[wrigby]

One of the use cases I can think of is "I have a set of scripts (shell, GDB, etc.) that are useful when troubleshooting servers, and I want to be able to use any of them at will once I've connected to a machine that's broken."

Even just having my own dotfiles (.vimrc especially) present on a machine that I'm troubleshooting is huge.

[amelius]

But how can you troubleshoot the remote machine if you're seeing your local filesystem?

Everything you run to test whether the remote is working uses only the CPU of the remote machine, not its files, which is where the problem usually is.

[wrigby]

Hah, that's what I get for skimming the article and assuming I knew what was going on here.

With that said, I guess the quickest thing that comes to mind is wanting to run my Jupyter notebooks on a machine with much beefier CPU and memory than my laptop. I was recently working on some lightweight ML stuff, which required training 3 SVR models. Each model really only took 30 seconds to train on my laptop (with a small, synthetic training set), but if cpu was in my workflow, I would have just done it on a beefier machine and saved a minute or two of time every time I wanted to test a new iteration.