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Playhead
Reverse engineer winning ADs at scale
Paste a link to any ad. Playhead watches the video and hands back the hook, shot length, every cut, every claim, and the second each one lands. Pass a whole set and each ad is read on its own, blind to the others, then compared. Label them winner and control, and it tells you what the winners do that the losers do not. Meta Ad Library, TikTok, YouTube, Instagram, or your own files. Underneath is a video API. Connect it to Claude, ChatGPT or Cursor, or call it from your own code.
Hey guys!
Your AI cannot watch video. Ask Claude or ChatGPT about a TikTok and it answers
from the title, the caption or a transcript. It never sees the picture. That is
why every ad teardown it writes sounds like every other one.
The obvious fix is to hand the model the frames. A minute of video is hundreds
of pictures, and the model pays for every one. So we did the opposite: Playhead
watches the video on our side and answers in words, with the second of every
moment. The frames never touch your context.
Our shot detection is new.
Playhead names every cut and every transition in an ad, to the frame.
Not "fast cuts". Cuts per second, the median shot length, and the second
each claim lands.
The part I did not expect was what made it actually useful for ads.
The first version read one ad and wrote a teardown. It was accurate and it was
worthless, because every ad opens on a face, cuts fast and shows the product by
second three. So do the ads that lose money. A trait shared by every winner and
every loser is a convention of the category, not a cause of anything.
So a set now takes labels. Three winners and two ads you killed, and the
question stops being "what do these have in common" and becomes "what do the
winners have that the controls do not". One word in a prompt, and it turned a
description into research. Every ad is still read blind to the others, so the
comparison cannot talk itself into a pattern.
About Playhead on Product Hunt
“Reverse engineer winning ADs at scale”
Playhead was submitted on Product Hunt and earned 2 upvotes and 1 comments, placing #146 on the daily leaderboard. Paste a link to any ad. Playhead watches the video and hands back the hook, shot length, every cut, every claim, and the second each one lands. Pass a whole set and each ad is read on its own, blind to the others, then compared. Label them winner and control, and it tells you what the winners do that the losers do not. Meta Ad Library, TikTok, YouTube, Instagram, or your own files. Underneath is a video API. Connect it to Claude, ChatGPT or Cursor, or call it from your own code.
On the analytics side, Playhead competes within Marketing, Advertising and Artificial Intelligence — topics that collectively have 977.5k followers on Product Hunt. The dashboard above tracks how Playhead performed against the three products that launched closest to it on the same day.
Who hunted Playhead?
Playhead was hunted by Maximilian. 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.
For a complete overview of Playhead including community comment highlights and product details, visit the product overview.
Maximilian