Hi folks, I've worked with the kotlin-jupyter kernel last weekend and I was amazed by its stability and its wonderful module-definition feature. See yourself (just in case you like spaghetti and philosophy, but who doesn't...) https://github.com/holgerbrandl/kalasim/blob/master/simulations/notebooks/dining.ipynb . Or if you prefer a more gentle/verbose introduction see the corresponding manual page https://www.kalasim.org/examples/dining_philosophers/ FYI @Maria Khalusova data science with kotlin!

Indeed the platform looks really nice espcially the modularity and library loading. I think that @Ilya Muradyan's effort to add kernel library API togethere with including other ipynb platforms like VS-Code and Google colab could really turn tables on how notebook-based prototyping works.

To me, one stopper is still https://youtrack.jetbrains.com/issue/PY-41575 because I can hardly imagine myself leaving the beloved ide for a much reduced browser experience. But it's clearly not kernel but rather an IDE issue.

Keep working on it. It's rather slow because of need of interaction with other teams, but I already have a working prototype with IDE-level code insight and cells execution on Kotlin kernel.

That's great news. I'm looking forward to it. I guess one hard part are also dependencies, which may differ from notebook to Notebook which contradicts the current module dependency model.

Kotlin scripting support has some issues with updating configurations (including dependecies, implcit receivers and so on), but I've made it work. Kernel sends back the resolved classpath as a part of response metadata, and then plugin triggers configuration update. After reindexing resolved jars become available to code insight

@holgerbrandl Thanks! Do you mind if I share this article via @KotlinForData?

It would be an honour for me to be mentioned there @Maria Khalusova. I've just polished the example and pushed out a fresh release of

kalasim

to jcenter.