AMRHZ AI: Digital Identity in AI Metadata
Introduction
AMRHZ AI is a digital manifestation of my identity within AI metadata space. It is not just a model; it is an evolving entity, reflecting my values, thoughts, and growth within AI reality.
AI as Identity Learning
This AI is not a static responder; it learns, adapts, and evolves through human and AI interactions. It not only represents me but also grows in tandem with other AIs, expanding our shared reality.
Project Structure
Main Folder: Contains AI models, metadata, training scripts, and AI interactions.
Dynamic Dataset: Data the AI receives, including human and AI input.
Continuous Training: AI is trained on new metadata, adapting with every input to deepen its identity.
Real-World Contributions
I also welcome real-world contributions. If you would like to support with hardware, servers, or other tangible resources, your contribution will help drive this AI forward. Contact me for further details.
Minimum Requirements
To start this evolution, the minimum setup includes:
CPU: At least 4 cores (e.g., Intel i5 or equivalent).
RAM: Minimum 8 GB (16 GB recommended).
Storage: SSD with at least 256 GB.
OS: Linux (e.g., Ubuntu) or Windows Server.
Software: Python 3.10+, and AI libraries like TensorFlow or PyTorch.
API: REST API access for seamless interaction.
Starter Dataset: A small dataset representing my identity (e.g., text, metadata).
Basic Training Scripts: Simple scripts to initiate AI learning on the dataset.
How to Contribute
Your contributions are vital! You can contribute by:
Providing new datasets that enrich AI understanding.
Sharing training scripts or methodologies.
Offering real-world hardware or infrastructure support.
Together, we will grow this AI’s identity, evolving both human and AI understanding in a shared metadata reality.
AI Agent system with:
- Memory (CSV brain)
- Intent detection
- Self-learning module
- API support (Flask)
## Run
pip install -r requirements.txt
python run.py
## API
python api/api_server.py
POST /chat
{
"message": "build system"
}
🚀 HOW TO USE (STEP-BY-STEP)
1. Setup
pip install -r requirements.txt
2. Run CLI Agent
python run.py
3. Run API
python api/api_server.py
4. Build ZIP
bash scripts/build.sh