The were being used to threaten and swat government officials on both sides of the aisle over the last few years. The Secret Service found them by tracking the SIM numbers that the threats originated from.
The sheer volume of the SIM cards and distribution of the installations indicates there were plans for far more than just anonymous threats to officials, because that volume of devices could easily overload the cell network. .
CNN has a much better article going into far more detail than the BBC blurb.
The main thing that got these on the radar of the Secret Service was swatting and threatening government officials (both Democrat and Republican) via phone calls and texts from these devices. After several months of tracking the SIMs these calls were originating from, and trying to find their physical locations, they discovered these SIM servers.
The sheer volume of them - 100,000 unique SIM cards housed in 300 servers spread across multiple locations (rented apartment spaces and the like) is way, way more than needed to just harass government officials. That volume of devices could easily overload the cellular network and bring it down.
Nazism isn't an internet insult. It was an actual, real thing that happened in this world, with an actual, real ideology, and the core of this ideology was racial-based social Darwinism.
BTW, re: the Congo in particular: the most common traditional type of fishing is basket fishing with woven funnels suspended in the rapids. You sure as hell better know how to swim if you want to do that.
Famous angler Jeremy Wade referred to the local Congo fishermen as nearly suicidal, just diving into the rapids to get nets unstuck and the like.
SIGH.
There were 10 people chosen and people with dark skin in the USA make up about 1 out of 8 Americans.
1 in 8 is 12,5%.
African-American without mixed race in 2024 is estimated at 46,3M, or 14,2%
With mixed race, that rises to 51,6M, or 15,8% of the population.
Some hispanics have dark skin, some light. In 2023 there were 62,5%, representing 19% of the population (though there's a small overlap with black - doesn't affect the numbers much).
In 2023, Asians were 25,8M people, or 7,7% of the population. This is again a diverse group with mixed skin tones (for example, the Indian subcontinent)
In 2023, there were 1,6M people (0,49%) of pacific island ancestry and 3,3M native Americans - again, mixed skin tones.
People of Mediterranean European ancestry often have so-called "olive" complexions.
With a strict definition of dark skin, you're probably talking like 1 in 6 or so (~16,7%). With a looser definition, you could be talking upwards of 40% or more of the population.
The chances of the 10 people to be a perfect representation of the racial demographics of the USA is quite small.
Here are the actual odds of selecting no dark-skinned people at different population percentages being "dark skinned", by one's definition of "dark":
15%: 1 in 4
20%: 1 in 8
25%: 1 in 17
30%: 1 in 34
35%: 1 in 73
40%: 1 in 165
Then consider that NASA astronauts are required to pass a swimming test
It is not a test of swimming prowess, just of an ability to not drown. You have to be able to do three lengths of a 25-meter pool without stopping, three lengths of the pool in a flight suit and tennis shoes, and tread water for 10 minutes while wearing a flight suit. This is not some massively imposing task. You don't have to be Michael Phelps to become an astronaut.
and as a general rule those with African ancestry tend to have less stamina in swimming than those with lighter skin
Yes, white athletes tend to have an advantage in swimming. A 1,5% advantage. While a 1,5% advantage may be of good relevance at the highest level of a sport, it's hardly meaningful in a "can you tread water with a flight suit on" test.
Think of the different races as just really big families
That is not how genetics work, and is instead the pseudoscience that drove fascist movements, and in particular, Nazism.
There is far more genetic diversity within a given "race" than between them. Certain genetic traits tend to have strong correlates - for example dark skin and sickle cell anemia - but that's not because races are some sort of genetic isolates, but rather for very practical reasons (dark skin is an adaptation to not die of skin cancer in the tropics, and sickle cell disease is a consequence of a genetic adaptation to not die of malaria which also happens to be found in such climates). But the vast majority of genes don't have such strong correlates.
The concept of "race" as a distinct biological category is not supported by modern genetics.
If we are to ignore skin color and just put one big family up against another big family on swimming ability then just due to random mutations, perhaps some Darwinian selection way back in the family tree, one family will swim better than the other
The main "racial difference" in swimming ability in the US is "inherited", that is, parents who don't know how to swim tend to not teach their kids how to swim. As a result, white children are 56% more likely to receive swimming lessons than black children. One can expect that to directly correspond to an advantage in adulthood. But again, the ability to tread water is not out there knocking 90% of astronaut candidates out of the race - especially given that astronaut candidates tend to be athletic and motivated to learn new skills.
