Jory MacKay

Six-step checklist for validating new product ideas, like talking to users, building a fake door, or testing a landing page. Useful when a team has an early idea and wants quick ways to know if real demand exists.

Karlo Mihanović

Explains how idea validation in product discovery checks whether there's real market need before investing in build. Useful when a team has a bold idea and needs a clear way to test whether it's worth pursuing.

Shana Rusonis

Explains why color contrast is a strong design principle to test, since contrasting elements grab user attention. Useful when running A/B tests on key calls to action and you want to know if a contrast change drives clicks.

Jeff Gothelf

Argues that the term MVP has lost its meaning and people use it to mean very different things, from rough tests to nearly finished products. Useful when teams keep arguing about scope or fall into building too much before testing assumptions.

Jordon Sansom

A short evaluative guide for asking whether a new idea deserves to exist before investing in it. Useful when the team is buzzing about an idea but no one has stress-tested whether it should be built.

Tolgay Budayici

Walks through how to define measurable hypotheses upfront so feature work is justified, tracked, and learned from. Useful when feature debates rely on opinion and you need a way to ground them.

Trevor Newberry

Frames 0-to-1 as the gap between idea and MVP, full of unknowns like market and resource risk, and shows how cheap prototypes plus discovery reduce those risks. Useful when stakeholders need a clear picture of what could go wrong before approving a new build.

Liat Ben-Zur

Liat Ben-Zur lists ten common PLG mistakes, like ignoring activation or skipping enterprise needs. Useful when a team is rolling out PLG and wants to avoid known traps.

Konstantin D

Konstantin D groups product risks into technical, market, financial, management, and security types and explains how to assess each. Useful when a PM wants a wide-net risk checklist before planning.

Raluca Piteiu-Apostol

Raluca Piteiu-Apostol covers Cagan's four product risk dimensions plus a fifth ethical risk for AI. Useful when a PM wants a current risk framework that includes AI.

Yana Yushkina

Yana Yushkina (Google) shows how to use data to validate hypotheses and gain influence with cross-functional teams. Useful when a PM struggles to win consensus and wants data to back design proposals.

Dave Masom

Frames product management as managing four risks: value, usability, feasibility, and business viability, and shows how discovery is really de-risking. Useful when a PM needs a shared vocabulary for talking about what could go wrong before building.

Julie Hall

HBR classic that lists five common reasons new products fail, including weak demand and poor consumer education. Useful when a product team wants the canonical list of launch failure causes to pressure-test their plan.

Stéphanie (Stef) Walter

Sixty UX cognitive bias cards and a workshop to help teams recognize bias across research, decisions, teamwork, memory, and thinking. Useful when a UX or product team wants a ready-made workshop kit to learn about bias as a group.

Kalina Tyrkiel

Walks through cognitive biases that shape both how users behave and how designers make choices. Useful when a UX team wants to spot bias in their own decisions and in the way users use a product.

Bhavya Singh

Bhavya Singh covers the craft side of running good product experiments. Useful when PMs want to move past templates and design experiments thoughtfully.

Tobias Scharikow

Tobias Scharikow walks through how to find the riskiest assumptions in a business model and turn them into testable hypotheses. Useful when a startup team has a stack of guesses and needs to know which to test first.

Ameet Ranadive

Ameet Ranadive argues PMs and founders fall in love with their ideas without checking that a real market opportunity exists. Useful when a team is excited about an idea and you want to slow them down to validate first.

Adam Fard

Adam Fard lists common reasons apps fail to keep users, including too many ads, slow load times, bad reviews, and clunky UI. Useful when a team has rough retention numbers and wants a quick checklist of suspects to investigate.

Thomas Nagels

Argues the MVP is the wrong tool for finding weak spots and offers RAT — the Riskiest Assumption Test — which scores each assumption by probability of being wrong times impact, then tests the top one. Useful when a team is racing to build an MVP but has not asked which single assumption could kill the idea.

Cesar Tapia

Carwow's product team shares how they list every assumption behind a feature, score them by risk, and design cheap tests for the riskiest ones before building. Useful when a team is about to invest in a new feature and wants to avoid building something the market will not want.

Michael Storrs

Argues that testing the assumptions behind an idea is faster and cheaper than building the product, and that the right assumptions to test are the ones the product depends on to work. Useful when a team is about to start building and you want to push them to validate the riskiest bets first.

Tristan Kromer

Tristan Kromer shares a new lean experiment template and warns readers not to use it without thinking. Useful when a team is about to adopt a template and needs a sanity check on doing it well.

Chris Stone

Chris Stone's Experiment Canvas template hosted on Miro, ready to copy and run. Useful when a team needs a shared visual to design and review experiments together quickly.

James Birchler

James Birchler explains how to write Lean Startup hypotheses you can actually learn from, not vague guesses. Useful when a team's hypotheses keep coming out fuzzy and unfalsifiable.

Tristan Kromer

Tristan Kromer argues that templates can hurt thinking, then shares his Lean Startup template anyway as a jumping-off point. Useful when a team is leaning on templates too hard and needs a reminder to think first.

Tim Herbig

Tim Herbig shares a Lean UX hypothesis template tuned for product managers, with a fillable example. Useful when a PM is forming a hypothesis for a sprint and wants a clean template to follow.

Nima Torabi

Lays out a framework for testing ideas during discovery, with examples of fast experiments per assumption type. Useful when a team has a backlog of ideas and needs to figure out which one to test first.

Tristan Kromer

Shares an updated lean experiment template that combines hypothesis, fail conditions, and observations, then argues teams should build their own version instead of copying it. Useful when picking or shaping the format you use to record experiments and decide which fields actually help your team avoid common traps.