tig VS typer

https://github.com/jonas/tig is one of the first things I install on a new dev machine. It's a really nice UI for staging files or hunks. Since it's just a companion to the git CLI, it feels much more focused than full-blown git GUIs, and doesn't do anything magical.

Related but I use tig, a TUI, a lot to examine the state of my working tree and index and stage/unstage/reset changes piecemeal. It works great.

I'd like to plug [tig](https://github.com/jonas/tig) for those who don't use emacs. I see lazygit recommended here too, but I've been using tig for years now and love it's simplicity.

Try tig

I'm normally a CLI git (and tig) user.

Sometimes when I work in command line I use tig (https://jonas.github.io/tig/). There is also similar tool lazygit (https://github.com/jesseduffield/lazygit)

And you accidently open a git TUI

tig +My custom command to fix MR comments by quickly editing an old commit's changes at the time when that commit was created. (Like a more controlled git-absorb that explicitly selects a commit to fixup and therefor avoids rebase-conflicts when squashing)

today I looked at tig which is a nice text based GUI, and I think I will never use git log again :-)

He is probably most well know for creating FastAPI that I taught to some of my clients and Typer that I've never used.

I have been using Typer on every one of my CLI projects which uses Click under the hood. The documentation is fantastic, the CLI app it produces looks great and lets you create things quickly. I high recommend it.

https://typer.tiangolo.com/

Adding CLI capabilities. My preferred library here is typer.

I just heard about this but it seems to be pretty much the type of thing you want and want fast.

Docstrings are for documenting how a function/ class/ method/ module works. Often you don't need to add a docstring to your main function because no one will be importing it to use elsewhere. And if you want it to run as a CLI, then there are better ways to document the available options. For example, typer does most of it for you, or in click you add the help text to the decorator.

Most computing tasks in airflow DAGs are KubernetesPodOperator containing a CLI (Python Typer). It allows us to pass arguments easily to run DAG manually if needed (the new UI to pass arguments to DAG in airflow 2.6 is really nice). Arguments allow us to replay DAG easily (change start / end dates for instance).

So it's basically the main .gitlab-ci.yml file plus a separate Python CI app using Typer for the AWS instrumentation.

Similarly for Typer, which is literally "the FastAPI of CLIs"[1]. Handy to type your `main` parameters and have CLI argument parsing. For more complicated cases, it's a wrapper around Click.

[1] https://typer.tiangolo.com/