Measuring runtime strain in autonomous AI systems

Hacker News

This is a small runtime safety primitive I’ve been working on. It computes a bounded “GV” signal from live agent behavior (token velocity, tool calls, errors, recursion, repetition) and emits: green / yellow / red → continue / slow / halt. The goal is runtime survivability rather than training-time alignment. No model introspection, deterministic scoring, and designed to be embedded directly into agent loops. There’s a short demo script in the repo that simulates an agent going unstable. Feedback welcome.

Visit product View on Hacker NewsJan 29, 2026

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

81%81% 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, personal

47%47% 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, podcasting

46%46% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: calls · Missing: plus, platform, intuitive

27%27% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue

24%24% 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

23%23% 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

6%6% predicted probability of success on BetaList, based on ML models trained on real launch data.

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