I built an app I always wanted to quickly share snippets

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Hi! I'm a backend architect by day, an indie iOS developer by night. This spring, I wanted to experiment with iMessage app extensions for another app I build, and I needed a proof-of-concept that binding a SwiftUI view in this iMessage context would work correctly. Not only this is possible, but this project became a bit more, because it transformed the way I started sharing piece of information I often need to have at hand. My first use case was my IBAN sharing, often shared with relatives. Each time I was asked for it, I would have to open my bank app, have a correct internet connection, and a few minutes of my time to find it, copy it, and removing the extra un-needed info from the pasted content. Instead, opening my newly created iMessage app, and taping it would simply paste it in the iMessage textfield, ready to be sent. SharePal was born. Other use cases were quickly found: URLs to my blog posts, apps, and social networks, but also hashtags I often use when I'm microblogging. This is why I also enabled the custom keyboard extension for this app. Behaving similarly to the iMessage app, it was _almost_ free to support. After finding a UI/UX that would feel right, and adding a few more features that felt right at home for this kind of app, including categories, access restriction by biometry, drag and drop, and Apple Shortcuts integration, the app launched this week in the App Store. I'm open to discussion about this app. Any improvement or feature request, bug spotting, or marketing advices… I take it all! I'll also be very happy to share implementation or technical details about this 100% SwiftUI app that packs way more than it seems.

Visit product View on Hacker NewsOct 8, 2023

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

92%92% 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

92%92% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

40%40% 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

18%18% 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.

Incorrect prediction on native model

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