Finding Your Neighborhood
Privacy-preserving community architecture
Finding Your Neighborhood explores how people can discover communities without surveillance, behavioral profiling, or centralized data collection.
The project also demonstrates my approach to AI-assisted engineering. AI accelerated implementation and challenged assumptions throughout development, while architectural decisions, integrity verification, and publication standards remained under human control.
FYN applies the same philosophy I bring to AI systems: minimize unnecessary data collection, keep behavior inspectable, and preserve user control.
- Static GitHub Pages architecture
- Python build system
- Build-time integrity verification
- Referrer-scrubbing architecture
- Client-side matching and discovery
- No database
- No tracking
- No advertising
Multi-Model AI Verification Workflow
Calendar Defender
A practical engineering project exploring collaborative AI review rather than depending on a single model to produce solutions.
Independent AI models reviewed architecture, challenged assumptions, explored edge cases, and critiqued implementation approaches before human synthesis.
The result was not simply better code. It was a repeatable engineering process that improved transparency, reasoning quality, and confidence in technical decisions.
Custom Static Publishing System
A Python builder that turns structured JSON into a portable, versioned website.
- Reusable templates and collections
- Build-time validation
- No runtime application or database
Enterprise Synthetic Student Data
Perl generators producing more than one million privacy-preserving Oracle records for performance testing.
- Representative randomized data
- FERPA-aware testing approach
- Repeatable enterprise-scale generation
