A GTM diagnostic for founders who built the product but still need the market to move.

Why this exists

AI made products easier to build. It did not make distribution easier. Most founders do not need another list of channels. They need to know which adoption problem they are solving. The playbooks are not stories to copy blindly. They are pattern libraries for choosing the next experiment.

Why URL-first

Your homepage is the version of your pitch the market actually sees. Reading it first costs you nothing and surfaces the gap between what you claim and what a stranger can believe. Long forms make you rationalize; a URL makes you honest. The three follow-up questions come after the first-pass result, not before it.

How the matching works

Rule-based, not a black box. We read the page for category, audience, pricing, proof and workflow signals, pick the most likely adoption bottleneck, then score all ten playbooks from 0-30 across six dimensions worth 5 points each.

• Problem similarity — Does this startup face a similar adoption, trust, or distribution problem?
• Audience similarity — Is the first audience structurally similar?
• Trust mechanism fit — Can this startup earn trust the way the reference startup did?
• Founder/channel fit — Can the founder realistically execute the motion?
• Visible proof loop — Can users, demos, artifacts, or public proof compound distribution?
• Timing/category fit — Is there existing market curiosity or a category narrative to ride?

24-30 strong fit · 18-23 partial fit · 12-17 weak fit · 0-11 poor fit. Below 18 we say the fit is weak and recommend a diagnostic experiment instead of pretending certainty.

The 10 researched playbooks

• Lovable — Expert-Proven, Beginner-Owned
• Cursor — Familiar Workflow, Expanding Autonomy
• Perplexity — Inspectable Trust, Repeated Habit
• Clay — Operator Superpower, Community Proof
• Replit — Zero-Setup Creation Loop
• Duolingo — Free Habit, Cultural Flywheel
• Cal AI — Camera Utility, Creator Portfolio
• Partiful — Host-Seeded Invitation Loop
• Too Good To Go — Neighborhood Density, Surprise Urgency
• Strava — Single-Player Proof, Social Motivation

Why the result is a hypothesis, not a truth

The diagnosis is built from public page signals and your own answers. It states its assumptions and tells you what would make it wrong. Treat it as the fastest available starting hypothesis. The goal is not the best GTM strategy. It is the next testable GTM move.