A blackboard architecture sounds simple until you build one. Then it sounds obvious — but only in retrospect.

Teachable Machine works because transfer learning constrains the problem. The same constraints that make fast demos possible also define where they fail.

Modern agent systems rediscovered coordination, but forgot the shared board that makes uncertainty explicit, auditable, and adaptable under real-world ambiguity.

Ten pressure-test questions that expose what polished AI demos miss, from hidden assumptions to user-facing failures, before production finds them.

AI demos can look brilliant while hiding brittle systems. This is about seeing through the applause and finding what breaks first.

AI agents plan like grandmasters in changing environments. The fix is simple and hard: teach them when to reassess, stop, and adapt.

AI made generation abundant. The new bottleneck is comprehension: can anyone read, challenge, and prune machine-written code before reality does?

For seventy years computers executed instructions. Reasoning models flipped the contract: inference over specification, and a new question of what must stay explicit.

A rebooted welcome note on writing in public, abstraction, and AI: why architecture is really about choosing assumptions you can live inside.