Yes. I use and enthusiastically endorse Math Academy. It is far and away the best self-learning educational resource I have ever tried; leagues better than Khan Academy.
I'm a self-motivated adult learner, so I don't know what it's like for kids. Though the program was originally designed for them, so I suspect their experience would broadly be similar to mine.
As other commenters have mentioned, you need to be okay with grinding through problem sets with no videos or UI pizzazz -- maybe this doesn't work for everybody. I'd compare it to the difference between trying to learn a language through scattered YouTube videos and Duolingo versus tandem and grinding on a good Anki set.
NB: I'm taking it for the Math for ML track and am currently most of the way through the Math Foundations III course. So I can only comment on the lower level courses.
These "what ifs" are kinda funny because the origins of JSX can be traced back to Facebook's XHP[1], which took explicit inspiration from E4X[2], an early JS standard that looked and behaved similar to the library described here.
Speaking as someone who's split ~half my time between Leipzig and Berlin for the last 5 years, this is not true.
Leipzig's club scene is an extension of its university population. It's younger, straighter, whiter, and about two orders of magnitude smaller. People visit the clubs while they're going to school there, then they graduate and move elsewhere. Often to Berlin.
Why does this illusion exist? Because Leipzig is about an hour away from Berlin by train. Berliners visit for a weekend and think "wow, it's like Berlin in the 90s! Still cheap! And look at all these cool young kids at these scrappy clubs -- so that's where the underground has gone!". Then they go back to Berlin and spread the word to credulous out-of-towners, who go on to repeat this truism to people who have never visited either city.
In reality, Berlin's club scene -- both "mainstream" and "underground" -- dwarfs that of any other city. Nothing short of an asteroid hit is likely to change that.
If we limit this claim to _just_ German manufacturing competitiveness (which is often synonymous with "European manufacturing" in the context of these discussions -- and I will assume this is true of your comment as well, given the nuclear energy remark), this really isn't true.
What changed my view on this was one of Adam Tooze's newsletters from last year.[1] The relevant points here are:
- Germany manufacturing is less gas-intensive than the global average.
- Energy costs only constitute a small and decreasing share of total industrial costs -- about 5.8% for the German manufacturing industry as a whole, and 3% for leading export sectors (namely the auto industry).
- Most German manufacturing -- and this is true of European manufacturing more broadly -- is primarily in high-margin, value-added sectors where quality, rather than cost, are the competitive factor.
Another important detail lost in the invocation of "cheap gas" is that Europe as a whole, and German in particular, has never had particularly "cheap gas." European natural gas prices have long been well above those in the US, and Germany's have been above the European average for the last 15 years or so.
I agree with both Tooze and the majority view that, notwithstanding the above points, Germany's post-war energy policy has been a catastrophe. It has certainly limited its manufacturing potential. But the effect of the Ukrainian war on its _already_ limited manufacturing sector, due to _already_ not-so-cheap energy, tends to be exaggerated.
Air travel accounted for 2.5% of CO2 emissions in 2020, and its total contribution to global warming was probably closer to around 3.5%.[1]
Only 11% of the world's population travelled by air in 2018, with at most 4% taking international flights, and 1% of the world's population accounting for more than half of total emissions.[2]
Passenger air travel is projected to grow by about 44% by 2050,[3] and will probably take up an even more substantial slice of overall emissions by then because technologies to decarbonize air travel (other than direct carbon capture) do not yet exist.
The argument you are making is probably least compelling when applied to air travel compared to any other form of consumerism, and HN's readership (generally speaking) is uniquely culpable here.
“Curtis’s stitched-together compositions are less collages than they are Rorschach blots: look into their murk, and you can find your own worldview confirmed.”
This passage, which I see as being core to the author’s critique, doesn’t really jive with me. Basically he’s saying these movies are enigmatic, they offer space for reflection, they resonate with a lot of people across the ideological spectrum.
Well, you can look at that cynically, or you could say that’s precisely — almost definitionally — what makes them effective art.
To go a little further than that: I don’t think it’s a fair claim. Each of his movies since Bitter Lake have had the same general arc of “emerging ideological apparatus promises to resolve social contradictions and empower the common person, fails to do so.” In this sense, his narrative angle is broadly _anti-confirmatory_.
Then there’s the aesthetic critique, which, whatever. I really can’t fault someone for finding fault with Adam Curtis’s style. And it’s least overbearing in TraumaZone out of all his movies I’ve seen, so I get why this author favors it.
