Comment by TXTOS

Honestly, the most disturbing moment for me wasn’t an answer gone wrong — it was realizing why it went wrong.

Most generative AI hallucinations aren’t just data errors. They happen because the language model hits a semantic dead-end — a kind of “collapse” where it can't reconcile competing meanings and defaults to whatever sounds fluent.

We’re building WFGY, a reasoning system that catches these failure points before they explode. It tracks meaning across documents and across time, even when formatting, structure, or logic goes off the rails.

The scariest part? Language never promised to stay consistent. Most models assume it does. We don’t.

Backed by the creator of tesseract.js (36k) More info: https://github.com/onestardao/WFGY