✅ High-Speed Processing – Optimized bitwise operations and low-level transformations.
✅ Multi-Threaded Execution – Parallel chunk processing for increased speed.
✅ 512-bit Strong Hash Output – Reduces collisions while maintaining efficiency.
✅ Advanced Mixing Function – Inspired by MurmurHash3 for better diffusion.
✅ Bitwise Rotation Optimization – Faster and more secure state transformations.
✅ Lightweight & Scalable – Low memory usage, works on large data sets.
✅ Strong Avalanche Effect – Small input changes drastically modify the hash.
✅ Cross-Platform & Extensible – Works seamlessly in Python, with future support planned for C/Rust.
Clone the repository:
git clone https://github.com/ProgrammerKR/HashX.git
Navigate to the directory:
cd HashX
from hashx import HashX
hasher = HashX()
print(hasher.hash("Hello, World!")) # Example usage
3F2A9D7B89C4E6A5D12F1E4B67A3C9D5E8F7B2A6C3D1E0F4B6A2D9C7E3F5A5B1
HashX has been tested against SHA-256, BLAKE3, xxHash, and MurmurHash3 for speed and efficiency.
For detailed benchmark results, check the full report:
📊 Benchmark Results
📢 Have questions or feedback?
Contributions are welcome! Feel free to fork the repo, submit issues, or create pull requests.
git clone https://github.com/your-username/HashX.git
git checkout -b improve-docs
README.md and add documentation updates).git add README.md
git commit -m "Improved documentation & usage examples"
git push origin improve-docs
This project is open-source and available under the MIT License.