As it happens, I’m about halfway through TraumaZone right now. It’s great. Poses some interesting questions, doesn’t offer any easy answers.
But I think the steam on FP hype was already starting to run out for unrelated reasons.
The briefest account I can offer on "peak FP" from my (web developer) perspective is that
1) functional programming was already enjoying a moment of renewed interest and vitality due to the increasing ubiquity of multi-core,
2) React/Redux -- which ~solved[1] many problems with increasingly complex frontend web/mobile state management -- really started to become mainstream around 2016-2017,
3) Node/TypeScript was in the midst of an popularity / enterprise adoption explosion (in part due to #1) and only served to amplify the general enthusiasm around FP among JS-literate engineers (in part due to #2).
In the intervening years, the React paradigm more-or-less "won" and multi-core is taken for granted. A huge chunk of our industry has probably never known a time where FP wasn't celebrated. For that reason, it no longer seems to be answering any pressing problems, and naturally there are fewer articles being written about it.
[1] I expect this to be a point of consternation, since this is HN, but the point is that React was at least _perceived_ to have been an antidote to a variety of issues people faced with Angular, Backbone, plain old jQuery apps, etc. and the unifying theme of those issues was (rightly or wrongly) perceived as "OO / mutable state bad."
I think the reluctance to blame phones is not just that people have a personal, dopaminergic fixation on them.
There are a couple of other factors at play:
1. Thinking people, especially of the sort that frequent Hacker News, want there to be a more complex and multivariate reason behind the "big problems" like loneliness and alienation. It feels wrong to just say "phones bad."
2. Many of us grew up with emerging technologies, like video games and the internet itself, that were reflexively rejected by generations older than us for reasons that seem poorly thought out in retrospect. We pattern match on the type of blithe Luddism we grew up with, and are instinctively reluctant to say "phones bad" for fear of falling symptom to it.
But I agree with you, and the thesis of this article. The "phones" argument has such obvious explanatory power for a wide range of regressive social phenomena that there needs to be overwhelmingly strong evidence for some other catalyst to drop it from consideration.
I would greatly appreciate a moratorium on this genre of article until there is compelling accompanying evidence that a meaningful portion of ChatGPT's users are unaware of these shortcomings. I have yet to encounter or even hear of a non-technical person playing around with ChatGPT without stumbling into the type of confidently-stated absurdities and half-truths displayed in this article, and embracing that as a limitation of the tool.
It seems to me that the overwhelming majority of people working with ChatGPT are aware of the "con" described in this article -- even if they view it as a black box, like Google, and lack a top-level understanding of how an LLM works. Far greater misperceptions around ChatGPT prevail than the idea that it is an infallible source of knowledge.
I'm in my 30s, so I remember the very early days of Wikipedia and the crisis of epistemology it seemed to present. Can you really trust an encyclopedia anyone can edit? Well, yes and no -- it's a bit like a traditional encyclopedia in that way. The key point to observe is that two decades on, we're still using it, a lot, and the trite observation that it "could be wrong" has had next to no bearing on its social utility. Nor have repeated observations to that effect tended to generate much intellectually stimulating conversation.
So yeah, ChatGPT gets stuff wrong. That's the least interesting part of the story.
Let me start by saying that I really like Elixir as a language and ecosystem. That said, I don’t think it’s a good choice for a first language.
Elixir excels for building highly available networked backend applications. Not that it can’t be good for other things, but I personally tried my hand at it as a scripting language (as a complete newcomer to programming might). For this purpose I had to learn things like Erlang interop, module composition, setting up a mix project, etc. — just to get a small project off the ground. Even as a seasoned developer familiar with syntactically and conceptually comparable languages (Ruby, OCaml…) I found the self-instructional overhead to be atypically high for a programming environment.
This is a bit of a tangent, but I’ve noticed that whenever “best first language” discussions come up, responses tend to be biased toward whichever language people first scrapped something together in for fun. For me that was TI-BASIC. By no means do I think that was a “good” first language - it’s just what was around. But it passed the litmus test of letting me quickly iterate from writing calculator-crashing output loops to making little games for my friends.
For its virtues, Elixir was originally developed to serve a community of experienced web devs trying to solve problems most newcomers don’t know about and won’t encounter — and it shows. I think most novices would be better served by a more “mainstream” scripting language like Python, Ruby, JS, etc.
