Vultrino – Let AI agents use credentials without seeing them

Hacker News

I built Vultrino because I kept running into the same problem: I want AI agents to do things that require credentials, but I don't want those secrets in prompts, logs, or context windows. It's not just API keys – it's signing commits with PGP keys, accessing databases, decrypting files, anything that needs a secret. The agent shouldn't see the credential; it should just be able to use it. Vultrino is a credential proxy that sits between your AI agent and the operation. The agent says "use my github-api credential" or "sign this with my pgp-key" and Vultrino handles the secret material without exposing it. Key features: - WASM plugin system – add any credential type (PGP signing plugin included, build your own) - MCP native – works directly with Claude - OAuth2 with automatic token refresh - Scoped API keys – agent X can only access credentials matching "github-*" - Encrypted storage (AES-256-GCM), SSRF protection, audit logging Written in Rust. Self-hosted, no cloud dependency.

Visit product View on Hacker NewsJan 28, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, claude · Missing: mac, macos, cursor

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.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

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

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

41%41% 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

34%34% 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

26%26% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

21%21% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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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