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Stack, an open-source Clerk/Firebase Auth alternative

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Stack, an open-source Clerk/Firebase Auth alternative

Hey HN! Happy to finally launch Stack. We made it because we like to put apps into production quickly, and authentication & user management was taking up way too much time. We have components like <SignIn /> and <AccountSettings /> that automatically adapt to whatever theme & design system you're using. Check the blog post to see the example with Radix UI and Joy UI. Also, there's an admin dashboard for monitoring and editing accounts. Stack is 100% AGPL/MIT-licensed, so you can self-host it. Cheers!

Visit productView on Hacker NewsApr 14, 2024
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144points
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: apps, user, using · Missing: mac, agents, macos
97%97% 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.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
80%80% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
61%61% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps, way · Missing: mobile apps, ios, personal
48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive
36%36% 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
4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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