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AgentMint – Cryptographic proof of human approval for AI agent actions
Hacker NewsAgentMint – Cryptographic proof of human approval for AI agent actions
Session auth says "agent can use Stripe." It doesn't say "Alice approved this $50 refund at 10:03am." AgentMint signs short-lived tokens proving a human approved a specific action. Ed25519 signatures Single-use (JTI tracking) 60s TTL default 3ms verify Full audit trail Built in Rust. Drop-in sidecar for your backend. https://github.com/aniketh-maddipati/agentmint
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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, stripe, single · Missing: mac, agents, macos86%86% 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.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting35%35% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive26%26% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released21%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.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: crypto · Missing: web3, chat, cryptocurrency6%6% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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