As the original submitter, I will one-up your ad hominem by pointing out that the blogger was an enthusiastic booster of peak oil theory in the mid-00s (as one might infer from the domain name). I don’t endorse her analysis, but I nevertheless found the data compelling and was interested to hear others’ perspectives.
Back in 2015, when this type of thing was still novel, I participated in an ISA program that required a $3,000 deposit. It was refundable if you failed a (demonstrably) good-faith effort to applying for jobs, or rolled into the payment once you landed one.
This was a substantial amount of money for me at that time, but I probably wouldn't have applied for the program had it not been for the ISA. I was unwilling to take on new debt, and I didn't have enough money for an upfront payment. The deposit seemed like a reasonable expenditure to save toward precisely because the ISA showed that the program itself had skin-in-the-game.
There's a strain of Marxism called Maoism Third-Worldism. (This relates to the article -- I'm getting to it.)
This is a contentious strain within Marxism because of the way it contorts some of Marxism's basic ideas to its own ends. The main one being Lenin's theory of "labor aristocracy." If you're not familiar with this, it's the idea that the working class in first world countries is effectively "bribed" by their national bourgeoisie through wages higher than those available for the same types of jobs in developed countries.
Maoism Third-Worldism extends this concept by claiming that, actually, first world laborers are not exploited _at all_. No matter how bad workers in developed countries have it, their class interests are fused with those of the national bourgeoisie and their share of imperial "superprofits" means that they effectively belong to the same class category as the latter. I'm simplifying somewhat, but this is the basic idea, and it's proven through hundreds and hundreds of spreadsheets. If you leaf through a book like Zac Cope's Divided World, Divided Class, half the book is citations, charts, and tables.
Now I admit I am a bit of a dilettante here, but to my knowledge there have been no serious attempts made to challenge the vast quantitative efforts made by Third-Worldist writers. This is at least partially explained by the fact that Marxist audiences tend to be a bit less quant-focused than their peers in mainstream economics (to put it lightly). Still, it upends some of the most basic contentions of Marxist analysis, so you'd expect there to be a more lively debate over all those charts and graphs. Right?
I claim there's a larger issue at play here: most people are driven to ideas within social sciences for basically pathological reasons. The ideas agree with them on some basic level, and the research is largely a pretext for legitimizing those ideas or creating a sort of ideological playing field for its adherents. People aren't drawn to Marxism or any of its sub-sub-ideologies because they started reading Capital on whim one day and found its ground-up arguments on the commodity form compelling, any more than libertarians start from a blank slate and are "woken up" by reading Hayek cover-to-cover.
So if you propose a wildly heterodox idea within one of these fields, no amount of math is going to help you. Most people, even academics within the same area of research, will either be drawn the idea - in which case, great, there's math to "back it up" - or repelled by it and dismiss it out of hand.
I'll show my hand here by saying the author's idea sounded kind of dumb to me so I didn't look at the spreadsheets.
I'm an American who did the same, and I echo this. The compensation tradeoff was much more pronounced when I moved to Berlin 4 years ago. Covid and remote work changed this: you can now make more-or-less median comp for a second tier US city after adjusting for cost of living (and before accounting for quality of life).
Everywhere's still low compared to the Bay Area, don't get me wrong. But it means you either need to be a salary maximalist, determined to nationalize, to have a strong pull toward a certain US city/company for the move to make as much sense these days.
Still, I'd encourage everyone regardless of home/destination to live and work at least some of their life in another country. The merits of doing so are vast, and extend well beyond renumeration.
The overwhelming consensus in this thread and in the replies to that tweet is dissent on the basis of the many completely obvious holes in the author's reasoning, which I won't get into here.
What I'd like to appreciate is the uniqueness of the blockchain space in its seemingly bottomless capacity to generate these bold propositions for which blockchain is _blatantly_ ill-equipped to solve whatever problem is at hand.
This is seen virtually nowhere else in tech with such regularity: engineers are obsessed with picking the "right tool for the job," entrepreneurs are obsessed with finding "product-market fit," etc.
This would make complete sense if the sector as a whole were, to put it crudely, one big Ponzi. The type that could influence otherwise intelligent, well-meaning, technically-inclined people to make glaringly absurd claims like in the linked thread as a pretext for luring in investors.
The typical response to that explanation is to split hairs over whether NFTs, or Bitcoin, or whatever token this guy is selling (I didn't check) fits the classical definition of a Ponzi.
