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ZON v1.0.0 - Entropy Engine (First Official Release)

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@ronibhakta1 ronibhakta1 released this 23 Nov 13:16
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v1.0.0
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ronibhakta1 Roni bhakta
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🚀 ZON v1.0.0 - Entropy Engine

First Official Release - Production-ready data serialization format optimized for LLM token efficiency.

🎯 What is ZON?

Zero Overhead Notation (ZON) is a human-readable data format that achieves 24-40% better compression than TOON and 30-42% compression vs JSON on real-world data, while remaining 100% readable and debuggable.

✨ Key Features

  • 🧠 Entropy Tournament: Automatically selects optimal compression strategy per column
  • 📊 8 Compression Strategies: ENUM, VALUE, DELTA, GAS_INT, GAS_PAT, GAS_MULT, LIQUID, SOLID
  • 👁️ Human Readable: Unlike binary formats, ZON is plain text
  • ✅ 100% Lossless: Guaranteed perfect reconstruction with safety anchors
  • ⚡ Zero Configuration: Works out of the box

📈 Performance Benchmarks

Standard Datasets

  • employees.json: 63.1% compression vs JSON, +9.7% vs TOON
  • complex_nested.json: 76.0% compression vs JSON, +76.6% vs TOON
  • orders.json: 30.3% compression vs JSON, +2.7% vs TOON

Real-World API Data

  • Random Users API: 42.4% compression, +40.4% vs TOON 🏆
  • StackOverflow Q&A: 42.4% compression, +40.4% vs TOON 🏆
  • GitHub Repos: 33.9% compression, +32.8% vs TOON
  • Average: 30.5% compression, +24.1% vs TOON

🔧 Installation

pip install zon-format
💡 Quick Start
python
import zon

# Your data
data = [
    {"id": 1, "name": "Alice", "role": "Admin"},
    {"id": 2, "name": "Bob", "role": "User"}
]

# Compress
compressed = zon.encode(data)

# Decompress
original = zon.decode(compressed)

🤖 LLM Integration

Works seamlessly with:

  • OpenAI (GPT-3.5, GPT-4)
  • Anthropic Claude
  • LangChain
  • LlamaIndex
  • Hugging Face Transformers
  • Cost Savings: Reduces LLM API costs by 30-40% through fewer tokens!

📄 License

Proprietary - Free for Production Use

  • ✅ Use in production (commercial/non-commercial)
  • ✅ Integrate into applications
  • ❌ Cannot redistribute source code

Copyright © 2025 Roni Bhakta. All Rights Reserved.

📚 Documentation

Acknowledgments

Inspired by TOON format. Tested on datasets from JSONPlaceholder, GitHub API, Random User Generator, and StackExchange API.

Ready for production use! 🎉

For licensing inquiries:
ronibhakta1@gmail.com

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Assets to attach (optional)

You can attach:

  • Source code (zip/tar.gz) - GitHub does this automatically
  • dist/ folder files if you built with python -m build (optional)

After publishing, you can:

  1. Share on Twitter/LinkedIn
  2. Post on Reddit (r/Python, r/MachineLearning)
  3. Submit to awesome-python lists
  4. Announce in LLM/AI communities

This creates a professional, comprehensive release that highlights ZON's unique value! 🚀

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