gogrepoc VS orange

Two projects I greatly appreciate, allowing me to easily archive my bandcamp and GOG purchases (after the initial setup anyways):

https://github.com/easlice/bandcamp-downloader

https://github.com/Kalanyr/gogrepoc

And I recently learned about archivebox, which I think is going to be a fast favorite and finally let me clear out my mess of tabs/bookmarks: https://github.com/ArchiveBox/ArchiveBox

I should really write up a full Linux and windows setup guide and submit it.

Likewise, people who use bandcamp are likely to use GOG, which also has purchase archiving tools available on GitHub. I’ve used https://github.com/Kalanyr/gogrepoc for a long time, but there are actually multiple projects now.

Tools like https://github.com/Kalanyr/gogrepoc use these blue dots to know which games are updated, without having to do an intensive and long full scan of you library.

See above that's what I used and it works perfectly and its gogrepoc the other one is vastly out of date. Don't use the one you linked too.

I download my GoG games & extras directly to my RAID 1 NAS using gogrepoc.

Personally I use a app called gogrepoc.py It creates a manifest based off of your account and allows you to generate a current log of games. And you can update those in the future so it spins through your entire list and will mark changes. Unfortunately it takes a while if your catalog is huge but it's a great way to keep everything up to date.

I use https://github.com/Kalanyr/gogrepoc

For windows, I found: https://github.com/Kalanyr/gogrepoc Didn't test that one tho.

Yes, there is. I use a program called gogrepo to automate the process of downloading games like this. I think there are a couple other similar projects out there (definitely a couple forks of gogrepo), but this one suits me.

Oy you're outdated then it's Gogrepoc now and it has many more features including a trash mode instead of keeping the old stuff ;)

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.