Hello everyone! I’m curious about the NLP workflows people are currently building with Galaxy and related tools. In my experience, tasks that look straightforward on paper often become surprisingly difficult when moving toward automation. Data cleaning, entity normalization, document classification, and extracting structured information from large collections of text can all introduce unexpected challenges. For those working with NLP pipelines, what task took you the longest to automate successfully? Was the bottleneck related to data quality, model performance, workflow integration, evaluation, or something else entirely? I would be interested in hearing about both research and real-world projects. Sometimes the most valuable lessons come from problems that did not go as planned. Looking forward to learning from the community's experiences. Thanks! https://wackygame.org/