Showing 13–24 of 24 articles tagged AI Architecture

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.

Your AI product market fit depends on a model that has not shipped yet. Build your product architecture for the capability curve, not today's snapshot.

I built AI voice receptionists that handle real phone calls for real businesses. Latency, conversation flow, graceful handoff. Here's what actually matters.

Chunking, retrieval, and grounding are not engineering details. They are product decisions that determine whether your AI feature helps or hallucinates.

Most AI tools are deployed but unused. The friction isn't capability. AI lives in a separate tab instead of where work happens. Build inline, not destination.

Most teams evaluate agents with manual chats and gut feel. A practical framework for eval suites that let you ship, starting with 20 examples, not 20,000.

A manager model checking every worker output increases unit cost by 2,500%. The fix: a spot-check architecture that can save 75% of your token margin.

A 5-step agent at 95% accuracy per step is only 77% reliable. The path forward isn't better agents, it's narrower ones. Three rules for workflows that ship.

Building for a single model is technical debt with a short shelf life. The winning strategy is orchestration, evals, and governance, not leaderboard loyalty.

Strip the vendor marketing from Google's AI Agent Handbook and three stack-agnostic architectural patterns emerge that every product builder should steal.