CATMAID (Collaborative Annotation Toolkit for Massive Amounts of Image Data) is a full-stack web application for collaborative connectomics research. It enables neuroscientists to trace neuron skeletons, annotate synapses, and analyze neural circuits across massive image stacks. The backend is a Django monolith with a REST API, PostgreSQL/PostGIS database, Celery for async tasks, and WebSockets for real-time collaboration. The frontend is a large JavaScript codebase using jQuery, Three.js, Pixi.js, D3, and custom widget system. Key features include skeleton tracing tools, 3D visualization, NBLAST neuron similarity, connectivity matrices, and review workflows. The codebase is well-structured with many reusable client-side utilities (event system, command pattern, caching, settings manager) and demonstrates strong engineering practices with CI/CD, tests, and Docker deployment. However, it carries some technical debt from vendored libraries and complex database migrations.