Unscanny – an app designed to turn screenshots into actions

Hacker News

Ever had to manually type out a phone number from a screenshot? Or worse — switch between your photo gallery and WhatsApp just to write someone a message? That frustration led me to build something simple but powerful. Meet Unscanny – an app designed to turn screenshots into actions. It all started when I kept seeing local businesses post their phone numbers on Instagram stories. No clickable links, just plain numbers. I had to screenshot, open my phone app, and type the number manually. Even worse, some said “DM us on WhatsApp or Telegram” — again, no links, just text. So I built Unscanny, an app that: Lets you share a screenshot directly from your gallery Detects phone numbers and emails in the image Gives instant actions like Call, Message on WhatsApp/Telegram, Send Email, or Copy But I didn’t stop there. Someone sends you their IBAN as an image? Unscanny detects it and lets you copy it instantly. Need to send crypto and they send their wallet address as a QR code screenshot? Unscanny scans it and gives you the address in one tap. All the annoying bits — gone. Available now: App Store: https://apps.apple.com/us/app/unscanny/id6739267314 Google Play: https://play.google.com/store/apps/details?id=com.goktug.uns... Built from real pain points. If you've ever typed something you could've just tapped — Unscanny is for you. Would love to hear your thoughts, feedback, or feature ideas!

Visit product View on Hacker NewsApr 30, 2025

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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, google, apps · Missing: mac, agents, macos

60%60% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

55%55% 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

45%45% predicted probability of success on AppSumo, 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

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

21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: crypto · Missing: web3, chat, cryptocurrency

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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