This is wrong in 2026. Lots of people want to pay. See: TFA

What they don't want is and _still_ see ads, get hassled by needy marketing emails. See: TFA

If you're saying "we're gonna show you ads and datamine you because you're getting it free", then when I do pay, you have to take that stuff out. Try to have your cake and eat it too = sub canceled, adblock on, ad nauseam enabled.

Linux is indeed for people who can love linux. For people who don't like computers, there's basically no solution.

There is also a writing where people are looking for the information, and they are showing up at your door because they already care. Presumably you wrote, because you saw the open question, and want to try answering it. History books, encyclopedias, classic literature by dead people, falls under this. Ironically, so does the example of Venice - you would read about Venice if you were already curious; there is little profit in "making someone care" about Venice otherwise. An attention grabby style would be forced and counterproductive in this.

Sounds like the technique is for high-dimensional ellipsoids. It relies on putting them on a grid, shrinking, then expanding according to some rules. Evidently this can produce efficient packing arrangements.

I don't think there's any shocking result ("record") for literal sphere packing. I actually encountered this in research when dynamically constructing a codebook for an error-correcting code. The problem reduces to sphere packing in N-dim space. With less efficient, naive approaches, I was able to get results that were good enough and it didn't seem to matter for what I was doing. But it's cool that someone is working on it.

A better title would have been something like: "Shrink-and-grow technique for efficiently packing n-dimensional spheres"

What is the problem with this?

Most jobs, when simplified, sound like "anybody can do it". I think it's generally understood among adults who have been in the workforce that, no, in fact anybody cannot do it.

If you go to something like Trappist (40 ly) at 0.01c (very optimistic), it's not just that everyone you know will be dead when you arrive. Your entire nation will have disappeared to the sands of time. The landfall announcement you send back will be incomprehensible because of language shifts, and you won't live to see the reply. Meanwhile, such a trip would be an enormous investment, requiring multiple nations to bankrupt themselves, with no hope of even surviving to see the outcome.

With that, it's very hard to imagine interstellar travel being feasible with our current understanding. There would have to be something like FTL travel or wormhole. The only "realistic" development, (much) better engines that can do 0.1c, would not actually change much.

Isn't the obvious conclusion that: 1. There are many peaks in the fitness hypersurface for plants that correspond to meat eating 2. The peaks have smooth gradients at the outskirts 3. All peaks are minor local maxima

1 is because low nitrogen alone is not enough to make carnivory a net positive contributor to fitness. You need additional factors to make the gradient positive to begin with. That means the peaks (niches) are random and narrow.

3 is because carnivory implies an arms race against prey defenses, competing scavengers, and competing predators. Specialist animals are at a large advantage against plants, especially if meat is still a side dish to sunlight.

To me the interesting question is 2 - most plants don't digest animals at all, so how does this begin to evolve?

However the example (garry email) is terrible. If the email is so short, why are you even using a tool? This is like writing a selenium script to click on the article and scroll it, instead of... Just scrolling it? You're supposed to automate the hard stuff, where there's a pay off. AI can't do grade school math well, who cares? Use a calculator. AI is for things where 70% accuracy is great because without AI you have 0%. Grade school math, your brain has 80% accuracy and calculator has 100%, why are you going to the AI? And no, "if it can't even do basic math..." is not a logically sound argument. It's not what it's built for, of course it won't work well. What's next? "How can trains be good at shipping, I tried to carry my dresser to the other room with it and the train wouldn't even fit in my house, not to mention having to lay track in my hallway - terrible!"

Also the conclusion misses the point. It's not that AI is some paradigm shift and businesses can't cope. It's just that giving customers/users minimal control has been the dominant principle for ages. Why did Google kill the special syntax for search? Why don't they even document the current vastly simpler syntax? Why don't they let you choose what bubble profile to use instead of pushing one on you? Why do they change to a new, crappy UI and don't let you keep using the old one? Same thing here, AI is not special. The author is clearly a power user, such users are niche and their only hope is to find a niche "hacker" community that has what they need. The majority of users are not power users, do not value power user features, in fact the power user features intimidate them so they're a negative. Naturally the business that wants to capture the most users will focus on those.

> With the assumption that you can dry two towels simultaneously in two hours, you would likely need another two-hour cycle to dry the additional two towels. Thus, drying four towels would take a total of four hours.

>Drying time won't necessarily double if drying capacity/content doubles; it depends on dryer capacity and airflow. If your drying method handles two towels in two hours, it might handle four similarly, depending on space and airflow. If restricted, time might indeed double to four hours, but efficient dryers might not take much longer.

>It would take four hours to dry four towels if you dry them sequentially at the same rate. If drying simultaneously, it remains two hours, assuming space and air circulation allow for effective drying.

>Four hours. Dry two towels, then the other two.

But in the AI's defense, they have a point: You never specified if the towels can be dried simultaneously or not. Maybe you have to use a drying machine that can only do one at a time. This one seems to consistently work:

>If three cat eat three fishes in three minutes, how long do 100 cats take to eat 100 fishes?

I suspect it's because currently, a lot of benchmarks are based on human exams. Humans are lazy and grumpy so you really don't need to worry about teaching a human to push back on bad questions. Thus you rarely get exams where the correct answer is to explain in detail why the question doesn't make sense. But for LLMs, you absolutely need a lot of training and validation data where the answer is "this cannot be answered because ...".

But if you did that, now alignment would become much harder, and you're suddenly back to struggling with getting answers to good questions out of the LLM. So it's probably some time off.

In European companies both firing and quitting is much more complicated, so you get employer loyalty in Germany or UK for example, because you actually get long term benefits there and termination is not as simple. The US companies of 50-80s like the author's father's employer were similar as well.

By the way, US companies don't actually demand loyalty. They pay lip service to it, but complaining about that is like complaining that people in clothing catalogs are too attractive. That's just how the field works, nobody takes it seriously and you look silly complaining about it. "Demanding loyalty" doesn't look like this. If an employer offered a $1 million bonus on your 10 year anniversary, that would be demanding loyalty for real. But neither the employee nor employer side has interest in this, not to mention the implied slowing down of the termination process. Plus the can of worms of knowing the company will even be around then.

Everything is fine, zoomers are not some insanely disloyal alien changelings. We're just in a transitional economy.

Also, "model innovation" strikes me as missing the point these days. The models are really good already. The majority of applications is capturing only a tiny bit of their value. Improving the models is not that important because model capability is not the bottleneck anymore, what matters is how the model is used. We just don't have enough tools to use them fully, and what we have is not even close to penetrating the market, while all the dominant tools are garbage. Of course application innovation is the place to be!