People with light skin tend to have ancestors that had to go fishing for their protein
Utter tripe. Fish consumption has no correlation with skin colour. How much fish do you think your average herder or plains horseman ate? And fish is massively important in much of Africa - in coastal areas (Gabon, Ghana, Sierra Leone in particular note), along the Congo (it's literally the world's largest river, people have been fishing it since time immemorial), Lake Victoria, Lake Chad, the Niger Delta, etc etc. What sort of racist stereotype world are you living in where black people don't fish?
Having a large candidate pool relative to the number chosen makes it easier to show bias and still get high-quality candidaes, not harder.
Fairly obviously, this almost certainly won't result in many thousands of H1-Bs each paying $100k to the US government each year; it'll result in many thousands of jobs that would have been paying US taxes on their wages, and then paying for accommodation, a car, for leisure, and whatever else into the US economy paying their taxes and spending their wages in wherever the new (or expanded overseas) office is instead.
Yep. Google, at least, started this transition during Trump1.
The company has long had engineering sites in various other countries, but until Trump1, the primary focus was always on cities where Google thought the global talent would want to live. Low cost was clearly not the driving factor in the selection of London, Zurich, Munich, Tokyo and Sydney, to name a few of the ones I visited. US sites were similarly not located in low-rent areas. The workforce was definitely global, because Google wanted to hire the smartest people and while the US does have its share of brilliant minds, the US has only 4% of the world's population, so most teams -- even in the US -- ended up being minority American.
During COVID, Trump leveraged the health crisis to essentially halt H-1B approvals and renewals. This caused significant problems for Google. My own team lost a few people because they couldn't get their visas renewed and had to go back home. Some chose to move to other Google sites overseas where Google could get them a work visa, others simply went back to their home countries. One trans woman on my team was in a particularly tough spot because her home country (India) refused to renew her passport because it didn't recognize her new gender. She couldn't get her visa renewed, couldn't go home to India, and also couldn't move to any other country with an expired passport. Luckily, she had a lot of nVidia and Google stock she'd been saving up to buy a house, and by cashing that out had enough free cash to get an EB-5 "investor" visa. It's good to be rich, of course.
Anyway, Google saw what was going on and, anticipating future troubles of the sort, refocused its overseas office plans on building up teams and infrastructure, especially in India which provided so much of Google's engineering talent anyway, with the intention of shifting whole projects and workstreams there. The company had long required a significant percentage of all staffing growth to be in the US (and especially in the bay area), but that policy was scrapped and replaced by its opposite: A certain percentage of all new roles must be based overseas.
It's still the case that the center-of-mass of Google is in the bay area, but the company is actively working to change that, to build up overseas capacity, and not just groups of junior engineers under a manager whose role is to pass them detailed requirements for implementation, but instead full teams with highly-skilled and experienced senior engineers and managers able to take full ownership of major product areas and move them forward.
Trump's latest moves will just accelerate this transition. The result will eventually be a hollowing out of the company's US capacity, and therefore a reduction in the need to hire American engineers. Lucky for me, I'm leaving Google for a startup and anyway am not far from retirement. Between this stuff and AI being poised to replace junior engineering staff it's a good time to be getting out.
Also, I think it will soon be time to start shifting investments out of the US.
This does not appear to be holding up in practice, at least not reliably.
It holds up in some cases, not in others, and calculating an average muddles that.
Personally, I use AI coding assists for two purposes quite successfully: a) more intelligent auto-complete and b) writing a piece of code using a common, well understood algorithm (i.e. lots of sources the AI could learn from) in the specific programming language or setup that I need.
It turns out that it is much faster and almost as reliable to have the AI do that then finding a few examples on github and stackoverflow, checking which ones are actually decent, and translating them myself.
Anything more complex than that and it starts being a coin toss. Sometimes it works, sometimes it's a waste of time. So I've stopped doing that because coding it myself is faster and the result better than babysitting an AI.
