Claude Code + Karpathy's Autoresearch = INSANE RESULTS!

Author: AI Andy - Bewertung: 8x - Views:3

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This is the full breakdown of how to build and automate your personal brand using AI — from idea sourcing to publishing across every major platform.

Here's what I cover:
Pulling real CTR data for 186 videos from the YouTube Reporting API and discovering a ten percentage point gap between old thumbnails and recent ones
Building 12 binary eval criteria derived from actual click-through rate patterns — not guesses, not vibes, data from your own channel
Discovering that clear visual hierarchy, clean backgrounds, and showing the result predict CTR far more than curiosity gaps or mystery framing
Wiring a Karpathy-style autoresearch loop that scores thumbnails, correlates eval scores against real CTR, and rewrites its own generation prompts automatically
Watching the machine run ten iterations in one session — eval score climbing from 8.7 to 11 out of 12 without a single piece of manual feedback
Setting up the daily loop that pulls overnight CTR data, processes A/B/C split test results, and improves packaging prompts from three real data sources every single day

If you're a creator publishing videos without a feedback loop on your thumbnails, this video shows you exactly how to turn your own CTR data into a self-improving packaging system. I break down the full build, the 12 eval criteria, and the patterns I found in 186 videos so you can apply this to your own channel.
Tools & Platforms Covered:
Claude Code — builds and runs the entire autoresearch loop
YouTube Reporting API — pulls real impressions and CTR data for every video
Gemini — scores thumbnails against binary criteria and writes data-derived improvement rules
Airtable — stores CTR data, eval scores, and performance tiers across all 186 videos
Andrej Karpathy's Autoresearch Framework — the open source foundation the loop is built on
Stop guessing which thumbnail will get clicked. Pull your own data, build the eval, and let the machine get better every single day while you sleep.


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00:00 // Intro
"00:26 // What Just Happened
02:28 // Pulling the Data
03:35 // Writing the Eval
05:30 // Wire the Loop
07:24 // Running the fast Iteration
10:27 // How to run it daily
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