hummuscience

Apparently this group was a bit late. Here is the first group with the same approach

https://www.nature.com/articles/s41586-026-10466-y

Get Uber drivers/taxis, truck drivers, ups/amazon delivery people etc. As your relay devices (and gives them extra cash for driving around)

For every train, there is a fixed number of tickets per price category. So sometimes, you can still find cheap tickets ("super sparpreis") a day before because thag specific train didn't have many bookings:)

The moment I started reading this, I got reminded of this recent study: https://arxiv.org/html/2503.10212v1

The scope is a bit different. The study uses an LLM to interpret pose estimation data and describe the behavior in each frame. The output is text which can be used to create embeddings of behavior. As someone who works in ethology, that's a clever (but maybe expensive) idea.

I think the author could use something similar. With multi-person pose estimation models.

Since its text, especially text with links to other articles, there is no need for tags.

If I had a clue how to do this (sorry, just a neuroscientist), I would probably create "communities" of pages on a network graph and weight the traversal across the graph network based on pages that the person liked (or spend X time on before).

This is protein on a western blot but the general idea is the same.

Streamrip on github

It depends.

40 out of 60 is easier to grasp for many people than 67%.

Percentages are better in cases where the numbers are harder to imagine (like your second example).

Not "the Chinese subsidiaries". Only one of them.

You can start with Behave by Robert Sapolsky