Instead of an external model guessing if a text is AI-generated, TWFF is a ZIP-based container (similar to an EPUB) that stores the document alongside a Process Transcript (JSON).

How it works: 1) It captures Revision Velocity: the delta between human drafting and AI injections. 2) It intercepts paste and AI-interaction events, wrapping them in deterministic metadata. 3) It’s local-first. The audit trail stays with the author until they choose to export the signed container.

This is a v0.1 reference implementation built in Python/NiceGUI. I’m looking for feedback on: > The container structure (XHTML vs. Markdown). > The JSON event schema. > The Revision Distance logic: can we create a fingerprint for human effort that is as difficult to fake as the writing itself?

MVP Demo: https://demo.firl.nl/

TWFF spec:https://github.com/Functional-Intelligence-Research-Lab/TWFF...