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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:)

Like a train ticket that spans the year break (depart December 31, arrive January 1).

Aw hell they picked up on that stupid American capitalist crap?

I worked in Germany in 2005 and back then everything was fixed price per kilometer for each train class, and you could get rail passes of sorts and get on whatever the hell trains you want during their validity. I'd take train roundtrips after work just to watch sunsets.


The price concept discussed above applies to long-distance trains. Local trains are still different, especially if you have the "Deutschlandticket" (germany ticket) you can just hop on any local train you like.

Dynamic pricing based on availability is much more efficient, which is a good thing.

I don't believe in "efficient" pricing being a good thing.

Railway systems with flat pricing and no stupid games are such a relief.


Okay then you need to accept some combination of higher average prices and sold out peak travel times.

> Aw hell they picked up on that stupid American capitalist crap?

Surge pricing is not an uniquely american concept.


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).


This is why I mentioned 2/3 - why keep a reductible fraction?


It's easy to misread as 40/60 (e.g. split)


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


You can start with Behave by Robert Sapolsky


Interesting recommendation, thanks!

https://www.thesummarist.net/summary/behave/


“Delve” is in the *first sentence* lol. GPT being GPT


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