March 31, 2014 One of the fundamental pieces of infrastructure for an effective software engineering team is their deployment pipeline. Here we cover a fairly basic but effective pipeline for deploying code.
December 3, 2012 A simple implementation of the war card game in Python, made for an interview some time back.
March 28, 2011 I'm a developer and I write a little bit. If you're curious about learning more about me, this is a good place to start.
November 3, 2008 An overview of my pipeline between development and deployment for Django projects. Fabric and Git turn a potentially unhappy task into something very quick and easy.
January 31, 2010 A look at using Node.js to write a log collection server and also log submission clients. My first experiment with Node.js, which really impresses me with how easy it is to write flexible, powerful and efficient code in Node.js.
April 10, 2011 In this article I'm releasing real-time analytics for this site, as well as analyzing historical data thus far.
September 5, 2010 Part of my day's experiment was to play with implementing Python datastructures which are implemented ontop of Redis. Here we take a look at dictionaries and lists, but it should be straightforward to extend this idea to sets as well.
April 6, 2011 Last year Digg released a Streaming API which exposes real-time activity on its site. It's available via JSONP, so this tutorial takes a quick look at dynamically populating a Flot.js graph using the API's data.
April 8, 2011 If you're doing analytics, reports or dealing with memory constraints in Redis, you're probably dealing with keeping two sorted-sets mutually consistent. This article also takes a look at using multi/exec to keep it fresh.
April 28, 2009 A look at the frontend engineer's primary pain point: product skew. While skew impacts everyone, it beats on the frontend engineer early and frequently. Think frontend engineer's have the easy half of engineering? Well, let's talk about that.
August 14, 2008 I used Google Insights to look at the global search popularity for a dozen programming languages. Although I wasn't inspired with any particularly valuable insights, its still fairly interesting to see the distributions.