Apparently it's based on GPT-3.5. I wonder how it compares to ChatGPT with GPT-4 for more advanced problems, for instance stuff which requires combining multiple web APIs or some degree of logic.
And the same applies to every other vehicle, but I suppose you would still get in a car on a rainy day?
You adjust speed and cycling style to the conditions, and for winter cycling there are plenty of good options for studded tires. The Nordics have succeeded in making winter cycling a more common activity, and you might want to check out the "winter cycling capital of the world": Oulu, Finland.
I did the transition by extracting keys from the desktop app using the scripts mentioned in this gist[1] and its comments. Of course, you should not do this unless you are comfortable verifying the security of the scripts yourself.
Importing to Aegis afterwards was quite straight-forward.
The first illustration shows how this works in tmux.
The illustration first has a split in left and right half (vertical split?) and then a split in top and bottom (horizontal split?) of each of the halves. So if you resize one of the panes vertically, it only affects the other pane in the same half of the window, while if you resize horizontally all the panes will be resized. Basically vertical resizing only resizes the inner split, while horizontal resizing resizes the outer split.
It seems Zellij somehow makes the order of the splits irrelevant.
This is of course a really neat solution, but the proof doesn't really give me much value as a reader.
I am much more interested in an explanation of how to find this solution, than a theoretical solution of why it is correct. Specifically I don't understand from the article why the trick of raising n to the power of LCM(phi(3), phi(5)) works.
Apparently it's based on GPT-3.5. I wonder how it compares to ChatGPT with GPT-4 for more advanced problems, for instance stuff which requires combining multiple web APIs or some degree of logic.