Supafine – Use simple prompts to generate or remix React UI components

Hacker News

Hey HN! Today I am releasing Supafine, a tool that lets you generate and modify React UI components using simple prompts. When I build apps, I tend to think in blocks, and in React, those blocks are components. But creating and tweaking them manually often feels slower and more repetitive than it should be. I built Supafine to make that process faster and more intuitive. Give it a try at: https://create.supafine.app Thanks for reading - would love to hear any feedback!

Visit product View on Hacker NewsJun 24, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: apps, using · Missing: mac, agents, macos

81%81% 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.

AppSumoStrong fit for a featured deal · Strong signals: intuitive · Missing: plus, platform, reviews

58%58% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Missing: https docs, excited, just released

57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: apps · Missing: mobile apps, ios, personal

35%35% 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

16%16% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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