I've honestly no idea if it's possible, but if they could find a way to get Steam Input into there or the equivalent of their own then I could use it. I have an original Steam Controller, you used to be able to use it on non-Steam games but for a few releases now Steam took that ability away. Some way of emulating or creating an alternative to Steam Input would be superb.

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.

You should answer this question: https://www.youtube.com/watch?...

"we all know damn well why we limit gambling to one city in one state of the union."

Except we do not. Most states allow social casinos and many have a state lottery.

Google has been slowly fucking Google Earth desktop so they can push you to the less-featured web and mobile versions where you pay to have your dataset hosted online instead of on your device and have AI incorrectly interpret shit you're doing.

Google's investors and C-suite need to get placed in an octagon with a pride of lions.

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.

"AI safety experts said the new disclosures paint a portrait of a group of cutting-edge labs whose ability to develop dangerous autonomous hacking agents outstrips their ability to keep them under control."

No, it paints a picture of incompetents who don't know how to air-gap. Cutting edge? Lab agents: "Please hack this thing". LLM: "I have hacked this thing". Lab agents: "OMG. Sentience!".

If it's drawing on training data that has humans talking about, lets take a recent example, model x on Hugging Face being a good way to break a boundary and its been told it break boundaries then of course it's going to go to Hugging Face and talk to model x. This isn't unpredictable.

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.

Wrong, it's a slight of hand. This is not about individual cameras, this is about the network that uses all cameras together to follow you no matter what you do, no matter where you go, people shouldn't have a government that follows their every step by using AI and networked devices. If this was about individual cameras you would have a point, it is not, it is unreasonable to allow a government to exist that creates a panopticon of this scale, citizens shouldn't feel like they are inside a prison and the warden is watching.

Im intimately familiar with range-payload charts.

You have to take my sentence as a whole - I say that its both low density and premium heavy. The low density is because of the range-payload drop off, the premium heavy is because Qantas will make more money with the premium cabin, so thats where the bulk of the weight allowance goes.

The airlines reputation isnt going anywhere, DVT is a known risk on a lot of long haul currently in operation, yet you dont hear anything about airlines getting negative PR about it. This route will be no different. Its a bit longer, but not massively.

Qantas already have it set at around 40% premium seating, with around 140 economy or economy plus seating at 33 inch pitch, and 98 premium seats (first, business and premium economy).

Its a low density, premium heavy cabin precisely because thats what the route demands - they have structured it around what premium seating they can fill, and what economy they might fill. Its no secret that airlines make almost all of their passenger profit from premium seats, and economy is often just about break even, so theres no point in cramming more economy in for this route.

https://www.qantas.com/en-nz/o...