Cut a feature or product idea down to its true minimum: the one core loop, the fakeable parts, and the learning the MVP must buy.
Cut this to a real MVP: {{idea}} (full vision, who it's for, what we're trying to learn/prove: {{learning_goal}}).
Cut ruthlessly: the ONE core loop that tests the learning goal (everything not in that loop goes), the concierge/fake list (what to do manually or hard-code that users think is real — a human behind the curtain, a canned dataset, a single hardcoded workflow), the segment cut (one user type, not all), the polish floor (what genuinely can't be rough — usually trust and data-safety — vs what can), and the honest week-count for the cut version. Then the discipline: the learning question the MVP answers, the metric that answers it, and the decision we've pre-committed to at each outcome. If the 'MVP' still takes a quarter, cut again — show me the next cut.
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