ReceiptIQ Pro – AI receipt scanner that tracks spending/budgets (iOS)

Hacker News

Hi HN! I’m a solo iOS dev who kept losing track of where my grocery and shopping money was going, so I built ReceiptIQ Pro. What it does: - Scan any receipt with on-device AI/OCR and instantly categorize the items. - Auto-build spending charts, budgets, and exportable CSVs—no manual typing. - Works offline; iCloud backup/restore optional. No data leaves your phone unless you choose to. - Small extras: warranty reminders, bill tracking, and income logging. Tech bits: - Swift/SwiftUI on iOS. - Apple’s Vision framework + my own ML post-processing. - A lightweight Python backend for optional analytics (not required for core features). I’d love feedback on: 1. Onboarding—does it make sense without a tutorial? 2. OCR accuracy on longer/foreign-language receipts. 3. Budgeting UX—too much, too little? App Store link https://apps.apple.com/ca/app/receiptiq-pro-ai-scanner/id674... Thanks for reading, and happy to answer anything!

Visit product View on Hacker NewsJul 23, 2025

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

76%76% 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: ios · Missing: supports, reddit linkedin, podcasting

70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: ios, apps, scanner · Missing: mobile apps, personal, entrepreneurs

58%58% 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

38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit

23%23% predicted probability of success on Acquire.com, 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

14%14% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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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