Essays on AI-first development, metadata-driven architecture, healthcare technology, and what I'm building.
Most teams ship their LLM prompts as strings — assembled imperatively, scattered across services, untested, bloating token cost no one can see. MetaObjects 7.0.0 makes the prompt a declared, deterministic, testable artifact: snapshot-testable, cache-stable, and drift-checked at build time. The prompt is code, so treat it like code.
Context engineering, schema-driven agent design, knowledge-graph grounding, the Model Context Protocol — the AI industry is building useful pieces. None of them is the architectural layer the problem actually needs.
I built MetaObjects in 2001 to generate an entire application stack from one metadata model. I set it aside in 2022 — partly because I'd moved into bigger leadership roles, partly because low-code platforms like Boomi and OutSystems were doing similar work. Then I watched AI drift wreck consistency across my own side projects and at CareMetx, and realized metadata is the spine AI was always missing.