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The open-source standard for deterministic process logging in Human-AI collaboration. Moving past AI detection toward transparency

10 starsPython

TWFF – A container format for declaring AI use in writing

by normanbell·Feb 19, 2026·2 points·5 comments
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AI Analysis

●●SolidZero to OneBig Brain

Deterministic audit trail vs. probabilistic detection, but adoption depends on ecosystem buy-in.

Strengths
  • •Conceptually elegant: separates content from process, enabling privacy-preserving sharing (JSON-only for research)
  • •Local-first architecture keeps telemetry on creator's machine until voluntary export
  • •Revision Velocity concept is genuinely novel—captures human effort as delta between drafting and AI injection
Weaknesses
  • •v0.1 spec with only Python/NiceGUI MVP; no multi-platform tooling or editor integrations yet
  • •Revision Distance fingerprinting is unvalidated; unclear if human effort is actually harder to fake than writing itself
Category
Other
Target Audience

Academic researchers, writers, publishers needing verifiable AI attribution without black-box detection

Similar To

EPUB · PDF metadata standards

Post Description

TWFF (Tracked Writing File Format) is my proposal for moving away from so called AI detection to verifiable declaration.

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...

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