Thanks, great to know. I hear you on at least knowing where you might sleep. Age 18-23 even this isn't that critical, when 'beach' or 'park' are viable answers.
The best part of interailling for me was turning up to a major train station, looking at the giant departure board, choosing an enigmatic destination and just getting on the train. No booking or reservations or even planning at any point.
This is absolutely needed for media companies in London, some of whom may pay freelancers many months after invoicing and only if the freelancer repeatedly reminds them and escalates towards legal threats. When I ran such a company we made a point of paying people the same day they invoiced in order to stand out as being great to work with, it was the easiest win ever.
They should give the data to people who ask, which will be a snapshot in time presented in a spreadsheet. Then what you are paying for is the interface that shows you who clicked yesterday with a thumbnail and a link to their profile, and who will click tomorrow, as long as you keep paying. But refusing the download option is not on.
Okay sounds like we agree that sugar and junk food should be on the wrong side of the line, but turns out those industries have very little stigma. Who is standing outside the school gates protesting against big cola? My point is it's complicated, ambiguous, sometimes hypocritical, differs by jurisdiction and so on. None of it is clear.
There are plenty of other products that literally ruin people's lives: alcohol, tobacco, sugar, pharmaceuticals, credit cards, firearms, timeshares, junk food. Society has them all on very different parts of a stigma spectrum.
Honest question: why is this line so clear for you?
Watch the video if you believe it's worth being clear. The credits are perfectly visible on both the LaserDisc AND the CED. Approx 22:00-24:00 is the laser disc and 25:00 onwards is the CED. Enjoy.
It says its tailored for beginners, but I don't know what kind of beginner can parse multiple paragraphs like this:
"How wrong was the prediction? We need a single number that captures "the model thought the correct answer was unlikely." If the model assigns probability 0.9 to the correct next token, the loss is low (0.1). If it assigns probability 0.01, the loss is high (4.6). The formula is
−
log
(
�
)
−log(p) where
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p is the probability the model assigned to the correct token. This is called cross-entropy loss."
You claim to cite 'technologies' but include a few brands and companies for some reason.
The one you keep citing, here and in the article, Quibi, lives on in technology-form (the spirit of your article we must presume) as an 8 billion dollar business in China and is rapidly upending every Hollywood film studio.
So, arguments about substantiation or even 'this time' fall flat in the face of not even understanding your own message.