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NeuraOS is a groundbreaking Linux-based operating system that integrates an Open Interpreter and a Large Language Model (LLM) to revolutionize user interactions and system management.

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NeuraOS

Overview

NeuraOS is a revolutionary Linux-based operating system that seamlessly integrates an Open Interpreter and a Large Language Model (LLM) to transform user interactions and system management. By leveraging advanced Artificial Intelligence (AI) capabilities, NeuraOS enables users to interact with their computer using natural language, automates routine tasks, intelligently manages resources, and provides a highly personalized computing experience.

Table of Contents

Features

  • Natural Language Interface: Interact with the operating system using conversational language, eliminating the need for traditional command-line or graphical interfaces.

  • Contextual Awareness: Maintains context from previous interactions, enabling more accurate and relevant responses.

  • Automated Task Management: AI anticipates user needs, automates routine tasks, and optimizes system performance dynamically.

  • Intelligent Resource Allocation: Dynamically manages system resources based on usage patterns and predictive models.

  • Secure and Private: Implements robust security measures to protect user data and ensure system integrity.

  • Scalable Architecture: Modular design allows for easy integration of additional functionalities and components.

Architecture

NeuraOS leverages a hybrid architecture that combines kernel-space and user-space components to deliver an intelligent and responsive operating system experience.

High-Level Architecture Diagram

Components Description

  1. User Interface: Interfaces through which users interact with NeuraOS, such as terminal, voice commands, or graphical interfaces.

  2. LLM Service: Runs the Large Language Model (e.g., GPT-4) to interpret and generate responses based on user inputs.

  3. Context Manager: Maintains the state and context of user interactions to provide coherent and contextually relevant responses.

  4. Command Interpreter: Translates interpreted natural language commands into executable system-level operations.

  5. NeuraOS Kernel Module: Acts as a bridge between user-space services and kernel-space operations using Netlink sockets.

  6. Security Module: Ensures that only authorized commands are executed, maintaining system security and integrity.

Installation

Prerequisites

Before setting up NeuraOS, ensure that your system meets the following prerequisites:

  • Linux Distribution: Preferably Ubuntu or similar Debian-based distributions.

  • Kernel Headers: Must match your current kernel version.

  • Python 3.8+

  • Git: For version control.

  • Internet Connection: Required for installing dependencies and accessing OpenAI's API.

Setup Scripts

NeuraOS provides a set of bash scripts to automate the setup process. These scripts handle project creation, dependency installation, kernel module compilation, installation, user-space service setup, and cleanup.

1. create_project.sh

Description:
Creates the NeuraOS project structure and populates essential files with their respective contents.

Usage:

./scripts/create_project.sh

2. install_dependencies.sh

Description:
Installs necessary system and Python dependencies required for NeuraOS.

Usage:

./scripts/install_dependencies.sh

3. compile_kernel_module.sh

Description:
Compiles the NeuraOS kernel module.

Usage:

./scripts/compile_kernel_module.sh

4. install_kernel_module.sh

Description:
Inserts the compiled NeuraOS kernel module into the running kernel.

Usage:

./scripts/install_kernel_module.sh

5. run_user_space.sh

Description:
Starts the NeuraOS user-space services, including the command handler daemon.

Usage:

./scripts/run_user_space.sh

6. clean_project.sh

Description:
Cleans the NeuraOS project by removing build artifacts and stopping services.

Usage:

./scripts/clean_project.sh

Configuration

OpenAI API Key

NeuraOS utilizes OpenAI’s GPT-4 for natural language processing. To configure NeuraOS, you need to provide your OpenAI API key.

  1. Obtain an API Key:
    • Sign up or log in to OpenAI.
    • Navigate to the API section and generate a new API key.
  2. Configure the API Key:
    • Open the llm_service.py file located in the user_space/ directory.
nano user_space/llm_service.py
  • Replace the placeholder 'YOUR_OPENAI_API_KEY' with your actual API key.
openai.api_key = 'sk-XXXXXXXXXXXXXXXXXXXXXXXXXXXXXX'
  • Security Tip: Avoid hardcoding API keys in scripts. Consider using environment variables or secure storage solutions.

