jdupes VS the-algorithm-ml

I recommend the use of jdupes, a fork of the well-known fdupes, to find duplicate files.

200 lines of Nim [1] seems to run about 9X faster than the 8000 lines of C in fdupes on a little test dir I have. If you need C, I think jdupes [2] is faster as @TacticalCoder points out a couple of times here. In my testing, `dups` is usually faster than `jdupes`, though.

[1] https://github.com/c-blake/bu/blob/main/dups.nim

[2] https://github.com/jbruchon/jdupes

There's always the well-respected tool, Czkawka. Or, of the CLI is your thing, jdupes is a good option.

My research into this many years ago turned out that jdupes was the right / best solution I could find for my usecase.

https://github.com/jbruchon/jdupes

Though that works fine from a script perspective I'd like some more interactive way of sorting directories etc. Identifying is just the first step, jdupes helps with linking the files (both soft and hard links comes with caveats though!) but that is mostly to save space, not to help in reorganisation.

I can work it out by looking at https://github.com/jbruchon/jdupes.

I have looked at a few alternatives and think jdupes is the one for me. Then I found out it was not multi-threaded so will give it a go but the developer of jdupes recomended fclones (https://github.com/jbruchon/jdupes/issues/186) if you were dealing with large file systems and wanted multi-threading. But as I am using a HD it may not be necessary.

jdupes is a fork of the old standby fdupes, but it has a Win32 release as well as supporting POSIX.

jdupes is a tuned fork of the well-known fdupes, and has Win32 releases.

I said “parts of the recommender system code.”

This is the kind of highly emotional reaction that’s not helpful.

Yes, I am quite familiar with building ML models, both training and building my own for which I’ve been paid large sums of money, and I’m here to tell you that you don’t know what you’re taking about.

There’s so much more information about an ML system than just the trained model that is important for understanding the effects of the system on a society, and its legal, ethical, and social ramifications.

Just seeing the type of RS being used, the ranking approach, and the information on SimClusters is enough for RAI folks to start to understand the ecosystem effects and how that can show up downstream in social effects.

https://blog.twitter.com/engineering/en_us/topics/open-sourc...

Blog summarising the change: https://blog.twitter.com/engineering/en_us/topics/open-source/2023/twitter-recommendation-algorithm

People who don't share your interests (or at least what Twitter thinks your interests are). This blog post explains it in detail.

Twitter's announcement | Main GitHub Repo | ML GitHub Repo | Engineering Blog Post

where? I searched through the repo and couldn't find it.