6 ms·

A set of top Computer Science blogs

293 points by johndcook 14y ago

Symmetry 14y ago
One of the CS blogs I read is Embedded in Academia, which has lots of posts on C compilers, what they optimize and where they can break http://blog.regehr.org/ http://blog.regehr.org/
- timtadh 14y ago
  I would also add Matt Might's blog http://matt.might.net/articles/ http://matt.might.net/articles/ we see articles from it on HN from time to time so you may already be familiar with it.
mjn 14y ago
A few others: Machine Learning (Theory), http://hunch.net/ http://hunch.net/, is a good place to learn about new machine-learning papers. It doesn't really do research exposition on the blog, but it posts about recent ML conferences, highlighting some of the papers the author finds interesting. Embedded in Academia, http://blog.regehr.org/ http://blog.regehr.org/, is about 50% personal stuff, but 50% posts on John Regehr's work on C-compiler fuzzing, with some interesting examples if you're into compilers or the finer points of C semantics. Proper Fixation, http://www.yosefk.com/blog/ http://www.yosefk.com/blog/, is by an embedded developer (not academic), and not always about research, but it has some good researchy and expository posts. For example, it has the best concise overview I've found of how SIMT/SIMD/SMT relate (http://www.yosefk.com/blog/simd-simt-smt-parallelism-in-nvidia-gpus.html http://www.yosefk.com/blog/simd-simt-smt-parallelism-in-nvid...). While it's a mathematics blog, Terence Tao's blog, http://terrytao.wordpress.com/ http://terrytao.wordpress.com/, has a lot of content likely of interest to computer scientists as well. In particular, his blog-exposition versions of papers are often a better introduction to recent research for nonspecialists than anything in the official published literature is. Tomasz Malisiewicz's computer-vision blog, http://quantombone.blogspot.com/ http://quantombone.blogspot.com/, has intermittent but often quite good posts on object recognition and similar topics. Of course I can't refrain from mentioning my own quasi-blog, http://www.kmjn.org/notes/ http://www.kmjn.org/notes/, though only about 1/4 of it is on computer science (about 4/5 of my day job is computer science, but online essays end up being mainly an outlet for everything else).
brewerja 14y ago
I really enjoy Eli Bendersky's posts: http://eli.thegreenplace.net/ http://eli.thegreenplace.net/
josefonseca 14y ago
Well, just yesterday I thought to myself: I should compile a good list of blogs for reference, instead of the usual marketing blogs disguised as Compsi(most of them are really just trying to sell you something, not discuss real CS). Thanks for sharing.
mindcrime 14y ago
For anybody looking to overdose on CS topics, you might also enjoy: http://www.reddit.com/r/compsci/ http://www.reddit.com/r/compsci/ and http://www.reddit.com/r/compscipapers http://www.reddit.com/r/compscipapers Also, http://machinelearning.reddit.com http://machinelearning.reddit.com and http://semanticweb.reddit.com http://semanticweb.reddit.com feature some high quality links and discussion that many HN'ers might find of interest.
- Homunculiheaded 14y ago
  Unfortunately I would recommend against all of these (except /r/compscipapers, only because I've never checked it out). /r/compsci is, in my experience, mostly lower level undergrads who really want to show how much they know, there's a lot of misinformation in comments that gets voted up, and a lot of missing nuance in any of the discussions. /r/semanticweb is very inactive, almost no discussion /r/machinelearning is pretty poor as well, better choices are http://metaoptimize.com/qa http://metaoptimize.com/qa http://blog.kaggle.com/category/how-i-did-it/ http://blog.kaggle.com/category/how-i-did-it/ and following ml topics on quora
  - mindcrime 14y ago
    The value there, IMO, is more the links than the discussion. It's not so much like HN, where the discussion itself is half (or more) of the value. But, for a quick, concise list of new links in those fields, I find those Reddits all very valuable. better choices are http://metaoptimize.com/qa http://metaoptimize.com/qa http://blog.kaggle.com/category/how-i-did-it/ http://blog.kaggle.com/category/how-i-did-it/ and following ml topics on quora Yes, those are excellent sites as well. There are also some good StackExchange sites that can help one get their fill of CS'y topics.
- Darmani 14y ago
  My favorite subreddits are http://www.reddit.com/r/reverseengineering http://www.reddit.com/r/reverseengineering and http://www.reddit.com/r/types http://www.reddit.com/r/types .
abhaga 14y ago
A good source of NLP goodness is http://nlpers.blogspot.in/ http://nlpers.blogspot.in/, written by Hal Daume, a CS prof at UMD. He covers recent NLP conferences and also his own work. Some of it is in the area of domain adaptation which is of interest to anyone trying to bring research papers to real world products.
stungeye 14y ago
Knowing and Doing, reflections of an academic and computer scientist: http://www.cs.uni.edu/~wallingf/blog/index.html http://www.cs.uni.edu/~wallingf/blog/index.html
Ironballs 14y ago
The Theory of Computing blog http://feedworld.net/toc/ http://feedworld.net/toc/ is an aggregator about theoretical CS stuff.
Bharath1234 14y ago
Great resources !! Thanks :)
lunchladydoris 14y ago
I must be an idiot. It took me a few Google searches and finally stumbling on it by accident to figure out how to subscribe to the RSS feed for Serious Engineering's dynamic Blogger blog, since the "Subscribe to this page..." option isn't available. In case you too struggle, there's a pop-out menu on the right. And now back to ramming crayons up my nose.
- mjn 14y ago
  Many RSS readers these days are pretty good at feed discovery if you just give them the blog URL. At least, both Google Reader and Newsblur (http://www.newsblur.com http://www.newsblur.com) seem to be able to dig up a feed for anything I've thrown at them.
possible 14y ago
Another: Math ∩ Programming http://jeremykun.wordpress.com http://jeremykun.wordpress.com He is at the beginning of his academic career (PhD student), but the posts are especially clear and well-written.
naerisot 14y ago
I like Scott Aaronson's blog (http://www.scottaaronson.com/blog/ http://www.scottaaronson.com/blog/), which has some posts about computational complexity and quantum computing.
Homunculiheaded 14y ago
The "How I did it"[0] section of Kaggle's "No Free Hunch" blog is really great for getting practical insights into solving machine learning problems. All the posts are short, and unless you're an expert in ML, will likely give you leads all a lot of new material to learn. The pragmatic bent is what really makes it such an excellent resource, there's a huge gap between the mathematical foundations of ML and the solving real world problems side of it. 0. http://blog.kaggle.com/category/how-i-did-it/ http://blog.kaggle.com/category/how-i-did-it/
drtomcrick 14y ago
Excellent, thanks for the extra blog links!
maboi 14y ago
CodeAvengers is an addictive hot new site that teaches novices the computer programming language of the web: Javascript. The site went live last month with 40 interactive lessons and games. The site aims to be the most fun and effective Javascript tutorial on the web. Script your future NOW, With CodeAvengers.com.

