Privacy-Focused Travel Journal Social Media App, Avare

Hacker News

Hi HN, I've been working on Avare, a travel journal map social media app, for the past 6 months as a solo project. It’s designed to blend seamlessly with iOS aesthetics, using UIKit in places where SwiftUI Map falls short. Key Features: - Privacy First: Entries are private by default; you control what to share. - No Tracking: Built with user privacy in mind. - Ad-Free: Enjoy a clean, distraction-free experience. - Offline Access: Access your content offline (creation/update requires internet). - Subscription: Unlock premium features like public sharing and unlimited locations/lists. Why I built it: I wanted a travel journal that prioritizes privacy and user control, with a design that fits well within the iOS ecosystem. Existing options didn’t meet my needs, so I created Avare to fill this gap. I’d love to hear your thoughts, feedback, and any suggestions for improvement. Download Avare: https://apps.apple.com/app/id6475641353 More info at https://avare.can.bi Thanks for checking it out! <3

Visit product View on Hacker NewsMay 25, 2024

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1points

Did not reach leaderboard

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

87%87% 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: apple, apps, user · Missing: mac, agents, macos

71%71% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

46%46% predicted probability of success on AppSumo, 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

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: subscription · Missing: arr, mrr, revenue

17%17% 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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