docker-socks-tunnel VS orange

I'm the maintainer of HTTP Toolkit.

In general the source for core components that can't really be used for anything else is AGPL, but all the generally reusable libraries are Apache 2 or similar. The goal is to share as much as possible, and let everybody access the source for everything, and fork for their own use or submit modifications or whatever, but without the risk that a proprietary product runs off with the core product in a different direction. Seems to be working reasonably so far!

Regarding Docker, you might also be interested in https://github.com/httptoolkit/docker-socks-tunnel (1MB Docker container that runs a socks proxy, for tunnelling traffic between networks) and this detailed breakdown of how the whole Docker interception setup works under the hood: https://httptoolkit.com/docs/guides/docker/#the-technical-de...

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.