What about Karpathy Loop? (thenewstack.io)

Andrej Karpathy's recent GitHub release of a 630-line Python script signals a significant leap toward autonomous machine learning research. By utilizing the AutoResearch framework, the script ran 50 experiments overnight, optimizing the learning process for small transformer models without any human oversight. This advancement highlights a new design paradigm in machine learning, illustrating how structured, time-boxed experimentation can drastically reduce the manual labor typically associated with hyperparameter tuning. Central to this innovation are three key principles: a single editable asset, a scalar metric for clear evaluation, and a fixed time budget for direct comparability of experiments. The implications for the AI/ML community are profound. This approach not only enhances efficiency but also enables the generalizability of autonomous experimentation beyond just machine learning, potentially benefiting domains like database optimization and customer support systems. The noteworthy aspect is the role of the Markdown file, which serves as the human-agent interface, detailing search strategies and constraints in a format that's easily interpretable by both humans and machines. The shift from coding to designing experimental protocols indicates a maturation in research practices, as effective experimental design now becomes the critical skill, ensuring that researchers focus on high-value decision-making rather than repetitive tasks.