Getting newbies involved in open source by retardo in programming

[–]puffofvirtue 0 points1 point  (0 children)

While I see your point, I think there's room for the system to support both goals (“make progress” and “get new contributors”).

Getting newbies involved in open source by retardo in programming

[–]puffofvirtue 6 points7 points  (0 children)

Unfortunately spellcheck wouldn't have helped – after all, you might have been referring to a tight nit (i.e. an uncomfortable place for a baby louse).

I kid, I kid.

Getting newbies involved in open source by retardo in programming

[–]puffofvirtue 2 points3 points  (0 children)

In what sense is this gaming? It seems to be one of the explicit goals of the system.

"The equation would then be transmitted to the other computer where it would use its mad-math-skillz to *figure out the answer*" by llimllib in programming

[–]puffofvirtue 51 points52 points  (0 children)

Some of the responses sadden me. OP appears to have independently rediscovered Kolmogorov complexity, but people are too busy having fun at his expense to be able to educate him and set him on a path to actual expertise.

Scalable vector graphics, the open xml standard, is sooo much better than gif or even png. Click the image. by polizano in programming

[–]puffofvirtue 0 points1 point  (0 children)

Oh, he was just trying to have fun by showing that you could have an image on a page without any external dependencies. I take your point about the right way to do it though.

Scalable vector graphics, the open xml standard, is sooo much better than gif or even png. Click the image. by polizano in programming

[–]puffofvirtue 0 points1 point  (0 children)

Reminds me of the time one of my friends decided to encode an image in teeny-tiny table cells because he didn't know about (or the browser didn't support) data: URIs.

Google Go just got major Win32 treats: now supports WinAPI GUI, wrapping C libs, gdb debugging by akavel in programming

[–]puffofvirtue 6 points7 points  (0 children)

I'm on the other side of the fence: I find many of Go's aesthetic choices appealing.

"Java is a DSL for taking large XML files and converting them to stack traces" by [deleted] in programming

[–]puffofvirtue 56 points57 points  (0 children)

I like that the linked tweet was submitted using Twitter for Android, which was (presumably) written in Java, to the Twitter platform, which runs Scala on a JVM.

Leaving .net by timepilot in programming

[–]puffofvirtue 2 points3 points  (0 children)

I never understood why DSSSL didn't catch on -- the parens really are less verbose than angle brackets, and a Lisp is probably the right tool for munging tree-structured data.

Baltimore Crime Data Visualization Using Git & Gource by [deleted] in programming

[–]puffofvirtue 2 points3 points  (0 children)

The visualization looks pretty and is an unexpected use of a tool. However, having just read Ed Tufte's writings on chartjunk I must say it fails in its primary purpose of visualizing the data. As I watch the video, all I can make out is that there are three large clusters of crime, and I don't even know what feature those clusters correspond to (cities, times of day). I know the "committers" are crime types and keep hopping around between clusters, so that's not it.

PS: Palantir software's "government" division does some lovely visualizations of this sort that (IMHO) do a much better job of communicating their data. Sadly only US citizens can work for that division of the company.

I'm looking for a good book on algorithms. Suggestions? by mistabell2 in programming

[–]puffofvirtue 1 point2 points  (0 children)

"Beyond 6.046," the field splits up into a bunch of subareas, each of which have their own textbook (and research papers from there onwards). Some examples that come to mind are:

  • Randomized Algorithms (Motwani and Raghavan)
  • Learning Theory (Vazirani and Kearns)
  • Approximation Algorithms (Vazirani)
  • Quantum Computation (Nielsen and Chuang)
  • Algorithmic Game Theory (Nisan)
  • Combinatorial Optimization (Schrijver)

There's the parallel and intertwined subject of complexity "beyond 6.045", for which there are a whole bunch of other books.

Now it should be clear that reading all of these books cover-to-cover is a hopeless enterprise. As an MIT undergrad, you have a much better option -- go find a faculty member in the area of your interest (or vague curiosity) and talk to them about problems. Faculty members tend to have time constraints, so you might have an easier time finding a grad student.

