Tailwind CSS Blocks ( Always Free)

Hacker News

Hello everyone, after being some time with no chance to work on the blocks, I removed some and instead I refactored them and I am working on the accessibility of these ones. Now you have around 134 blocks and more on the way. http://wickedblocks.dev/ This is what you will find. LEFT HEADERS 7 blocks CENTERED HEADERS 10 blocks RIGHT HEADERS 8 blocks AUTH 5 blocks PRICING 12 blocks CARDS 15 blocks ALERTS 6 blocks GRIDS 10 blocks MODALS 4 blocks CLIENTS 10 blocks CONTENT 13 blocks FORMS 11 blocks NAVIGATION 8 blocks SIDEBARS 5 blocks FOOTER 10 blocks hope you all find this helpful. Ask me here or on Twitter. @Mike_Andreuzza /Mike

Visit product View on Hacker NewsJun 2, 2021

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Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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

72%72% 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.

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

50%50% 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: way · Missing: mobile apps, ios, personal

36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

32%32% 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

27%27% 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

20%20% 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

11%11% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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