I wrote a book on how to make iOS/Android apps with Ruby on Rails

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Hi all! Joe Masilotti here. After 2+ years my book, "Hotwire Native for Rails Developers", is finally available for sale. It contains everything I know about building, shipping, and maintaining iOS and Android apps with Ruby on Rails and Hotwire Native. Always with one simple goal in mind: making it easier for Rails developers to build mobile apps. The book teaches you how to: - Build your first Hotwire Native apps on iOS & Android - Add navigation, modals, and native tab bars - Mix in native screens and components - Send and route push notifications - Ship to physical devices via TestFlight and Play Store

Visit product View on Hacker NewsSep 25, 2025

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

90%90% 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 · Strong signals: apps, physical · Missing: mac, agents, macos

79%79% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: mobile apps, ios, apps · Missing: personal, entrepreneurs, video

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

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

64%64% 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

38%38% 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

36%36% 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.

Correct prediction on native model

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