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Agent Chaperone – Screen AI agent tool calls and results with Jev
Hacker NewsAgent Chaperone – Screen AI agent tool calls and results with Jev
Hi everyone. TypeSafe released Jev last week, and it pushed me to build agent-chaperone. It is a screening layer for AI agent tool calls and tool results. It screens risky, off-task, or secret-leaking actions before they run, and prompt injections or other suspicious instructions before the agent reads them. Repo: https://github.com/agent-chaperone/agent-chaperone Website: https://agentchaperone.dev/
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Product HuntOn track for Day 1 leaderboard · Strong signals: agent · Missing: mac, agents, macos80%80% 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.
TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal57%57% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: calls · Missing: plus, platform, intuitive45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting44%44% 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: io · Missing: https docs, excited, just released36%36% 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, revenue25%25% 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, crypto19%19% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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