docker Deployment

# Build the Docker image
docker build -t mindmirror:latest .

# Run the container
docker run -d -p 8080:8080 mindmirror:latest

Usage

Once running, access the dashboard at http://localhost:8080 to:

• Monitor video feeds and AI analysis
• Configure IoT device connections
• Manage autonomous agents
• View real-time metrics and logs

Architecture

MindMirror follows a modular architecture:

1. Video Module: Handles video capture, streaming, and frame processing
2. AI Module: Manages agentic AI models and inference
3. IoT Module: Manages device connectivity and communication protocols
4. Dashboard Module: Provides real-time visualization and control interface

Configuration

Configuration is typically managed through environment variables or a config file. See the documentation in the config/ directory for details.

Contributing

Contributions are welcome! Please follow these steps:

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

License

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

Author

SleepyStack

Acknowledgments

• Video processing libraries and frameworks
• IoT device manufacturers and APIs
• Open-source AI and machine learning communities

Support

For issues, questions, or suggestions, please open an issue on the GitHub repository.

---

Note: This is an active development project. Features and APIs may change.