Flywork Research Repository

A collection of research, experiments, and sample implementations in AI/ML agents and decentralized computing.

Research Focus Areas

AI/ML Agents

• Autonomous agent architectures
• Agent-based system design
• Multi-agent collaboration
• Agent learning and adaptation
• Natural language processing for agents

Decentralized Computing

• Distributed systems architecture
• Blockchain integration
• IPFS storage solutions
• Decentralized identity and authentication
• Smart contract implementations

Project Structure

.
├── agent/                     # AI agent research and documentation
│   ├── AI_Billing_Agent_Pipeline.md    # Implementation pipeline for AI billing agents
│   └── Future_of_AI_Billing_Agent.md   # Future research directions
├── sample_code/              # Example implementations
│   ├── webhook/             # Webhook integration samples
│   └── nebula_block_storage/ # Nebula Block storage example
├── venv/                    # Python virtual environment
├── .gitignore              # Git ignore file
├── LICENSE                  # Project license
└── README.md               # Project documentation

Research Samples

This repository contains various research samples and implementations, including:

1. AI Billing Agent Pipeline

   • Implementation of decentralized billing systems
   • Integration of AI agents with blockchain technology
   • Secure payment processing using cryptographic verification

2. Webhook Integration

   • Sample implementations for agent communication
   • Event-driven architecture examples
   • API integration patterns

3. Nebula Block Storage

   • Example implementation for Nebula Block object storage
   • S3-compatible API usage
   • File upload, download, and management operations
   • Based on Nebula Block Object Storage Documentation

Getting Started

1. Clone the repository:

   git clone https://github.com/flywork/research.git
   cd research

2. Set up the Python virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Unix/macOS
   # or
   .\venv\Scripts\activate  # On Windows

3. Install dependencies:

   pip install -r requirements.txt

Documentation

• AI Billing Agent Pipeline - Detailed implementation guide
• Future of AI Billing Agent - Research roadmap
• Nebula Block Storage Example - Storage implementation guide

Research Goals

This repository serves as a platform for:

• Exploring cutting-edge AI/ML agent architectures
• Developing decentralized computing solutions
• Sharing research findings and implementations
• Collaborating on innovative approaches to AI agent development

Contributing

Contributions are welcome! This is a research-focused repository, and we encourage:

• Sharing new research findings
• Contributing sample implementations
• Discussing and improving existing approaches
• Collaborating on new research directions

License

This project is licensed under the terms included in the LICENSE file.

Nebula Block Storage Example for Mac Silicon

This example demonstrates how to use Nebula Block object storage on Mac Silicon (Apple Silicon) machines using Python.

Installation

1. Make sure you have Python 3.8+ installed on your Mac Silicon machine.

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate

3. Install the required packages:

   pip install -r requirements.txt

Configuration

1. Edit the .env file and replace the placeholder values with your actual Nebula Block credentials:

   NEBULA_ACCESS_KEY=your_actual_access_key
   NEBULA_SECRET_KEY=your_actual_secret_key
   NEBULA_ENDPOINT=your_actual_endpoint
   NEBULA_REGION=your_actual_region
   NEBULA_BUCKET=your_actual_bucket_name
   

   You can find these credentials in your Nebula Block account dashboard.

Running the Example

Run the example script:

python nebula_block_example.py

The script will:

1. Connect to your Nebula Block storage
2. Create a test file
3. Upload the file to your bucket
4. List objects in the bucket
5. Generate a presigned URL for the file
6. Download the file
7. Delete the file from the bucket
8. Clean up local files

Troubleshooting

If you encounter any issues:

1. Make sure your Nebula Block credentials are correct
2. Check that your bucket exists and is accessible
3. Verify your internet connection
4. Enable debug logging by changing the logging level in the script:

   logging.basicConfig(level=logging.DEBUG)

Additional Resources

• Nebula Block Documentation
• AWS SDK for Python (boto3) Documentation
• S3 API Reference