In March 2026, three LLM agents generated over 600,000 lines of code, ran 850 experiments, and helped secure a first-place finish in a Kaggle playground competition. Success in modern machine learning competitions is increasingly defined by how quickly you can generate, test, and iterate on ideas. LLM agents, combined with GPU acceleration, dramatically compress this loop.  Historically, two bottlenecks have limited this experimentation: How quickly you can write code for new experiments. How quickly you can execute those experiments. GPUs and libraries like NVIDIA cuDF, NVIDIA cuML, XGBoost, and PyTorch have largely solved the second problem. LLM agents now address the first problem—unlocking a new scale of rapid, iterative experimentation. This blog post describes how I used LLM agents to accelerate the discovery of the most performant tabular data prediction solutions.…