korin43
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I think the problem with this is that markets are a complicated and highly inefficient tool for coordinating resource consumption among competing individuals without needing an all-knowing resource-allocator. This is extremely useful when you need to coordinate resource consumption among competing individuals, but in the case of programming, the functions in your program aren’t really competing in the same way (there’s a limited pool of resources, but for the most part they each need a precise amount of memory, disk space, CPU time, etc. and no more and no less).
There also is a close-enough-to-all-knowing resource allocator (the programmer or system administrator). The market model actually sounds like a plausibly-workable way to do profiling, but it would be less overhead to just instrument every function to report what resources it uses and then cental-plan your resource economy.
In short, if everyone is a mindless automaton who takes only what they need and performs exactly what others require of them, and if the central planner can easily know exactly what resources exist and who wants them, then central planning works fine and markets are overkill (at least in the sense of being a useful tool; capitalism-as-a-moral-system is out-of-scope when talking about computer programs).
Note that even in cases like Amazon Web Services, the resource tracking and currency is just there to charge the end-user. Very few programs take these costs into account while they’re executing (the exception is EC2 instance spot-pricing, but I think it’s a stretch to even call that agoric computing).
Also, one other thing to consider is that agoric computing trades off something really, really cheap (computing resources) for something really, really expensive (programmer time). Most people don’t even bother profiling because programmer time is dramatically more valuable than computer parts.
The thing I don’t understand is how the market got (and stays) this way. Slice successfully created a new (much lower margin) service for this. Why is everyone else putting up with 30% fees on something that’s trivial to replace? For example, why aren’t all of the businesses using ChowNow?
Presumably part of this is that some ordering systems get top billing in places like Google Maps, but given that Google Maps seems to show every order system under the sun, it can’t be *that* hard to get a new one in there.
Also that article seems to equivocate between services like UberEats that provide their own delivery drivers and are plausibly worth paying a large fee to and services like GrubHub that are just online order systems and could presumably be trivially replaced.
Looks like you can watch the game vs TLO here: https://www.youtube.com/watch?v=DpRPfidTjDA
I can’t find the later games vs MaNa yet.
Haha writing my comments was way easier since you already covered the hard parts in the article so I can just make short comments about the few places where I disagree.
I feel like this article is more optimized for European / conservative US fashion. In most of the places I’ve lived in the US, you could follow basically the same rules but go significantly more casual. For example, you still want to get basically the same colors, material, logos, etc. but get jeans, t-shirts, and (maybe) nice-looking hoodies instead of button-up shirts, chinos and sweaters.
I think shirts like this could help your status within small subcultures. I think the article is more about how to dress to maximize status for the overarching culture. Depending on your goals it could plausibly be worth it to optimize for a subculture instead, although I think the cases of that are probably uncommon (since most subcultures are fine with normal fashion too).
I upvoted this article because the general advice is very good, although I disagree with most of the specific advice (the brands, which pieces of clothing are most important). Fancier companies are generally nice in ways that have nothing to do with fashion (nicer materials, more comfortable). Pretty much any brand works fine if you can find the right fit and colors. Although you may need to explore multiple brands to find clothes that fit you, it doesn’t mean you have to go straight to expensive clothes. I can’t find anything that fits me at Walmart but everything at Target does, and they’re very similar prices.
I started wearing relatively expensive clothing in the last few years, but it’s entirely for reasons that aren’t obvious visually (jeans with a very slightly stretch around the waist are a lot more comfortable, wool shirts dry quickly and don’t smell bad after physical activity).
Please link more of your posts here. I looked through the history on your blog and there are quite a few that I think would be relevant and useful for people here. In particular, I think people would get a lot out of the posts about how to make friends. Some other other posts have titles that look interesting too but I haven’t had time to read them yet.
I wonder if it’s just the field I’m in, but this doesn’t match what I’ve seen as a software engineer. Companies frequently retroactively create opening if someone good enough applies (I’ve seen this happen at every company I’ve worked at, and it’s the official policy at my current company).
I also don’t think the people in charge of hiring care that much about salary (they don’t want to pay more than they need to, but realistically, how good someone is and how long they’ll stay at a company matter a lot more). Part of it is that the pool of qualified applicants is much smaller than most people think so the situation of deciding between two (good enough) candidates for one opening is rare (it has never happened to me).
I’ve been using Standard Notes. It’s basically just a networked text editor which can display structured text nicely.
