In My Pocket – An offline personal data vault

Hacker News

I built a privacy-focused iOS app to solve a personal frustration: the constant hunting for information like bank details, loyalty cards, and documents that we need to access regularly. In My Pocket is designed with these principles: - Complete data privacy: Everything stays on your device - No internet connectivity required - No cloud syncing/storage - Support for text, images, and PDF documents - Biometric security for sensitive information - OCR functionality to extract text from images and PDFs Tech details: - Built with SwiftUI - Uses local device storage only - Implements biometric authentication - Features document categorization with tabs and sections - PDF rendering and text extraction capabilities - QR code generation for sharing I built this primarily for myself but thought others in the HN community might find it useful, especially those concerned about privacy. It's available on the App Store. I'd appreciate any feedback or suggestions for improvement.

Visit product View on Hacker NewsMar 21, 2025

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

88%88% 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.

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

58%58% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: code · Missing: mac, agents, macos

44%44% 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, personal · Missing: mobile apps, entrepreneurs, apps

42%42% 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 · Missing: https docs, excited, just released

42%42% 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

14%14% 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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