According to God of Prompt on X, Andrej Karpathy open-sourced a 630-line repository that lets an AI agent autonomously run end-to-end ML research loops on a single GPU, including generating code changes, launching training runs, evaluating validation loss, and committing improvements to git without human intervention (as reported by God of Prompt citing Alex Prompter’s video and link to github.com/karpathy/autoresearch). According to Alex Prompter on X, each dot in Karpathy’s demo graph represents a full LLM training run of roughly 5 minutes, with the agent iteratively discovering better architectures and tuning hyperparameters, enabling back-to-back experiments overnight and side-by-side comparisons of research strategies via different prompts. From an industry perspective, this agentic workflow suggests immediate opportunities for MLOps teams to automate hyperparameter optimization, architecture search, and ablation studies, reduce researcher time-to-insight, and standardize experiment tracking through git-native versioning, according to the posts. The original source code is hosted on GitHub under karpathy/autoresearch, and the functionality and claims described are attributed to the authors’ X posts; practitioners should validate performance and safety constraints on their own workloads before adoption.