293 points by johndcook 14y ago

One of the CS blogs I read is Embedded in Academia, which has lots of posts on C compilers, what they optimize and where they can break http://blog.regehr.org/ http://blog.regehr.org/

timtadh 14y ago
I would also add Matt Might's blog http://matt.might.net/articles/ http://matt.might.net/articles/ we see articles from it on HN from time to time so you may already be familiar with it.

mjn 14y ago

A few others: Machine Learning (Theory), http://hunch.net/ http://hunch.net/, is a good place to learn about new machine-learning papers. It doesn't really do research exposition on the blog, but it posts about recent ML conferences, highlighting some of the papers the author finds interesting. Embedded in Academia, http://blog.regehr.org/ http://blog.regehr.org/, is about 50% personal stuff, but 50% posts on John Regehr's work on C-compiler fuzzing, with some interesting examples if you're into compilers or the finer points of C semantics. Proper Fixation, http://www.yosefk.com/blog/ http://www.yosefk.com/blog/, is by an embedded developer (not academic), and not always about research, but it has some good researchy and expository posts. For example, it has the best concise overview I've found of how SIMT/SIMD/SMT relate (http://www.yosefk.com/blog/simd-simt-smt-parallelism-in-nvidia-gpus.html http://www.yosefk.com/blog/simd-simt-smt-parallelism-in-nvid...). While it's a mathematics blog, Terence Tao's blog, http://terrytao.wordpress.com/ http://terrytao.wordpress.com/, has a lot of content likely of interest to computer scientists as well. In particular, his blog-exposition versions of papers are often a better introduction to recent research for nonspecialists than anything in the official published literature is. Tomasz Malisiewicz's computer-vision blog, http://quantombone.blogspot.com/ http://quantombone.blogspot.com/, has intermittent but often quite good posts on object recognition and similar topics. Of course I can't refrain from mentioning my own quasi-blog, http://www.kmjn.org/notes/ http://www.kmjn.org/notes/, though only about 1/4 of it is on computer science (about 4/5 of my day job is computer science, but online essays end up being mainly an outlet for everything else).

brewerja 14y ago

I really enjoy Eli Bendersky's posts: http://eli.thegreenplace.net/ http://eli.thegreenplace.net/

josefonseca 14y ago

Well, just yesterday I thought to myself: I should compile a good list of blogs for reference, instead of the usual marketing blogs disguised as Compsi(most of them are really just trying to sell you something, not discuss real CS). Thanks for sharing.

mindcrime 14y ago

For anybody looking to overdose on CS topics, you might also enjoy: http://www.reddit.com/r/compsci/ http://www.reddit.com/r/compsci/ and http://www.reddit.com/r/compscipapers http://www.reddit.com/r/compscipapers Also, http://machinelearning.reddit.com http://machinelearning.reddit.com and http://semanticweb.reddit.com http://semanticweb.reddit.com feature some high quality links and discussion that many HN'ers might find of interest.

