Brice focused on backend stability and API correctness for the DataDog/watermarkpodautoscaler repository, addressing a critical bug in the recommender’s watermark handling logic. Using Go and leveraging Kubernetes expertise, Brice resolved an issue where fractional watermark values below 1.0 were incorrectly truncated due to invalid float conversion. This fix ensured that the recommender API accurately preserved and represented fractional watermarks, improving the reliability of recommendation signals and reducing downstream edge cases. Brice’s work emphasized careful debugging, code review, and validation practices, contributing to overall code health and maintainability, while prioritizing maintenance and stability over new feature development during this period.