- Introduction
- Quick Start
- Architectural Overview
- Roadmap
- Data and Workflow Descriptions
- Core Principles and Mental Models
- Contributing
- License
- Acknowledgments and Credits
The AI-Native Programming Paradigm project revolutionizes software development by enabling AI to directly produce optimized machine code, with human-readable representations available on demand.
- Clone this repository:
git clone https://github.com/bmeyer99/ai_native_programming_paradigm.git
- View documentation directly on GitHub or a Markdown viewer.
The project is based on a three-layer architecture:
- Intent Layer: Captures developer intent.
- Semantic Layer: Maps intent to optimized machine code.
- Execution Layer: Ensures high-performance execution.
For detailed architecture and workflows, refer to the Idea Flow document.
The development roadmap includes:
- Foundation and Research
- Core Technology Development
- Development Tooling
- Integration and Interoperability
- Scaling and Optimization
- Ecosystem Growth
For details, see the Project Roadmap.
- Testing Framework: Implements a multi-layered verification system. See Comprehensive Testing Architecture.
- CI/CD Integration: Automates confidence score integration and verification checks. See CI/CD Pipeline.
Key principles:
- Intent-Driven Development: Focus on human intent rather than code syntax.
- Verification-Centric Architecture: Ensure correctness and traceability at every layer.
Explore more in Paradigm Principles.
- Add new pages: Create markdown files under
docs/and link them appropriately. - Edit existing pages: Update relevant markdown files in the
docs/folder.
[Specify your license here].
Thanks to all contributors and resources that supported this project.
Feel free to copy this structure and replace placeholders with your specific project details. Let me know if you need more assistance!