Permit Elements- UIs to let your customers manage their own damn RBAC

Hacker News

Hi HN! Co-Founder of Permit.io here, I’ve built access-control to my products, thousands of times throughout my career - and at no point did I want to. We adopted OPA, created OPAL.ac (open-source), and Permit.io on top - so no developer would have to build permissions again. To truly solve this problem end-to-end we’re releasing Permit-Elements ( https://permit.io/elements ) - embeddable UIs providing the interfaces you need so your end-customers can control access-control (e.g. user-management, audit-logs, approval flows, permission requests, api-key management, …) Check out the full tutorial: https://youtu.be/xGYdDF65lkQ The solution highlights: - Authorization for Authorization (who can control who controls permissions) - Security (auditing, real-time decision making and meeting industry standards) - An easy integration (generate and embed a JS snippet) There’s a lot more to do, we’d love your feedback on Permit in general, this feature, and others. Chat with us on Slack ( https://bit.ly/permit-slack ) Thanks, Or Weis

Visit product View on Hacker NewsJan 26, 2023

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

93%93% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

84%84% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

64%64% 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 · Missing: mobile apps, ios, personal

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

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

25%25% 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

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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