pebblexe

Wow, thanks for mentioning that. I am used to being impressed by people on here, but the idea that he is this talented and blind is beyond my comprehension.

Man I suck as a programmer; I complain about every little thing and barely ever program.

The idea of it registering as a printer is ingenious.

FreeBSD also has netmap https://www.freebsd.org/cgi/man.cgi?query=netmap&sektion=4

Hopefully they introduce namespaces (like matriona) and the type system of strongtalk with gradual typing.

edit: just realized this was an april fools :(

The best book I know about research is "Apprentice to Genius: The Making of a Scientific Dynasty":

https://www.amazon.com/dp/0801847575

It's a fantastic book.

I remember seeing this posted here and I was really impressed by it: https://github.com/yinwang0/ydiff

This video of him is fantastic: https://www.youtube.com/watch?v=BkvEpoqFx6c

I remember reading somewhere that there is funding/support from China. I can't find any support for that.

I stumbled upon this site in my search though http://red.reb4.me/

What's the difference between 'determines' and 'influences'? I mean isn't that just a sliding scale? If a concept is incredibly difficult to formulate in a given language, could it not be said that the language has limited my thinking?

Do you have to manipulate text? Perl is probably better than C for that.

Do you have to write a program that operates on lists? Lisp probably is better than Java.

Do you have to write a formal proof? Coq is probably better than Python.

Do you have to write distributed networking code? Erlang is probably better than PHP.

How can the strengths of each language not be direct support for linguistic relativity? All that means is that certain concepts are easier to manipulate and understand in certain languages.

Where is it written that programming languages can't support the theory of linguistic relativity?

Are you kidding me?

Are you claiming the Sapir-Whorf hypothesis isn't true on ycombinator?

This is a forum famous for talking about blub languages and lisp. And if it's true for artificial languages, that's a strong indicator that it's true for human ones.

But sadly there are very few studies comparing programming languages and I've never seen one comparing human languages qualitatively.

Sorry about the title. Thank you mods for fixing it.

You're right, I'm sorry.

I don't mean to imply that I've done this (aside from copypasting from that wikibook). I just think that understanding why it's easy to do this for lisp is important.

Does anyone know of an easy way to interact with performance monitor counters in Linux? I've been reading stuff by Brendan Gregg about DTrace and the importance of PMCs, and it made it sound like Linux doesn't have the equivalent.

"A Field Guide to Genetic Programming" (https://www.amazon.com/Field-Guide-Genetic-Programming/dp/14...) is one of the better books I've read on the matter (but I've yet to read anything by Koza). W. Langdon's webpage (http://www0.cs.ucl.ac.uk/staff/W.Langdon/) has a lot of great information.

I agree entirely with that.

The only thing you need to do to understand lisp's entrancement is to write an interpreter for it, and in doing so you'll see why it's easier to write a lisp interpreter than it is for any other language. All you need to understand the appeal is a look at https://github.com/kanaka/mal.

Personally I learned the appeal of lisp while following this tutorial on writing a scheme in Haskell: https://en.wikibooks.org/wiki/Write_Yourself_a_Scheme_in_48_.... There's a reason there'll never be a tutorial like that for a toy Haskell interpreter.

You can read the full research article here: https://rd.springer.com/article/10.1007/s00114-017-1440-1

I've always thought that without spiders vertebrates would never have had a chance to become dominant.

As the paper notes, this is a different perspective on generative adversarial networks:

  Goodfellow et al. (2014) proposed generative adversarial nets
  (GANs). GANs, while independently invented, are essentially
  based on the same idea as Turing Learning.

I found this while trying to remember the other APL derivative that was posted here: http://arcfide.github.io/Co-dfns/

Reminded me of this fantastic video from Zed Shaw: https://vimeo.com/2723800