when hiking/walking, i'd rather it stay northbound and click that compass to keep it that way.

otherwise, the pace i'm walking is so slow that the thing keeps changing up what direction it thinks i'm walking in, spins around 3 times, and throws a whole new side trek onto my route.

when i keep it northbound, it still adds that useless side-trek, but at least it isn't spinning all the time.

5000 feet (or even 1000) is deceptively tough when it is in the other direction. (I know the Rescuer knows this but saying it for the general).

Canyons and mountain routes can be deceptively difficult. Examples include the Grand Canyon, obviously, but also, say, starting a hike from a high point along the Blue Ridge Parkway or (in Shenandoah NP) Skyline drive. You head down, not up, a generally easy descent to see the landmark...and then after having been out for 2-3 hours, you have to climb back up. So the hardest part of the hike is when you're already tired...

Park rangers and most guide books will remind guests of this, but not every app (esp not google maps) will.

For biking, google maps for desktop is reasonably well at giving you a chance to see the elevation changes on the route to figure out where the toughest climbs are going to be...but I think it still presumes a steady pace rather than the "I quit, I'm getting off and walking" pace that my unconditioned 50-something legs force me to do.

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.

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.

Set up two companies in a state or country where there are no legal issues. One says "invest in us, if [event] happens by [date], we will pay off more than you invest because company #2 will turn all their assets over to us" and the other says "invest in us, if [event] does not happen by [date], we will pay off more than you invest because company #1 will turn all of their assets over to us." Set up both companies to liquidate and return assets to stakeholders on [date].

Then list the companies on an exchange accessible to US residents or if that's not possible, create a pair of holding companies or closed-end mutual funds that own 100% of company #1 and company #2 above and list them on an exchange accessible to US residents.

If this would be a legal way for New York residents to invest in the outcomes of events, it sounds like Kalshi is just doing the above but without all of the middlemen and inefficiencies that come with middlemen.

If this is not legal, I would ask "why not?"

Even that one with the pig?

I forget what it was called.

on second thought, run for the hills!

Man, this makes me feel just a bit better about spending the last 30 years in various types of tech support.

Fuck sales!

What the hell is that? Is that a salesman in PC-speak?

Waiting for Cyberpunk 2077 update with an eeeeeevil Brendan.