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Anonymous Age Verification

Hacker News

Anonymous Age Verification

So I'm not an expert in this area, but here's an attempt at cost effective, anonymous, age verification flow that probably covers ~70% of use cases in the United States. The basic premise is to leverage your bank (who already has had to perform KYC on you to open an account) to attest to your age for age-restricted merchant sites (pornhub, gambling, etc) without sharing any more information than necessary. Flow works like this: 1) You go to gambling.com 2) They request you to verify your age 3) You choose "Bank Verification" 4) You trigger a WebAuthn Credential Creation flow 5) gambling.com gives you a string to copy ------------- 6) You log into your bank 7) You go to bank.com/age-verify 8) You paste in the string you were given 9) The bank verifies it/you and creates a signed payload with your age-claims (over_18: true, over_21: false) 10) You copy this and go back to gambling.com --------------- 11) You paste the string back into gambling.com 12) You perform WebAuthn Auth flow 13) gambling.com verifies everything (signatures, webauthn, etc) 14) gambling.com sets a session-cookie and _STRONGLY_ encourages you to create an account (with a pass key). This will prevent you from having to verify your age every time you visit gambling.com The mechanics might feel off, but it feels like this in the neighborhood of a way to perform anonymous age verification. This is virtually free, and requires extremely light infra. Banks can be incentivized with small payments, or offer it because everyone else does and don't want to get left behind.

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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.
best fitHighest predicted score across all platforms for this description.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
49%49% predicted probability of success on TrustMRR, 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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31%31% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
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30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.
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15%15% 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.

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