NewCircle Developer Stream
Stream is a constantly updated source of free, educational content on open source development. Also, check out our bookshelf for in-depth tutorials.
The Observer in Python works a bit differently than it does in other languages. This short tutorial will introduce you to how it works in Python and get you started on how to use it.
As companies strive to deliver software faster, “classic” software testing needs to be modernized. Melvin Laguren goes into some of these testing strategies in further detail.
Pattern matching is a killer feature in Scala. Those of you coming from a Java background might find this particularly interesting, because even with Java 8, there’s nothing like this in Java.
Blaze is an open source project from Continuum Analytics. It’s a project under evolution, "an ambitious effort to provide uniform, Pythonic interface to modern datasets and computation platforms."
Bokeh is a data visualization library that lets Python programmers and data scientists create interactive, novel, plots for the web. This talk overviews its capabilities and demos its latest features.
Software peer review is essential on a modern development team. Learn how to keep your code healthy, and your people happy in this 15 minute talk from Forward JS.
Twelve talks from the Bay Area's elite conference for open web, open source, and web accessibility.
Karl Groves discusses modern web toolsets and how Tenon.io uses many of these tools to create its product - an automated web accessibility testing API.
What's new in the Python packaging community? Noah Kantrowitz outlines what's happened, what's going to happen, and how to incorporate the latest techniques into your Python environment.
Noah Kantrowitz overviews the various tools available for application deployment today, discusses their tradeoffs, and helps shine a light on which might be the appropriate platform for your project.
Andrew Godwin discusses the reasons behind Lanyrd's decision to move from MySQL to PostgreSQL, then from AWS to Softlayer, and what their team learned along the way.
The Django Debug Toolbar can be extremely helpful, but the interesting bugs only happen in production. Simon Willison offers advice on asking “what went wrong?,” and, “what’s going to go wrong?”
The story of taking two APIs, each with their respective issues, and updating them to create a single API for the modern era.
Nathan Yergler, Principle Engineer at Eventbrite, talks about how they took their code base, that's been around for quite some time, and built a culture of testing around it.
Releasing a new feature means takings into consideration how it will interact will all of your previous features. Feature flags are a tool to help confront this issue.
Nathan Yergler explains how Eventbrite adapted their code base for internationalization and discusses some of the unique challenges they faced along the way.
A series of 15-minute talks on Eventbrite and Lanyrd, two-large scale, layered, sites built on Python and Django.
Greg Sadetsky delivers an introduction for anyone interested in getting started with Python. He begins by setting up the environment, then demonstrates the power of a few simple lines of code.
What are descriptors and why do I care? If you're asking yourself this question, Simeon Franklin explains how they work and when they might come in handy.
What are metaclasses and why would we ever need them? In this talk, Jess Hamrick explains why you might use this Python feature, taking examples from her own adventures.
A 5-minute introduction to context managers.
Understanding what is going on in your business involves lots of data, but how can a company begin to make sense of so much information? Learn how Adroll does it in this advanced level talk.
When dealing with exponential growth, how should your company scale their infrastructure? Will experienced this firsthand, at SocialCode, and reveals solutions to the issues they faced.
Elasticsearch is a distributed data store that's very good at searching and analyzing data. Find out how Python makes it possible in this 20-minute demonstration!
A 5-minute introduction to prediction, given historical data, using Python.