by larryste | 2026-03-05 19:39:51+00:00
## What It Does

The tool generates compressed "shorthand" representations of codebases:
- 40k LOC → 8k lines (78% compression)
- Detects duplicate patterns automatically
- Generates layered documentation (overview → modules → files)
- Supports Tree of Thoughts (ToT) and Swarm reasoning for complex decisions

## Key Features

*Core:*
- AST-based parsing (Python, extensible to other languages)
- Pattern detection with similarity clustering
- Duplication analysis with refactoring recommendations
- Parallel indexing for 1M+ LOC codebases

*AI Integration:*
- Multi-model support (Ollama, OpenRouter, Claude)
- ToT templates for architecture decisions
- Swarm personas for code review (Skeptic, Architect, QA, etc.)
- Automated reasoning for complex operations

*CLI Commands:*
```bash
ai-shorthand generate /path/to/code --output ./analysis/
ai-shorthand debug /path/to/code --output bugs.md
ai-shorthand reasoning tot "Feature Name" --output feature_tot.md
ai-shorthand reasoning swarm "Feature Name" --output feature_swarm.md
```

## Validation Study

I ran a 5-phase validation study implementing 5 features in a real codebase:

| Method | Time | Quality | Cognitive Load |
|--------|------|---------|----------------|
| Manual | 15 min | 8.0/10 | 5/10 |
| AI | 15 min | 9.0/10 | 3/10 |
| AI+ToT | 20 min | 9.0/10 | 4/10 |
| AI+ToT+Swarm | 25 min | 9.5/10 | 4/10 |

*Results:*
- +12.5% code quality with AI
- -40% cognitive load
- 0 bugs across 731 LOC
- 3x ROI (time saved vs invested)

Full report: `validation/FINAL_VALIDATION_REPORT.md`

## Technical Details

*Architecture:*
- 7 core modules (file discovery, AST parser, pattern scanner, etc.)
- 31 unit tests (100% pass)
- Parallel processing with ProcessPoolExecutor
- LRU caching for pattern similarity (1024 entries)
- Security hardening (path validation, symlink protection)

*Performance:*
- 118 files (40k LOC) in 0.61s
- 857 files/second throughput
- 3.6x faster than sequential processing

## Example Usage

```bash
# Install
pip install ai-shorthand-tool

# Generate shorthand for a codebase
ai-shorthand generate ./my-project --output ./analysis/

# Get AI intelligence report
ai-shorthand intelligence ./my-project --provider ollama

# Debug analysis with refactoring plan
ai-shorthand debug ./my-project --output refactor.md

# Generate ToT template for complex feature
ai-shorthand reasoning tot "Add Authentication" \
--complexity medium --output auth_tot.md
```

## Why I Built This

I was tired of:
1. AI tools that add bloat instead of improving code quality
2. Code analysis tools that don't integrate with AI reasoning
3. No validation studies for AI development tools

So I built one with actual metrics and validation.

## What's Different

*Not just another AI wrapper:*
- No feature bloat (removed 70 lines of my own AI slop during development)
- Actual code quality improvements (split 541-line main() into modules)
- Validation study with real data (not marketing claims)
- Open source and reproducible

*Bridges hardening + AI:*
- Integrated with super_editor_complete.py (1,721 LOC hardened tool)
- Added ToT/Swarm hooks for high-risk operations
- Opt-in AI (doesn't force AI on simple operations)

## Tech Stack

- Python 3.8+
- Click (CLI), Rich (output)
- AST (parsing), difflib (similarity)
- Optional: Ollama/OpenRouter/Claude for AI features

## Roadmap

*v2.2 (Q2 2026):*
- Web UI for visualization
- VS Code extension
- Multi-language support (Go, JS/TS)

*v3.0 (Q4 2026):*
- Autonomous refactoring agent
- CI/CD integration
- Cloud deployment

## Feedback Wanted

1. Is the validation study convincing?
2. What features would you actually use?
3. Any showstoppers for adoption?

Happy to answer questions!