Sn
SnippQR - A Custom QR Code Generator
Hacker NewsSnippQR - A Custom QR Code Generator
We just launched SnippQR (http://qr.snipp.com) – a free custom QR code generator. We first built our generator for internal use to create QR codes for clients like ESPN, Taco Bell and Sección Amarilla but quickly realized that this could be a great tool to share with everyone. Yes, QR codes are still relevant. According to eMarketer, 36% of adults aged 18-34 have used a QR code at least once – that’s greater than the percent of people who have used Twitter. QR code usage in China has quadrupled in the past year, to over 9 million scans per month.
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Made the leaderboard
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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting62%62% 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.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue24%24% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: code · Missing: mac, agents, macos16%16% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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