Contributed to the mlflow-website and mlflow/mlflow repositories by delivering two key features focused on AI observability and developer onboarding. Built a comprehensive Braintrust vs MLflow comparison blog post, incorporating detailed tables, architecture analysis, and practical code snippets to guide user evaluation and adoption. Enhanced the trace page with onboarding instructions, multi-framework code blocks, and an interactive empty-state message, implementing polling logic in React and TypeScript to enable real-time trace updates without manual refresh. Emphasized UI/UX design and state management to streamline onboarding and improve trace monitoring reliability, with all work centered on front end development using JavaScript and CSS.