homeshick VS orange

I have a work mac, work linux, and home mac. I want the same terminal-based development environment on all of them, but each requires just a little bit of customization.

For example, the .gitconfig for work is different from home (e.g. my username/email). Ditto for my .ssh/config and my shell aliases.

I also use Nix to manage all my tools, and the home-manager configuration is slightly different between mac & linux due to platform support.

I've gone through a few iterations of home-built solutions, including extending homeshick[1], before discovering YADM which implemented everything I had done but better.

[1] https://github.com/andsens/homeshick

I do roughly the same and then manage them with 'homeshick' ( https://github.com/andsens/homeshick )

Have a look at https://github.com/andsens/homeshick project, it makes this workflow much easier.

Homeshick for dotfiles: https://github.com/andsens/homeshick

Docker for Obsidian and Alfred syncing - the three target limit on the free tier is just barely enough for 2 of my own computers and my work laptop.

I've also got a Brewfile for installing the basic tooling on macOS

I also have a "how to set up a new computer/server" document on Notion that I use so I don't forget any steps.

This is the exact reason I use Fish. The only thing I _need_ to get installed on random servers is Fish itself.

No need to install and configure oh-my-$shell or other huge monstrosities. Most of my stuff comes from a simple homeshick[1] sync with a few files in it.

[1] https://github.com/andsens/homeshick

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.