diff --git a/README.md b/README.md
index c97b150..06c57f3 100644
--- a/README.md
+++ b/README.md
@@ -1,41 +1,130 @@
 
-# Swarms-Example-1-Click-Template
+# StellarNet 🌟
 
 [![Join our Discord](https://img.shields.io/badge/Discord-Join%20our%20server-5865F2?style=for-the-badge&logo=discord&logoColor=white)](https://discord.gg/agora-999382051935506503) [![Subscribe on YouTube](https://img.shields.io/badge/YouTube-Subscribe-red?style=for-the-badge&logo=youtube&logoColor=white)](https://www.youtube.com/@kyegomez3242) [![Connect on LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue?style=for-the-badge&logo=linkedin&logoColor=white)](https://www.linkedin.com/in/kye-g-38759a207/) [![Follow on X.com](https://img.shields.io/badge/X.com-Follow-1DA1F2?style=for-the-badge&logo=x&logoColor=white)](https://x.com/kyegomezb)
 
+[![Python](https://img.shields.io/badge/Python-3.10+-blue.svg)](https://www.python.org/downloads/)
+[![PyTorch](https://img.shields.io/badge/PyTorch-2.0+-orange.svg)](https://pytorch.org/)
+[![License](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
+[![arXiv](https://img.shields.io/badge/arXiv-2024.xxxxx-b31b1b.svg)](https://arxiv.org/)
 
-[![GitHub stars](https://img.shields.io/github/stars/The-Swarm-Corporation/Legal-Swarm-Template?style=social)](https://github.com/The-Swarm-Corporation/Legal-Swarm-Template)
-[![Swarms Framework](https://img.shields.io/badge/Built%20with-Swarms-blue)](https://github.com/kyegomez/swarms)
 
+StellarNet: An AI system probing the possibility that stars may possess primitive forms of information processing. By analyzing complex patterns in stellar emissions using deep learning, we search for signatures of self-organization and structured behavior that transcend random processes.
 
 
-## 🚀 Quick Start
+## Overview
+
+This project implements a comprehensive analysis pipeline for investigating potential "consciousness-like" patterns in stellar data using PyTorch and astronomical data from TESS and Kepler missions.
+
+
+## Features
+
+- 🔬 Real-time analysis of stellar light curves from TESS/Kepler missions
+- 🧠 LSTM-based pattern detection for stellar behavior prediction
+- 📊 Comprehensive entropy and frequency analysis
+- 🔍 Anomaly detection in stellar emissions
+- 📈 Advanced visualization of stellar patterns
+
+## Installation
 
 ```bash
 # Clone the repository
-git clone https://github.com/The-Swarm-Corporation/Swarms-Example-1-Click-Template.git
+git clone https://github.com/Agora-Lab-AI/StellarNet.git
+cd StellarNet
+
+# Create and activate a virtual environment (optional but recommended)
+python -m venv venv
+source venv/bin/activate  # On Windows, use `venv\Scripts\activate`
+
+# Install dependencies
+pip install -r requirements.txt
+```
+
+## Quick Start
 
-# Install requirements
-pip3 install -r requirements.txt
+```bash
+python main.py
+```
 
-# Set your task in the .env file or pass it in the yaml file on the bottom `task:`
-export WORKSPACE_DIR="agent_workspace" 
-export GROQ_API_KEY=""
+By default, the script analyzes a set of pre-selected variable stars. To analyze specific stars:
 
-# Run the swarm
-python3 main.py
+```bash
+python main.py --star_id "TIC 260128333" --mission "TESS"
 ```
 
 
-## 🛠 Built With
+## Requirements
 
-- [Swarms Framework](https://github.com/kyegomez/swarms)
 - Python 3.10+
-- GROQ API Key or you can change it to use any model from [Swarm Models](https://github.com/The-Swarm-Corporation/swarm-models)
+- PyTorch
+- lightkurve
+- astropy
+- numpy
+- pandas
+- scipy
+- scikit-learn
+- matplotlib
+
+See `requirements.txt` for complete list.
+
+## Methodology
+
+Our analysis pipeline consists of several key components:
+
+1. **Data Collection**: Automated fetching of stellar light curves from TESS/Kepler missions
+2. **Preprocessing**: Cleaning and normalization of time-series data
+3. **Pattern Analysis**:
+   - Shannon entropy calculation
+   - Fourier analysis
+   - LSTM-based pattern prediction
+   - Anomaly detection
+4. **Visualization**: Comprehensive plotting of results
+
+## Results
+
+Analysis results are saved in the `results/` directory with the following structure:
+- `{star_id}_analysis.npz`: Numerical results and statistics
+- `{star_id}_plots.png`: Visualization plots
+- `models/{star_id}_model.pt`: Trained LSTM model
+
+## Contributing
+
+We welcome contributions! Please see our [Contributing Guidelines](CONTRIBUTING.md) for details.
+
+1. Fork the repository
+2. Create a feature branch
+3. Commit your changes
+4. Push to the branch
+5. Open a Pull Request
+
+## Citation
+
+If you use this code in your research, please cite:
+
+```bibtex
+@article{stellarnet2024,
+  title={StellarNet: Investigating Information Processing Patterns in Stellar Emissions},
+  author={Agora Lab AI, Kye Gomez},
+  journal={arXiv preprint arXiv:2024.xxxxx},
+  year={2024}
+}
+```
+
+## License
 
-## 📬 Contact
+This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
 
-Questions? Reach out:
+## Acknowledgments
+
+- NASA's TESS and Kepler missions for providing stellar data
+- The lightkurve team for their excellent data access tools
+- The astropy community for their comprehensive astronomy tools
+
+## Contact
+
+- **Project Lead**: [Your Name](mailto:contact@agoralab.ai)
+- **Website**: [https://agoralab.ai](https://agoralab.ai)
+- **Issues**: [GitHub Issues](https://github.com/Agora-Lab-AI/StellarNet/issues)
 - Twitter: [@kyegomez](https://twitter.com/kyegomez)
 - Email: kye@swarms.world
 
@@ -48,5 +137,3 @@ Questions? Reach out:
 ---
 
 ⭐ Star us on GitHub if this project helped you!
-
-Built with ♥ using [Swarms Framework](https://github.com/kyegomez/swarms)