Runtime AI safety via a continuous "constraint strain" score

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Hi HN — I’ve been working on a small open-source experiment around runtime AI safety. The idea is to treat AI risk not as a binary “safe/unsafe” state or post-hoc failure analysis, but as accumulated constraint strain over time — similar to how engineers think about mechanical stress. The project defines a simple, model-agnostic signal (GV) and a lightweight runtime monitor (Sentinel) that classifies risk into bands (green/yellow/red) and suggests interventions (alert, throttle, human review). This is an early MVP — intentionally minimal — meant to explore whether continuous, quantitative safety signals are useful before failures occur, especially for agents and LLM-based systems in production. I’d really appreciate feedback, criticism, or pointers to prior art I should study. Repo: https://github.com/willshacklett/gvai-safety-systems

Visit product View on Hacker NewsJan 27, 2026

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