Quite a few times things which were deemed misinformation back during the COVID times turned out to be different than official sources said (at first or later).
If the best available evidence indicates X, but you believe Y based on gut feel, then later solid evidence of Y is developed, were you right? Further, should this experience convince you to trust your gut over the best available evidence in the future?
there are lots of those, often with their own "store." I find batteries like that a lot.
But this was explicitly a Walmart listing, by Walmart, rather than a 3d party listing.
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.
For deaf, since one of the features is captioning a speaker.
On the one hand, I know all too well that the AI will screw it up some.
However, if you watch closed captioning, you know that the captions are already frequently messed up, long before even AI was a possible strategy. Usually the live captioned stuff had lower quality, but you'd see it in scripted shows too.
I also wonder about the converse, captioning someone using sign language for those that don't know it.
But that FOV is just so tiny....
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.
It's not just amazon.
I ordered a thermostat for my mustang last week. It was described as "sold and shipped by Walmart."
A couple of days later, I found an Autozone box on my porch. And not just the box, but the shipping return address was to auto zone!
??
the real tragedy of Viet Nam was that the US achieved *exactly* what it set out to do--which was a really stupid thing to do and waste lives upon.
The mission was *not* to defeat the north Vietnamese, but to keep them on their side of an imaginary line. US troops that went over the line got called back.
When the US finally decided it wanted to stop playing, the north wouldn't let them simply leave. To get them to talk, the US bombed them into submission, for crying out loud.
By any *military* standard, Viet nam was an overwhelming success for the US. US troops controlled whatever ground they chose, and won all of the battles.
But "resist aggression and stay on your side of the line" is a *stupid*, even criminal, thing to ask of a military. As is the lives it through away for idiocy.
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.
My sister opened a computer store in Hawaii. She sells C shells down by the seashore.
