Machine Learning (Theory), 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/, 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/, 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-nvid...).
While it's a mathematics blog, Terence Tao's blog, 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/, 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/, 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).
Also, http://machinelearning.reddit.com and http://semanticweb.reddit.com feature some high quality links and discussion that many HN'ers might find of interest.
/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
and following ml topics on quora
better choices are http://metaoptimize.com/qa 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.
And now back to ramming crayons up my nose.
Math ∩ Programming
He is at the beginning of his academic career (PhD student), but the posts are especially clear and well-written.