Looking for beta testers for my macOS meeting reminder app

Hacker News

Hey HN! I built Chime - a macOS app that shows full-screen meeting alerts for people who lose track of time while in deep work mode (like me). The problem: Calendar notifications are too subtle when you're in deep focus. I kept missing meetings/reminders and felt terrible about it. The solution: Full-screen alerts 5 minutes before meetings that you literally can't ignore (time is customisable). Plus auto-detection of Zoom/Teams/Meet/etc links for one-click joining. Tech stack: Native Swift/SwiftUI, EventKit for calendar integration, local-first (your data stays on your Mac). Landing page: https://usechime.app I'm looking for ~50 beta testers to try it via TestFlight before the official launch. Especially interested in: - Techies who have this same problem - Feedback on the alert timing and design - Edge cases with different calendar setups - Privacy-conscious users (happy to discuss implementation) If you're interested, there's a waitlist link on the site. I'll send TestFlight invites to the first respondents. Would also love general feedback on the concept!

Visit product View on Hacker NewsOct 27, 2025

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3points

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

85%85% 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: mac, macos, user · Missing: agents, agent, cursor

84%84% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

28%28% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

28%28% 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

22%22% predicted probability of success on Acquire.com, 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

22%22% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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