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Gray-market operations usually use crypto for transactions, e.g. distributors of estradiol for trans HRT. Gray market is cheap and doesn't depend on the political climate. Quality control is a problem, though.

I realize the morality of such a thing is subjective, so I'll just say that I've known a number of individuals, especially minors, who have relied on gray market sources.

Amazon pays $20/hour because most people don't have the mental constitution to put up with it. It's pretty dull.

When I was hired as a part-timer (no interview!) at a warehouse there were about a dozen of us. In a month maybe four were left. It wasn't terribly physically difficult, though I was fairly sore. But there was nothing good about the work.

It was just repetitive, isolated tasks where you have to consciously decide not to meet the unrealistic standard, or just blindly follow the orders on the device, or guess how fast to work to avoid a strain injury while not being obviously slow.

To give some examples, we'd take bags (totes) from specific locations as indicated on our device, pack them on a large push-cart, and shuttle them halfway across the warehouse. Or we'd pick packages for a specific location off a conveyer belt (which usually moved too fast). Or we'd spend 5+ hours, alone, unfolding bags and putting them back onto the shelves.

There's probably some analogy to be made with software development here, based on what I've heard, but I'm only a CS student.

As someone who just converted a bicycle to ebike (via hub motor kit installation), it's not really that hard to do a traditional conversion.

The worst part was filing down the metal brake assembly so it didn't rub the hub motor on the rear wheel. I assume that was a very unique compatibility issue. Besides that, it was no more difficult than assembling a computer, just a bit of screwing and wrenching which any bike owner would be able to do (add in some spitting and cursing if you've never worked on a bike before, as I hadn't). Done in a weekend.

The second hardest part was figuring out what to buy. The most straightforward option is a full hub motor conversion kit, which should work for any commuter application. Trying to individually buy parts (likely from aliexpress) is cheaper but a hassle to coordinate, since there's no standard for ebike part compatibility. Then there's also the choice between PAS and torque-sensing, throttle, etc.

I ended up buying the minimal throttle conversion kit from Grintech (~$1200 total) and installing it on a used hardtail mountain bike I got for $200. The bike goes ~26mph on a 48V battery, 30mi range, works like a charm.

Verification is sometimes less conceptually difficult than solving. I'd say for most well-defined problems, verifying is simpler.

E.g. finding a general solution for a cubic polynomial is difficult. Proving that a solution is correct is conceptually trivial: substitute a solution for x, and simplify. Many mathematical problems are well-defined in this way.

In the case of a compiler (CompCert), the program is already, in part, being written according to the language spec. So that definition can be used in verifying a compiler. In a domain where there is no standard specification or required properties, then coming up with a spec is hard (probably as hard as coming up with a solution).

Do you have any objective data for this? In my experience, most subjective claims about men vs. women are unresolvable as either true or false. At best, the discussion degenerates into a Jung-like discussion of anima and animus.

"All of humanity's problems stem from man's inability to sit quietly in a room alone." - Blaise Pascal

Translations vary slightly.

AlphaStar (2019) refined through self-play but was initially trained on human data. I don't know of any other high-level Starcraft AI, but if you do let me know.

LLMs read and write human-code because humans have been reading and writing human-code. The sample size of assembly problems is, in my estimate, too small for LLMs to efficiently read and write it for common use cases.

I liken it to the problem of applying machine learning to hard video games (e.g. Starcraft). When trained to mimic human strategies, it can be extremely effective, but machine learning will not discover broadly effective strategies on a reasonable timescale.

If you convert "human strategies" to "human theory, programming languages, and design patterns", perhaps the point will be clear.

But: could the ouroboric cycle of LLM use decay the common strategies and design patterns we use into inexplicable blobs of assembly? Can LLMs improve at programming if humans do not advance the theory or invent new languages, patterns, etc?

> Can you sit down with an unfamiliar domain and develop enough genuine curiosity to get good at it, without a syllabus or a credential dangling in front of you?

Do I have faith that I'll be compensated according to my developed ability?

Looking broadly at the recent past, the correct answer seems "no".

I've known many people who met through games. They offer something similar, in the sense that you can meet new people and learn about them.

The synchronous nature of multiplayer games leaves most of this expression implicit rather than explicit, though, so for some people it doesn't fit the same need. It's a kind of role-play.

I think most people are, for lack of a better metaphor, blood-sucking vampires for honest, explicit, and carefully-crafted communication. People are pleased when I offer it, but they struggle to offer it back, so I learn to not bother. Most relationships degenerate into expressing things better left unsaid, or being entirely superficial.

A case study of myself as an overeager math student:

I used to focus so much on finding "elegant" proofs of things, especially geometric proofs. I'd construct elaborate diagrams to find an intuitive explanation, sometimes disregarding gaps in logic.

Then I gave up, and now I appreciate the brutal pragmatism of using Euler's formula for anything trigonometry-related. It's not a very elegant method, if accounting for the large quantity of rote intermediate work produced, but it's far more effective and straightforward for dealing with messy trig problems.

Agreed. I think the divide is between code-as-thinking and code-as-implementation. Trivial assignments and toy projects and geeking out over implementation details are necessary to learn what code is, and what can be done with it. Otherwise your ideas are too vague to guide AI to an implementation.

Without the clarity that comes from thinking with code, a programmer using AI is the blind leading the blind.

The social aspect of a dialogue is relaxing, but very little improvement is happening. It's like a study group where one (relatively) incompetent student tries to advise another, and then test day comes and they're outperformed by the weirdo that worked alone.

Writing may not be produced for the prestiege of its result, but written words still serve an essential purpose for communication. I think that, as with any essential art, e.g. cooking, people will experiment with it to fit their needs.

Writing is also peculiar in that it is easily referenceable with a deep history, so it serves as a way to compare one's own ideas to others. Memes are similar in principle, but tend towards esotericism and ephemerality in a balkanized internet.

I prefer a more direct formulation of what mathematics is, rather than what it is about.

In that case, mathematics is a demonstration of what is apparent, up to but not including what is directly observable.

This separates it from historical record, which concerns itself with what apparently must have been observed. And it from literal record, since an image of a bird is a direct reproduction of its colors and form.

This separates it from art, which (over-generalizing here) demonstrates what is not apparent. Mathematics is direct; art is indirect.

While science is direct, it operates by a different method. In science, one proposes a hypothesis, compares against observation, and only then determines its worth. Mathematics, on the contrary, is self-contained. The demonstration is the entire point.

3 + 3 = 6 is nothing more than a symbolic demonstration of an apparent principle. And so is the fundamental theorem of calculus, when taken in its relevant context.

I think that humans can find new frontiers to struggle on and develop mental faculties for, even if the prior frontiers are solved.

"Problem-solving" might be dead, but people today seem more skilled in categorizing and comparing things than those in the past (even if they are not particularly good at it yet). Given the quantity and diversity of information and culture that exists, it's necessary. New developments in AI reinforce this with expert-curated data sets.