Boarding Pass Wallet with Leaderboard, Analytics and Trip Planning

Hacker News

Hi Guys, A long time ago, I began collecting my boarding passes. I've always been a travel fanatic. I thought I'd love to build a travel app that is more than just flight bookings. This is a way to store your travel memories. I've gamified it a little bit by adding a leaderboard. It's optional as well. Just upload your boarding pass and Borderly does the rest. I'd love for you guys to try and give me feedback. I'm also open to new features and ideas to make it as comprehensive and fun as possible. I'm planning to make a "Make it out of the group chat" feature where you can plan with friends and also record and split expenses while on a trip. It's available on the Apple App Store: https://apps.apple.com/in/app/borderly/id6753683444it

Visit product View on Hacker NewsMar 27, 2026

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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: apple, apps, new · Missing: mac, agents, macos

73%73% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

37%37% 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

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

26%26% 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

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

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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