He Finally 10x Claude Code With This Method

Loop engineering is how people get Claude Code to build on its own for hours. Thank you Upstash for sponsoring! Check them out at https://upstash.com/?utm_source=ailabs1 and Run AI coding agents safely in the cloud with Upstash Box. We set up Karpathy's loop on our own apps, found the one issue it can't see, and fixed it with a loop that improves itself. It's the step past prompt engineering for agentic AI and AI automation. Community with All Resources: https://ailabspro.io/?v=qLfSDQ5NGh0 The Roundup, our daily newsletter covering the AI stories that matter. Join now: https://www.theroundup.so/ WTF is loop engineering? It's building agentic loops: an AI agent makes a change, a separate check scores it, and the agent keeps the change only if the score improves. Loop AI engineering took off with Andrej Karpathy's AutoResearch. His agent was allowed to change only one training file and could never touch the file that scored it. A plain-English file called program.md told it how to run each round. Over two days it ran 700 experiments and found 20 changes that made the model train faster. Shopify's CEO ran the same kind of loop on one of his own models overnight: 37 experiments, and the model performed 19% better by morning. This is a full long video on loop engineering. Before the setup, we cover when a loop is worth building at all. The task should be one you repeat often. You need a usage limit that can handle the token cost. The work has to be checkable without a human, using a clear score. And the agent has to be able to run what it built and see what breaks. Then we walk through our Claude Code loop engineering workflow: - a project context skill that works as the app's memory bank - a build skill that drives the whole loop - a write checks skill that writes the checks before any feature gets built - an approve checks program that locks the checks in a folder the agent can't edit - a fresh feature builder agent for every feature - a results file that records every round The skills and agents around the loop are where loop engineering and harness engineering overlap. Loop engineering example #1: we asked the loop to add pickup ordering to a restaurant website. It wrote 11 checks, and all 11 passed in the first round. Then it built the order form the checks never tested and connected it before marking the feature done. Loop engineering example #2: the auto loop, a loop on top of the loop. It reads how each run went, finds habits that keep repeating, and rewrites the "How to work" part of program.md so the next feature starts from the method that worked. It can never edit the checks. On a project management app, it caught a shared database that passed all 10 checks while the app never saved anything to it. It also caught a mentions feature that clashed with older parts of the app, and it fixed both habits for the features that came next. Loop engineering in Claude works the same whichever model you run, Fable included. We built this for Claude Code, but the AI automation ideas carry over if you build with ChatGPT, OpenAI's Codex, or any other AI agent. 0:00 Intro 0:51 Karpathy's Method 2:32 When to Use Loops 4:02 The Setup 6:41 Sponsor 7:37 Running the Loop 8:38 The Habit Problem 9:56 Auto Loop in Action Hashtags #ai #claude #claudecode #chatgpt #openai #codex #aiautomation #loopengineering