Homunculiheaded 14y ago
Unfortunately I would recommend against all of these (except /r/compscipapers, only because I've never checked it out). /r/compsci is, in my experience, mostly lower level undergrads who really want to show how much they know, there's a lot of misinformation in comments that gets voted up, and a lot of missing nuance in any of the discussions. /r/semanticweb is very inactive, almost no discussion /r/machinelearning is pretty poor as well, better choices are http://metaoptimize.com/qa http://metaoptimize.com/qa http://blog.kaggle.com/category/how-i-did-it/ http://blog.kaggle.com/category/how-i-did-it/ and following ml topics on quora
- mindcrime 14y ago
  The value there, IMO, is more the links than the discussion. It's not so much like HN, where the discussion itself is half (or more) of the value. But, for a quick, concise list of new links in those fields, I find those Reddits all very valuable. better choices are http://metaoptimize.com/qa http://metaoptimize.com/qa http://blog.kaggle.com/category/how-i-did-it/ http://blog.kaggle.com/category/how-i-did-it/ and following ml topics on quora Yes, those are excellent sites as well. There are also some good StackExchange sites that can help one get their fill of CS'y topics.
Darmani 14y ago
My favorite subreddits are http://www.reddit.com/r/reverseengineering http://www.reddit.com/r/reverseengineering and http://www.reddit.com/r/types http://www.reddit.com/r/types .

abhaga 14y ago

A good source of NLP goodness is http://nlpers.blogspot.in/ http://nlpers.blogspot.in/, written by Hal Daume, a CS prof at UMD. He covers recent NLP conferences and also his own work. Some of it is in the area of domain adaptation which is of interest to anyone trying to bring research papers to real world products.

stungeye 14y ago

Knowing and Doing, reflections of an academic and computer scientist: http://www.cs.uni.edu/~wallingf/blog/index.html http://www.cs.uni.edu/~wallingf/blog/index.html

Ironballs 14y ago

The Theory of Computing blog http://feedworld.net/toc/ http://feedworld.net/toc/ is an aggregator about theoretical CS stuff.

Bharath1234 14y ago

Great resources !! Thanks :)

lunchladydoris 14y ago

I must be an idiot. It took me a few Google searches and finally stumbling on it by accident to figure out how to subscribe to the RSS feed for Serious Engineering's dynamic Blogger blog, since the "Subscribe to this page..." option isn't available. In case you too struggle, there's a pop-out menu on the right. And now back to ramming crayons up my nose.

mjn 14y ago
Many RSS readers these days are pretty good at feed discovery if you just give them the blog URL. At least, both Google Reader and Newsblur (http://www.newsblur.com http://www.newsblur.com) seem to be able to dig up a feed for anything I've thrown at them.

possible 14y ago

Another: Math ∩ Programming http://jeremykun.wordpress.com http://jeremykun.wordpress.com He is at the beginning of his academic career (PhD student), but the posts are especially clear and well-written.

naerisot 14y ago

I like Scott Aaronson's blog (http://www.scottaaronson.com/blog/ http://www.scottaaronson.com/blog/), which has some posts about computational complexity and quantum computing.

Homunculiheaded 14y ago

The "How I did it"[0] section of Kaggle's "No Free Hunch" blog is really great for getting practical insights into solving machine learning problems. All the posts are short, and unless you're an expert in ML, will likely give you leads all a lot of new material to learn. The pragmatic bent is what really makes it such an excellent resource, there's a huge gap between the mathematical foundations of ML and the solving real world problems side of it. 0. http://blog.kaggle.com/category/how-i-did-it/ http://blog.kaggle.com/category/how-i-did-it/

drtomcrick 14y ago

Excellent, thanks for the extra blog links!

maboi 14y ago

CodeAvengers is an addictive hot new site that teaches novices the computer programming language of the web: Javascript. The site went live last month with 40 interactive lessons and games. The site aims to be the most fun and effective Javascript tutorial on the web. Script your future NOW, With CodeAvengers.com.