I know it’s kind of a weird thing for this post to do, but this one finally gave the push I needed to setup decent journaling software, so I can do better planning, and also have something to reference in daily stand-up meetings instead of trying to come up with a summary of the previous day on the spot.
Not sure why this got voted down so badly, but I can’t get the link to work. Maybe you missed something when posting it?
This seems to be conflating two completely different phrases that use the word security. Security mindset has nothing at all to do with working for a government agency or being a spy. It’s a similar concept to “antifragility” except that you’re assuming that bad things don’t just happen by chance.
Wouldn’t this just push the problem back, so everyone would fight over Phd programs so they can get a guaranteed income? I imagine this would select for people who are good at impressing schools over people who are good at research.
The purpose of this post is not to argue these claims in depth but to summarize the Critical Rationalist view on AI and also how this speaks to things like the Friendly AI Problem.
Unfortunately that makes this post not very useful. It’s definitely interesting, but you’re just making a bunch of assertions with very little evidence (mostly just that smart people like Ayn Rand and a quantum physicist agree with you).
I don’t know much about the specific goal you’re working on, but my experience with CS has been that the best way to learn is to work on real problems with people who know what they’re doing. I’ve learned significantly more from my internship and jobs than I did in school, and that seems to be pretty common. Rather than trying to design a curriculum, I’d advise trying to find someone doing what you’re interested in and get a job/internship/apprenticeship working with them. After you’ve done that for a few years, I suspect you’ll know what you’re not getting out of the current deal and can either go off on your own or find a different set of teachers.
I feel like you may have gone too far in the other direction then, since what I got out of this was definitely “there wasn’t any evidence for heliocentrism and people just liked it better for philosophical reasons”. As far as I know, the standard science education explanation for heliocentrism involves newtonian physics, observations that people weren’t able to at this time (like you said, Tycho tried), and hindsight.
Can you expand on what the evidence that should have convinced people was? I feel like this article is a puzzle that’s missing key information.
Why would a human withdraw from the account but an AI wouldn’t? It seems like you’re assuming either:
The correct decision is to not withdraw the money. No human could be smart enough to figure this out, but an AI would. Are you an AI?
The correct decision is to withdraw the money, and the AI is stupidly not doing it. Why is the AI stupider than a human?
I suppose Bitcoin’s wastefulness would be a good cover for an AI wanting to use a bunch of computers without making people suspicious. I doubt it’s the fastest way a super intelligent AI could make money though.
You explain why new researchers would want to join, but why would top researchers want to? It seems like they lose money and time in exchange for that warm feeling you get when helping people. Would that be enough?
In terms of legality, worker owned corporations exist, but I suspect it would be hard convincing people to give unrestricted funding to the corporation (I think most government grants are fairly specific about what you can spent the money on?).
My (outsider) perspective of the field is that private funding for academic style research is uncommon, and generally involves the funder directly hiring the researchers, which seems to have some things in common with what you’re saying (although since the researchers typically doesn’t own any portions of the organization, they have presumably have fewer incentives to mentor other people).
If non academic research counts (researching something so you can build a product), then a think something similar to what you’re proposing happens in some parts of the startup scene. For example, a group of people get together with an idea for a new product, start a company, research how to create/improve the product. Once the company transitions from solving scientific/technical problems to solving organizational problems, the founders leave and join or found new startups. The main difference here is that it’s a short term cycle instead of a long term commitment, but that doesn’t seem to stop people for providing mentoring.
When dealing with resources on the internet, you’re running into the “trading off something cheap for something expensive” issue again. I could *right now* spend several days/ weeks write a program that dynamically looks up how expensive it is to run some algorithm on arbitrary cloud providers and run on the cheapest one (or wait if the price is too high), but it would be much faster for me to just do a quick Google search and hard-code to the cheapest provider right now. They might not always be the cheapest but it’s probably not worth thousands of dollars of my time to optimize this more than that.
Regarding writing a program to dynamically lookup more complicated resources like algorithms and data.. I don’t know how you would do this without a general-purpose programmer-equivalent AI. I think maybe your view of programming seriously underestimates how hard this is. Probably 95% of data science is finding good sources of data, getting them into a somewhat-machine-readable-form, cleaning them up, and doing various validations that the data makes any sense. If it was trivial for programs to use arbitrary data on the internet, there would be much bigger advancements than agoric computing.