I build AI-native systems, engineering workflows, and production-minded infrastructure. I care about clear specs, reliable execution, and software that holds up outside demos.
This is where I share the public edge of my work across agentic workflows, model-tool interfaces, and operationally sound systems.
- Agentic engineering workflows, spec-driven development, and systems that help teams move from intent to implementation with less ambiguity.
- MCP servers, prompt and context systems, and practical interfaces between language models, tools, and real-world data.
- Cloud, observability, and local-to-production infrastructure with an emphasis on repeatability, debugging, and operational clarity.
I am interested in systems where models are not left to improvise over critical facts. That usually means deterministic tooling, clear interfaces, source-grounded outputs, and enough structure to make failure modes visible.
I care about reducing the gap between "we have an idea" and "this is implementable." That includes better specs, better feedback loops, and workflows that make AI assistance more disciplined instead of more chaotic.
I like stacks that are observable by default and simple enough to operate under pressure. Tracing, logs, metrics, and repeatable environments matter more to me than clever abstractions.
- I optimize for clarity before speed-for-speed's-sake.
- I prefer explicit tradeoffs, concrete interfaces, and systems that can be debugged by someone other than the original author.
- I am most useful where product ambiguity, technical depth, and execution pressure overlap.