noobhacker

It's not basic supply and demand--indeed, most economists are pro immigration. Bryan Caplan went as far as advocating for open border (admittedly he's more libertarian than most).

Labor market is complicated because jobs are not a finite pool that people compete over. New workers are also new consumers, who create new jobs as well. If more workers are always bad for other workers, declining birth rate (ie fewer future workers) would be a good thing.

Profit margin is the wrong metric in this debate. The person you are replying to is saying that the insurance companies as a whole is a waste, including all the salaries paid to their employees.

I'd love to learn phonetics, but with written materials like this it feels like learning to ride a bike by reading a book. I'm still at a loss with regards to how to actually make the sound. Without a teacher, I don't think I even realize my own mistakes.

How did you learn to physically make the sound? How much time did you spend practicing daily?

Thanks so much for the advice. I too hope to do stand up one day.

I stalked the post's author (your?) LinkedIn and didn't see any background in statistics. How do you know to design this experiment so nicely?

Excellent experimental design and analysis!

Is there a way for me to buy you a coffee (or some cash equivalent)? I'd love to incentivize such rigor in online discourse.

An obvious way is to try out readwise ;) but I mostly read online articles and (pirated) books, so I'm not sure I'm a good use case.

Buddhism is about recognizing that everything is one, i.e. there's no sky/earth, but just sky-earth as one thing altogether.

Of course you don't have to believe it or find it useful, I'm just explaining Buddhist thought in general and Thich Nhat Hanh's thoughts in particular here.

Does your son have other alternatives to learn programming and make money other than Roblox?

If there are, then it's a great lesson about looking outside of one's immediate circumstance and striving towards something better.

Could you elaborate on how this Hamilton framework is a case of Dependency Inversion (which in my understanding is about removing dependency on low level class and using dependency on high level class instead)?

What's the low and high level classes in this application?

A great way is to encourage them to question the rules that they have to follow, including your own.

That's something they already do, and most enjoy doing. We just need to guide them to question the rules in a coherent manner.

Besides critical thinking and argumentation, by letting them question your own rule, the meta skill you're demonstrating here is intellectual humility, IMO the most prized quality of a great thinker.

Facebook, as a business, is primarily interested in making money and would love to avoid all these messy issues regarding what to censor so that it can focus on selling ads.

Therefore, FB would love to have a legitimate external authority, e.g. the government, mandating what's allowed or not. Unfortunately, no one is willing to do that dirty work, and thus forcing Facebook to do it themselves.

(Granted, I've simplified the matter quite a bit here by saying "Facebook, as a business". Facebook is also a collection of individual employees who have their own beliefs and do shape FB in their image.)

Awesome! Could you speak a bit about what you have in mind to incorporate actual experiment data into the algorithm?

This is an accurate summary of the article.

However, in my personal experience, it is not an accurate summary of the Bay Area. The startup scene may be truly this toxic, but I think such toxicity plagues any small, homogeneous group of people. It is hardly a tech problem. Indeed, mom-and-pop shop in small towns can be very abusive too.

To offer a counter anecdote, my experience with FAANG has been very positive. My workplace is the most meritocratic, efficient, and transparent organization that I've been in. (And I did spend 6 years on the East coast doing things unrelated to tech.)

I'm certain that the author's experience is true. I also want to caution against generalizing these issues to all of tech in the Bay.

Could you suggest a low cost option for meditation retreat? I'd really like to do one, but the idea of paying $1000 for what's supposed to be a no-frill, minimalist, anti-materialistic experience still trips me up. (As a reference, I rarely spend more than $500 for any kind of leisure trip.)

I understand that I'm being naive here, that "the Dharma is free but the light is not." Perhaps if cheap retreat is not an option, you can also reframe my thinking about the cost.

The article you linked says the opposite of what you quote.

Right after the quote, the article's authors say that these claims [about Europe being economically interdependent before WW1] are exaggerated and this do not disprove the theory that interdependence prevents war.

I read through the top 3 links and would love to hear you elaborate more on how these are counterpoints to the assertion that the Times would not print a story not independently corroborated.

The first link regarding Jayson Blair is the clearest counterpoint, showing a Times reporter fabricating story. This is absolutely an example of the NYT failing. But the same Wikipedia article says that the NYT, on their own initiative, investigated and fired Jayson Blair in a very public manner.

After internal investigations, The New York Times reported on Blair's journalistic misdeeds in an "unprecedented"[14] 7,239-word front-page story on May 11, 2003, headlined "Times Reporter Who Resigned Leaves Long Trail of Deception."[2] The story called the affair "a low point in the 152-year history of the newspaper."

To me, publishing Jayson Blair's articles is a huge mistake by the Times. But the way they handle it proves that they do hold their journalistic values in high regard.

The 2nd article itself admits that what the Times published is supported by many others in the media. (Their point is that the entire media is lying.) The audio proof that the 2nd article mentions is also not included, so I don't know if I should trust it more than I trust the Times.

The 3rd link is not easy to understand for me since there isn't a lot of context.

I also interpret the article as the author grieving her mother, whose Koreaness is the most distinguishing feature. Had her mother been unique in her love for a particular music genre, for example, the author would have written about walking into a coffee shop playing that kind of music.

