The thing I bounced off isn't the high-concept art, or the abstract math. It’s the combination of the two without enough bridge between them. You have to infer too much about how the poetic layer, the mathematical notation, and the actual machinery relate.
You can do mind-expansion by induction in a math journal. This is not that venue. And this project is too good to waste by letting people walk away confused.
I’d love a very plain “one clock, end to end” walkthrough: primitives, composition, graph, rendered result.
Outside the US, broadband providers are still huge but differences in regulation mean there are five or six of them in most markets.
Overall, I think my point still stands: claiming that you can't build a giant company without Silicon Valley's growth formula is ridiculously narrow. The vast majority of big companies are built by other methods.
And many more who sell crops, chemicals, power, machine parts, cars, concrete...
Many companies have billions of customers and no trace of lock-in or virality at all. You might want to shift your perspective a little and remember that there is a world outside of Silicon Valley.
Unix famously comes with a lot of documentation, but it's still famously hard to learn. Why?As a teacher and cognitive scientist, the reason is interesting: Unix documentation, starting with its man pages, is presented in a format that makes it easy for experts to look up details, not for beginners to learn concepts or build skills. O'Reilly, Sam's, APress and others have built large...
It is true that there are some corners of the internet where probably-healthy people meet to discuss what it's like to have disorders that they probably don't have, or in some cases disorders that might not even exist.
But you really ought to talk to some actual doctors and patients before you conclude that their problems aren't real, or can be overcome by pretending they don't exist.
Which is good, because Mercury is mostly metal, whereas e.g. Mars is mostly rock and thus not useful for this kind of work
The biggest per-student spender on education in the developed world is the US:
> In 2019, the United States spent $15,500 per full-time-equivalent (FTE) student on elementary and secondary education, which was 38 percent higher than the average of Organization for Economic Cooperation and Development (OECD) member countries of $11,300 (in constant 2021 U.S. dollars). At the postsecondary level, the United States spent $37,400 per FTE student, which was more than double the average of OECD countries ($18,400; in constant 2021 U.S. dollars).
https://nces.ed.gov/programs/coe/indicator/cmd/education-exp...
And everybody knows that US outcomes in primary and secondary education are not great. (Actually they're considerably better than many realize, but not as good as, say, Finland, which spends much less). So budgets aren't everything.
And yet, at both the top and in the broad middle, US universities have some of the best outcomes in the world. So big budgets can still be good.
Human brains are complicated. Big groups of them are more so. Transferring output of some brains to other brains in big groups is not at all straightforward.
It's no wonder people would rather swap platitudes about bigger budgets.
If you read PAIP today, the most likely reason is that you want a master class in Lisp programming and/or want to learn a lot of tricks for getting good performance out of complex programs (which used to be part of AI and is in many ways being outsourced to hardware today).
None of this is to say you shouldn't read PAIP. You absolutely should. It's awesome. But its role is different now.
VS Code's goal is to get the low-effort 30% of devs who want something that will just work right out of the box, while providing enough functionality/customization to attract a significant fraction of the remaining 70%. And given that, it's pretty good.
But I'm skeptical it will ever be as good for someone who does want to make the investment in something like Emacs.
That's the kind of thing people say when math tells them what they want is not feasible. Surely the math must be wrong!
At least wait for some corroborating evidence to come in before you start writing policy, because you'll hurt people if you get it wrong.
It isn’t precise. But back in the real world, the “chemical imbalance“ explanation serves the very important purpose of explaining to a lot of ordinary people who otherwise wouldn’t understand that bipolar people who are manic or depressed are not in control of what they do. And that’s important, because when they’re manic or depressed, bipolar people often act like bad people.
This is false. I don’t know what this author was doing. Even reading Wikipedia would give you a better understanding than that.
Initial treatment for patients presenting with acute mania are antipsychotics like Risperdal or Geodon. After that they might try mood stabilizers like lamotrigine or seroquel, or any number of other things that are much more recent than lithium.
If you want to learn more about the wild world of psychopharmaceuticals, I recommend crazymeds.com, which also tells you a lot about how mentally ill people often view themselves. It’s fascinating even if you’re not crazy.
Soon you'll be dead, and everyone you love, everyone you know. And everyone who knows them, and everyone who knows them, and so on on down the line. And not just dead. Forgotten. Completely.
