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AuthForge – open-source auth for AI agents (early preview)

Hacker News

AuthForge – open-source auth for AI agents (early preview)

Hi HN, I'm building AuthForge - authentication infrastructure for AI agents. Early preview at https://auth-forge-web-two.vercel.app : When AI agents need to access your tools (Slack, GitHub, Jira), how do you handle auth? Current options are expensive proprietary platforms or DIY OAuth with security risks. : - Zitadel for identity/SSO - Ory Hydra for OAuth 2.1 (MCP-specific flows) - Cerbos for policy engine (time/context-based rules) - HashiCorp Vault for token management - All Apache 2.0 licensed : Is this overengineered? Should I start with just basic OAuth + agent registry and add complexity later? Or is the enterprise stack necessary from day 1? Background: 15+ years building infrastructure. Currently building AI agents and hitting auth pain points that existing solutions don't address. : https://github.com/ashishjsharda/authforge First docker-compose stack: ~2 weeks Looking for honest technical feedback. Thanks!

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, mcp · Missing: mac, macos, cursor
90%90% 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
66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
40%40% 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 · Missing: mobile apps, ios, personal
34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
24%24% 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
23%23% 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.

Correct prediction on native model

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