Google Maps is a shitshow if you try to use it for anything other than the Yellow Pages.

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.

There is an old technique - the guaranteed winner by random chance:

You get 32 stock brokers. On Monday the boss picks 2 stocks. 16 say that stock is going up, 16 say down, and they each call 100 people and give their prediction. That evening the 16 that were 'right' call their 100 back and brag. Tuesday they repeat only with 8 on each side. Repeat on Wednesday with 4 winners. Thursday they have 2 winners. Friday they have 1 winner that brags to his 100 prospects:

"Look, I happened to predict the winner every day this week. I can not guarantee that will happen every day, but if you want to hear my predictions next Monday, I need you to move your account to my firm."

And the gullible fool does it, not knowing that mathematically the predictions were guaranteed to work on 1 of the 32 brokers. Just math, not competence.

Betting on AI was not some super genius move - especially using margin. All it involved was taking the popular opinion and going all in. While it worked you look like a genius. When it fails, you lose everything.

He just played the odds and won for a while. But the math was never going to have him win forever.

10% drop is large but not horrific. This is basically normal variation, not something real.

Apple is one of the "FANG" stocks - Facebook, Apple Netflix, Google. They are huge companies that have had major gains as compared to most other stocks. Over time some people just bought those stocks.

This drove the prices of their stock up a lot from greedy people The current drop is just undoing some excess greed, not really that big a deal.

On 7/31/2026, it hit a low of 300. Before 7/31/2026,. the last time Apple was at 300 was 7/2/2026

In other words, it gave up less than a month of gains.

This is not that big a deal - yet. I would be shocked if it dropped to 272. Why? Because that is just below the previous 'low point' on 6/25/26. Going below that number not only indicates a significant drop, but it established a trend of new lows being less than the last lows.

Yes. That's exactly what I meant. I definitely did not misspell 43 as 53 because I was touch typing.

Electrical vs ICE does not affect New vs used. It's not higher gas prices.

The main reason used EVs are not losing value as much as they did in the past is that the fear of used electrical batteries no longer being as strong.

Previously people were extremely worried that EV batteries were going to quickly lose their charge. There were concerns that older batteries could not be trusted. They thought a used battery was a liability. Worse, everyone kept talking about how the NEXT generation of battery would be so much better. For this reason a lot of people would rather get the newest technology which meant new EVs rather than used ones.

Over time we have found that batteries from used EVs stand up pretty well. Moreover, most people now think that 'new' battery technology is likely to be used for things besides cars (i.e. home back up) so the new cars use the same old batteries.

Combined with the fact that EVs generally have less maintaince issues, people have begun to realize that used EVs are a pretty good deal.

Did not know that. Thanks. But fighting online piracy was only the pretext; the real goal was the usual expansion of government (and thus their) power.

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.

Seven out of 50 is remarkably small. I am shocked they did not get more.

Now, I guarantee you, those affected will realize that the cybersecurity are not joking around. They will improve security.

And about a year later their new security protocalls will spread to the other 53 states. By the time the next group tries this crap, it will be much harder to do.

Those illegal narcotics are physically in New York right now. Are the internet servers?

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.

There is a story about how dealer plates have small numbers on them that Flock cameras do not see. The system then accuses people of car theft.

They are proven as not reliable. If I were a judge I would rule that the police must treat Flock camera information as an unreliable, anonymous source. No drawing guns on people tracked or accused because of Flock information and their info cannot be used to obtain a warrant.