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I built RoQR, a privacy-respecting platform for dynamic QR codes

Hacker News

I built RoQR, a privacy-respecting platform for dynamic QR codes

Hi HN, For the last few months, I have been building RoQR, a privacy-respecting SaaS platform for dynamic QR codes. Dynamic QR codes let the code creator see statistics about the number of scans and update the target that the code links to without changing the code itself. RoQR is different from other dynamic QR code platforms in a few ways: Privacy respecting RoQR is a marketing tool, but we aim to be the most privacy-respecting alternative in our niche. Unlike a lot of other companies in the space, we don’t collect any personally identifiable information about the users that scan our codes, we respect [DNT-headers]( https://allaboutdnt.com/ ), we don’t use cookies to identify repeat scanners and we don’t offer tracking pixels that track end-users across the web. My hope with RoQR is to do for QR codes what [Plausible]( https://plausible.io ) (big fan!) is doing for website analytics. Cost-aligned pricing All of our plans include our full set of features. The only factor that determines the cost is the number of monthly scans. I think this better aligns our costs with revenues, and makes all of our improvements available to all our customers. Open source [RoQR is open-source]( https://github.com/roqr/roqr ), licensed under the AGPL. If you’re interested, I have a [longer post]( https://roqr.app/blog/introducing-roqr/ ) on our blog announcing the launch. Happy to answer any questions and would love to hear any feedback you might have.

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Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
81%81% 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: open source, ide, io · Missing: https docs, excited, just released
61%61% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: personal, month, monthly · Missing: mobile apps, ios, entrepreneurs
57%57% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: user, code, open · Missing: mac, agents, macos
46%46% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, users · Missing: plus, intuitive, reviews
43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: revenue, saas · Missing: arr, mrr, profit
21%21% 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.

Incorrect prediction on native model

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