entr VS mercury

entr ("Event Notify Test Runner"; GitHub), is a command-line tool written by Eric Radman that allows running arbitrary commands whenever files change.

I use something very similar on https://lunar.fyi and https://lowtechguys.com but I wouldn’t call this “simple” anymore.

They use Jinja templating, I prefer Slim (https://github.com/slim-template/slim#syntax-example) which has a more Pythonic syntax (there is plim [0] in Python for that)

I use Tailwind as well for terse styling and fast experimentation (allows me to write a darkMode-aware and responsive 100 line CSS in a single line with about 10 classes)

For interaction I can write CoffeeScript directly in the page [1] and have it compiled by plim.

I run a Caddy static server [2] and use Syncthing [3] to have every file save deployed instantly to my Hetzner server.

I use entr [4] and livereloadx [5] to rebuild the pages and do hot reload on file save. All the commands are managed in a simple Makefile [6]

———

You can already see how the footnotes take up a large chunk of this comment, this is not my idea of simple. Sure, the end result is readable static HTML and I never have to fight obscure React errors, but it’s a high effort setup for starters.

Simple for me would be: write markdown files for pages, a simple CSS for general styling (should be optional), click to deploy on my domain. Images should automatically be resized to multiple sizes and optimized, videos re-encoded for smaller filesize etc.

I have mostly implemented that for myself (https://notes.alinpanaitiu.com/How%20I%20write%20this%20blog...) but it feels fragile. I’d rather pay for a professional solution.

[0] https://plim.readthedocs.io/en/latest/

[1] https://github.com/FuzzyIdeas/lowtechguys/blob/main/src/rcmd...

[2] https://caddyserver.com/docs/command-line#caddy-file-server

[3] https://syncthing.net

[4] https://github.com/eradman/entr

[5] https://nitoyon.github.io/livereloadx/

[6] https://github.com/FuzzyIdeas/lowtechguys/blob/main/Makefile

Try entr for fast reloading. Another one is hupper.

entr

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.