Usage

Running User-Space Services

To start NeuraOS user-space services, execute the run_user_space.sh script. This script initiates the command handler daemon, which listens for commands from the kernel module and executes them accordingly.

./scripts/run_user_space.sh

Expected Output:

NeuraOS: Starting user-space services...
NeuraOS: Starting command_handler.py as a background process...
NeuraOS: Command handler started with PID: 12345
NeuraOS: Logs can be found at NeuraOS/user_space/command_handler.log
NeuraOS: User-space services are running.

Sending Commands

NeuraOS allows users to send natural language commands through the llm_service.py script. The script interprets these commands using GPT-4 and communicates them to the kernel module for execution.

Example Command:

python3 user_space/llm_service.py "Open Firefox browser."

Expected Output:

NeuraOS: Sent command to kernel: open_application:firefox

Result:

The Firefox browser should launch automatically.

Example Commands

  • Open an Application:
python3 user_space/llm_service.py "Launch the text editor."
  • Shutdown the System:
python3 user_space/llm_service.py "Please shut down the system."
  • Restart the System:
python3 user_space/llm_service.py "Restart my computer."
  • Check System Status:
python3 user_space/llm_service.py "How is my system performing?"

Note: Implement appropriate command handling for system status queries.

Security Considerations

NeuraOS integrates AI capabilities directly into the operating system, necessitating stringent security measures to protect against potential vulnerabilities.

Authentication and Authorization

  • Permission Verification: The kernel module verifies user permissions before executing sensitive commands (e.g., shutdown, restart). Only authorized users (e.g., root) can perform such actions.
  • Secure Communication: Ensure that only trusted user-space services can communicate with the kernel module. Implement additional authentication mechanisms if necessary.

Input Validation

  • Sanitization: All incoming commands are validated and sanitized to prevent injection attacks or malicious exploitation.
  • Command Whitelisting: Restrict the set of executable commands to a predefined list to minimize risk.

Data Privacy

  • API Key Protection: Securely store and manage the OpenAI API key to prevent unauthorized access.
  • Data Handling: Limit the amount of user data processed and ensure compliance with data protection regulations (e.g., GDPR).

Logging and Monitoring

  • Audit Trails: Maintain logs of all executed commands and access attempts for auditing purposes.
  • Real-Time Monitoring: Implement monitoring tools to detect and respond to suspicious activities promptly.

Development

Contributing

Contributions are welcome! To contribute to NeuraOS, follow these steps:

  1. Fork the Repository: Click the “Fork” button at the top-right corner of the repository page on GitHub.
  2. Clone Your Fork:
git clone https://github.com/ttracx/NeuraOS.git
cd NeuraOS
  1. Create a Branch:
git checkout -b feature/your-feature-name
  1. Make Changes: Implement your feature or bug fix.
  2. Commit Your Changes:
git add .
git commit -m "Add feature: your feature description"
  1. Push to Your Fork:
git push origin feature/your-feature-name
  1. Create a Pull Request: Navigate to your repository on GitHub and click “Compare & pull request.”

Code Structure

  • Kernel Module (kernel_module/): Contains the Linux kernel module source code and Makefile for compiling the module.
  • User-Space Services (user_space/): Includes scripts for LLM interaction, command handling, and context management.
  • Setup Scripts (scripts/): Bash scripts to automate project setup, dependency installation, compilation, and cleanup.

Testing

Implement comprehensive testing to ensure system stability and security.

  • Unit Testing:
    • Kernel Module: Use kernel testing frameworks like KUnit to write unit tests for kernel functions.
    • User-Space Scripts: Utilize Python’s unittest framework to test individual components like llm_service.py, command_handler.py, and context_manager.py.
  • Integration Testing:
    • Test the end-to-end flow from sending a natural language command to executing the corresponding system command.
  • Security Testing:
    • Conduct vulnerability assessments to identify and mitigate potential security risks.
  • Performance Testing:
    • Measure system latency and resource usage under various workloads to ensure optimal performance.

