joiguru

There are two primary criticisms of this move in this thread:

C1) This is just hurting ordinary Russian citizens and serves no other purpose. I empathize strongly with the Russians. But I also empathize with the Ukrainians. Since Russia has nuclear weapons, the only thing rest of the world can do is impose sanctions. Sanctions work in the long run, but they take a long time to show effects. Ukrainians do not have a long time. All of these moves will cumulatively amplify the unrest among common Russian people, giving Putin less time to sit back and let his plan go to work. Will these be enough? Only time can tell. But ignoring the impact of secondary and tertiary effects for the primary negative effects is short sighted.

C2) This is just virtue signaling. Virtue signaling may be a component of this move, but this is business for Cogent. They make money moving data, and Russian money has lost a lot of its value. So I am not convinced this is just virtue signaling, there is definitely a business component.

Filling out tax forms and FBAR requirements were a pain.

One theoretical objection to this idea is that distances measured have an accuracy limit of Planck length (1.616e-35 meters). So if your number needs more precision then "just mark" step can't be done.

Surprising-ness is a not a good metric to measure social/psychological science research by. Often a statement and its opposite can be intuitive.

If the opposite result was stated here one could have easily come up with many "intuitive" explanations.

What would be the benefits of this over neovim-qt?

In regular natural language, cutting the ball into 5 pieces implies cutting 5 contiguous pieces.

If I say a cake is cut into 5 pieces, no person will consider that each piece contains parts from all parts of the cake.

"cut the ball into 5 pieces" is not the best description. A better one is: 2a. Split the ball into infinite pieces 2b. Divide the infinite pieces into 5 groups

The basic idea is as follows.

Lets say you are building an ML model to decide whether to give someone insurance or not. Lets also assume your past behavior had some bias (say against some group). Now ML model trained on this past data will likely learn that bias.

Part of modern ML focus is then to understand what bias exists in data, and how can we train models to use the data but somehow counteract that bias.