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.

I mostly do heavy multi-disciplinary engineering problems (these I also use for checking an AI, as they're typically not good at these sorts of problems), complex coding, OS analysis, stuff like that. However, sometimes I do throw the occasional odd-ball - I've used ChatGPT to propose a workable quantum mechanics that will cope with Doctor Who canon, for example, and to produce an outline for a story in which symphonic metal appears in 1964 that is compliant with current sociological and psychological models of behaviour.

Claude Opus 4.6 was coping surprisingly well with just about everything I threw at it (but ran out of credits fast), but Opus 5 is churning out incoherent babblings to the point I'm worried I may have accidentally summoned Cthulhu.

But Gemini, Grok, and DeepSeek got hopelessly confused on just about everything past a very low level of complexity. They can handle large problems, yes - Gemini has a huge context window - but complex interactions baffle them.

ChatGPT is able to identify issues correctly, but can only outline solutions, it's just not good at depth. 5.6 is a lot better, but still not good at deep answers. ChatGPT is also prone to agreeing for the sake of it, which makes me nervous about trustworthiness.

That is fair enough. I've been trying out Kimi on the free model, and have subscriptions to Claude and ChatGPT. If Kimi is actually as good or better than ChatGPT, at the pro level, then it might be worth my while moving over as ChatGPT has become very disk-hungry of late and I'm pushing right to the very limits on what it can reliably process.

Although the Chinese models are all considerably smaller, it's not obvious to me that they're as good or better. They could be, but I'm not convinced the benchmarks that currently exist are stressing the models enough to establish where models actually are any good.

You use the can of grue repellant in the lake to get past those.

That humans have sufficiently directional hearing is perhaps the most impressive part. Once you have that, then the rest really just follows,

However, this continues the unexpected senses in humans, one of the first discovered was that humans have a weak magnetic sense.

The potential, both in fact (if you can "see" walls and gaps by clicking then you can presumably navigate a cave even if your light source goes out) and in fiction (this one should really be obvious), is considerable.

These are the same lazy dolts that call them selves "Software Engineer" yet don't know shit on how software actual works beyond an ID, don't understand software developer ethics, and think the can be sloppy because "computers have a lot of RAM" completely blind to the idea there janky ass software isn't the only thing running on a computer.

Modern IDEs have lead to this industry being full undisciplined half wits.

I literally do. Stop being bad at your job.

"I know it is counter to modern coding practices"
it is not. It's literally best practice. Coders not doing this are bad at their job, and should be fired.