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Create TailwindCSS Skeleton Loaders with a Single Click in VSCode
Hacker NewsCreate TailwindCSS Skeleton Loaders with a Single Click in VSCode
Hey Hacker News! I've developed Tailwind Skeleton Generator, a tool for quickly generating animated loading skeletons with Tailwind CSS. It's available as a web app and a VSCode extension. With the VSCode extension, you can right-click any code block and select "Create Tailwind Skeleton" to instantly create a skeleton. This project aims to simplify adding responsive and polished UI elements. Try it out and let me know your thoughts or suggestions!
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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting77%77% 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 · Strong signals: new, single, code · Missing: mac, agents, macos73%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, intuitive47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: hacker news, io · Missing: https docs, excited, just released38%38% 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, personal29%29% 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, revenue17%17% 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, crypto5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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