Developers and their customers mostly gave up design many years ago and used frameworks like Bootstrap because they are good enough, they are cheap to create, they increase speed to deliver with no external designer in the loop, etc. That made many sites look alike. AI designed web sites are the next natural step.
20 year olds are bewildered when they see me opening a computer and replacing stuff instead of bringing it to a shop. "Where did you learn to do that?" It used to be the only way, everybody with a computer did it. The strange thing is that it's still possible but they don't think about it.
It's simply not true at all that everyone who owned a (i'll be generous and assume you're talking about PCs) computer serviced their own computer upgrades and part swaps. In the 80s and 90s most people would take the whole PC to the store and get a whole new PC. The consumer market has always been dominated by pre-builts.
About as neat a trick as opening a slot machine, pulling out the mech and fixing a jam.
There is a massive qualitative difference between API knowledge and foundational knowledge. The former is tied to the usefulness of the platform, the person with a macbook or an iphone looks at you the same way you look at a person fixing their car or slot machine. I for one am sick of the gross fetishization nerds do of cheap knowledge.
The same thing that makes your knowledge useful (usable) is the same thing that makes it useless (negative utility). You can only change your likely PC parts because it's long been standardized and a whole industry has ossified around those standards. You've confused learning about computers with learning about a standard. Someone else would roll their eyes at your statement, "well duh of course you can't take an IBM 360/40 to the shop"
I've been working on a Rails + Vue project for 3 years. Plenty of experience of Rails (all of its years) but not much experience of SPA frameworks before then. The first year has been slow on the Vue side because I had to learn its weirdnesses (among the top 5: how you have to call dispatch("file/function") instead of calling that function right away and all the contort ways of passing data up and down between component trees). Then LLMs started to help and after three years I noticed that I could handle the code made alone. I would have probably reached this point in six months. On one side LLMs are crutches that slow down learning. On the other they made me deliver software faster, at least at the beginning.
That's correct. Furthermore
if RAM prices keep going up and staying up, many people won't buy a new PC and they will switch everything on their phones. So the current market could be the undoing of Windows.
Fighting sports are divided by weight (boxing, judo, etc) but no woman would even be close to winning in the same weight category of men, so we will never see a woman in those sports at the Olympics or anywhere it matters.
And who would pick a woman to play in a team of volleyball, basketball, soccer? I think that historically the only sport in which men and women are absolutely equal is shooting. Maybe curling but it's usually the man that sweeps the ice (a little bit of extra strength.)
That could be a useful feature. It's got vibes from the 90s, when there were a lot of different browsers and some of them allowed users to annotate pages and links [1]. I'm sure that there are a number of extensions to do that and still it's OK to have it in the browser by default.
From what I see on the other side of the ocean, the same applies to Europe, at least to Italy. Add to the list: wake up early, drive to customers all the day long, learn to always smile and be kind to customers even when they don't deserve it.
If I care a little bit about that random number I might reach for my phone and look at the digits of the seconds of the current time. It's 31 now. Not appropriate for multiple lookups.
Yes, there is probably some variable context in every chat (like date and time). Could work as a good seed but I guess you should ask the LLM to really make an effort to produce a seriously random number. (Actually I've just tried, even if you ask it to make an effort, the number will be always the same).
That happened at toddler stage of brain development and of knowledge buildup.
Let's suppose that you meet adults that never saw cats and dogs. You show them a picture a cat and a dog. Do you expect that they need to see 100 of them before telling the difference?
If you see one picture of a zebra, fly to Africa, see a real zebra, you recognize it as a zebra. But zebras are really unmistakable.
If you see a picture of an oryx and a picture of a kudu, maybe you remember the shape of their horns and a picture is enough.
Enter waterbucks and steenboks. That starts to require a little more training.
Go all the way from mammals to insects. Bees and wasps and ants are still in the one picture is enough category. But what species of ants those on the wall of my house belong to?
I believe that ease of detection depends on how much things stand out on their own. Anyway, we do use a fundamentally different way of training than neural nets because we don't rebuild ourselves from scratch. However birds and planes fly in totally different ways but both fly. Their ways of flying are appropriate for different tasks, reach a branch or carry people to Africa to look at zebras.
Humans can learn to recognize the difference between male and female newborn chickens, not sure if you can train an AI to do that since we humans don't know how we tell the difference we just learn how to by practicing enough. It is a skill any human can learn quite quickly, it isn't hard we just don't know how it works.
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