DummyIDP – Test SAML SSO and SCIM Directory Sync

Hacker News

We heard from people ( https://news.ycombinator.com/item?id=41674109#41675409 ) a while back that there's demand for a lightweight SAML testing tool. So we made one! DummyIDP sends real SAML/SCIM messages to your app – just like Okta or Entra would. But it's radically simple. All of the settings are on the same page. You don't even need to create an account. It's just ready to go. You can test IDP- initiated SAML login. You can test SP- initiated SAML logins. You can provision or deprovision users or edit their permissions. You can test just about everything you could reasonably expect your enterprise customers to do. Here's a demo: https://www.youtube.com/watch?v=K2EMzbJhVag It's a free and open source tool. (See: https://github.com/ssoready/dummyidp ) We hope you find it useful.

Visit product View on Hacker NewsOct 15, 2024

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Hacker NewsStrong engagement from HN community · Strong signals: open source, 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.

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

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

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

28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

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

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

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