[Looking for testers] Free brain-grounded creative analysis for short-video ads - building toward a proper platform, looking for ad creators to test the platform
Hey r/alphabetausers,
I've been building a research demo I think this community might want to break.
The short version: you give me a short-video ad (TikTok / Reel / YouTube Short / a pre-launch cut), and within ~24 hours you get back a creative brief that combines:
- A neural attention curve — predicted moment-by-moment brain attention from a digital-twin viewer, generated by TRIBE v2 (the fMRI foundation model Meta AI published earlier this year)
- A brain timeline strip — when each of 12 brain regions peaks during your video (audio, visual, language, memory, reward, decision, emotion, etc.)
- A simulated audience panel — any number of ICP personas matched to your audience, reacting in their own voices (verbatim quotes, what they'd scroll past, what they'd save, what specific lines they trip on)
- A specialist agent panel — Creative Director, Performance Marketer, Brand Strategist — each grounded in the neural data
- Quick Wins — algorithmic outputs like "best 6-second clip for paid spark", "optimal CTA placement (second 57)", "cut to 76s — last 36s have flat-low brain attention", "brand recall index: 70/100"
Two PDFs come back: a plain-language summary and a full agency brief. You also get the standalone visualizations as PNGs.
What I want from testers:
- Submit a video you've already launched (so we can compare predictions vs your actual platform analytics) or a pre-launch cut you're polishing
- Read the report
- Tell me what's right, what's wrong, what's useless, what's missing
- If possible, share your actual platform analytics post-launch (skip rate, AWT, like-spike timing) — that's the calibration data that turns the system from research demo into a real predictive engine
- That's it. Free. No catch. No upsell. Not selling anything yet.
Why I'm being transparent about the limits:
The model has known biases I disclose in every report. It over-predicts watch time. It biases like-spike timing late. It can't predict platform retention curves directly (different mechanism). The qualitative diagnosis — friction points, hook problems, brand encoding gaps — is what's actually working well right now. The quantitative predictions need calibration data to sharpen, and that's exactly the dataset I'm trying to build.