TRIBE v2 Brain-Response Breakdown

Cortical-response model output for Convex shorter v2, a 1:38 Lawn clip processed on the RTX 5090 rig.

June 1, 2026· Prepared by Codex· Research sandbox only

★

How to read this

TRIBE v2 predicts cortical-response maps from video, audio, and language features. The timeline below is useful for comparing where the model response rises, falls, or shifts. It is not a virality score, preference score, clinical interpretation, or proof that a specific edit will perform better.

Clip-boundary correction

TRIBE emitted padded timeline segments beyond the 98.26-second source. Report-facing outputs are trimmed to the original MP4 duration; preserved raw artifacts retain the padded model output for audit. The downloadable bundle includes both the corrected report-facing files and the preserved raw padded artifacts.

Clip duration

1:38

99 retained one-second segments

Prediction array

99 × 20,484

seconds × fsaverage5 vertices

Words transcribed

373

20 sentence events

Total runtime

11.6 min

warm dependency caches, 1080p input

Timeline

The first panel shows overall cortical-response magnitude and its 95th-percentile envelope. The second shows second-to-second map change. The third compares left and right hemisphere magnitude. Vertical guides mark the strongest response-magnitude moments.

Sampled Cortical Maps

Twelve evenly spaced cortical maps summarize how the predicted surface response develops across the clip.

Strongest Response Moments

These are the largest per-second mean absolute cortical-response values. Nearby transcript words are included as editing landmarks, not causal explanations.

Time	Mean absolute response	Nearby transcript words
`1:17`	0.1724	all of the niceties you would need with this. And the experience of building Convex, especially with agents is only getting
`0:04`	0.1681	kind of hesitant to work with just because I use them in every single one of my projects. It is a phenomenal piece of software
`1:38`	0.1588	you should go check out at davis7.link slash Convex.
`0:10`	0.1402	building without. And that piece of software is Convex. It's so easy to pitch. It is everything you need
`1:33`	0.1289	do anything wrong. The whole system is seamless. It is what Firebase should have been and so much more that
`1:23`	0.1244	they recently introduced the NPX Convex AI files command, which allows you to instantly install
`0:14`	0.1169	is everything you need for your backend and your database all rolled up into one. They have automatic client
`0:40`	0.1165	They have everything from file storage to cron jobs, to workflows and queues and

Largest Map Transitions

These seconds have the largest change in the predicted cortical surface map compared with the prior second. They can be useful places to inspect cuts, topic changes, visual reveals, or pacing shifts.

Time	Change RMS	Nearby transcript words
`0:01`	0.0789	Today's sponsor is one that I was honestly kind of hesitant to work with just because I
`1:37`	0.0728	so much more that you should go check out at davis7.link slash Convex.
`0:07`	0.0719	of my projects. It is a phenomenal piece of software that I cannot imagine building without. And that piece of software
`1:00`	0.0716	on for like internal tools, all on Convex. And they also have a component system built on top of their primitives,
`1:31`	0.0679	project so that the agent will not hallucinate and do anything wrong. The whole system is seamless. It is what Firebase
`1:15`	0.0574	and background queuing and all of the niceties you would need with this. And the experience of building Convex,
`0:33`	0.0520	and mutations in TypeScript and they just kind of work naturally back and forth together with full type safety. They
`0:51`	0.0479	Convex and it will work incredibly. I've been building everything with this from PickThing 2.0 entirely

Aggregate Diagnostics

Metric	Value	Meaning
Mean absolute response, average	0.1062	Average magnitude across time and cortical vertices.
Mean absolute response, maximum	0.1724	Largest one-second response-magnitude moment.
Change RMS, average	0.0349	Typical second-to-second shift in the cortical surface map.
Change RMS, maximum	0.0789	Largest second-to-second map transition.
Left hemisphere magnitude, average	0.1020	Average model-output magnitude on left-hemisphere vertices.
Right hemisphere magnitude, average	0.1104	Average model-output magnitude on right-hemisphere vertices.

Run Details

Source: Public Lawn watch page
Input SHA-256: 45a339036c32381a5a9f64087235e6e0fce6b172aeb48234e1ab2537df3d95cf
GPU: NVIDIA GeForce RTX 5090
CUDA: PyTorch 2.7.0+cu128 · compiled CUDA 12.8 · capability [12, 0]
Numerical sanity: Finite predictions float32 from -1.0380 to 1.0505
Events: {"Audio": 2, "Sentence": 20, "Text": 1, "Video": 2, "Word": 373}
Raw padded prediction array: [219, 20484] preserved alongside the clip-boundary-corrected array.

Research-use boundary

The released TRIBE v2 source and weights are licensed under CC-BY-NC-4.0. Keep this analysis in the research sandbox unless commercial permission or an acceptable legal path is confirmed.