KornShell

The KornShell (ksh) is a standard/restricted command and programming language developed by AT&T.

Installation

First, install an implementation from the official repositories:

http://www.kornshell.com/ || ksh

More implementations are provided in the Arch User Repository:

  • loksh — A Linux port of OpenBSD's ksh
https://github.com/dimkr/loksh || loksh-gitAUR
  • OpenBSDs Korn Shell — Porting of the OpenBSD version of ksh to GNU/Linux.
https://github.com/ibara/oksh/ || okshAUR
  • MirBSD™ Korn Shell — Enhanced version of the public domain ksh.
http://www.mirbsd.org/mksh.htm || mkshAUR

Making a ksh implementation your default login shell

Change the default shell for the current user:

$ chsh -s /bin/ksh_implementation

Uninstallation

Change the default shell before removing the ksh packages.

Warning: Failure to follow the below procedure may result in users no longer having access to a working shell.

Run following command:

$ chsh -s /bin/bash user

Use it for every user with m/ksh set as their login shell (including root if needed). When completed, the mkshAUR package can be removed.

Alternatively, change the default shell back to Bash by editing /etc/passwd as root.

Warning: It is strongly recommended to use vipw when editing /etc/passwd as it helps prevent invalid entries and/or syntax errors.

For example, change the following:

username:x:1000:1000:Full Name,,,:/home/username:/bin/mksh

To this:

username:x:1000:1000:Full Name,,,:/home/username:/bin/bash

See Also
gollark: https://media.discordapp.net/attachments/680631064447680542/860888281489997834/Eja1jGxWkAYd3DU.png?width=661&height=422
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gollark: If it's false, why is it real, true, and in Minoteaur 7?
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gollark: ?tag create top2vec The assumption the algorithm makes is that many semantically similar documents are indicative of an underlying topic. The first step is to create a joint embedding of document and word vectors. Once documents and words are embedded in a vector space the goal of the algorithm is to find dense clusters of documents, then identify which words attracted those documents together. Each dense area is a topic and the words that attracted the documents to the dense area are the topic words.
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