I created an open-source headless onboarding library for React

Hacker News

ONBOARDING_LIB is a tiny headless onboarding library for React apps, that makes it easy to implement fully accessible, customisable and persisted onboarding flows. To try out an onboarding flow that takes you through ONBOARDING_LIB, check It out here: https://onboarding-lib.vercel.app/ Key features: Persistance: all form values are stored using unstorage with 500ms debouncing. Consistent form handling: Form values handled with react-hook-form, for easily defining your onboarding schema with Zod and building accessible and consistent onboarding forms. A way to run side-effects during step transitions Configurable A headless approach, giving you flexibility to implement your onboarding however you like

Visit product View on Hacker NewsFeb 29, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: apps, tiny, using · 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.

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

70%70% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

65%65% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: apps, way · Missing: mobile apps, ios, personal

49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

29%29% 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

16%16% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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