AlphaEvolve, leveraging Gemini technology, marks a significant advancement in reinforcement learning (RL) application across various domains, especially in coding and algorithm design. It showcases a speedup of up to 32.5% in FlashAttention kernel implementations in Transformer-based models, with notable rediscoveries and improvements in state-of-the-art solutions, indicating high potential for generating novel knowledge through AI systems. The method of employing self-play and verifiable outputs addresses the data scarcity issue in training AI, leading to efficient algorithm development without extensive human input. Users express excitement about the implications of this technology in software engineering, including the potential obsolescence of traditional coding interviews and the capability to autonomously produce and refine code.