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Taildashboards – Dashboard Templates Built with Tailwind CSS and Alpine

Hacker News

Taildashboards – Dashboard Templates Built with Tailwind CSS and Alpine

A website where I publish one-page dashboard templates I designed and coded using Tailwind CSS and Alpine.js. It's a passion project of mine because I love designing dashboards and this project will enable me to try out new things, experiment and hopefully provide value to all of you who are looking for a great starting point or inspiration for your web applications. Feel free to check them all out, use them in your upcoming projects and stay tuned as I work on introducing new ones in the future

Visit product View on Hacker NewsSep 28, 2023

Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, using, code · 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

70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

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

nativeThis product was originally launched on this platform.

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

49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

19%19% 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

15%15% 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.

Incorrect prediction on native model

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