Eve VS mercury

There's also https://github.com/mech-lang/mech which is a sort of descendant of Eve https://witheve.com/ . That too seems to be getting close to hiatus. It's a bit of a shame since it seems like quite a nice paradigm for some stuff like GUIs, interactive stuff, and discrete event simulation, but I suppose the paradigm is both a bit obscure and different enough from everything else that it becomes a "boil the ocean" situation where one or a few people try and hack away but aren't really able to get much traction and eventually tired themselves out.

Interesting perspective. It reminds me of Eve [1], which was all the rage over here a few years ago.

[1] https://witheve.com/

You can read more about it here: http://witheve.com

From what I can tell, this language builds on the learnings of Eve[1] without investor money. Their active repository seems to be on GitLab[2] despite the links on the landing and in the README. It doesn't seem to have a lot of contributors or even people observing it, so I thought the HN crowd might want to look into this.

[1] https://github.com/witheve/Eve

[2] https://gitlab.com/mech-lang/mech

I helped with the Eve language, which was an attempt down this path (https://witheve.com)

After that project ended I started working on my own attempt (https://GitHub.com/mech-lang/mech).

Someone else posted a link to futureofcoding.org, which is a community that works on these types of projects. You can find a lot more there.

Also sounds like http://witheve.com

Some langs have been made more or less like this (ex: http://witheve.com).

Do you mean this Eve? I'm trying to figure out what you meant :)

http://witheve.com/

I'm build dashboards in Jupyter Lab. My plotting libraries are Altair, matplotlib, seaborn, Plotly - all work well in notebook.

My favorite is Altair. It provides interactivity for charts, so you can move/zoom your plots and have tooltips. It is much lighter than Plotly after saving the notebook to ipynb file. Altair charts looks much better than in matplotlib. One drawback, that exporting to PDF doesn't work. To serve notebook as dashboard with code hidden, I use Mercury framework, you can check example https://runmercury.com/tutorials/vega-altair-dashboard/

disclaimer: I'm author of Mercury framework https://github.com/mljar/mercury

Web App is using two open-source packages that I've created:

- MLJAR AutoML - Python package for AutoML on tabular data https://github.com/mljar/mljar-supervised

- Mercury - framework for converting Jupyter Notebooks into Web App https://github.com/mljar/mercury

You can run Web App locally. What is more, you can adjust notebook's code for your needs. For example, you can set different validation strategies or evalutaion metrics or longer training times. The notebooks in the repo are good starting point for you to develop more advanced apps.

The GitHub repository https://github.com/mljar/mercury

We are working on open-source web framework Mercury that converts Python notebooks to Web Apps.

It is very opinionated:

- it has no callbacks - we automatically re-execute cells below updated widget

- it has no layout widgets, all input widgets are always in the left sidebar

Thanks to above decisions you don't need to change notebook's code to have web app and fit to the framework.

The simplicity of the framework is very important to us. We also care about deployment simplicity. That's why we created a shared hosting service called Mercury Cloud. You can deploy notebook by uploading a file.

The GitHub repository https://github.com/mljar/mercury

Documentation https://RunMercury.com/docs/

Mercury Cloud https://cloud.runmercury.com

Analysis was done in Jupyter Notebook with Python 3.10, Pandas, Matplotlib, wordcloud and Mercury framework.

Analysis was done in Jupyter Notebook with Python 3.10 kernel, Pandas, Matplotlib, wordcloud and Mercury framework to share notebook as a web application with widgets and code hidden. Gif created in Canva.