Performance Optimization

To ensure NeuraOS operates efficiently, implement the following performance optimization strategies:

Asynchronous Processing

  • Non-Blocking Operations: Ensure that command interpretation and execution are handled asynchronously to prevent blocking critical system processes.

Resource Management

  • Dedicated Resources: Allocate dedicated CPU and memory resources for the LLM service to avoid contention with other system processes.
  • Cgroups and CPU Affinity: Use cgroups to limit the resources available to user-space services and set CPU affinity to bind processes to specific CPU cores.

Caching Mechanisms

  • Command Caching: Implement caching for frequently used commands to reduce processing time and enhance responsiveness.

Hardware Acceleration

  • GPU Utilization: Leverage GPUs for faster AI processing if available, reducing latency in command interpretation.

Efficient Communication

  • Netlink Sockets Optimization: Optimize Netlink socket communication for low latency and high throughput.

Troubleshooting

Common Issues

  1. Kernel Module Fails to Load:
    • Symptoms: Errors during insmod execution, missing kernel symbols.
    • Solutions:
      • Ensure kernel headers match your current kernel version.
      • Check for syntax errors in ai_os_module.c.
      • Verify that the Netlink protocol number (NETLINK_USER) is not conflicting with existing protocols.
  2. User-Space Services Not Receiving Commands:
    • Symptoms: Commands sent via llm_service.py are not executed.
    • Solutions:
      • Confirm that the kernel module is loaded (lsmod | grep ai_os_module).
      • Check logs using dmesg | tail for any errors.
      • Ensure that command_handler.py is running without errors.
  3. Applications Fail to Launch:
    • Symptoms: Commands like “Open Firefox” do not launch the application.
    • Solutions:
      • Verify that the application name is correctly specified.
      • Ensure that command_handler.py has the necessary permissions to launch applications.
      • Check the command_handler.log for any error messages.
  4. System Shutdown/Restart Commands Not Executing:
    • Symptoms: Commands to shut down or restart the system are ignored or result in errors.
    • Solutions:
      • Ensure that the user executing the command has root privileges.
      • Verify that permission checks in the kernel module are correctly implemented.
      • Check dmesg | tail for any permission-related warnings.

Log Files

  • Kernel Logs: Use dmesg to view kernel module logs.
dmesg | tail
  • User-Space Logs: Check command_handler.log located in the user_space/ directory.
cat user_space/command_handler.log

Debugging Steps

  1. Verify Kernel Module Status:
lsmod | grep ai_os_module
dmesg | tail
  1. Check User-Space Services: Ensure that command_handler.py is running.
ps aux | grep command_handler.py
  1. Test Communication: Send a simple command and verify if it’s received and executed.
python3 user_space/llm_service.py "Open Terminal."
  1. Review Permissions: Confirm that the executing user has the necessary permissions to perform system-level operations.

License

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

Author

Tommy Xaypanya
Chief AI Scientist
Email: [email protected]
LinkedIn: linkedin.com/in/tommyxaypanya
GitHub: github.com/tommyxaypanya

Disclaimer

NeuraOS is an experimental project intended for educational and conceptual purposes. Implementing an AI-driven operating system involves complex challenges that require professional expertise. Always consult with experienced kernel developers and AI specialists when undertaking such projects.

Acknowledgements

  • OpenAI for providing the GPT-4 API.
  • The Linux Kernel Community for their extensive documentation and support.
  • All contributors and testers who have helped in developing and refining NeuraOS.

Additional Notes

  • Environment Variables:
    For enhanced security, consider storing sensitive information like the OpenAI API key in environment variables rather than hardcoding them into scripts.

  • Automated Scripts:
    Ensure that all scripts have execute permissions. If not, you can set them using:

chmod +x scripts/*.sh
  • Virtual Environments: It’s recommended to use Python virtual environments to manage dependencies and prevent conflicts.
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
  • Continuous Integration: Implement CI/CD pipelines to automate testing and deployment processes for NeuraOS.
  • Documentation: Maintain comprehensive documentation for each component to facilitate easier maintenance and onboarding of new contributors.

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NeuraOS is a groundbreaking Linux-based operating system that integrates an Open Interpreter and a Large Language Model (LLM) to revolutionize user interactions and system management.

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