The ARC-AGI benchmark has garnered significant attention recently as a challenging problem for large language models (LLMs) to solve. Ryan Greenblatt achieved 71% accuracy on a set of examples where humans typically achieve 85%, marking a state-of-the-art (SOTA) performance. The benchmark, which offers a $1 million prize, involves generating many possible Python programs to implement transformations, using a carefully-crafted few-shot prompt, generating ~5k guesses, and selecting the best ones using examples and a debugging step. Some experts argue that solving ARC-AGI does not equate to achieving artificial general intelligence (AGI) but recognize it as a valuable challenge highlighting LLMs' weaknesses in cell-based rules like the Game of Life. Another attempt using GPT-4o reached 50% accuracy, demonstrating progress through clever tricks and increased computational search.