Learn Tailwind CSS with a Challenge

Hacker News

Hello everyone! http://30daysoftailwindcss.com/ Here we have another challenge. This one is about Tailwind CSS, I was astonished it wasn't already existing so here we have it up and running. You can also follow an optional path with 30 challenges, from super basic to a bit more challenging, like to create a whole UI, why not, right? You will see it soon as you land. For references read Tailwind docs. - tailwindcss.com If you need inspiration, you can find it in codepen.io. Anyways, if you have any feedback or questions, just do it right here or on twitter. - https://Twitter.com/mike_andreuzza

Visit product View on Hacker NewsMar 8, 2021

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4points

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

76%76% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

62%62% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

53%53% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

43%43% predicted probability of success on AppSumo, 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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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