Showing 1–12 of 21 articles tagged AI Architecture

Digital agents were the first act. Physical AI is the next product frontier: robots, sensors, factories, vehicles, and supply chains.

AI software quality is a production discipline. Code got cheap, but review, evals, rollback, and observability did not.

Energy, chips, systems, models, applications. Every layer matters. Only one pays compounding returns. A framework for picking yours.

Six production chat surfaces, a habit of breaking every AI chat in the wild, and the defence-in-depth stack that keeps your prompts contained.

Chat is the wrong interface for AI agents in professional software work. A well-written issue is a better agent instruction than any prompt.

A 97% attack detection rate sounds fine until an agentic system has tool access, private data, and a path to action. Then it is a breach rate.

When AI writes the code, green CI isn't enough. The new discipline is understanding and defending the choices the model made — not just the ones you made.

The 'never rewrite' doctrine was based on rewrite cost. AI has collapsed that cost to days. Pre-launch rewrites are now a product strategy, not a failure.

AI coding is the sixth abstraction layer in 80 years. Every previous layer was dismissed as not real programming by the practitioners of the one below.

DAU, time-in-app, and NPS were built for a world where humans do the work. AI products need different metrics. A framework for what to measure and why.

Scaffolding gives you 10-20% gains that the next model wipes out. The bitter lesson for product builders: give the model tools and a goal, not a workflow.

Weekend build to 145K GitHub stars to acquisition in weeks. The pattern: agents that execute locally instead of chatting in a browser window win on adoption.