Upload a video or audio clip. In about a minute, NeuroVidz maps how a brain would respond to its picture AND its sound — an engagement score that shows its components, a per-second emotion timeline, and timestamped suggestions for your next edit. Most tools focus on the frames alone; NeuroVidz listens too, so podcasts, music, and voice-driven clips score like a listener feels. If it can't produce a confident read, it refunds the credits. Start free, no card — founding 50 accounts get 40 credits.
Hey Product Hunt — we're Uddalak and Shivam, the team behind NeuroVidz (built at our studio, Sandmatter).
We made this to answer a question every creator has asked: how will this clip land — before it goes out? Analytics tell you what happened after publishing; we wanted that insight available while there's still time to edit.
NeuroVidz analyzes a clip the way a brain would experience it. Every second is distilled into perceptual signals — motion, faces, pacing, sound — and mapped onto a seven-network model of how a brain responds. You get an engagement score that shows its components, a second-by-second emotion timeline, and clear, timestamped suggestions for your next edit.
The part we're proudest of: it listens. Most tools focus on the frames alone; NeuroVidz also hears the music, the pauses, and the delivery — so podcasts, songs, and voice-driven clips get a read that reflects what a listener actually feels.
Honesty is a design principle: when the signal is too weak for a confident read, it says "no clear read" — and refunds those credits automatically. You're never charged for a result we wouldn't stand behind.
It's live today — start free, no card required. The founding 50 accounts receive 40 credits and a founding badge; every account after starts with 20. You can also explore a full sample result without creating an account: neurovidz.com/c/sample
We're both here all day and would genuinely value your feedback: what would make this a daily tool for you?
saw your reply to Aidan below - appreciate the direct answer that it's a forward model against published neuroimaging weights, not fitted to real retention data. genuine follow-up: if it isn't validated against actual audience retention, what's the evidence that the engagement score correlates with what real viewers do, versus just being internally consistent with itself? asking because "stimulus-driven, not viewer-specific" is honest but it also means the score could be confidently wrong in a way no refund policy would catch.
The audio-aware angle is genuinely interesting, since so many tools ignore the sound side. One thing that would help me decide if it's worth using long-term though is some kind of comparison view, so I can drop in two versions of the same clip and see how the engagement scores and emotion timelines actually differ. That would make the suggestions feel way more actionable instead of just a score in a vacuum.
I like that the product is explicit about what it measures and, just as importantly, what it doesn't claim to predict. Tools that acknowledge uncertainty tend to be much more useful than ones that always return a confident score.
One thing I'd love is the ability to compare two versions of the same clip side by side, so I can see how a re-edit changes the engagement score and emotion timeline. Would make A/B testing hooks and thumbnails way easier.
Honestly the audio piece is what sold me here, finally something that gets that podcasts and music-driven clips need attention too. One thing I'd love: a way to compare two versions of the same clip side by side so I can see which edit is actually pulling more engagement second by second. Would make the refund policy almost unnecessary.
The refund on low confidence reads is a smart call. Most analytics tools will give you a number no matter what, even when the input is too messy to trust.
Honestly the audio part is what sold me, most engagement tools totally ignore that. One thing though, would be cool if you could compare two versions side by side before exporting, like drop in a re-cut and see how the emotion timeline and score shift. That would make the refund policy almost irrelevant because people would just keep tweaking until the numbers move in the right direction.
The thing I'd want to understand is what the prediction is validated against. "How a brain would respond" can mean a model fit to EEG data, or one fit to actual retention curves from real viewers, and those two behave very differently once a clip looks unlike whatever it was trained on.
Very cool - congrats on the launch.
uploaded a 30 second podcast clip and the per-second emotion timeline actually picked up on the pause before the punchline, which i didn't expect from a tool like this. the refund promise is a nice touch too.
This is a fascinating angle for video creators — most analytics tools tell you what happened (views, drop-off points) but not why. Curious what's actually being measured here: is this modeling predicted neural response from visual/audio features, or do you have real EEG/biometric data feeding the model? That distinction matters a lot for how much I'd trust the output as a creator deciding what to cut.
About NeuroVidz on Product Hunt
“See how a brain reacts to your clip”
NeuroVidz launched on Product Hunt on July 20th, 2026 and earned 106 upvotes and 28 comments, placing #19 on the daily leaderboard. Upload a video or audio clip. In about a minute, NeuroVidz maps how a brain would respond to its picture AND its sound — an engagement score that shows its components, a per-second emotion timeline, and timestamped suggestions for your next edit. Most tools focus on the frames alone; NeuroVidz listens too, so podcasts, music, and voice-driven clips score like a listener feels. If it can't produce a confident read, it refunds the credits. Start free, no card — founding 50 accounts get 40 credits.
NeuroVidz was featured in Analytics (172.9k followers), Artificial Intelligence (474.1k followers) and Video (1.9k followers) on Product Hunt. Together, these topics include over 128.8k products, making this a competitive space to launch in.
Who hunted NeuroVidz?
NeuroVidz was hunted by Uddalak Datta. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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Uddalak Datta