Nope! We shouldn't. Speculation is precisely what we should do! That's what a critical review of research involves. We should take this as excellent motivation for further research. It could be true, but we haven't done nearly enough work to conclude as much. I'm not saying it's wrong, I'm saying it's uncertain, and it's strictly unscientific to proceed by assuming something uncertain is the truth just because it's intuitively compelling. Intuitively compelling narratives are very dangerous in science, because they make you believe you have a shortcut to the truth and they're vindicated right up until they aren't. It's not a good habit.

Happiness is incredibly complicated. Contradicting evidence and results abound across studies of life satisfaction. Measuring the impact and success of advertising itself is highly complex; the researchers have shown this correlation under their current methodology. What are we to conclude if someone uses a different methodology to study the same topic, equally as valid, and comes away with a different conclusion? That's very possible, and we can't dismiss it. The researchers themselves hedge their claims and don't come out and say they've found causation.

I don't really have a dog in the race with advertising. But I really despise this kind of reporting by HBR, because the result is threads like this one where people walk away with unproven "truths" that become part of the popular zeitgeist because they just seem to vindicate obvious beliefs. Today it's advertising, tomorrow it'll be something else. Given the replication crisis we're undergoing, we shouldn't take anything away from studies like this except that further research is required.

A single study of two things as complicated as advertising measurement and happiness/satisfaction, and the intuitively compelling conclusion is immediately embraced as the truth because it's "obvious."

Edit: Again with the downvotes...come on HN. I'm not defending advertising, I'm criticizing the reporting on a study in a world which has a rampant replication crisis.

Capitalism affords many opportunities for someone to be unhappy and unsatisfied; advertising may cause this or simply exacerbate what is already there.

I'm disappointed with the way HBR reported this. The researchers are explicitly quoted as saying they found a "negative connection", which, yes, is an inverse correlation. No matter how intuitively compelling, you can't just extrapolate that to a conclusion of causation as the author of this article proceeded to do right in the introduction.

Edit: Why is this being downvoted? Do you disagree with my point that we can't extrapolate causation from correlation, or do find my comment to be off topic?

Which of course leads to the ethical question: is it right for people to live in ignorance if it makes them happy, it it's not their choice? Is it fundamentally better for people to be happy rather than aware of massive inequality (up to and including significant poverty)? How much would be appropriate to hide, for how much additional happiness? Is it better in the long run for some to be unhappy if it brings attention to inequality?

I don't have any of those answers, but they're interesting and challenging questions.

The author is making the point that this shouldn't take much effort because Apple already uses different icons.

> So what should Apple do? Customized icons for different types of drives would be a good start. Time Machine drives get custom icons automatically, as do many other types of storage devices that were more common in the past, so surely Apple could design different icons for SSDs and Fusion Drives, and then display them appropriately based on the type of drive.

Apple could repurpose one of the existing SSD icons pretty easily.

Of course, at RenTech in particular the employee-only fund is the good fund. But that's not always the case, so the parent commenter has a point. Deferred/locked up compensation can really suck.

RenTech and TGS had a spectacular year as always. But even if you don't work at a firm like that, being on the right team at a strong second tier firm can be extremely lucrative.

I have not seen any of the typical sell side grind common to investment banking among hedge funds. New grads hired to reputable hedge funds typically work comparable hours to their friends who work at Google/Facebook. They might earn 20 - 100% more though.

I also know another new grad recently hired at Hudson River Trading, and another new grad recently hired at Jump Trading. Both are earning over $400k per year, but they work 45 hour weeks. And since RenTech has been mentioned in this thread: the people I know there have wonderful work life balance.

I have found that most popular conceptions of bad work life balance in finance apply to banking and sell side, not to the buy side. Working as a developer at a bank usually sucks, both financially and culturally, in my experience. Not always, but commonly. Working at a hedge fund can be very nice both financially and culturally.

> Bcrypt is not a KDF and not a hash function

This is true, but it's also a good example of what I was saying in my other comment. bcrypt is an example of a password hashing function which is not itself a KDF, but which can be used to construct a KDF.

All password hashing functions can be used to construct key derivation functions or simply are key derivation functions. But not all password hashing functions are key derivation functions. Whether or not it would be advisable to use a given password hashing function as a KDF depends, of course. In bcrypt's case you can construct a reasonable KDF. For example: https://github.com/pyca/bcrypt/blob/master/README.rst

It's essentially like rectangles versus squares. You can create a key derivation function out of anything which passes all the criteria of a password hashing function. But it won't be a particularly performant or useful key derivation function. Likewise you can create a password hashing algorithm out of a dedicated key derivation function, but that's insufficient on its own.

There's no need to get bogged down in the details, just continue recommending a reputable implementation of these algorithms. On the other hand, if you'd like to learn more out of intellectual curiosity, Boneh & Shoup's textbook is good (work in progress) [1]. Galbraith's textbook includes chapters which cover the topic to a depth that's beyond what you're looking for, but you'll learn whatever it is you want to know [2].

Finally, more accessible, informal answers that get the basic idea across are [3], [4].

1. https://toc.cryptobook.us/

2. https://www.math.auckland.ac.nz/~sgal018/crypto-book/main.pd...

3. https://security.stackexchange.com/questions/95410/what-is-t...

4. https://crypto.stackexchange.com/questions/70716/key-derivat...

I also don't really think you're using the right definition of computable here. You make it sound as though we're estimating, or truncating uncomputable numbers to make them computable when you say:

> From that definition, it's quite obvious that everything you compute has to be continuous, because you are never sure of what other decimals may be coming up, so whatever you compute has to be close enough.

It's not about being close enough or estimating, they're categorically different things. You can't obtain an uncomputable number, even by estimating, to any meaningful precision with a finite amount of time. So what are you saying here?