Claude Code · Anthropic
Study what works across peer channels before planning content
The two creators behind a small newsletter and podcast used Claude Code to analyse the thumbnails of 776 videos across 57 YouTube channels and pull out what the best-performing ones have in common.
What they did
They described the goal in plain English, had Claude Code gather the videos into a database, and iterated on the analysis by giving feedback on each result. They also built a channel analyser that finds slightly larger peer channels and suggests video ideas only when a pattern shows up in at least three examples.
How it went
The poster says the thumbnail study, which would have taken three days by hand, was done in an afternoon.
Worth knowing
The post does not share the findings in detail or explain how the analysis was checked, and it promotes the authors' own workshop.
Try it with Claude Code
Collect the last <number> videos from these channels: <list of peer channels>. For each, save the title, view count relative to the channel's average, and the thumbnail image into a local database. Then compare the top 20 percent against the rest and tell me which visual patterns appear in at least three top videos, with links to the examples.
We wrote this prompt from the case. Replace the parts in angle brackets with your own details.
What this label means: User reports
Someone using it at work posted about it. Any numbers are theirs, so we write "the poster says".
Real cases of AI agents at work, from every major agent, each linked to where it was first reported. Rewritten in our own words, most with a prompt you can try. These are not Wize customers.