I say "typical" because we re-engage in this discussion anew every day here on HN -- apparently in direct proportion to the total cryptocurrency market cap, with the occasional two-or-three year relief period when retail money dries up.
Deliberately choosing to not not conduct business with certain groups on the basis of their political ideologies is a political action. It implies a disposition toward trade and exchange that is itself political.
Consider the fact that you cannot legally offer a service or a fee to terrorist groups (however that may be defined), money launderers, industries from countries under sanctions... the list goes on.
These limitations are political in nature and could be revised, expanded, or eschewed via political action. But even if they were all eliminated, this would not constitute a "depoliticization" of trade: such an action could be characterized variously as libertarian, free-market, laissez-faire, or something of that nature but it would nevertheless be explicitly political.
This article focuses heavily on Facebook’s Libra, which to my knowledge has recently scaled back from offering a currency balanced by (and collateralized against) a basket of commodities to offering a handful of 1-to-1 fiat-anchored stablecoins.[1] This is way less technically ambitious, and seems pretty similar to the old Facebook Credits system of in-app purchases.
Where this gets interesting for me is 1) stablecoins like DAI, which collateralize using other cryptocurrencies, 2) projects like Celo, which are doing something similar to Libra but with way less initial runway for backing EU-sanctioned reserves, and 3) projects like Reserve Protocol, which aim for something similar to Libra’s original goal (achieving “stability” relative to an algorithmic balance between the real value of currencies, goods, etc).
From the article it’s not obvious to me whether a lot of this could be evaded simply by not marketing your project as a stablecoin.
This article makes it sound like the main bottleneck in the first-run recycling process is sorting.
Could anyone with knowledge around ML, robotics and the like chime in on napkin math around novel labor-reducing approaches to improving the operating economics here? From the sound of it, this is not really a solvable problem short of some leapfrog technological advance.
Even then, you don’t want to spend more energy on sorting automation than you get back from the recycling. And this would only make sense if the diminishing returns after the first or second recycle made it reasonable to be issuing so many consumer plastics in the first place.
I'm sympathetic to marketing counterfactuals, so I wanted to like this article, but its claims did not sway me.
- The appalling environmental footprint of the dairy industry was never addressed.
- The canola oil claims cited were nowhere near conclusive (see twanvl and NotOscarWilde's comments below).
- The nutritional comparison seemed to me to be more or less trivial.
Okay, it's got a higher glycemic index and similar sugar content compared to cow milk. If you share the author's concern about tacitly "health-adjacent" marketing, this is problematic, but not an outright lie. At worst the product has a pretty comparable nutritional portfolio to cow milk. Seems appropriate for a "milk substitute," no?
The author engages in a little deceptive rhetoric himself by setting a 12oz serving as the baseline. It's true that a portion that size is bad for you. Lattes are bad for you. Like many people, though, I only rarely use milk substitutes to add a dash of not-coffee to coffee that would otherwise be too hot or too burnt. Guess I shouldn't be concerned?
- Most glaringly, the moral dimension of dairy consumption is never addressed. I won't harp on about this too much as many other commenters have already, but this seemed like a glaring omission: who cares about sugar content if the alternative is needless suffering? I guess you could have the best of both worlds by not drinking a milk substitute in the first place, but that's not Oatly's market segment, so...
Overall, this article came away as basically validating Oatly's marketing claims to me. Which is frustrating, since as I stated above, I'm biased toward marketing scrutiny!
The title is reflective of the article itself, which uses data to show that the wealth of an NYC neighborhood’s inhabitants is on average directly correlated with the extent to which those inhabitants left during the last few months.
Alternatively the article could have “just” listed the boroughs as you suggest, but this would raise the question of why some boroughs emptied out and not others (also why there is visible clustering within those boroughs’ neighborhoods). This is the question the article tries to address.
I'm a self-motivated adult learner, so I don't know what it's like for kids. Though the program was originally designed for them, so I suspect their experience would broadly be similar to mine.
As other commenters have mentioned, you need to be okay with grinding through problem sets with no videos or UI pizzazz -- maybe this doesn't work for everybody. I'd compare it to the difference between trying to learn a language through scattered YouTube videos and Duolingo versus tandem and grinding on a good Anki set.
NB: I'm taking it for the Math for ML track and am currently most of the way through the Math Foundations III course. So I can only comment on the lower level courses.