spoilerjs – Reddit-style spoilers with particle animations

Hacker News

Hello HN! I just published my first npm library as a small way to give back to the open source community. I built `spoilerjs`, a lightweight web component that lets you hide text with an animated spoiler effect. Think Reddit spoilers, but with more flair! It works with plain HTML, React, Vue, or Svelte, and you can customize attributes like particle density, velocity, and scale. The effect is totally inspired by the Telegram app! Demo: https://spoilerjs.sh4jid.me NPM: https://www.npmjs.com/package/spoilerjs GitHub: https://github.com/shajidhasan/spoilerjs I'm sure there are probably some bugs and rough edges, but I'd love to hear your feedback! Thanks!

Visit product View on Hacker NewsOct 27, 2025

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

87%87% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

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

Hacker NewsStrong engagement from HN community · Strong signals: open source, ide, io · Missing: https docs, excited, just released

60%60% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

47%47% predicted probability of success on AppSumo, 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.

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

19%19% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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