How Wayfair cut ML model costs by 90% (twice!) with Cursor

This post details how Wayfair's Applied Research team leveraged Cursor's agentic capabilities to significantly reduce ML model inference costs and accelerate research cycles. Key contributions include: demonstrating a 94% cost reduction for a tag-validation model through an automated experimentation sprint involving 110 model variations in four days; achieving a further 90% cost reduction in a subsequent sprint by using the productionized model as a baseline and layering genetic-algorithm searches; highlighting the effectiveness of scaled agent parallelization (20+ agents concurrently), cloud agents for continuous experimentation, and cross-platform surfaces (desktop/CLI); and showcasing how researchers can define specs, set cost guardrails, and steer agents for open-ended research projects, compressing months of work into days. The post also mentions the use of custom low-precision kernels for MoE training as part of the underlying infrastructure.