Beautiful iOS Habit Tracker with clean and simple user interface

Hacker News

Hello, I’m excited to introduce Rovelist, a habit-tracking app designed to make building positive habits effortless and enjoyable. Whether you're aiming to stay consistent or simply track progress. Why Rovelist? Reminders Set multiple reminders per habit so you never forget to stay on track. 11 Home & Lock Screen Widgets Quickly check progress, compare streaks, and stay motivated without opening the app. Time Travel Habit Log Missed a day? Need to adjust past logs? Easily edit habit history without restrictions. Flexible Tracking Options Track habits traditionally with streaks or use 'Just Track' mode to log progress without pressure. Motivation That Works Celebrate wins with fun confetti animations and get a 'Perfect Day' popup when you complete all habits for the day. Personalized Habit Setup Customize habits with unique colors, emojis, and sounds for a tailored experience. You can even set different check-in sounds for each habit to make tracking even more personal and fun. Habit Notes Attach personal notes to each habit log to reflect on progress and stay mindful of your journey. Privacy & Security Keep your habits secure with a safety lock, ensuring only you have access. All your data is stored locally on your device, giving you complete control over your information. Habit Organization Group habits into categories for a structured and clutter-free experience. Advanced Habit Management Archive, restart, or undo today’s habits whenever needed. Custom Habit Frequencies Set habits to repeat daily, weekly, or monthly—perfect for any routine. I’d love to hear your feedback and ideas! Try it out and let me know what you think.

Visit product View on Hacker NewsMar 24, 2025

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Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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

89%89% 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: user, notes, open · Missing: mac, agents, macos

64%64% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: excited, ide, io · Missing: https docs, just released, exist

24%24% 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

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: introduce · 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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