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...
normanbell•1h ago
Can't I just script a 'human-like' delay and spoof the log? Currently, yes. But v0.1 is about the container. Future iterations will look at signing and making it as computationally expensive to fake the process as it is to just write the text.
Is this just surveillance? It’s an Opt-in Declaration. The user owns the file and the log.