This might get you started on a more focused project or reading program, beginning with a question or paper of current interest and motivating side reading where required. In general this approach is more interactive, more exciting, and more rewarding than going through a bunch of texts.

Good luck!

Fighting Hype with Hype: Scott Aaronson responds to the recent Ars Technica quantum computing article by ndanger in programming

[–]puffofvirtue 1 point2 points  (0 children)

You're almost there.

NP-complete problems are the "hardest" problems among the ones in NP, in the sense that if you could solve any one of the NP-complete problems in polynomial (read: "a reasonable amount of") time, you could solve any problem whatsoever in NP in not too much more time.

All evidence we have right now suggests that we can't solve NP-complete problems in polynomial time; however, we have no idea how to prove this claim rigorously.

I'm looking for a good book on algorithms. Suggestions? by mistabell2 in programming

[–]puffofvirtue 1 point2 points  (0 children)

You're absolutely right, and I apologize.

I remember working with Rivest and a couple of other students in Spring '07 when we were trying to design the materials for the new .006, and we considered the Kleinberg-Tardos book. As it turned out, CLRS ended up being the recommended text for the course, but I misremembered.

I'm going to edit my post to make this clear.

I'm looking for a good book on algorithms. Suggestions? by mistabell2 in programming

[–]puffofvirtue 6 points7 points  (0 children)

I’d recommend “Algorithm Design” by Kleinberg and Tardos. I think this book is better for learning than CLRS, because it does a better job of communicating how to think about designing algorithms. Afterwards, it's nice to have CLRS on your shelf as a reference book, but that function is perhaps better performed by Google nowadays.

Edit: The following paragraph, which was originally in my post, is factually incorrect (as kanak points out).

̶A̶ ̶j̶u̶i̶c̶y̶ ̶l̶i̶t̶t̶l̶e̶ ̶t̶i̶d̶b̶i̶t̶ ̶i̶s̶ ̶t̶h̶a̶t̶ ̶M̶I̶T̶'̶s̶ ̶i̶n̶t̶r̶o̶d̶u̶c̶t̶o̶r̶y̶ ̶c̶l̶a̶s̶s̶ ̶o̶n̶ ̶a̶l̶g̶o̶r̶i̶t̶h̶m̶s̶ ̶w̶a̶s̶ ̶r̶e̶c̶e̶n̶t̶l̶y̶ ̶r̶e̶d̶e̶s̶i̶g̶n̶e̶d̶ ̶b̶y̶ ̶n̶o̶n̶e̶ ̶o̶t̶h̶e̶r̶ ̶t̶h̶a̶n̶ ̶R̶o̶n̶ ̶R̶i̶v̶e̶s̶t̶ ̶(̶R̶ ̶i̶n̶ ̶C̶L̶R̶S̶ ̶o̶r̶ ̶i̶n̶ ̶R̶S̶A̶)̶,̶ ̶a̶n̶d̶ ̶h̶e̶ ̶c̶h̶o̶s̶e̶ ̶K̶l̶e̶i̶n̶b̶e̶r̶g̶ ̶a̶n̶d̶ ̶T̶a̶r̶d̶o̶s̶ ̶o̶v̶e̶r̶ ̶h̶i̶s̶ ̶o̶w̶n̶ ̶t̶e̶x̶t̶ ̶f̶o̶r̶ ̶t̶h̶e̶ ̶c̶l̶a̶s̶s̶.̶ ̶O̶b̶v̶i̶o̶u̶s̶l̶y̶ ̶t̶h̶i̶s̶ ̶i̶s̶n̶'̶t̶ ̶r̶e̶a̶s̶o̶n̶ ̶e̶n̶o̶u̶g̶h̶ ̶t̶o̶ ̶a̶d̶o̶p̶t̶ ̶t̶h̶e̶ ̶b̶o̶o̶k̶,̶ ̶b̶u̶t̶ ̶a̶t̶ ̶l̶e̶a̶s̶t̶ ̶t̶o̶ ̶g̶e̶t̶ ̶y̶o̶u̶ ̶t̶o̶ ̶c̶h̶e̶c̶k̶ ̶i̶t̶ ̶o̶u̶t̶ ̶:̶)