AI product management
Use AI to move faster without skipping problem framing, customer evidence, or evaluation.
Practical guides for founders, first PMs, product engineers, and product builders: discover customer problems, decide what to build, and ship with AI.
Field guides for small teams building software, AI products, and better product habits.
Use AI to move faster without skipping problem framing, customer evidence, or evaluation.
Go from a real customer problem to an AI feature you can evaluate, release carefully, and improve.
Practical workflows for people close to the code: learn from users, choose a small bet, and give coding agents useful context.
Turn customer signals into a focused plan, and test whether people value what your team is building.
A practical way for solo founders and product engineers to choose the next build using customer evidence, product usage, and small validation tests.
Practical guides, product frameworks, and Specky updates — sent when it’s worth your time.
A seven-step AI-native product development workflow for product builders and engineers: test a real user problem, decide where AI belongs, define boundaries, evaluate real tasks, and learn from rollout.
A practical, copyable PRD template for AI coding agents. Define the user problem, keep scope clear, give the agent the right context, and agree on how to verify the result.
A practical feature launch checklist for small product teams: choose the audience, ground the promise in evidence, coordinate the rollout, and measure adoption.
How to choose software that connects customer evidence to product decisions, specs, delivery work, and outcomes—with a practical evaluation checklist.
Use this product problem statement template to move from a feature request to user evidence, capture what is still unknown, and give product and engineering a clear next step.
Turn interview notes into useful product evidence with a lightweight workflow for observations, themes, contradictions, and next decisions.
A practical customer discovery workflow for product engineers: choose a decision, ask about recent behavior, separate evidence from assumptions, and test the smallest useful change.
A practical framework for deciding when an AI model adds product value, when a rule is enough, and what to evaluate before release.