Python

When people think computational geometry, in my experience, they typically think one of two things: 1. Wow, that sounds complicated. 2. Oh yeah, convex hull. In this post, I'd like to shed some light on computational geometry, starting with a brief overview of the subject before moving into some practical advice based on my own experiences in computational geometric programming with Python.

More often than not, the software we write directly interacts with what we would label as "dirty" services. In layman's terms: services that are crucial to our Python application, but whose interactions have intended but undesired side-effects—that is, undesired in the context of an autonomous test run.

Python is amazing. Surprisingly, that's a fairly ambiguous statement. What do I mean by 'Python'? Do I mean Python the abstract interface? Do I mean CPython, the common Python implementation? Or do I mean something else entirely? Maybe I'm obliquely referring to Jython, or IronPython, or PyPy. Or maybe I've really gone off the deep end and I'm talking about RPython or RubyPython (which are very, very different things). While the technologies mentioned above are commonly-named and commonly-referenced, some of them serve completely different purposes (or at least operate in completely different ways). In this post, I'll start from scratch and move through the various Python implementations, concluding with a thorough introduction to PyPy, which I believe is the future of the language.

It’s always fun to put your programming skills on display. A while back, I figured it’d be cool to try and control my laptop via my Android mobile device. Think about it: being able to play and pause music, start and stop programming jobs or downloads, etc., all by sending messages from your phone. Neat, huh?

But this isn’t just another article about cohort analysis. If you already know the importance of the topic and want to skip the introduction, you can jump to the simulator, where you can either simulate startup growth based on retention, churn, and a number of other factors, or analyze your own PayPal logs with the code I’ve open sourced. If, however, you don’t realize that these are some of the most important metrics around–continue reading.

Clouds must be efficient to provide useful fault-tolerance and scalability, but they also must be easy to use. CloudI (pronounced "cloud-e" /klaʊdi/) is an open source cloud computing platform that is most closely related to the Platform as a Service (PaaS) clouds. CloudI differs in a few key ways, most importantly: software developers are not forced to use specific frameworks, slow hardware virtualization, or a particular operating system. By allowing cloud deployment to occur without virtualization, CloudI leaves development process and runtime performance unimpeded, while quality of service can be controlled with clear accountability.

Porn is a big industry. There aren’t many sites on the Internet that can rival the traffic of its biggest players. And juggling this immense traffic is tough. To make things even harder, much of the content served from porn sites is made up of low latency live streams rather than simple static video content. But for all of the challenges involved, rarely have I read about the developers who take them on. So I decided to write about my own experience on the job.