oivey

Will the intelligent agents be plugging the infected USB sticks in?

Bonkers to call college graduates deadbeats. These aren’t addicts or slackers. They had to have some level of achievement their whole lives and managed to finish a degree.

Getting on disability is insanely difficult and pays jack shit. That is not a US equivalent by any measure.

Excess solar power generated by ordinary consumers is probably being priced correctly

Do you have any evidence for this position? Is this just regulations giving you bad vibes? I’m pretty sure everyone was quite aware the sun doesn’t shine at night whenever the previous rules and regulations were written. Your analysis isn’t breaking new ground.

No? Christianity, for example, has changed massively over time. You can pick any denomination, and it’s true.

You can also look at new religions, denominations, or sects popping up. The purpose of religion is at its core supposed to be spirituality.

At a certain point, grades become arbitrary and won’t necessarily select for the best candidates. Obviously the current system doesn’t, either.

The actual solution is to increase the number of slots for training doctors to match the huge number of qualified applicants. It makes even more sense given that there is a shortage of doctors and health care costs are astronomical.

It’s free, but it’s not like they’re running Gmail as a charity, either. It has revenue and contributes to their other businesses.

Even that is arguably not lucky, it just followed a non-obvious trajectory. Graphics uses a fair amount of linear algebra, so people with large scale physical modeling needs (among many) became interested. To an extent the deep learning craze kicked off because of developments in computation on GPUs enabled economical training.

Again, the old car comparison is demonstrably untrue. To put the same example forward, computer modeling has wildly changed and accelerated car design in ways that were impossible for any sum of money in the 70s.

I think part of why this is hard to believe is that people strongly believe in the concept that time is money. On the margins for decisions like hiring someone to mow your lawn, it is true. For large scale things, you often cannot accelerate processes no matter how much money you dump into it. A good example of this is how long it has taken China to industrialize.

To be clear also, you have to prove your point that #2 is outpacing #1. The fact that the price keeps going up is not proof as there are other explanations. The poor quality of domestic manufacturers and their bad business practices, for example.

You are getting a multi-million dollar truck for $50k.

You’re not, though, because that truck never did and never could exist. A modern F-150 isn’t a 70s F1 car made cheap by new tech. This isn’t something you can wave away with an argument equivalent to “we put 1000 research points in the tech tree.”

When the US economy was working well, products got better and cheaper over time. Tech and increased labor productivity drove that. Now, tech and labor productivity has continued to increase, yet consumer prices have far outpaced inflation.

I don’t think this is true. Advancements in technology often make things possible that previously were not at any price. Engines, for example, are better than ever in part due to computer modeling that would have been impossible in the 70s. Same deal with aerodynamics, safety features, and a million other things. In the 70s, you couldn’t have those things for any price. They required decades of development in other sectors to open possibilities for automobiles.

That is a low n, but I’m not sure what the alternative is. Surely random anecdotes (n=1) are even less powerful?

That’s a strange comparison to make. Those are entirely different sectors and sorts of engineering projects. In this example, also, SpaceX built all of that on Earth.

Why not do the obvious comparison with terrestrial data centers?

Lockheed, Boeing, Northrop, Raytheon, and all the others are private companies, too. NASA and others generally go through contractors to build things. SpaceX is on the dole just like them.

The satellite is built on Earth, so I’m not sure how it dodges any of those regulations practically. Why not just build a fully autonomous, solar powered datacenter on Earth? I guess in space Elon might think that no one can ban Grok for distributing CSAM?

There’s some truly magical thinking behind the idea that government regulations have somehow made it cheaper to launch a rocket than build a building. Rockets are fantastically expensive even with the major leaps SpaceX made and will be even with Starship. Everything about a space launch is expensive, dangerous, and highly regulated. Your datacenter on Earth can’t go boom.

A truck is sluggish because of its weight and inertia. It’s a law of nature. What law of nature is making Gitlab slow?

Besides what all these other commenters are saying, probably many of the people running these small lunch shops in Japan are the owners, not waged employees. On top of that, that business probably isn’t viable for 8 hours per day.

If you were open every day of the year and assume no seasonality, that means your first 49 orders every day go just to regulatory fees.

This looks crazy because it is incorrect. In your premise, that 9% profit margin includes the regulatory costs for a brick and mortar restaurant already. The only way your logic works out is if truck regulations are on average $30k more expensive than a regular building, which they almost certainly are not.

You can’t even begin to do the calculation without knowing the breakdown underlying the profit margin you cite.

$250k per house is an absolutely insane level of mismanagement.

I'm not sure it's common knowledge, but it is general knowledge. Not all HNers are writing web apps. Many may be writing truly compute bound applications.

In my experience writing computer vision software, people really struggle with the common sense of how fast computers really are. Some knowledge like how many nanoseconds an add takes can be very illuminating to understand whether their algorithm's runtime makes any sense. That may push loose the bit of common sense that their algorithm is somehow wrong. Often I see people fail to put bounds on their expectations. Numbers like these help set those bounds.

No one is suggesting “wasting time on non problems.” You’re tilting at windmills.

These are the metrics underneath it all. Profiles tell you what parts are slow relative to others and time your specific implementation. How long should it take to sum together a million integers?

It isn’t what you said. If you want, you can write your own matmul in Numba and it will be roughly as fast as similar C code. You shouldn’t, of course, for the same reason handrolling your own matmuls in C is stupid.

Many problems can performantly solved in pure Python, especially via the growing set of tools like the JIT libraries I cited. Even more will be solvable when things like free threaded Python land. It will be a minority of problems that can’t be, if it isn’t already.

How is it not general knowledge? How do you otherwise gauge if your program is taking a reasonable amount of time, and, if not, how do you figure out how to fix it?

People generally aren’t rolling their own matmuls or joins or whatever in production code. There are tons of tools like Numba, Jax, Triton, etc that you can use to write very fast code for new, novel, and unsolved problems. The idea that “if you need fast code, don’t write Python” has been totally obsolete for over a decade.

It’s a nice and productive language. Why is that incomprehensible?

The core libraries to me include all the numerical and other major scientific computing libraries. I’m guessing those were laggards due to things like that string/byte change and probably changed to the CPython API.

Did you ever look into why the transition took so long for OAuth libraries? Did you consider just rewriting one yourself?

Virtually all the graphics? Modern computers are very fast.

I think we essentially agree. My comments about maintainers wasn’t referencing the Python language maintainers. The print change certainly shouldn’t have blocked anyone.

More interesting is how long it took core libraries to transition. That was my primary blocker. My guess is that there were fairly substantial changes to the CPython API that slowed that transition.

Other changes to strings could be actually dangerous if you were doing byte-level manipulations. Maybe tools could help catch those situations. Even if they did, it took some thought and not just find/replace to fix. The change was a net benefit, but it’s easy to see why people might be frustrated or delay transition.

You can rivet people onto the outside of the ISS to radiate heat, too, but it may be detrimental to the overall system.