I often make the argument about whether metric is "better" being split by "is it better on paper" vs "is it better in day to day life" - and most metric stuff breaks down in the latter, meaning, the non metric systems seem to be perfectly fine in day to day life, but almost always better on paper.
Like Celsius - sounds great on paper, not any better (and in some ways, worse) in day to day weather/temperature.
One of the few exceptions I find is metric paper sizes. It's such a beautiful way of designing the sizes that is not only perfect on paper (pun proudly intended), is also totally practical in real life.
He is explaining that if you design an app that displays as desired on the outer display (in portrait mode). Then if you display that app to the inner display (also in portrait mode), it will show up exactly the same, just with everything twice as large.
This obviously makes life easier for both users and developers.
Moreover, app developers for iOS have to support fewer aspect ratios than developers of Android apps. In theory, this allows for more attention to be given to the UI design for each aspect ratio - rather than having to use a spring-style stretch layout.
If I were to guess, this does look like a new ratio to support, but at least it's just 1 instead of 2.
Now when I use an internet search, the top answer is an AI guess/approximation to the result and can be completely inaccurate. I have to ignore that and move onto the traditional results and hope that they aren't A.I. slop websites.
>A BBS chatbot from the 80s (LISA) was almost as good as modern LLMs and that was just based on key words.
Of course, because it's not like whole world went crazy after ChatGPT and hundreds of billions of dollars are being constantly invested into many of technology stacks - software, hardware, semiconductor engineering in order to push this technology even further, lol.
Maybe in a year or so or maybe never. It depends on how 14a turns out and if it is comparable to TSMC 2NM. They may also choose to utilize 18a-p / 14a for other chips and not the M-series.
Usually, chronically-overweight ppl have tried regulating weight with all kinds of diets, lifecycle interventions, regular exercise, etc, and a lot of the time the thing that actually worked was the GLP-1. I think this speaks a lot to the variance in metabolism, hunger, cravings etc across the population.
The rub is that if they did a good job with those things, they would have worked. Most people cannot accurately account for calories and poorly estimate how hard they are actually working out.
I think this is a commonly-held misconception. Even when individuals accurately account for calories and workout intensity (using hard data, not feels), theres a large variance across individuals, broadly attributable to 'metabolics'.
While that is true there is variance it doesn't vary to the point of overcoming the basic thermodynamics at play. Facts are, you eat fewer calories, you lose weight. You burn more calories than previously without eating to make up the difference, you lose weight. There is no obese person that can starve themselves and not lose weight. It just doesn't work like that. The energy has to come from somewhere.
What often happens when people say "it doesn't work for me" is that they think they are working hard in the gym but aren't actually, or they think they are eating healthy without realizing all the little snacks or drinks they consume are putting some thumbs on the scale.
Sure the thermodynamic reduction of `weight-loss := energy-in < energy-burned` is true, but youre erroneously extending that reduction to assert basically ~variance in outcomes is explained by effort and measurement error~.
Most pointedly, theres a number of studies that measured outcomes from over/under-feeding identical twins [1] or varying exercise in identical twins [2] and found significantly wider variance WITHIN twin pairs than across the whole study. Phrased differently, calorie intake + exercise doesn't predict the outcome of weight even when controlling for hereditary factors, and looking at the geometric mean masks this.
TLDR the same amount of exercise + number of calories in one person doesnt predict the outcome in another. Hence why at-home delivery services doesnt eliminate the need for GLP-1 drugs.
[1]: n=12 twin pairs, Bouchard et al., NEJM 1990.
[2]: "Mirror image experiment", Bouchard et al., Obesity Research 1994.
I've been overweight many times in my life, and I've been able to use diet and exercise to get my weight back to a healthy level.
Each occasion took a large amount of willpower and the healthy habits didn't stick around forever.
I started taking tirzepatide weekly and the most striking effect was a marked decrease in cravings. Where previously it took all my willpower to ignore cravings, it has now become difficult to overeat. I would have to force myself.
Now I wonder if its like this for those who have never had issues with their weight. Are there people who insist it's easy but have never experienced cravings?
In C's defence, you only have forward-compatability of string handling to the extent that your string type will not change in the future. Byte arrays are great for this. All string encodings in the foreseeable future can be encoded in bytes. You do not need to recompile coreutils once someone invents utf8-plus.
If you wrote your standard lib in whatever was better-than-C at the time, you might have settled for sized strings (who needs strings longer than 65535 bytes?) instead of \0, and you might have had utf32 or some kind of wide char representation.
Not anymore, during covid many things changed, many people moved on with their lives and other people prefer to argue about interviews and market instead of hard tech
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