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Stack Auth (YC S24) – the open-source Auth0/Clerk alternative

Hacker News

Stack Auth (YC S24) – the open-source Auth0/Clerk alternative

Hi HN! We're Zai and Konsti, and we're building Stack Auth [0], an open-source managed authentication & authorization platform. Stack Auth was born out of years of frustration with the incumbents. We wanted to build something that is developer-friendly and open-source at the same time, yet fully managed. User data is quite critical, and you generally don't want to get locked into a specific vendor. On the other hand, building and maintaining auth infrastructure on-top of existing libraries is tedious work as you grow. Hence: - Stack is 100% open-source, licensed under MIT and AGPL. - You can self-host, or choose to use our managed hosting. - If you choose the latter, there's absolutely no reliance on us; if you think we're doing a bad job at any point in time, you can export all your data and start self-hosting instead. Also, we're more than just authentication — we have authorization built-in, and a frontend to manage your users. You can use any language to access these features through our REST API [1]. We also have a bunch of components for sign in, password reset, organizations. For now, we only support Next.js frontends and backends in any language with our API, though our docs also list the client endpoints, and some contributors have been building frontends for other languages. For more info, check out our GitHub repo [2]. Any feedback is highly appreciated! Thanks all! [0] https://stack-auth.com [1] https://docs.stack-auth.com/rest-api/auth [2] https://github.com/stack-auth/stack

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3points
4comments
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, open · Missing: mac, agents, macos
92%92% 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: organizations · Missing: supports, reddit linkedin, podcasting
85%85% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, io · Missing: https docs, excited, just released
83%83% 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 · Strong signals: users · Missing: mobile apps, ios, personal
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, host, friendly · Missing: plus, intuitive, reviews
41%41% 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
15%15% 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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