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I made a 2FA generator with a convenience feature

Hacker News

I made a 2FA generator with a convenience feature

Check out VerifyGate, an open-source, online two-factor authentication app that lets you generate time-based one-time passwords (TOTPs) without storing any sensitive information and also shows you the 2 next upcoming passwords for convenience. Simply enter your secret key, click "Generate," and get the current and next TOTPs instantly. It's a lightweight solution designed for maximum security and privacy, especially useful for those who don’t want to store their secret keys in a traditional 2FA app. Would love feedback from the HN community!

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Actual performance

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
71%71% 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: open · Missing: mac, agents, macos
57%57% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
41%41% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
23%23% 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, revenue
17%17% 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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