SuperAlarm – Alarm app for heavy sleepers

Hacker News

Hey HN! I made a new app called SuperAlarm. If you’re a heavy sleeper who struggles to wake up, this feature-packed alarm app is designed just for you. [Top features] 1. “Missions” you must complete to turn off the alarm, ensuring you get out of bed. We’re starting with math missions but let us know what other challenges you’d like to see! 2. Custom alarm tones selected by our team to jolt you awake. Tell us your favorite alarm sounds and we’ll add them. 3. Set a daily memo that displays after you turn off the alarm. Start your morning with a motivating message or reminder to kickstart your day right! 4. When you snooze, SuperAlarm goes into iPhone’s dynamic island to display time left until next ring. 5. Clean, intuitive interface combined with powerful functionality. Most feature-rich alarm apps are expensive, but SuperAlarm is totally free, supported by unobtrusive ads. We’ll be adding an affordable one-time purchase option to go ad-free. We built SuperAlarm because we were frustrated with oversleeping and found existing alarm apps lacking in features, design, or price. Our goal was to create the ultimate alarm clock experience to help you start your day right. SuperAlarm is available now on the App Store(Android version coming in May). Please check it out and share your feedback - we’d love to hear your thoughts, suggestions and feature requests. We’re committed to making this the most powerful, customizable and user-friendly alarm clock app available.

Visit product View on Hacker NewsApr 24, 2024

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

92%92% 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, user, new · Missing: mac, agents, macos

83%83% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: intuitive, friendly, interface · Missing: plus, platform, reviews

56%56% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, 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.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

20%20% 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

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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