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Gazepass Passwordless Login API

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Gazepass Passwordless Login API

Hey everyone , Taus from Gaze here. We are super excited to share our passwordless login API with you! Here's a video on how it works: https://youtube.com/watch?v=nb3vU9kCTGg . You can also try it out: https://gazepass.com/ Passwords add friction during registration and login. Our mission at Gaze is to eliminate them from the Internet. We do this by enabling brilliant hackers such as yourself to remove passwords from your products. Meet Gazepass—a fully passwordless login API that lets your users sign up with just a few clicks, and subsequently login seamlessly. Here are the highlights: - Social Accounts: Users can register with Google or by typing in their email address (more social logins to come in the future) - Native Biometrics: They can subsequently login using native biometric auth (like Apple Face ID, Windows Hello or Android's biometric-enabled devices) - Camera Face ID: If they don't have a biometric sensor on their device, they can choose to sign in with camera face ID—our own face ID technology that runs on any webcam/front facing camera - User Choice: If your users prefer not to use biometrics at all, they can disable biometrics altogether and login with a single click Integrating Gazepass on your website is easy too! We have already released our JS SDK for integration on websites. We will be releasing our mobile SDKs very soon as well (currently under development). You will also find relevant API endpoints on our docs (which are constantly being improved -- launching comprehensive docs very soon). This is just the very first version of our product—there's lots more in store. We would love to hear from you and get your thoughts/feedback. You can join our Slack workspace by visiting https://gaze.ai/slack -- we'll be waiting to start working with you! Alternatively, you can reach out to me via email anytime: t@gaze.ai . Early adopters (before Nov 30) get 1 year of all existing features (unlimited logins/users) for free!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
92%92% 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: apple, slack, google · Missing: mac, agents, macos
87%87% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, exist, existing · Missing: https docs, just released, lua
62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: video, google, users · Missing: mobile apps, ios, personal
55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: soon, users · Missing: plus, platform, intuitive
44%44% predicted probability of success on AppSumo, 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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