Their GitHub does ;-) I enjoyed the article. We are doing a more genAI-oriented notebook env variant for louie.ai aimed at database/API users preferring natura

Their GitHub does ;-) I enjoyed the article. We are doing a more genAI-oriented notebook env variant for louie.ai aimed at database/API users preferring natural-language-first vs typical Jupyter/observable/hex/etc python wrappers, and for their automatic reactivity portion, a lot of similarity here with the road we went down. I appreciate seeing some of that reasoning laid out. Especially neat is the use of static analysis of global assignments vs the typical input/output variable syntactic convention. Few go so fine-grained (enso, frp, ...). My personal experiences with automatic reactivity (OSS'ing the first web frp system, and many after) and last 10 years as a heavy Jupyter notebooks user with many enterprise/gov/tech DS+operational teams actually steered us down a different path. We separate out the auto-reactivity to only auto-trigger when switched into a 'live' dashboarding view. By default, when in the authoring notebook view, we stick to Jupyter-style manually triggered run / runall. When authoring, the notebook accidently running a bunch of database or OpenAI or bigquery calls when you didn't intend can be... problematic. The underlying cell-based reactivity model we went with is largely the same, but when an analyst is dealing with big database calls and returns, a world of a difference in UX. Fwiw, if anyone uses Splunk/databricks/OpenSearch/etc and curious about a team genAI-first learning system with auto-viz-reactivity, would love to chat!