Labelbox has introduced several updates aimed at enhancing data exploration, platform navigation, and model error debugging. These updates include natural language querying, which allows users to quickly search for specific data using simple text phrases, and new filtering capabilities that enable more granular searches across large datasets. The platform now supports saving and revisiting search queries, offering a streamlined way to manage and analyze data. Additionally, the navigation interface has been redesigned for improved usability, with vertical scanning and a more accessible notifications layout. The platform also introduces enhanced ontology creation processes, requiring users to specify media types to ensure consistency and reusability across projects. Labelbox Model has been updated to aid in diagnosing model performance issues, offering features such as auto-generated metrics and tools to inspect model failures, uncover data patterns, and curate better datasets. These changes are designed to improve efficiency and accuracy in managing and analyzing AI and data labeling tasks.