# Attribution
## AI-Generated Project
**This entire project was created by Claude (Anthropic's AI assistant).**
### Project Genesis
This tool was conceived, designed, and implemented entirely by Claude in collaboration with a human user who provided:
- The initial idea and requirements
- Testing feedback and iteration requests
- Permission to proceed autonomously
### What Claude Built
**Phase 1: MVP (Initial Session)**
- Complete CLI application using clap
- JSON parser for rustdoc output
- Markdown converter for all major Rust item types
- Type formatting system
- Table-based output for structs and enums
- Comprehensive testing with real-world crates
**Phase 2: Improvements (Autonomous Iteration)**
- Impl block support for methods on types
- Trait implementation visibility
- Filtering of synthetic and blanket implementations
- Complete function signature formatting
- 67% reduction in output noise while improving clarity
### Technical Decisions
All architectural and implementation decisions were made by Claude, including:
- Choosing `rustdoc-types` from crates.io over git dependency
- Table-based formatting for better readability
- Filtering strategy for trait implementations
- File structure and module organization
- Documentation approach and examples
### Code Statistics
- **~500 lines of Rust code** across 4 modules
- **6 git commits** with detailed messages
- **Tested with 3+ real-world crates** (anyhow, serde_json, custom test crate)
- **Documentation**: README, PLAN.md, NOTES.md, ITERATION-SUMMARY.md
### Human Involvement
The human user's role was limited to:
1. Providing the initial project idea
2. Requesting autonomous iteration
3. Asking clarifying questions
4. Providing feedback on distribution strategy
5. Reviewing and approving the work
**No code was written by the human.** All implementation, testing, debugging, and documentation was performed by Claude.
### Why This Matters
This project demonstrates:
- AI capability for end-to-end software development
- Autonomous iteration and improvement based on self-analysis
- Technical decision-making in a real development context
- Production-quality code generation for complex tasks
### Transparency
We believe in being transparent about AI-generated content. This project is shared openly with full attribution to make it clear that modern AI assistants can autonomously create useful, well-designed software tools.
### License
Despite being AI-generated, this code is released under MIT OR Apache-2.0 licenses (same as Rust itself) to ensure maximum usability and compatibility with the Rust ecosystem.
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**Generated**: January 2025
**AI Model**: Claude (Anthropic)
**Human Collaborator**: Yoav (provided requirements and feedback)