Bi

Bionictoggle package to toggle bionic reading in blogs

Hacker News

Bionictoggle package to toggle bionic reading in blogs

Hello HN community. This is my very first post here. I created a NPM package called `bionictoggle`, a ready to integrate React component that can be used in blogs. The toggle enables/disables Bionic reading which facilitates readers read the text quickly. You can read about Bionic Reading in detail from their official website. P.S - I guess Bionic reading is a copyrighted(Correct me if I'm wrong) and I've created this solely for the purpose of learning about how to publish a npm package. Please do share your thoughts about it. I'll be glad to look into them and improve. Thanks.

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
79%79% 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 · Missing: mac, agents, macos
74%74% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
47%47% predicted probability of success on TrustMRR, 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
40%40% 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
33%33% 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
17%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, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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