WinR Lab (jev Hook Classifier)

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  WinR Lab x jev

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  Replit & RevenueCat Blueprint

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  Growth & Paywall Funnel

Inspiration

I wanted to figure out why certain creator short-form videos blow up while others get ignored. Instead of guessing, I built WinR Lab to reverse-engineer viral videos and break down their exact hooks and structures.

What it does

WinR Lab is an AI-powered reel decoder. It analyzes top YouTube Shorts and TikToks, automatically classifying their hooks, topics, and script structures so creators can spot viral patterns instantly.

How we built it

Built with React, Vite, and Tailwind CSS. We designed a clean research dashboard featuring interactive archive grids, live simulation controls, and clear visual breakdown metrics.

Challenges we ran into

Fitting all the stats, reel grids, and detailed classifications onto a single screen without messy scrollbars took some layout tuning. We solved it with a compact, card-framed desktop layout.

Accomplishments that we're proud of

We built a polished dashboard that looks and feels like a professional creator intelligence platform.

What we learned

Keeping data dense yet easy to digest is tricky, but using visual progress bars for hook types (Curiosity, Recognition, Result) makes patterns pop instantly.

What's next for WinR Lab (jev Hook Classifier)

Adding live web scrapers for automated daily reel ingestion and AI script generation based on top-performing hooks.

Built With

• react
• revenuecat
• stripe
• tailwind-css
• typescript
• vite