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Orangutan Card Trick

Orangutan Card Trick

A great video for April Fools day! Maybe you’ve seen the Orangutan card trick in which the magician Matt G seems to push a card through a piece of glass and the primate grabs and plays with the card. It took the internet briefly by storm and had many scratching their heads.

Captain Disillusion has a whole channel on YouTube that takes apart popular trick videos and shows exactly how its done – and he recently walked through this video too.

In his videos, he walks through not only how it’s done, but how you can go about figuring it out. I especially like this teaching component so you’re not fooled by the latest TikTok or viral video. As the internet becomes more and more AI generated and bot ridden, learning just how easily you can be fooled is more important than ever. Or, as science tells us, maybe we should all be stepping away from social media if we want to be happier.

Bonus points for him doing a double-pane passthrough at the end to show that even trickier versions can be done even cleaner.

Best and Worst College Degrees

Best and Worst College Degrees

Today, somewhere around 4 in 10 recent college graduates find themselves employed in roles that don’t need their degree. They just can’t find jobs relevant to what they studied, so they settle for something lesser. This is the definition of underemployment, and it’s a growing problem.

Many also wonder if college is still a good choice for their child. The answer is a big maybe. College is not the best time or place to figure out your career direction. It’s the most expensive time of life and unfortunately, too many programs aren’t yet aligned with real-world workforce needs.

But if you’ve done the career exploration and have a clear picture of 1.) the lifestyle you want to live and 2.) the career that best intersects with that lifestyle and your talents, interests and abilities, and if college is a necessary step to reach that destination, then go.

Degrees that don’t hire well:Degrees with the lowest underemployment:
Criminal justice: 71.5%
Performing arts: 65.9%
Art history: 62.3%
Leisure and hospitality: 57.6%
Liberal arts: 56.7%
Animal and plant sciences: 56.3%
Fine arts: 55.5%
Miscellaneous technologies: 54.8%
Business management: 53.6%
History: 53.5%
Nursing: 11.1%
Special Education: 12.1%
Computer Engineering: 13.3%
Elementary Education: 13.5% 1.5%
Civil Engineering: 15.9%
Computer Science: 16.7%
Chemical Engineering: 17.8%
Aerospace Engineering: 17.9%
Early Childhood Education: 18.2%
General Education: 19.6%
Mechanical Engineering: 20.3%
Miscellaneous Education: 20.6%
Electrical Engineering: 20.9%
Accounting: 21.0%
Secondary Education: 22.0%
Pharmacy: 23.5%
Industrial Engineering: 24.6%
Architecture: 25.1%
Miscellaneous Engineering: 26.2%
Mathematics: 27.6%

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R.E.P.O delivers the laughs

R.E.P.O delivers the laughs

In this 4 player game, you and your friends try to recover artifacts to reach a set amount. But the artifacts you collect don’t always cooperate. In fact, they can turn into monsters, explode, or latch onto your face and cause you to projectile vomit on your teammates.

Choosing something: the 37% rule

Choosing something: the 37% rule

It was the year 1960 and a brainteaser was formulated as “The Secretary Problem”. You need to hire a secretary; there are n applicants to be interviewed. You meet each of them in a random order. You can rank them according to suitability, but once rejected an applicant they cannot be recalled. How can you maximize the probability of picking the best person for the job? 

Other versions of this include the “fiancé problem” (same idea, but you’re looking for a fiancé instead of a secretary) and the “googol game” – in which you are flipping slips of paper to reveal numbers until you decide you’ve probably found the largest of all.

The answer is… surprisingly predictable, it turns out.

“This basic problem has a remarkably simple solution,” wrote mathematician and statistician Thomas S Ferguson in 1989. “First, one shows that attention can be restricted to the class of rules that for some integer r > 1 rejects the first r – 1 applicants, and then chooses the next applicant who is best in the relative ranking of the observed applicants.”

So, when faced with a stream of random choices and wanting to pick the best, the first thing you do is reject everyone. That is, up to a point. Once you reach that point, just accept the next applicant, suitor, or slip of paper, that beats everything you’ve seen so far.

The statistics are fascinating; and it says that you reject the first 37% of applicants and then take the next one that’s better than what you’ve seen in the rejected pool.

This works if it’s apartments, job candidates, or potential life partners.

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