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EventHorizon Engine - Documentation Index

Welcome to the EventHorizon Engine documentation! This guide will help you navigate all available resources.

📚 Documentation Structure

🚀 Getting Started

  • QUICKSTART.md - Get up and running in 30 seconds
  • README.md - Comprehensive project overview and technical specifications

📖 Detailed Information

  • CHANGELOG.md - Recent updates and performance improvements
  • ROADMAP.md - Development timeline and future plans
  • CREDITS.md - AI-assisted development acknowledgments and philosophy

💻 Code Organization

  • include/ - Header files with API definitions
  • src/core/ - Core implementation files
  • bench_all.c - Comprehensive benchmark suite

🎯 Quick Navigation

New Users Start Here:

  1. Read QUICKSTART.md for immediate usage
  2. Run .\run.ps1 or make run-bench to see it in action
  3. Check README.md for detailed concepts

Developers Start Here:

  1. Read README.md for architecture overview
  2. Browse header files in include/ for API reference
  3. Study bench_all.c for usage examples
  4. Check TODO.md for contribution opportunities

AI/ML Researchers Start Here:

  1. Read "Core Capabilities" section in README.md
  2. Study the State Collapse mechanism
  3. Review Neuroplasticity implementation in include/eh_neuro.h
  4. See benchmark results for performance characteristics

Edge AI Engineers Start Here:

  1. Read "Edge AI Deployment" section in README.md
  2. Review platform-specific optimization tips
  3. Check memory footprint and energy consumption data
  4. Study arena allocator implementation for zero-allocation patterns

📋 Documentation by Topic

Architecture & Design

  • Memory Arena: README.md → "Core Components" → "Memory Arena"
  • DAG Structure: include/eh_dag.h + README.md
  • Beam Search: include/eh_engine.h + implementation details
  • State Collapse: README.md → "Technical Specifications"

Performance & Benchmarks

  • Benchmark Results: README.md → "Real-World Execution Results"
  • Scalability: README.md → "Physical Scalability Matrix"
  • Updates: CHANGELOG.md → Performance improvements

API Reference

  • Engine: include/eh_engine.h - Main inference API
  • DAG: include/eh_dag.h - Graph structure
  • Arena: include/eh_arena.h - Memory management
  • Neuro: include/eh_neuro.h - Neuroplasticity
  • Scoring: include/eh_scoring.h - Routing core
  • Tokenizer: include/eh_tokenizer.h - Text processing

Platform-Specific

  • Edge Devices: README.md → "Edge AI Deployment"
  • Raspberry Pi: Platform-specific compilation tips
  • Mobile: Memory optimization strategies
  • Microcontrollers: Minimal footprint configuration

Development

🔍 Find What You Need

"How do I..."

...run the benchmark? → QUICKSTART.md - Commands section

...compile manually? → QUICKSTART.md - Manual Compilation section

...use the API? → QUICKSTART.md - Quick API Example → Header files in include/

...optimize for my platform? → README.md - Edge AI Deployment section

...understand the architecture? → README.md - Core Components section

...see performance data? → README.md - Real-World Execution Results → CHANGELOG.md - Performance comparison

...learn about AI collaboration? → CREDITS.md - Complete methodology

...contribute? → TODO.md - Planned features → CREDITS.md - Development philosophy

📊 Key Specifications (Quick Reference)

Language:        Pure C99
Dependencies:    None (only stdlib + libm)
Memory Footprint: 63 MB peak RSS
Inference Speed:  278,133 passes/sec
Energy:          0.054 mJ/inference
Collapse Ratio:  93.58%
FLOPs Saved:     93.21%
License:         Apache-2.0

🎓 Learning Path

Beginner (Just want to use it)

  1. QUICKSTART.md - 5 minutes
  2. Run benchmark - 2 minutes
  3. Try API example - 10 minutes

Total: ~20 minutes to productive use

Intermediate (Want to understand it)

  1. README.md - Full read - 30 minutes
  2. Browse header files - 20 minutes
  3. Study benchmark code - 20 minutes

Total: ~70 minutes to solid understanding

Advanced (Want to modify/extend it)

  1. All documentation - 1 hour
  2. Read all headers thoroughly - 1 hour
  3. Study implementation files - 2 hours
  4. Experiment and benchmark - 1 hour

Total: ~5 hours to mastery

🤝 About This Project

EventHorizon Engine is an AI-assisted open source project that demonstrates:

  • What's possible with human-AI collaboration
  • Production-quality systems programming with AI help
  • Transparent disclosure of AI contributions
  • Real-world validation and benchmarking

See CREDITS.md for full details on the collaborative development process.

📞 Need Help?

  1. Can't find something? - Use Ctrl+F in README.md
  2. Compilation issues? - Check QUICKSTART.md troubleshooting
  3. Performance questions? - See benchmark results in README.md
  4. Want to contribute? - Check TODO.md for ideas

Happy coding! 🚀

EventHorizon Engine - Human creativity meets AI execution