Pulsable – Auto-Generate Skeleton Loaders from Real Elements

Hacker News

Hey HN! I built Pulsable, a lightweight library for creating skeleton loaders, and here’s the cool part—you don’t need to design skeletons separately! Pulsable can automatically convert your real elements into skeleton loaders, saving you time and effort while keeping your design consistent. GitHub: https://github.com/abdheshnayak/pulsable Demo: https://codesandbox.io/p/sandbox/pulsable-kyzztl Features Auto-Generated Skeletons: No need to design custom skeleton loaders. Pulsable converts your real elements automatically. Lightweight & Dependency-Free: No external libraries required. Customizable: Modify shape, size, colors, and animation speed. Minimal Setup: Seamless integration into any project. Enhanced User Experience: Creates a better perception of speed. Why I Built This Creating separate skeleton loaders can be tedious and prone to inconsistency. With Pulsable, the UI elements themselves become skeletons, ensuring consistency while speeding up the development process. I’d love your feedback and suggestions! Let me know if there are features you'd like to see or ways to improve it.

Visit product View on Hacker NewsOct 16, 2024

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

76%76% 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.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

66%66% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

57%57% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

33%33% 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, para · Missing: mobile apps, ios, personal

31%31% 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

12%12% 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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