Does that change your priorities in life at all?
I take two things away from this exercise.
1. Try to be happy in the moment.
2. Try to build things that will outlast you. Nobody remembers the names of the architects of the Roman aqueducts, but people in Italy still benefit from their work today.
The problem is that it only covers half of what you need to know in linear algebra. What about the Spectral Theorem? What about positive definite matrices?
I saw enough books on the list that I recognized and benefited from to convince me that the author has some idea of what they're talking about. That's enough to convince me that some of the other books on the list might be worth checking out.
Once you have a book title in mind, there are lots of resources you can use to find out whether it's worth reading -- Amazon comments, book reviews, the publisher's website, the table of contents, published excerpts, etc etc etc.
The most important part of a book search on the internet today is title discovery: picking one book to look at out of the gazillion books available. The author seems to have taken a pretty good stab at that.
But if you want to really appreciate macros, you'll need to read some books. There's a lot to learn.
My introduction to lisp was ANSI Common Lisp and On Lisp by Graham. On Lisp is all about macros. You need ACL to understand On Lisp. What you learn is pretty impressive -- I seem to recall one of the later chapters of On Lisp features a compiler for Prolog in two and a half pages of code -- but it requires a certain amount of supporting material. Still, if you want the most direct route to understanding (some of) lisp's greatness, these two would be it.
Another possibility is Paradigms of Artificial Intelligence Programming by Norvig. This one teaches you Common Lisp in the introduction, but I'm not sure it's enough by itself for you to really understand some of the later chapters. If you're prepared though (read ANSI Common Lisp first), this book is a gem. It's less about AI than about transforming and optimizing programs. So, code as data.
You might also look at Practical Common Lisp by Siebel. I haven't read this, but a lot of people liked it, and the code is very real-world (a little dated now, though). It's available online here: http://www.gigamonkeys.com/book/
In general, you wind up learning a couple of different lisps. Common Lisp and Scheme have the best literature, but the lisp that's most in use today is Clojure. Clojure's macro system is a refinement of Common Lisp's. For learning clojure there are a lot of teach-yourself-X-in-21-days type books. The best of them is the O'Reilly book: https://www.oreilly.com/library/view/clojure-programming/978...
Scheme doesn't (always) have macros, but I'd be remiss if I didn't suggest something. The Schemer books are some of the most effective pedagogy I have seen on any subject: The Little Schemer, The Seasoned Schemer and The Reasoned Schemer. They are very cute, but don't let that fool you. They get hard (in TLS's case maybe too hard) at the end.
Finally, there is one of the most important CS books of all time: The Structure and Interpretation of Computer Programs (SICP). It will change the way you think about programming forever. It also explains some important details about how lisp works, and it's so definitive that a lot of them aren't covered elsewhere. ("SICP already did that...") I reread my copy every five years or so, and I always come away knowing something new.
You don't need to read all this stuff to be a good lisp programmer. One or two of these would probably be enough. But I think it's important to have choices. If you want to talk in more depth, my email's in my profile.
Let's talk. We can brainstorm something. Email's in my profile.
And his wikipedia page has been thoroughly whitewashed, which suggests he is not content with the reputation he deserves.
- thirsty (no tech for carrying water)
- hot (no air conditioning)
- hungry (hunter gatherers don't eat every day)
- practicing "open defecation"
- covered with insect bites
- riddled with intestinal parasites
- frequently sick (no vaccines/antibiotics)
- one compound fracture away from death by sepsis
and liable to be murdered by humans from other tribes or, according to some studies, possibly from your own if you ever stop pulling your weight.
In the mechanics library that comes with the book (which I'm building on in my own work), functions can take either numerical or symbolic values. If you have a computation involving symbolic values, you can manipulate it just like you would with pencil and paper (except you can operate at a higher level of abstraction, never get writer's cramp, and never have to laboriously recopy line after line of symbols to make sure you got the right number of minus signs). If, as so often happens, you find yourself up against an intractable integral, you pass the whole thing to a numerical solver and get a number back right away. With enough calculations you can build up a qualitative understanding of the system's behavior. My understanding is that this is what mechanics people do all day, but not how mechanics is taught to newcomers.
I thought 't Hooft was saying you have to learn all of this stuff. But it sounds like you're saying you can do good work in physics without some of this information. Is that true? What can you dispense with?