Your AI product doesn’t need another model—it needs a better system and better context.

In these two articles, I share the most concentrated, hard‑won lessons I’ve published this year from taking DocuMentor AI into production: how to architect a hybrid local+cloud stack, and how to engineer the context so users never have to write prompts.

You’ll learn:

• How to design a Chrome‑first hybrid architecture that balances Gemini Nano and cloud models without leaking complexity to users

• A reusable provider abstraction and routing strategy that gracefully degrades between local and cloud execution

• How to use personas (role, skills, goals, preferences) so recommendations and explanations actually change per user

• Content decomposition patterns that turn messy pages into the right slices of context for each feature

• How to adapt prompts and workflows across local and cloud models without forking your entire product

If you’re building AI extensions, devtools, or any local+cloud system, these two pieces are the clearest blueprint I’ve published this year for shipping something users actually rely on.

Start here (architecture + execution layer):
Engineering a Hybrid AI System with Chrome’s Built‑in AI and the Cloud

Then read the follow‑up (context + UX layer):
Engineering Context for Local and Cloud AI: Personas, Content Intelligence, and Zero‑Prompt UX