Category
AI Product Building
Practical posts on shipping AI products: architecture patterns, evaluation frameworks, pricing models, multi-model orchestration and UX design.
Category
Practical posts on shipping AI products: architecture patterns, evaluation frameworks, pricing models, multi-model orchestration and UX design.
Showing 13–24 of 28 articles in AI Product Building

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.

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.

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

The best AI products aren't imagined. They're discovered by watching how people misuse your existing ones. A framework for finding what to build next.

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.

4 engineers, 10 days, a new product line. AI coding agents collapsed build economics. If code is a commodity, your moat is data, integrations, and trust.

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.

AI is not a feature, it is a new compute paradigm. Bolting GenAI onto legacy platforms destroys unit economics. If the AI is optional, it's a gimmick.