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.