Bsharp

I agree 100%! I first learned to program in Python using Project Euler and they taught me a ton. Now I've solved 111 of them!

I'm currently trying to learn Rust, and since it's so new everything is geared towards people with experience. So I started Rust For Beginners:

http://www.rustforbeginners.com

I'm trying to make it a guide to the language, and programming in general, for true beginners. The very first post of actual coding (after installing, command line basics, and compiling/running with cargo) is a walk through of the first PE problem.

I know how you feel about most beginners guides not actually being suited for beginners. Since I'm a beginner, I'm hoping I'll be able to write one.

Feel free to check it out, and constructive criticism is encouraged!

You mentioned actuarial - as an actuary interested in quant work, I'm surprised they are so well-represented on your team. Do you find lots of them in the industry? I'm taking the Quantitative Finance specialty actuarial exams and figured it'd be a long shot to break in, but maybe there's hope.

Really enjoyed this article - seemed to give a pretty objective look at what happened then and parallels to now.

What I liked most though was the author's honest assessment of what happens if Uber and others win. Most people seem to imagine this utopia of competition between the ride share companies, but given that Uber is already engaging in immortal practices to eliminate competition and hiring people to schmooze regulators, I doubt that's the direction we're headed. If they get their way, we'll be complaining about them in 20 years when they are established, another company comes along, and Uber tries to regulate them away.

This is all sick in it's own way, but it's not capitalism. Why do people equate every system in which someone makes a profit with capitalism?

Approve!

I don't understand why Average Joe thinks he can beat investment firms long term with staff dedicated to tracking each and every tradeable asset. Who do you think you're trading with?

If you want to gamble on a company or a market shift then fine, but for the most part that's just what it is - gambling.

Given the size of Big Oil, Big Agriculture, and Big Auto, I don't think you'll see a major shift to electric in 10 years.

I have more faith in freedom than faith in a group of humans with the same greedy intentions as any other human, but with regulatory power over everyone else in the market.

You were effective at using big words and quoting an economist, but not at providing any counterargument to the essay.

So you don't shower then? Just hand-washing?

> "My advice is to attempt startups in the center of a confluence of macrotrends"

That's basically like saying "buy the winning lottery ticket" in my opinion. There are lots of eyes watching for macrotrends, and lots of people trying to capitalize on that. Identifying the trend is part of success, sure, but that's the easy part - the hard part is separating yourself somehow. Luck isn't the whole reason for that separation, but it's a significant part of it.

I do agree that many founders don't recognize their luck. To their credit though, I think they often emphasize the "work hard, be smart" part instead of saying "I knew I was better than all my competitors because..."

> After another three generations or so, chips will probably reach 5nm, and at that point there will be only 10 atoms from the beginning to the end of each transistor gate, he said. Beyond that, further advances may be impossible. "You can't build a transistor with one atom," Samueli said.

I don't get it - did people assume that chips could get infinitely small?

Seems pretty obvious that Moore's Law would fail at some point.

Disclaimer: I didn't know about this law before today so I'm probably ignorant of something.

I'm going to assume the revenue numbers you mentioned are US numbers.

The entire population isn't trying to be musicians - you're only competing with other musicians for that money. This website[1] offers various estimates for the number of active musicians, but the numbers are quite scattered due to the subjective and numerous definitions of "musician" (orchestra player doesn't need royalties, rock band member does, music teacher doesn't, etc.).

I'm going to use the number of albums produced to determine the number of active musical acts who would get paid by Spotify, which according to the source was 76,875 in 2011, and one album per active musical act seems reasonable. Assuming that is a US number for conservatism (not a global number) then we get 76,875 active musical acts competing for your $2.5B. Since most musicians won't assume that they will become superstars, using the 80/20 rule we'll chop off the top 20% of active musical acts and the top 80% of the revenue (which imo is probably most artists that anyone knows) to get 61,500 musical acts competing for $500M. Now lets assume 3 musicians collecting royalties per musical act (seems reasonable, lots of 4-5 member bands, lots of solo artists, conservative) for a total of 184,500 musicians competing for that $500M. $500M supports 11,111 people under your $45,000/year income assumption, meaning 6% of the bottom 80% of active musicians would get a livable income from Spotify alone. As other posters have pointed out, musicians have many other sources of income such as other streaming services, touring, record sales, merch, etc.

