decomp.me VS orange

https://github.com/decompme/decomp.me

I’ve posted this before but I love it so much I gotta do it again.

You plug in a piece of ASM from a video game ROM, and it gives you a first pass decompilation. On the left hand side you can edit this decompilation, and on the right there’s a side-by-side diff of the target ASM and what your source currently compiles to. It’s slightly gamified, looks great, is super easy to fork/share, and can be pretty addicting once you get into it. Super cool community.

Checkout http://decomp.me - it’s a community built tool used by a lot of video game decompilation projects. You put in the original bytecode, it will attempt a decomp, and then you fiddle with the source (using the same toolchain & flags known/best guessed to be used by original devs) until it matches perfectly. It’s super cool.

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.