We go to H Mart mainly for their vegetables too, especially leafy green veggies (so many different kinds of choy!). I think leafy greens tend to go bad faster in general, and that's not any indication of H Mart's quality.

Their seafood is so cheap they are the entire reason why I get to eat salmon at all ($3 per pound!).

I used to dislike "rich kids." What worked for me is to look inside and ask myself exactly why I don't like them.

It turns out that, at least for me, there isn't any valid reason. It boils down to me being upset at their 1) privilege, or 2) difference.

I was upset that they had privileges that I didn't. But was that their fault? Not really. We can't really fault individuals for having advantages in life---indeed, my intellect and work ethics are also things I were born with.

I was upset that they are different from me. Put into words, this sounds mean and petty, but that's really the root of much hate. They eat differently, wear differently, play differently. But being different is certainly not wrong! If I hate on rich kids for being different, that's just pure bigotry, no more no less.

So I try to judge "rick kids" just like I do everyone else. Do they treat others nicely? Do they elevate people around them? And despite the stereotype, I've found that many rich kids are just as nice. Indeed, I certainly plan to raise my kids, who are born wealthy, to grow up as nice people.

Econometric models typically do not say anything about causality. For example, the coefficients in OLS are just correlation, i.e. one unit increase in X is associated with $\beta$ increase in Y. Let's say that we regress "happiness" on "being married." It's impossible to tell which way does the causal relation flow, or if "happiness" and "being married" are both caused by a third factor altogether.

In contrast, causal models explicitly think about when we can claim that an effect is causal.

I honestly don't know why it's better to ban denial rather than trying to engage and educate. It's been very confusing to me. Could you please explain?

What are the recommended background readings to best prepare founders for this program? IMO it helps a lot when participants already use the same framework and speak the same language, so that more time can be devoted to specific problems.

Obviously for people that hang out on HN, we already had some shared understanding. But a list of recommended readings to make sure all gaps are filled would be very useful as well.

I used to get stressed listening. Now I just re-frame the problem to solve as "making the other person feel better" -- this way I don't feel the urge to offer solutions.

As engineers and problem solvers, we should know how to define the problem properly.

Since location and family income are perfectly observable variables, shouldn't we use them directly instead of race?

That's the opposite of my experience and understanding as a fresh PhD. In my field, social science, PhD students 100% own their research agenda. We get paid via a combination of grant (i.e. free money) and RA / TA work (i.e. money you work for). The better the program, the more of the money is free. And you always have the option to apply for external grants to fund yourself. In my (very fortunate) case, I rarely work on others' projects for more than 5 hours a week.

My friends in the natural sciences are more tied to the projects of their supervisors. But in the end they are still expected to be a mature researcher with their own agenda.

Caveat: I'm only certain that this is true for top-50 programs.

As a male, I used to also be upset about charges of discrimination against woman when I know in my heart of hearts that I don't mean to do anything badly. Geez, I'm just here at a conference to hang out with people also interested in technology. At what point did I do wrong?

Over time, I realize that no one needs to do anything wrong for unfortunate things to happen. Even though I didn't do anything wrong and didn't mean anyone harm, the unfortunate lack of females at conference still happens. It happens because of historical accidents like home video games that introduced more boys to technology, for example.

So nowadays I try not to interpret complains about diversity as a complaint about me personally. (And I try to remind my feminist friends not to appear like they are complaining about men personally.)

I also no longer get upset about women-only events and organizations. They are free to gather and support one another, just like we have association of business owners, school alumni, even guys named Smith! They are building each other up instead of tearing other groups down, so there's nothing wrong about that.

I think if you give the article a second chance, you'll see that it does not advocate for the self-promotional behavior you dislike.

The article doesn't tell us to "listing out insignificant contributions to FOSS they have made, especially when not asked." Rather the article tells us to research about how we can add value to our prospective employers beforehand.

Stated in that way, the article's message is quite "boring", but also true.

As a TA of statistics for non-statistics major, I find it hard to choose the appropriate level of complexity because every audience has a different expectation. I'd encourage you to communicate with your doctors that you'd like the full details and terminology. Otherwise they simply can't know what you want.

Why do you think there are millions to be made here? I'm sort of familiar with international development, and I'm surprised by that estimate.

That's entirely possible. I'm thinking more about the labor force of the next 40-50 years. In 100 years (or however long it takes for AI to improve itself), engineers may also be obsolete.

The argument that AI will take all the job is commonly made, but I agree with you that it's a poor argument.

Much more plausible and concerning, however, is that AI will stratify the labor force into 3 groups: highly-paid engineers (who manage the AI), highly-paid services job (i.e. doctors, lawyers, psychiatrist), and poorly-paid services job (i.e. barista).

This stratification has already happened as our economy advances (you mentioned baristas---in the past, those workers could have earned middle class manufacturing wages). The wage distribution in the services sector nowadays looks like a dumbbell [1].

Wage stratification can lead to political stratification as poor people don't have the resources to fight for their rights. Political stratification can lead to further wage stratification, continuing the cycle. That's why I believe that it's important to deal with the AI threat, now.

[1] https://seii.mit.edu/research/study/how-does-growth-in-the-u...