June 2026 monthly work summary for stanfordnlp/dspy: Focused on improving developer experience for the experimental RLM module through comprehensive documentation enhancements and navigation improvements, enabling faster onboarding and API adoption. Key outcomes include a Diving Deeper guide detailing design, functionality, API usage, and runtime considerations, integration into the MkDocs navigation, and a new link to an example dataset. These changes boost accessibility, reduce time-to-first-use, and clarify how to configure and experiment with RLM in real workflows.

May 2026: Delivered a major homepage redesign and documentation overhaul for stanfordnlp/dspy, anchored by a new 12-page Getting Started tutorial and refreshed learning/reference docs. Implemented extensive cross-linking to Diving Deeper, added two new deep-dive pages, and updated navigation/naming to kebab-case for improved discovery. Strengthened the docs build/deploy pipeline (static MkDocs site on Vercel, venv-based Python environment, and explicit install/build overrides) to ensure reliable production-ready docs. Business impact: faster onboarding, clearer guidance for users, and reduced maintenance overhead; demonstrated skills in MkDocs, typography, cross-linking, deployment automation, and collaborative documentation practices.