20+ years in tech → researching AI-native development at scale → Building the future, one experiment at a time
Builder, tinkerer, and occasional heretic when it comes to legacy dev processes. Most at home where systems, teams, and AI meet.
I'm researching AI-orchestrated development methodology through building production-grade platforms. Every repository here is a live lab exploring how technical leadership + AI-assisted development can deliver interesting projects at 10x velocity.
The vibe: hands-on technical leadership, staying current by building real things with modern tools.
- DocLoom CLI — Document intelligence + RAG orchestration + multi-provider LLM abstraction
- BrightPath Companion — Privacy-first AI with consent-gated providers + local-first architecture
- Corporate Dynamics Simulation — Multi-provider AI orchestration + enterprise reliability patterns
- Language Tutor — Cultural context processing + resilient provider architecture for APAC markets
And plenty of side experiments testing architecture, deployment, and workflow patterns for human–AI collaboration at scale.
- AI Team Orchestration — Managing specialized models as collaborators, not just tools
- Enterprise Reliability — Circuit breakers, semantic caching, provider abstraction for production
- Distributed Systems — Remote-first architecture and coordination across global teams
- Velocity + Quality — Patterns that enable speed without trading off standards
- 📝 Prompt Engineering — Refining human–AI communication
- 🧪 Model Experimentation — Evaluating the latest releases in context
- ⚙️ Development Workflows — Optimizing AI-assisted coding practices
- 🛡️ AI Workflow Integration — Embedding models into real-world SDLCs
Note: these tools and models evolve fast; this list may not exactly match what I’m using right now.
If you’re exploring how AI changes the way teams build, ship, or scale — I’d love to trade notes.
Find me:
- 🌐 Website: take a look
- 📧 Email: drop a line
- 💼 LinkedIn: connect here
"Make it so." — Picard ✨