Email builder for managing Auth0 Email Templates

Hacker News

Hi HN, From our Auth0 experiences and hearing from other devs, we found that managing custom email templates for Auth0 tenants could be improved. Earlier this year, I introduced Tabular (email builder) on HN. We've now added a direct integration with Auth0, that lets you directly upload Tabular emails to Auth0. To set it up, you'll have to create a Machine-to-Machine Application in your Auth0 tenant with permissions to modify 'email_templates' in your Auth0 Management API and use it with Tabular. See our link for a full guide. The integration lets you directly upload all Auth0 email template types: verify_email verify_email_by_code welcome_email enrollment_email reset_email (legacy) change_password (legacy) password_reset blocked_account stolen_credentials mfa_oob_code user_invitation I’d love to hear your feedback. Auth0 integration guide: https://tabular.email/integrations/auth0 Try out a password reset email template: https://tabular.email/demo/Passwordresetnotification--57901b...

Visit product View on Hacker NewsNov 15, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, user, email · Missing: agents, macos, agent

68%68% 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 · Missing: supports, reddit linkedin, podcasting

65%65% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: builder · Missing: plus, platform, intuitive

53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

52%52% 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

39%39% 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

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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