I've done some trail running. Not enough to make a hiker look silly, but a bit, and a lot of hiking. I was hiking in Ethiopia one time and we kept having to duck off the trail to let women carrying bundles of stuff on their heads pass. The real kicker was when a guy on crutches who didn't have any feet passed us.

People do dumb stuff in the mountains all the time. A couple of people apparently tried to hike this trail last winter in shorts.

This is a BC provincial park so there is undoubtedly a great big sign at the trailhead telling you how long to expect it to take and advising minimal equipment you should make sure to bring. There's also an official website (advising only experienced hikers go beyond a certain point) and many, many other websites.

I had to take an organizational behaviour course as part of my CS degree in the 90s. "Organizational behaviour" is basically how to get your employees to work harder without actually paying them more. This kind of lying was standard practice back then too, and certainly not just in some little part of California.

Adoption is a function of existing adoption and existing availability of resources.

Extremely good OS' and truly amazing computers have failed, not because they were too expensive or had issues, but because they were perceived to be unpopular OR simply lacked enough software that could run on them.

Extremely bad OS' and truly awful computers have succeeded for the opposite reason.

Linux has an extremely good architecture, at least for machines that are mostly operating with CPUs. When most of the work is done by the GPU, it's less obvious that Linux provides the necessary mechanisms to manage resources and abstract away the hardware specifics. That doesn't mean I expect Linux to start fading, but I don't think a centralised OS is necessarily where things will go. However, the reason it hasn't done better so far is simply because potential users and potential software houses had a negative view of it (even though actual users were almost invariably loving the system).

Windows is crashing now not because it is a slow, bug-ridden piece of spyware (which it is, but users have never cared about that in the past), but because it is perceived in increasingly negative light.

They can get vertical samples from space and the ground. What they can't get from space or the ground is how the internals of a system evolve.

The problem with "models" in the abstract is that there are a lot of them and some of the time the thing you want is to know the same point relative to the system and not to the mudball.

Lots of people have gut feelings about AI, positive or negative.

Most of the AI research I can see people doing is focusing on single-element problems (such as finding counter-examples to a mathematics hypothesis) or solving very simple engineering systems (at most a dozen or so components). Most of the benchmarks are even simpler (write a short story at the level generally asked in English classes of primary school kids).

As a result, I think that people have developed either a very cynical outlook or a very optimistic look. Neither of these is entirely realistic, for the simple reason that no actual useful problem fits any of those descriptions.

Digging much deeper, I'm finding that when problems get into the hundreds or thousands of elements, even ChatGPT Sol 5.6 and Claude Fable 5 struggle badly to keep track of accurate relationships, let alone any level of detail.

If you want to solve a relatively simple coupled system (whether to use solar panels or direct solar heating, and which side of the house to place them), I'm sure AI is fine. If you want AI to write a five hundred word story detailing a conversation between Kim Possible, Rufus, and a smoked herring, I'm sure it could cope. Just.

If you want it to do anything hard enough that a human could do with an assistant, the tests I've been using (meaningful rather than synthetic problems) suggest you've developed a fantastically expensive blue smoke generator. We are going to need to see the inference engines developed for previous generations of AI, and the reasoners developed for the semantic web, and perhaps tools that haven't begun to be imagined yet, before AI can do anything non-trivial.

A balloon is not static, it moves with the atmospheric system it is in. Ergo, it tells you how that system evolves. A drone cannot do this.

A drone can stay put, relative to the ground, telling you how a location's atmosphere evolves. A balloon cannot do this.

These are solving completely different types of problem.

You could, of course, construct a hybrid, as balloons are traditionally single-shot. If a balloon carries the measurement gear and a drone capable of carrying that same gear PLUS a deflated balloon, then you can have something that operates as a balloon until it reaches some pre-set location, whereupon the balloon's gas is released and the drone flies the ensemble to a collection point.

Lawsuits are not guilty verdicts. Also, "lines of code" have nothing to do with whether they were copied, as in copy-paste, or filtered through somone's brain first.

You clearly don't know what you're talking about and are too lazy to even do a simple Google search, so what youthinks is pretty irrelevant.

It obviously gives us guarantees in terms of what the source will do and what the compiled code should need.

These do not provide memory safety, so memory bugs would not be solved by these. They will find unexpected behavioural issues, though.

The precondition and postcondition statements are not cunningly-disguised assert statements because they're not asserted at runtime. They're enforced at static check and/or compile time.

The policy hints mean that if the kernel is doing something you're not expecting, you find this when testing the code. Equally, though, it means that users cannot cause the software to venture into realms unknown through many of the usual attack vectors because those will require permissions that aren't there.

It seems like an unnecessary button. LinkedIn already has an icon that labels slop posts generally. It's a blue field with white letters "in".

You're going to buy a car with a list price of $X. Then you find out that it was in a bad hailstorm, or driven by the dealership owner's kid all summer or something. Nothing has changed, so you're still willing to pay list price, right?

More similar to this situation, you're selling your corner store. Corner stores of that kind are typically worth about $Y. However, a new school opened close by and your store is suddenly making twice as much as similar stores. Nothing has changed, so you're still willing to sell for $Y right?

When a human reads code and then produces very similar code, that is infringement.

It is not, any more than when someone writes a book about elves, some Slashdotter parrots something they heard somewhere, or a half decent programmer writes any of the algorithms found in Numerical Methods. The GP is correct, copyright protects the specific expression, not the idea, and both law and precedent protect the case where you read something, put it down, then generate your own. Only substantial portions of exact text and distinctive and recognizable things like character names are protected, and even then only when there are no circumstances like a limited number of ways to write something.

Proving you did that can be tricky. THAT's why "clean-room" implementations were invented. You document the fact that the implementer had no contact with the original so any resulting expression is obviously not infringing.

GCC is covering their asses, as they should. They wrote a completely unenforceable policy so if some bit of code is found to infringe on something for whatever reason they can just remove it and blame the dastardly policy violating original submitter.