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Shadcn/UI theme editor – Design and share Shadcn themes

Hacker News

Shadcn/UI theme editor – Design and share Shadcn themes

Hey, I built https://ShadcnThemer.com - a web app for creating and sharing themes for shadcn/ui, made with my some of my favorites, Next.js 15, Tailwind CSS 4, Drizzle ORM, and Supabase. The goal was to make it easy to visually design shadcn color themes, preview them live across various example UIs, and export them straight into your projects (as CSS or via the shadcn CLI registry command). I had a bit of experience going into this because I built the Theme Studio for VS Code in the past, but it was fun using a modern stack and leveraging Cursor to help me along the way this time. GitHub: https://github.com/miketromba/shadcn-themer

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Actual performance

136points
41comments
Made the leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: cursor, visual, using · Missing: mac, agents, macos
76%76% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
74%74% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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