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.
Your first PM should create a shared way to decide and learn, not import a large-company process. This 30-day plan is for founder-led, AI-native teams that need clearer priorities without losing customer closeness.
Practical guides, product frameworks, and Specky updates — sent when it’s worth your time.
A practical way for solo founders and product engineers to choose the next build using customer evidence, product usage, and small validation tests.
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.
Andrew Ng’s 1:0.5 PM-to-engineer ratio was one team’s proposal, not an industry benchmark. Here’s what the debate means for AI-assisted teams—and how to keep product and engineering aligned.
A practical guide to product-market fit for AI startups: measure repeat use on real customer jobs, task outcomes, retention, trust, and cost without relying on one score.