How I built Remembered checkout for Voice

Hacker News

If you ever built a voice agent that takes orders or books appointments you know its essential to collect payments to avoid cancellations/no-shows. However, most consumers won't complete paying because of the friction involved (stumbling for the card, speaking the card number CVV etc). I built Ringup to help voice agents recognize repeat callers with just their phone number and charge their saved card in seconds. I've been asked what makes this lookup possible. Its the newly launched identity APIs from US carriers (T-Mobile, ATT, Verizon) as part of CAMARA project. These set of open source APIs now make it easy for developers to reliably verify identity directly from carriers and is much more robust than sending SMS OTP. If you're a Voice AI developer or know someone who is, I am offering free credits for 5 users in exchange for any feedback. Try it out.

Visit product View on Hacker NewsJul 31, 2026

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Actual performance

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, user · Missing: mac, macos, cursor

88%88% predicted probability of success on Product Hunt, 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

70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

Hacker NewsMay not resonate with HN audience · Strong signals: open source, ide, io · Missing: https docs, excited, just released

48%48% 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 · Strong signals: users · Missing: plus, platform, intuitive

30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

22%22% 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.

Correct prediction on native model

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