I built a simple, fast, privacy-first document scanner for iPhone

Hacker News

I’m an indie iOS developer and I built a small tool because I couldn’t find a simple, fast, privacy-respecting document scanner on the App Store. Most scanners today require an account, upload files to their cloud, or push subscriptions aggressively. I wanted something that opens instantly, works offline, exports clean PDFs, and doesn’t track anything. So I made Scan. It opens as fast as the Camera app, processes documents locally, and doesn’t require an account. The design is inspired by the older, simpler iOS UI from the iOS 6 era because I’ve always liked how clean and fast it felt. I’d love feedback, especially from other developers: – Is the interaction flow intuitive enough? – Does the “privacy-first + old-school design” direction make sense in 2025? – Anything obviously missing for a 1.0? Happy to answer questions.

Visit product View on Hacker NewsNov 23, 2025

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

75%75% 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: open · Missing: mac, agents, macos

69%69% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

57%57% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

50%50% 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

30%30% 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 · Strong signals: subscription · Missing: arr, mrr, revenue

26%26% 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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