Adam, head of design at Weights and Biases, introduces Weave, a tool designed to aid in building AI applications by providing visibility into performance and iteration progress. He showcases his personal AI assistant, Winston, which can perform tasks like summarizing articles and posting to Slack. Weave logs interactions as traces, helping identify issues like hallucinated tools and enabling quick iterations. By adding decorators to code, Weave captures inputs and outputs, facilitating observability. Adam emphasizes the importance of iteration speed in AI app development, using Weave's playground to experiment with prompt edits and model settings. Evaluations in Weave involve datasets and scoring functions to systematically assess changes, ensuring Winston's reliability across diverse inputs. Adam highlights the use of LLM judges for qualitative feedback, aiding in refining Winston's capabilities. He discusses model selection, balancing cost, speed, quality, and reliability, and compares different models using Weave's evaluation tools. Adam concludes by showcasing Weave's ability to track changes and evaluate model versions, aiding in informed decision-making for AI app development.