2,127 karma · joined September 9, 2022
According to Chinese claims, the number of soldiers killed was 32 on the Chinese side and 65 on the Indian side in Nathu La incident; and 36 Indian soldiers and an 'unknown' number of Chinese were killed in the Cho La incident.[8] "
War might be overstating it a bit, "incident" might be more appropriate, but we can round up in the spirit of comity.
So adding it all up, the Chinese had 1-2 small foreign wars per decade in the 50s-70s, zero since 1979. It still doesn't justify the phrasing "threatening all their neighbors" in 2025, aside from Taiwan specifically.
In the case of the line of control with India, it's reached the point where they're having ritualized fistfights at high altitude for pride, that's just comical. It's not threatening.
China conducted one several-week war against Vietnam and annexed Tibet, both over 50 years ago. Other than the longstanding dispute with Taiwan, who are they threatening? Some quibbles over a few Himalayan mountains with India?
That notwithstanding, Xiaomi cars are nicer than Teslas. They're called "the Apple of China" for a reason.
In an organization of high-minded individuals, the information asymmetry goes both ways, I've been a manager and a dev and I tried to be high-minded and do my best to smooth that assymetry for the greater good in both roles.
But sometimes, especially the last 3-4 years since it got tough, there's a lot of people trying to hold on by any means necessary. Information asymmetry isn't the problem there, it's incentive assymetry. What if you're just not that good at tech and got promoted to 1st, maybe 2nd level manager in the good years? What's your incentive?
(Not all managers, this was a special degenerate case, but it's worth considering that different people have different goals/incentives/values. It's not always a straight line to "delivering customer value" that is only held up by a lack of people skills.)
The "EMR over S3" paradigm is based on the assumption that the data isn't read all that frequently, 1-10x a day typically, so you want your cheap S3 storage but once in a while you'll want to crank up the parallelism to run a big report over longer time periods.
I once interviewed with a company that did some machine learning stuff, this was a while back when that typically meant "1 layer of weights from a regression we run overnight every night". The company asked how I had solved the complex problem of getting the weights to inference servers. I said we had a 30 line shell script that ssh'd them over and then mv'd them into place. Meanwhile the application reopened the file every so often. Zero problems with it ever. They thought I was a caveman.
"We're not hiring but AI is in the news" = "We're not hiring because of AI! Don't sell our stock!" It's independent of actual current or future AI adoption.
You still have to load a 64-byte cache line at a time, and most CPUs do some amount of readahead, so you'll need a pretty large "blank" space to see these gains, larger than typical protobufs.