Quoter – Daily Inspirational Quotes App

Hacker News

I've been working on an app called Quoter, designed to deliver daily inspirational quotes from renowned authors and thinkers. With Quoter, you can swipe right to like a quote or left to dislike it, making it easy to engage with content that resonates with you. You can save your favorite quotes and organize them into custom collections. Registered users can also add their own quotes. Sharing quotes with friends or on social media is straightforward. Plus, registered users can back up their quotes to keep them safe, even if you reinstall the app. I created Quoter because I wanted a simple, customizable way to get daily inspiration. There are a lot of quote apps out there, but I felt they lacked interactive features and user-friendly design. Quoter introduces a swipe feature, adding a playful element to the browsing experience. The focus on simplicity ensures that using Quoter is straightforward and intuitive. Quoter is currently available in the USA, Canada, Australia, Ireland, UK, and New Zealand. You can download it on iOS: https://apps.apple.com/us/app/quoter-daily-inspiration/id647... and Android: https://play.google.com/store/apps/details?id=com.alexfeller... I'd love to get your feedback and hear any suggestions for improvement. Thanks!

Visit product View on Hacker NewsMay 20, 2024

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

91%91% 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.

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

68%68% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

61%61% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: apple, google, apps · Missing: mac, agents, macos

49%49% predicted probability of success on Product Hunt, 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

25%25% 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 · Strong signals: active · Missing: arr, mrr, revenue

19%19% 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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