VigiloAuth – An OpenID Connect and OAuth 2.0 Identity Provider in Go

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I've open-sourced and OpenID Connect and OAuth 2.0-compliant identity provider called VigiloAuth, written in Go. It supports: - Authorization Code Flow (with PKCE) - Client Credentials, Resource Owner Password - Token refresh, revocation, and introspection - Dynamic client registration - OIDC Discovery, JWKS, and UserInfo endpoints - Audit logging Everything is configurable: token lifetimes, rate limits, password policies, SMTP, throttling, HTTPS enforcement, etc. Vigilo passes the OpenID Foundation's Basic and Comprehensive Authorization Server tests (test logs included in the repo). Not officially certified yet, but working on it. This was my senior project in university, and I’m continuing to build it out with features like RBAC, TOTP, social login, realm support, and an admin UI. The internal code could use some refactoring, so I’m very open to feedback, suggestions, and contributions. Repo: https://github.com/vigiloauth/vigilo

Visit product View on Hacker NewsMay 29, 2025

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

71%71% predicted probability of success on Indie Hackers, 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: ide, io · Missing: https docs, excited, just released

52%52% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

43%43% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

33%33% 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

14%14% 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.

Incorrect prediction on native model

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