Show HN: ZTGI-AC – An AI that checks its internal stability before answering (ztgiai.pages.dev)

ZTGI-AC is a new LLaMA-based assistant paired with a ZTGI-Shield that self-checks its internal stability before replying. On each turn the Shield compresses hidden internal signals — described as “noise” and “discordance” — into a single risk scalar. If that scalar crosses thresholds the system shifts between SAFE → WARN → BREAK modes, progressively shortening and softening replies; under Tek-Taht principles the model surfaces a filtered answer while leaving the final decision to the user. Users see observable indicators (Energy, p(Ω), Gate) that hint at the model’s internal dynamics, but the exact equations and parameters remain private. PRO access is supported and keys are kept in the browser’s localStorage rather than being logged in plaintext to the server. For the AI/ML community this is an interesting practical attempt at runtime introspection and behavior gating: it combines a base LLM with a lightweight safety monitor that produces an actionable risk scalar and mode transitions, enabling graded degradation instead of binary blocking. That makes it useful for human-in-the-loop workflows, safer content generation, and research into internal-signal-based calibration and adversarial robustness. The trade-offs are transparency versus security — internal metrics are hidden even as external indicators are exposed — so its value will depend on how well the risk scalar aligns with real safety concerns and how robust the gating is across prompts.