RotaFlow – A privacy-first shift calendar built with SwiftUI

Hacker News

I built RotaFlow to solve the UX disaster of current shift work apps. Most incumbents are web-wrappers laden with ads. The Stack: * Language: Swift 6 / SwiftUI. * Persistence: Core Data synced via CloudKit (NSPersistentCloudKitContainer). * Architecture: MVVM with a custom "LoopEngine" for O(1) shift calculation. The Interesting Part: I implemented a "Future Cliff" paywall. The app is fully functional for the next 30 days. Accessing dates beyond t+30d triggers a blurred UI layer. This aligns monetization with the value of long-term planning (vacations/weddings) rather than daily usage. I'm a solo dev and handled everything from the icon design to the CloudKit schema. Website: https://www.rotaflow.app App Store: https://apps.apple.com/app/id6758777908 Feedback on the onboarding flow (specifically the pattern builder dial) would be appreciated.

Visit product View on Hacker NewsFeb 27, 2026

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

73%73% 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

66%66% predicted probability of success on Indie Hackers, 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

42%42% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

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

30%30% 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

24%24% 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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