Skeleton Mammoth – lightweight and reusable skeleton loader screen

Hacker News

After a long investigation of the topic of skeleton loaders, I've decided to create my own improved universal solution. I've cherry-picked, combined and improved the best practices for its development, and now I am happy to present you – Skeleton Mammoth. The library uses pure CSS without any dependencies and is not tied to a specific framework. The main thing I aspired to and come up to, the ability to reuse the skeleton without the need to develop new or significantly change existing components, along with the ability to configure it. I invite each person to familiarize themselves with the library. I will be glad for your support and any contribution/suggestions/ideas. Below, I will post a list of useful links, to explore. 1. https://github.com/WOLFRIEND/skeleton-mammoth/ - GitHub repository of the library. 2. https://skeleton-mammoth-demo.onrender.com/ - Live demo. 3. https://dev.to/wolfriend/skeleton-mammoth-or-how-ive-been-so... - Article about the process of creating the library.

Visit product View on Hacker NewsJul 31, 2023

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

67%67% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

58%58% 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

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

38%38% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

30%30% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

13%13% 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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