Humanpass – Proof of humanness using WebAuthn, no data collected

Hacker News

Hi HN, I built humanpass because every human verification system today asks you to give up something — your time (CAPTCHAs), your money (paid blue checks), or your biometric data (Worldcoin). humanpass uses WebAuthn/passkeys to verify there's a real person behind the screen. You authenticate with your device's biometrics, get a temporary link (expires in 60 seconds), and share it anywhere. Whoever clicks it can see the verification is real. Your biometrics never leave your device — the server only receives a cryptographic assertion. No signup, no email, no passwords. The only data stored is a random user ID and a public key. Some technical details: - Built with Hono on Cloudflare Workers, D1 for storage, Workers KV for ephemeral links - Blocks known virtual/emulator authenticators by AAGUID - No analytics, no third-party scripts - Chrome extension for one-click verification - AGPL-3.0 licensed Live at human-pass.org. Would appreciate feedback on the threat model and attack surface.

Visit product View on Hacker NewsFeb 27, 2026

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Actual performance

2points

Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

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.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

50%50% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntUnlikely to reach the leaderboard · Strong signals: model, user, email · Missing: mac, agents, macos

47%47% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

24%24% 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

20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: crypto, paid · Missing: web3, chat, cryptocurrency

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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