This is a small experimental AI project I’ve been building called *ZTGI-AC*.

Most LLMs generate an answer immediately, but ZTGI-AC does something different: before responding, it runs an internal stability check.

It evaluates: • risk • jitter • dissonance • SAFE/WARN/BREAK modes • INT/EXT gating (self-monitoring loop)

Only after the internal signals stabilize does it generate a reply.

This project explores whether self-evaluation loops can reduce chaotic or unstable outputs in LLM-like systems.

*Demo:* https://ztgiai.pages.dev (Non-commercial, early prototype.)

I’d love feedback from the HN community — especially around: • whether self-monitoring loops are meaningful, • potential improvements to stability metrics, • and how this idea compares to classical alignment approaches.

Thanks for taking a look!