Archive
A journey through everything I've published, organized over time.
How the MCP Apps extension lets tools return interactive UI resources — and how it compares to A2UI and AG-UI for product teams building on LLM responses.
How Google's declarative agent UI spec lets LLMs propose native component trees across trust boundaries — without executing generated code.
What the Agent–User Interaction Protocol standardizes, how it differs from A2UI and MCP, and when your agentic frontend needs it.
How text streams, SSE data streams, and UIMessage parts carry LLM output to the frontend — and what frontend engineers need from each.
How JSON Schema, tool use, and typed objects form the first protocol layer between LLM responses and frontend interfaces.
A factual map of the open specs and transport layers that turn model output into product UI — from structured outputs and streaming to AG-UI, A2UI, and MCP Apps.
How AI products can evolve from single prompts into durable workflows with memory, checkpoints, and state.
A practical way to separate suggestions, generated artifacts, and user-approved actions in AI products.
How schemas, components, and model responses work together to make AI interfaces predictable.
Why AI products should treat chat as an entry point into structured UI, workflows, and decisions.
A practical guide to chat UX, generative UI, and AI frontend architecture — what Interface Lab covers, why it matters, and where to start.