Shrinkqr

TrustMRR

ShrinkQR is an all-in-one smart shortening platform that transforms long, complex data into clean, customizable short links and beautifully branded QR codes. Whether it’s a website URL, email address, phone number, WiFi credentials, WhatsApp chat, or geolocation pin, ShrinkQR makes sharing effortless and professional. It also provides powerful analytics that show exactly how, when, and where your content is being accessed. Note: The app was not officially launched yet!

Visit product View on TrustMRRJul 1, 2025SaaS Software stripe

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Actual performance

Did not reach leaderboard

Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.

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

54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

41%41% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

18%18% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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