Google Authenticator Compatible Hardware Token

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I recently implemented 2FA for OpenVPN using Google Authenticator. The verification code I'm using is largely based on the Google2FA class for PHP written by Phil Taylor found below: http://www.idontplaydarts.com/2011/07/google-totp-two-factor-authentication-for-php/ One challenge I had was not everyone has a smartphone (sigh). So I needed an inexpensive hardware token option. I found that the Authenex A-Key 3610 is compatible. [ If anyone knows of other compatible tokens please comment. ] It's not dirt cheap but it's cheap enough for our intended audience: $25 per unit if you get less than 100 and down to half that for larger quantities. They send you a list of SNs to private key associations for the hardware tokens you've purchased. The only catch is that they represent the 160-bit key in hex so if you're using RFC4648 formatted Base32 (e.g. the default for Google Authenticator) you just need to convert those keys. After that you can add them to your DB just like you add keys for Google Authenticator and they'll work with the same verification code.

Visit product View on Hacker NewsJan 28, 2015

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, using, code · Missing: mac, agents, macos

85%85% 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 · Strong signals: vpn, compatible · Missing: supports, reddit linkedin, podcasting

77%77% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

57%57% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

56%56% 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 · Missing: plus, platform, intuitive

42%42% 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

19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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