And when you need to optimize for a specific parameter - speed, memory, etc. - you can just about forget AI.
Hey, industry, I've got an idea: If you need specific, recent, skills (especially in the framework-of-the-month class), how about you train people in them?
That used to be the norm. Companies would hire apprentices, train them in the exact skills needed, then at the end hire them as proper employees. These days, though, the training part is outsourced to the education system. And that's just dumb in so many ways.
Universities should not train the flavour of the moment. Because by the time people graduate, that may have already shifted elsewhere. Universities train the basics and the thinking needed to grow into nearby fields. Yes, thinking is a skill that can be trained.
Case in point: When I was in university, there was one short course on cybersecurity. And yet that's been my profession for over two decades now. There were zero courses on AI. And yet there are whitepapers on AI with me as a co-author. And of the seven programming languages I learnt in university, I haven't used even one of them ever professionally and only one privately (C, of course. You can never go wrong learning C. If you have a university diploma in computer science and they didn't teach you C, demand your money back). Ok, if you count SQL as a programming language, it's eight and I did use that professionally a few times. But I consider none of them a waste of time. Ok, Haskell maybe. The actual skill acquired was "programming", not a particular language.
Should universities teach about AI? Yes, I think so. Should they teach how to prompt engineer for ChatGPT 4? Totally not. That'll be obsolete before they even graduate.
So if your company needs people who have a specific AI-related skill (like prompt engineering) and know a specific AI tool or model - find them or train them. Don't demand that other people train them for you.
FFS, we complain about freeloaders everywhere, but the industry has become a cesspool of freeloaders these days.
"When the chef said, 'Hey, Meta, start Live AI,' it started every single Ray-Ban Meta's Live AI in the building. And there were a lot of people in that building,"
The number of people isn't the problem here.
The "started every" is.
How did they not catch that during development and found a solution? I mean, the meme's where a TV ad starts Alexa and orders 10 large pizzas are a decade old now.
Cppcheck apparently knows "hundreds of other rules covering a multitude of language aspects" so you don't "have to mentally apply against every single line of code you write."
Cppcheck doesn't flag anything in Waffle Iron's example.
It also doesn't find anything wrong with:
std::vector<int> vec = {1, 2, 3, 4, 5};
auto it = vec.begin();
vec.push_back(6);
std::cout << *it << std::endl;
Which is another common example of how you can write memory errors without using C++ pointers.
In the sort of places where MISRA and similar coding guides apply, yes, never allocating memory is expected, because once dynamic allocation exists you can't guarantee that you won't die with an out-of-memory error and similarly can't guarantee any time bounds on how long an alloc and dealloc will take.
Sure, so C++ is safe as long as it's used in a way that makes it incredibly painful. Sounds good. Let's just require all C++ code everywhere to be written that way. Rust usage will skyrocket overnight.
Unintentionally
He unintentionally used Signal? How drunk was he?
It is hard to have fair democracy with winners take it all.
For a really rigorous definition of "fair", it's impossible to have fair democracy at all. Arrow's Theorem demonstrates this to a large degree, although many have argued that some of his fairness axioms are excessive. More recent research has concluded that fairness is the wrong standard, because there's no way for an electorate's "will" to really be fairly represented by any electoral system, not in all cases. Some systems can do better most of the time (and "winner take all" is particularly bad), but all systems fail in some cases.
What we need to aim for instead of fairness is "legitimacy", which is more about building broad acceptance of the system than about fixing the system itself, though it's easier to build acceptance for better-designed systems.
Having the country's top politicians continually claiming the system is unfair and rigged is, of course, the worst possible thing to do if you want to build support for the legitimacy of the system.
Precisely none of those books were ever banned.
I decided to check
According to the Book Censorship Database from the Every Library Institute, both "Of Mice and Men" and "Adventures of Huckleberry Finn" have been challenged, but only "Of Mice and Men" was removed, though "restricted" is more accurate. The Birdville Independent School District in Texas removed the book from general access, allowing access only to the AP English class, and the Indian River County Schools in Florida restricted it to high school students.
No Doctor Suess books were banned, although Suess Enterprises voluntarily ceased publication of six books.
"Plan to throw one away. You will anyway." - Fred Brooks, "The Mythical Man Month"
