AI Technologies
AI Product and Platform Work
AI is now part of both my product thinking and my engineering workflow. In current private product work I am building AI-enabled experiences around conversational coaching, prompt-driven workflows, content generation, and automation. That work includes modern .NET web applications, chat-oriented user experiences, provider abstraction, observability, and careful system boundaries around data, validation, and supportability.
LLM, Voice, and Media Orchestration
My recent private project work goes beyond simple prompt experiments. It includes multi-provider LLM integration, AI chat experiences, text-to-speech orchestration, image and video generation pipelines, continuity and quality gates, and configurable provider patterns designed so the underlying AI services can evolve without forcing a full application rewrite.
Modern AI Stack Experience
The technologies I am currently working with in this space include .NET 10, Blazor, ASP.NET Core, SignalR-style real-time UX, Azure OpenAI and Azure AI services, Azure Speech, Redis, Cosmos, SQL Server, OpenTelemetry, Seq, Application Insights, and feature-flag driven rollout patterns. My public seq-mcp project complements that by showing how I think about AI tooling, structured integrations, and operational diagnostics for agent workflows.
AI-Assisted Engineering Workflow
I actively use GitHub Copilot, Codex, and OpenAI ChatGPT as part of my day-to-day workflow. I use them differently depending on the need: inline coding assistance and scaffolding with Copilot, repository-aware task execution and refactoring help with Codex, and broader problem solving, research, planning, and iteration support with ChatGPT. I also use AI tools for selected personal productivity and ideation workflows when they are a good fit.
Practical AI Discipline
My approach to AI is pragmatic rather than hype-driven. I care about preview-first behavior, auditability, quality gates, test coverage, operational visibility, and human review. That mindset comes from having lived in QA, production support, and engineering leadership roles where “interesting” is never enough unless the software is reliable and maintainable.
Summary
- GitHub Copilot, Codex, and ChatGPT workflow integration
- AI-enabled chat and coaching experiences
- LLM provider abstraction and orchestration
- Azure OpenAI and Azure AI service integration
- Text-to-speech, image, and video generation pipelines
- Prompt workflows, quality gates, and validation
- Real-time and interactive AI user experiences
- Observability, testing, and responsible AI implementation
- MCP and AI tooling integrations