jamesjporter

I wonder what this means for the future of OpenStack?

This sounds really cool; I'd love to see a blog post about it :)

It's basically an attempt to bring the user interface of Mathematica or MATLAB (the "notebook") to the Python world. You can get an idea of what this is like in Mathematic by watching Wolfram's demo: https://www.youtube.com/watch?v=_P9HqHVPeik

We used to use gluster in production but had similar problems to raggiskula and are currently in the process of moving away from it.

This comment overstates the current power of Julia's parallel programming model — as of now Julia has no real tools for shared-memory parallelism and probably will not for another few versions or so. For distributed memory Julia is great, but please do not use Julia if you are being hindered by the GIL.

(NB I say this as a big Julia evangelist. it has a lot of potential but is not really there yet on a number of things, this being one of them.)

It's not a genetic algorithm, but FFTW's planning system actually does perform experiments to optimize for a particular machine's hardware.

    ydash = [1 => y[1]]

creates a Dict that maps for from Int64 to Float64, `=>` is used for declaring Dict literals in Julia. c.f.: http://docs.julialang.org/en/latest/stdlib/base/?highlight=d...

I wish someone would try this again for Emacs :(

it's interesting how some software problems have garnered many really good solutions while others are still mired in stuff like this.

e.g. no matter what your preferred source control tool is I think we can all agree that there are some pretty good options out there these days. compare that to the insanity that prevails in packaging in every corner.

I think he might mean "shaking things up" (which Node certainly has done) rather than "taking over the world" (which, as you point out, it has not).

This already exists as a prototype as part of IJulia, see Leah Hanson's awesome blog post about it here: http://blog.leahhanson.us/julia-calling-python-calling-julia...

The collaboration between the scientific Python and Julia communities in recent months has been awesome to watch.

It's less a matter of ability and more a matter of having the time/motivation. If you took a bunch of practicing natural scientists and/or engineers and forced them somehow to enroll in a course teaching Scheme/Clojure/etc., would many of them do well? Almost certainly. If you gave them the choice between a Lisp and something like Julia or MATLAB to use in their everyday work when they need to do some computation? They'll probably choose the later because it's easy and familiar and doesn't have a huge up-front time investment; scientists tend to be very busy people.

I agree with collyw; Lisp is too much for most scientists, who just care about getting their research done and can't be bother to learn all this weird FP/paren stuff [1]. Supplying an obvious, familiar syntax that looks like math written on paper for, e.g., matrix operations, is critical. This is actually one of the core tensions in Julia development imho: balancing having a sane, well designed language (from a programmer's perspective) vs. having a tool that allows scientists to quickly and easily crank out results.

[1]: Note; I am not insinuating that Lisp is bad, I like it personally. Just relaying the response you will get from most practicing scientists who are not trained as programmers.

Julia is designed to be a good general purpose language. There are already a bunch of database drivers; a simple web framework, etc., etc. http://docs.julialang.org/en/release-0.2/packages/packagelis...

Us Emacs users unfortunately have to remap it to control to retain sanity on standard keyboards :P

What tom said; also typing a url in a browser is a lot easier than installing something. This might sound silly but for the typical computer user (and even many nerds!) it makes a big difference.

For those interested in this sort of thing, Julia's Gadfly can also use D3 as a backend: https://github.com/dcjones/Gadfly.jl (although to be fair gadfly has more in common with ggplot than matplotlib)

ido doesn't become really amazing until you add ido-ubiquitous (which makes most minibuffer completions use the ido interface, as opposed to just file opens) and flx-ido (which allows fuzzy matching).

This makes a ton of sense, but it might be unorthodox enough to scare off some users thinking it was a scam of some sort.

Hacker School was an amazing experience for me; I had been programming on and off for ~2.5 years when I attended last summer. Feel free to drop me a line if you want to chat more about it (email in profile)

FWIW, running Wes' code with a fresh pull of Julia today shows some improvement in the pure-Julia benchmarks at least:

    Array operations took 79.55683398 ms
    inner took 23.88444026 ms

    BLAS took 48.182116560000004ms

Obviously its possible that this is just due to differences between our machines; just figured I'd throw it out there.

Julia is a really cool language; I came to it from a python background and in a lot if ways it feels like "python done right"

This is correct. The point of attending HackReactor, Dev Bootcamp, etc., is to gain a specific set of skills (often starting from zero) that will get you a job. The point of attending Hacker School is to become a better programmer, whatever that might mean to you (note that you have to already be a programmer in order to know that you want to do this).

EDIT: I do think it's unfortunate that Hacker School is basically unique in this regard; its an amazing thing.

Congrats on the release to everyone involved; very excited about the future!

Cool project, I've been thinking about writing one for MIPS (I'm taking a computer architecture class this quarter :))

+1 for installing python from homebrew; letting the default OSX installation be used at all has caused me mad problems in the past.

BWT is mindblowing. So many applications; e.g. it's used by pretty much all popular biological sequence alignment software to compress genomes so they fit in memory during the alignment :D

The story I read about it claimed the following:

He had made changes/improvements to various FOSS projects he was using at Goldman that he wanted to submit as patches upstream but his bosses wouldn't let him. He figured that since he was leaving he might as well take the FOSS code he had modified with him so he could submit patches later. The legality of this maneuver is tenuous at best, as others in this thread have noted, but in my opinion probably doesn't merit years of hounding prosecution and criminal charges.

I agree with paulgb and others -- most of my past work with python could be called scientific programming in one way or another and pip has been enormously useful.

This seems like a really expansive way to say don't do something if you don't enjoy it, which I think is good advice for anyone, not just undergraduate hackers.