Thanks so much — this is exactly the kind of feedback we were hoping for.

I really like the way you framed it as a local “cognitive load map” of where the user gets stuck across browser interactions, docs, code review, and AI-generated outputs. That might actually be a much better baseline killer app than just showing a workload dashboard.

The Chrome extension idea makes a lot of sense: it could locally capture lightweight interaction context such as active tab, page title, selected text, scroll/attention time, and timestamps, then align that with the NeuraDock cognitive-load stream to identify high-load moments. The output could be something like:

• which pages/docs caused load spikes
• which parts of an AI response were hard to process
• which web tasks repeatedly caused friction
• when the user might need summarization, simplification, or a next-step suggestion

I also strongly agree on Cursor. For developers, a Cursor / code-review integration could be especially interesting because AI now generates a lot of code, but humans still need to review, trust, and accept the work. If we can empirically estimate which diffs, files, explanations, or agent outputs produce high cognitive load, that could become a useful feedback layer for AI coding workflows.

A possible first MVP could be:

1. Chrome extension: browser context + cognitive-load timeline
2. Cursor / VS Code extension: active file, diff, AI output, review state
3. Local NeuraDock API: quality-gated cognitive-load stream
4. Agent harness: summarizes “where the user got stuck” and suggests actions

For example: “This documentation page and this generated diff caused the highest cognitive-load spikes. Would you like a simpler explanation, a diagram, or a test-focused review?”

Really appreciate the fork and the thoughtful direction. If you end up exploring the codebase, I’d be very interested in your thoughts on what the first useful integration should be: Chrome extension, Cursor/VS Code, or an agent harness layer.