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PolicyLayer – Non-custodial spending limits for AI agents

Hacker News

PolicyLayer – Non-custodial spending limits for AI agents

PolicyLayer enforces spending limits on AI agent wallets without custodying private keys. The problem: AI agents are moving into production with irreversible financial capabilities, but every agent is one bug or prompt injection away from draining a wallet. Traditional options are bad—shared seed phrases (compliance nightmare), custodial wallets (counterparty risk), manual approval (defeats automation), prompt-level limits (easily jailbroken). How it works: - Drop-in wrapper for existing wallet SDKs (Ethers, Viem, Coinbase CDP, Privy, Solana) - Two-gate enforcement: Gate 1 validates against policies, Gate 2 detects tampering via SHA-256 fingerprinting - Keys never leave your infrastructure—we only see transaction metadata - Fail-closed: if anything fails, transactions stop (not continue) - Policies configured in dashboard: daily caps, per-tx limits, recipient whitelists Also supports X402 (HTTP 402 payment protocol) for agents paying for APIs—per-endpoint limits, auto-discovery of new paid endpoints, duplicate payment prevention, and circuit breakers. SDK is open source. Feedback welcome — what would make this more useful for your agents?

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, new · Missing: mac, macos, cursor
86%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.
Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created
62%62% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
41%41% 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
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: exist, open source, existing · Missing: https docs, excited, just released
25%25% 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
18%18% predicted probability of success on AppSumo, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: paid · Missing: web3, chat, crypto
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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