orange VS zig

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.

There is an issue proposing this approach: https://github.com/ziglang/zig/issues/4284

https://github.com/ziglang/zig/issues/224

e.g.:

> > When debugging/prototyping, it's useful to comment out a line without having to refactor, e.g.

When writing code in a scripting language, sometimes you need that extra bit of performance (or maybe an async feature from Zig).

NodeJS is by no means a slow runtime, it wouldn’t be so popular if it was. But compared to Bun, it’s slow. Bun was built from the ground up with speed in mind, using both JavascriptCore and Zig. The Bun team spent an enormous amount of time and energy trying to make Bun fast, including lots of profiling, benchmarking, and optimizations.

ntdll.dll!RtlUserThreadStart()

There are valid reasons to use APIs from NTDLL. Where I disagree with zig#1840 is the idea that it is always better to use NTDLL versions of API. Every other software ecosystem uses the standard Win32 APIs and diverging from that without a good reason seems like a good way to have unexpected behavior. One concrete example is most users and programmers expect Windows to redirect some file system paths when running on WOW64. But this is implemented in Kernel32, not ntdll.

https://github.com/ziglang/zig/issues/11894

Zig also has an IRC channel on libera (#zig) that is moderated by Andrew Kelley.[1]

[1] https://github.com/ziglang/zig/wiki/Community