Also Skyrim VR was breathtaking, especially with mods to improve the scenery and clunky menus.

I'm pretty interested in this and would like to participate in a study as well if given the opportunity. I think one of the potential causes and solutions is it being a self-fulfilling prophecy that may be overcome with self-efficacy as described here [1] for seasickness. My only evidence is that I never knew motion sickness was a possibility when I first tried VR, and my friends with the worst motion sickness were worried about it before they even tried VR, even if they never got seasick or carsick.

From the article and other comments, they also mention figure skating which I never thought of before, and I play ice hockey which is likely similar enough. For reference I've been playing VR since 2016 and never once experienced motion sickness, from any style of game/locomotion.

[1] https://psycnet.apa.org/doiLanding?doi=10.1037%2F0021-9010.8...

  sp current | pyxr -0 -g "(Artist)\s+(.+)\n(Title)\s+(.+)" -p "{3} by {1}"

There's pyan3 [1] which although doesn't support python 3.7+, I've still had luck with v1.0.4 which works better for me than its most recent version with python 3.11, but there can be some weird issues though depending on your code style.

A quick search also turned up crabviz [2] which has support for more languages than just python.

[1] https://github.com/Technologicat/pyan

[2] https://github.com/chanhx/crabviz

> By precisely timing the duration of the occultation from many sites simultaneously, they can refine their knowledge of the size and shape of the asteroid.

> Betelgeuse is the 10th brightest star in our skies (+0.5 magnitude), so observers need only modest equipment to participate.

> the easiest way to capture the event is to use a simple DSLR camera on a tripod ... video frames must have a short (few-millisecond) exposure time ... Millisecond accuracy timing is crucial ... [an app] for timestamping occultation observations is called Occult Flash Tag (Android) or AstroFlashTimer (iPhone)

[1] https://github.com/elesiuta/picosnitch

This gives sufficient notice to fix things if a key were to become compromised.

It looks like you're using an average lifetime of 9 years for the 2.1x factor. I rarely see phones past 6 though and that's approximately how long they receive updates for.

I've also never seen a used iPhone that cheap where I am. Here a new 15 max is $1450 and a used 13 max is $1000.

Using a 6 year life:

$1000 / (6-2) = $250

$1450 / 6 = $242

So if you use a phone to the 6 year point, you are paying less for a better device, and without any unknown damage or wear. Migrating less often to a new device is also a small time saver. Even if you go to 9 years, the new iPhone is only 1.13x more.

So on average their predictions may have been pretty good, but since each transaction also depends on the other party to accept their offer, and whether they get outbid, most of their predictions where the offer actually goes through would be on the tail end of where they slightly overestimated the price.

This tweet from the article summed it up nicely

> Zillow made the same mistake that every new quant trader makes early on: Mistaking an adversarial environment for a random one. https://twitter.com/0xdoug/status/1456032851477028870

I was lucky to make and learn from that mistake pretty quickly with some algorithmic trading on much smaller amounts. With housing transactions being much larger and slower, you wouldn't learn this lesson until it was too late. Models never perform as well in practice as they do in theory, and you need to remember to account for both known unknowns and unknown unknowns.

Same here! That's why for small things I made pyxargs [1] to use python in the shell. In another thread I also just learned of pyp [2] which I haven't tried yet but looks like it's even better for this use case.

[1] https://github.com/elesiuta/pyxargs

[2] https://github.com/hauntsaninja/pyp

I didn't really highlight it in the documentation but it can also run commands in parallel as multiple windows in a multiplexer so it's easier to checkup on their progress or interact with them.

I made something similar myself, pyxargs [1], which doesn't have all the magic of pyp, but although being geared towards more xargs like usage, it also functions somewhat similarly.

For example, although not as clean as the magic variables x, l or line, you can use '{}'

  ls | pyp 'x[:3]'  ==  ls | pyxargs --pyev "'{}'[:3]"

[1] https://github.com/elesiuta/pyxargs

This sounds similar to how image recognition was done before deep learning [1]

[1] https://www.youtube.com/watch?v=8SF_h3xF3cE&t=1358s

If it's even still possible to install/activate them. Whenever any of them became available on Steam I would rebuy them there just for the convenience.

I already packaged it for nearly every distro, but unfortunately most don't have dash [2] in their repos so the user needs to install it separately, and I was hoping that snap would be an easier solution for that.

[1] https://github.com/elesiuta/picosnitch/blob/master/snap/snap...

[2] https://repology.org/project/python:dash/versions