Ex

Excompt – Export Any Web UI to Tailwind CSS Component

Hacker News

Excompt – Export Any Web UI to Tailwind CSS Component

I'm excited to share a tool I've developed called Excompt. It's designed to help us, the TailwindCSS aficionados, streamline our workflow. Excompt is a browser extension that lets you effortlessly export UI components from any website and converts them into TailwindCSS-styled components. Whether you're working on an HTML, React, or Vue project, Excompt can seamlessly fit into your workflow. Here are a few features I think you’ll love: One-click component export straight from your browser. Automatic conversion into TailwindCSS classes. A web dashboard to save, organize, and manage your components. I created Excompt to solve a problem I frequently encountered: wanting to reuse and repurpose well-designed components I saw online. Now, I'm looking for feedback from fellow TailwindCSS enthusiasts. How does Excompt fit into your development process? Are there any features you'd like to see? You can try Excompt here: https://excompt.com . I'm here for a discussion, to answer questions, and to collect all the feedback I can to make Excompt even better. Can't wait to hear what you think!

Visit product View on Hacker NewsMar 24, 2024

Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting

85%85% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

73%73% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: excited, io · Missing: https docs, just released, exist

40%40% 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 · Missing: mobile apps, ios, personal

33%33% 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

10%10% 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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