Pinplanet – pin where you’ve been

Hacker News

A while back my girlfriend was telling me about how she’d love it if there was an app where she could curate all her travel memories in one place and easily show people. Being a developer, I decided to give it a shot and initially created a web app. After that gained some traction and I also noticed some limitations on iPhones, I took the plunge to learn swift and build an iOS app. So after many difficult months of working full time and developing this on the side, I finally feel comfortable showing it off! You can pin past trips and plan new ones. On a 3D globe! Your pins can include spots you visited (restaurants, hotels, parks, etc.), reviews, photos, and travel buddies. You can also create custom screenshots of your pins to easily share. Tech stack is swift, django, and postgres for those that are curious. I would really appreciate feedback. Please rip the app to shreds if you think it sucks. App store link: https://apps.apple.com/us/app/pinplanet-app/id6443833392

Visit product View on Hacker NewsJul 19, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: apple, apps, new · Missing: mac, agents, macos

82%82% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

80%80% predicted probability of success on Indie Hackers, 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

48%48% predicted probability of success on TrustMRR, 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

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

nativeThis product was originally launched on this platform.

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

22%22% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · 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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