In the rapidly evolving field of AI product development, the author reflects on the lessons learned from past experiences, emphasizing the need to adapt to shifting model capabilities rather than relying on complex engineering solutions that may become obsolete. The text discusses the importance of understanding the unique value propositions of AI products and being agile in adjusting roadmaps to leverage new model capabilities. The author recounts specific challenges faced, such as the initial failure of the Notebook Agent due to premature development and the blind spots created by focusing on "pretty good" solutions rather than exploring new opportunities. Key strategies now include validating features with early user engagement, identifying capability shifts in models, and the willingness to abandon projects that rely on clever hacks to compensate for current model limitations. The narrative encourages celebrating failures as part of the learning process and highlights the exciting potential of building AI products during technological advancements.