BitTorrent Makes Twitter’s Server Deployment 75x Faster

“Some of the biggest Internet brands have declared their love for BitTorrent in recent months. Both Facebook and Twitter are using BitTorrent to update their networks and not without success. In Twitter’s new setup the BitTorrent-powered system has made their server deployment 75 times faster than before…”

Walmart Node.js Memory Leak

“A few weeks ago, Eran Hammer of Walmart labs came to the Node.js core team complaining of a memory leak he had been tracking down for months. By expending a lot of effort over those few months he had taken the memory leak from a couple hundred megabytes a day, down to a mere eight megabytes a day. That in and of itself is an impressive feat, however, the source and final portion of the leak was elusive.

So he spent more effort and found a test case that reproduced with only Node APIs, which placed the fault right at Node.js’ doorstep. He found that the more HTTP client requests he did, the more memory his Node process would consume, but it was really slow. Here’s the graph from his initial data:…”

How Python became the language of choice for data science

“Nowadays Python is probably the programming language of choice (besides R) for data scientists for prototyping, visualization, and running data analyses on small and medium sized data sets. And rightly so, I think, given the large number of available tools (just look at the list at the top of this article)…”

How I Learned to Stop Worrying and Love Golang

“Here’s a riff on Malcolm Gladwell’s rule of thumb about mastery: you don’t really know a programming language until you’ve written 10,000 lines of production-quality code in it. Like the original this is a generalization that is undoubtedly false in many cases – still, it broadly matches my intuition for most languages and most programmers1. At the beginning of this year, I wrote a sniffy post about Go when I was about 20% of the way to knowing the language by this measure. Today’s post is an update from further along the curve – about 80% – following a recent set of adventures that included entirely rewriting’s core dispatcher in Go. My opinion of Go has changed significantly in the meantime. Despite my initial exasperation, I found that the experience of actually writing Go was not unpleasant. The shallow issues became less annoying over time (perhaps just due to habituation), and the deep issues turned out to be less problematic in practice than in theory. Most of all, though, I found Go was just a fun and productive language to work in. Go has colonized more and more use cases for me, to the point where it is now seriously eroding my use of both Python and C.

After my rather slow Road to Damascus experience, I noticed something odd: I found it difficult to explain why Go worked so well in practice. Sure, Go has a triad of really smashing ideas (interfaces, channels and goroutines), but my list of warts and annoyances is long enough that it’s not clear on paper that the upsides outweigh the downsides. So, my experience of actually cutting code in Go was at odds with my rational analysis of the language, which bugged me. I’ve thought about this a lot over the last few months, and eventually came up with an explanation that sounds like nonsense at first sight: Go’s weaknesses are also its strengths. In particular, many design choices that seem to reduce coherence and maintainability at first sight actually combine to give the language a practical character that’s very usable and compelling. Lets see if I can convince you that this isn’t as crazy as it sounds…”

Machine learning is way easier than it looks

“After all, you’re teaching machines that work in ones and zeros to reach their own conclusions about the world. You’re teaching them how to think! However, it’s not nearly as hard as the complex and formula-laden literature would have you believe.

Like all of the best frameworks we have for understanding our world, e.g. Newton’s Laws of Motion, Jobs to be Done, Supply & Demand — the best ideas and concepts in machine learning are simple. The majority of literature on machine learning, however, is riddled with complex notation, formulae and superfluous language. It puts walls up around fundamentally simple ideas.

Let’s take a practical example. Say we wanted to include a “you might also like” section at the bottom of this post. How would we go about that?…”