Thanks! I think the "deploy and connect" workflow is itself not super painful, but even if you're invested in VSCode, doing that again and again every day is pretty annoying (and it certainly was for me when I used to do ML), so hopefully the ease of use is valuable for people.
Thanks! When I was doing ML research, every moment that my GPU setup was top of mind was a point of frustration, so hopefully we're moving the dial a bit towards making compute an abstraction you don't have to worry about.
It seems like there are two ways of using Jupyter with Modal. One is adding Modal decorators to specific functions within a notebook, and for that use case, I think Modal is fantastic (if you are using GPUs on a function-by-function basis)!
On the other hand, if you are trying to run an entire notebook on a remote machine by starting a Jupyter server with Modal, then the workflow with Modal is not that different from other clouds (e.g. you can start an EC2 instance and run a Jupyter server there). For that, Moonglow still makes it easier by letting you stay in your IDE and avoid juggling Jupyter server URLs.
Also, you might need to use a specific cloud e.g. if you have cloud credits, sensitive data that needs to stay on that cloud or just expensive egress fees. One of Moonglow's strengths is that you can do your work in that cloud, rather than having to move stuff around.
