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Swap contacts by scanning QR codes on your phone

Hacker News

Swap contacts by scanning QR codes on your phone

We recently developed a way to get rid of paper business cards and their shortcomings (e.g.: clutter). It's called Napkkin (www.napkkin.com), and it lets you create a business card on your phone and swap it with people you meet by scanning a QR Code. You can get referred by your new contacts and get notified in real-time so you can track who's helping you with word-of-mouth referrals. We've got mobile/desktop web apps and Android/iPhone native apps set up. We're looking for feedback, so let us know what you think!

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Actual performance

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TrustMRRFits verified-revenue profile · Strong signals: apps, way · Missing: mobile apps, ios, personal
69%69% predicted probability of success on TrustMRR, 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 · Missing: supports, reddit linkedin, podcasting
58%58% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Strong signals: apps, new, code · Missing: mac, agents, macos
53%53% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released
45%45% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
23%23% 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
1%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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