yadm VS orange

Everyone hand-rolls their own dotfile management system, but YADM already does everything you need:

https://yadm.io/

I've been working around this using tools built on top of git like [yadm](https://github.com/TheLocehiliosan/yadm) and relying on `ls-files` to list all my tracked dotfiles and their paths.

Still having everything in one place would make things much simpler. Great idea!

I wonder if the program i use to manage my dotfiles could help manage your scripts and extend your setup to all your desktops? Its called yadm (https://yadm.io/) it makes it so easy to have a laptop and a desktop or two.

I really like that one but still prefer yadm because you can just edit your files as usual and then yadm add them wherever you are.

If you haven’t already, this is the time to install a tool like yadm and get your computer configuration into version control. Your command-line tools can be managed by yadm directly, your system settings can mostly be managed with a yadm bootstrap script that runs things like defaults write, and the software you install can be managed with a Brewfile that the yadm bootstrap script uses to install software with Homebrew. Don’t manually download Xcode, use xcodes to do it.

I personally use YADM. It's basically a git repo on my home folder, that only tracks what I explicitly set. And you can setup bootstraps to do what you said, install a bunch of stuff or make custom changes. In it's essence, it's a set of bash/sh files that are executed sequentially when you launch the yadm bootstrap command.

I know I've tooted its horn before, but Orange3 is a pretty neat Python-based GUI platform that makes this and a metric buttload of other statistical/ML techniques available to non-programmer types.

Just watch out for null character `x00` in the corpus. That always seems to kill it stone dead.

https://orangedatamining.com/

https://orange3.readthedocs.io/projects/orange-visual-progra...

For all you folks who aren't ace programmer types, the Orange3[1] platform gives you a very miniaturized[2] ability to turn out these sorts of visualizations very rapidly. It's not the most stable thing in the world, but the node-based ML workflow designer is worth the price of admission all by itself.

[1] https://orangedatamining.com/

[2] The Wikipedia extension in Text limits each search result to 25 articles, so sucking all of Wikipedia is . . well, Orange text analytics crashes when I look at it sideways with a null character, so let's not think about what would happen.

First is identifying the "similar" things in a corpus. Best way I know to do that, for non-programmer audiences, is the Orange Data Mining tool, which gives you a node-based text mining interface to perform statistical analysis on text. Hierarchical Clustering shows - very rapidly - how similar your "modules" are, which ones are most similar. There's many other techniques (semantic viewer, similarity hash, etc) as well - the right one will depend on how your content is laying about.

Both tools are ripoffs of a data mining framework named Orange 3

You should also check out Orange Data Mining, it allows to create a lot of charts, filter data from a chart to another, build ML models, predictions and a lot more. And you can do it with zero code.

You can start with a free GUI based tool Orange. It is a component based data science workflow tool, which you can use to handle 60-75% of the traditional data science tasks from classification, regression, to basic neural networks.