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Scan QR codes to check in guests registered via Google Forms

Hacker News

Scan QR codes to check in guests registered via Google Forms

Hi HN! I made a no-code platform for creating physical data collection apps, using QR codes [1]. It does not yet have a self-service config UI though, which limits adoption. That's why I recently released a Google Forms™ add-on for QR code check-in, based on the platform. This focused use-case makes it easy to provide a fully self-service config UI. How it works: 1. Create your Google Form as you normally would [2] 2. Activate the add-on if you hadn't already [3] 3. Craft a confirmation email to be sent to each form responder Upon each form submission, the add-on will send a PDF with a unique QR code (a V4 UUID) to the responder. Have guests present this code at the event, and record check-ins in bulk using the included QR scanner. See here [4] for more information, or try the Google Sheets™ version [5] (which doesn't send email). [1] https://admin.trak.codes/ [2] https://forms.google.com/ [3] https://workspace.google.com/marketplace/app/qr_code_ticket_... [4] https://blog.darkaa.com/qr-code-pass-per-response-google-for... [5] https://workspace.google.com/marketplace/app/qr_code_pass_fo...

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73%73% 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.
TrustMRRFits verified-revenue profile · Strong signals: apps, google, scanner · Missing: mobile apps, ios, personal
56%56% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Strong signals: google, apps, email · Missing: mac, agents, macos
55%55% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
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48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.
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44%44% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
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17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
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1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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