Limen – modern, composable auth for Go

Hacker News

I built and released Limen v0.1.0: https://github.com/thecodearcher/limen Writeup/docs: https://limenauth.dev/blog/introducing-limen It’s inspired by better-auth’s plugin-first DX, but designed for idiomatic Go. The core handles sessions/cookies/security primitives, and auth methods are composed as plugins (credential auth, OAuth providers, and 2FA today). It exposes an http.Handler, so it works with plain net/http and frameworks like Gin/Echo/Chi/Fiber without framework lock-in. This is the first public release. I’d really appreciate feedback.

Visit product View on Hacker NewsApr 25, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: code, plain · Missing: mac, agents, macos

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

67%67% predicted probability of success on Indie Hackers, 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

60%60% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

43%43% 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

22%22% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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