In August 2025, Q focused on backend development and documentation for the chalk-ai codebase, delivering three features centered on scalable analytics and improved usability. Q implemented approximate top-k aggregation support in chalk-go, adding a configurable protobuf field to WindowAggregation for efficient analytics queries. They also enhanced the chalk-ai/docs repository by introducing and documenting the approx_top_k aggregation function, providing practical usage examples to guide adoption. Additionally, Q updated windowed feature documentation syntax to align with evolving codebase conventions. Their work, using Go, Protocol Buffers, and Markdown, demonstrated depth in both technical implementation and cross-repository documentation alignment, supporting faster iteration.