broot VS tokei

Take a look at broot https://github.com/Canop/broot

A past coworker implemented a system like this. It was awful. He was the gatekeeper because the numbers and names had to be "just so" to meet his approval, and he was the most senior person on the team. He was neurotic in general and a pain to work with.

The idea of limiting yourself to a few top-level categories in a directory hierarchy and then doing the same with subdirectories makes sense, but adding numbers is a bad idea. It just creates more work, and other people have to learn your idiosyncratic nomenclature. Just give the directories good names and get on with it. Search really isn't as bad as the article suggests, especially with something like broot [1].

[1]: https://github.com/Canop/broot

I've used a lot of the tools mentioned here in comments, but I think just for finding a directory/file broot[1] is much faster and easier than others. Though it is also quite feature rich but mostly it's just write a fuzzy search term that could even be sub-sub-directory and open, extremely quickly.

[1] https://github.com/Canop/broot

`broot` (https://github.com/Canop/broot) is another file manager with a curious interface that seems to fill a similar niche.

Of course, there are many other file managers to choose from (mc, ranger, nnn, lf, ....), but most of them don't show nested subdirectories by default.

As a macOS user, it boils my brain whenever I've to type in something like ~/Library/Application Support/org.rust-lang.Cargo/config.toml. macOS users have been begging CLI tools to support XDG variables on macOS too. Setting defaults is a strong indication to the community what should be the "preferred" locations. The defaults defined in your article will invariably lead to some authors saying that if that path is good enough for cargo, then it is good enough for their tool. Even the latest draft RFC acknowledges that macOS should use XDG variables too. I've written more about this here.

You may be interested in broot

I think you’re conflating different projects.

There are projects that aim for a better user experience, with better command line interface, defaults, performance and UI. These are of course breaking changes and the programs can’t be used as drop in replacement. Some examples are

- ls => exa (https://github.com/ogham/exa)

- grep => ripgrep (https://github.com/BurntSushi/ripgrep)

- cat => bat (https://github.com/sharkdp/bat)

- tree => broot (https://github.com/Canop/broot)

The person you’re replying to was speaking of a different project - uutils (https://github.com/uutils/coreutils). These are drop in replacements with identical interfaces (modulo bugs).

Even better broot, previously adding view verb to config:

Broot: https://github.com/Canop/broot

So If we would only count code and not comments, it is only 9489 LoC Rust. Which would be about 0.03% and if we take all lines and not only LoC it would be around 0.05%

[0] https://github.com/XAMPPRocky/tokei

[1] https://github.com/torvalds/linux/commit/b401b621758e46812da...

thirdly: found this (https://github.com/XAMPPRocky/tokei) and wanted to analyze languages used on my system, didn't see a package manager (apt) for it that I had. So i installed cargo via apt-get rustup. Added the bin folder to $PATH via PATH=$PATH:~/.cargo/bin. But did not make it permanent. And stupidly rand tokei on "/", realizing how long and unhelpful that would be killed it. Then ran it in a dump folder with some very nested repo dumps, and tons of wolfram.nb files. After killing that too, and attempting to kill via system monitor. Still have two of those as zombie processes.

tokei

For what it's worth, tokei seems to be named after tokei.

BTW, for more info on how it is done, you can check out tokei which is the library use by onefetch for code statistics.

With pytokei you can count code quickly using all the power from tokei, but from python.

Tokei is a nice utility to count lines and stats of code. It is very fast, accurate, and has a nice output. It supports over 150 languages and can output in JSON, YAML, CBOR, and human-readable tables.