So yes, since you have a 6% chance of earning a livable income from a singular source of music streaming while being in the bottom 80% of active musicians royalty-wise, I'd say it's definitely worth it to keep doing what you love. This also ignores the many non-royalty-based careers for musicians, such as teachers and session artists.

[1]:http://money.futureofmusic.org/how-many-musicians-are-there/ - "Other sources of data"

> money this guy has made

I think you mean "beer this guys has drunk"!

Reading comprehension fail on my part!

This site finds similar artists to the one you search for. It has no reason to expect you'll like The Offspring when you entered Symphony X.

It's like telling a friend you like Band X, and they say "Oh man, if you like Band X, you'll probably like Similar Band Y! or Similar Band Z!" It would be odd if you said you liked Jay-Z and someone recommended The Clash because they're so different, even though it's not uncommon to like both.

Awesome site! I'll definitely be coming back often.

Very interesting! Hacker School seems like something I'd like to do once I get my coding chops above absolute beginner level, it's nice to read a personal account of someone's experience. Hopefully I'll be applying sooner than later!

I agree that that would be more honest, if it's possible - maybe this company could add something like that to their product line. I just think that, although it isn't technically correct, non-tech parents would probably consider this "building a computer", and if their goal is to open their children's minds to computers this will probably do a great job.

This is a product for children and people who find computers intimidating. It's meant as a fun introduction to computers and programming.

Your comment is like complaining that a LEGO helicopter isn't a real helicopter, and that kids who build them are simply learning how to follow instructions.

You should change the title - the content is interesting enough that you don't need the mystery, and many people who would otherwise be interested probably won't click.

Cool stuff though!

Awesome website! Don't have much else to add - others will be better at pointing out UI/UX issues - just wanted to show support!

Can you quote where he "claimed that this analytic framework was the end-all in terms of analyzing the policy"?

If you're expecting research-level detail in a blog post from someone who is admittedly not an economist, your expectations are too high. The post was meant to present a simple Monte Carlo simulation in Python using a current topic as its subject. He isn't writing a NYT Op-Ed citing his results as evidence that BJ > BI based on sophisticated modeling techniques.

Unrealistic results are an indicator that something might be wrong with the model design or model choice which deserves investigating. Why is that such a bad thing?

If you're modeling investment growth and your model is:

  future_value = investment_amt * (interest_rate ^ num_years)

or

  future_value = investment_amt * (1 + (interest_rate * num_years))

you might realize after running it a few times and seeing incorrect results that the model is flawed (first case) or inappropriate (second case) and fix it to:

  future_value = investment_amt * ((1 + interest_rate) ^ num_years)

And in addition, after making the 60%-80% adjustment, if you still think the results look wrong, you now know that the model itself may require tweaking or redesign.

I really don't understand all the negativity here.

Expert opinions are often based on economic models which use incredibly simplified assumptions and questionably appropriate mathematical techniques. At least this guy is willing to post his model for the world to pick apart.

All of your concerns are addressed in his article:

> The basis of any informed discussion is a mathematical model. The best way to think of a mathematical model is a way to force everyone to clearly enumerate all assumptions being made, and to accept all logical reasoning that follows from those assumptions. Given a model, everyone involved in a discussion can agree that either the conclusions of the model are correct, or one of the assumptions going into the model must be false. This is very important when disagreement is reached - disagreement in the conclusions implies disagreement with some assumption, so it makes sense to figure out which assumption is the cause of disagreement.

It’s also important not to be blinded by a model. The involvement of numbers does not make an argument empirically correct - it simply makes it more understandable and less likely to be logically flawed.

He knows the limitations of mathematical modeling and is simply presenting a basic simulation of these two policies subject to various assumptions.

You're leaving the part out where he says "and write some fucking code." He wants people to show him he's wrong with analysis/simulation instead of tell him he's wrong with their opinions, which is entirely valid.

Many researchers in less mathematically rigorous fields lack basic statistical knowledge, such as how a p-value is calculated or what a chi-squared test is. And that isn't to be snarky towards them - they have a lot to absorb in their own field. It just means that they are slower to adopt or even know about various mathematical techniques.

This reminds me of the med researcher who rediscovered integration in 1993 and was cited many, many times:

http://care.diabetesjournals.org/content/17/2/152.abstract

Good article, it's nice to see someone not only provide some actual analysis, but also be clear with their assumptions and welcome critique.

Researchers should just be forced to append ", probably." to their paper's title and any conclusions reached.