Just yesterday at work I had a good real-life use case for generic functions that deserves a blog post.

Three years ago, Lynn Greiner interviewed key developers responsible for the direction and development of PHP, Perl, Tcl, Python, Ruby, and JavaScript to find out where they believed the languages were headed. In this follow-up discussion, she asks these dynamic language leaders what has changed since then.

Sun's John Rose announced the first OpenJDK build with support for the invokedynamic keyword. In related blog posts, Rose and Java SE spec lead Danny Coward provide some background on this important JVM milestone.

ZeroTurnaround released JavaRebel 1.2, the latest version of its Java class re-loading tool. In this interview with Artima, ZeroTurnaround chief architect Jevgeni Kabanov explains JavaRebel's support for the Spring framework, as well as other new features of the latest release.

This third installment describes other subtilities, dark corners, and bugs of Python's super.

Block-based storage is well-known to most developers, since many disks and network-attached storage devices appear as block devices to software or to the operating system. Amazon.com this week launched a virtualized block-based storage service for its Elastic Compute Cloud.

The committee to oversee the next version of the JavaScript language decided to abandon a multi-year effort at standardizing ECMAScript 4, slated to be the next generation of JavaScript, and to revert to a more modest update to the language. Packages and Java-like classes won't be part of the next standard, for instance.

In his latest blog post, Steve Yegge says that requirements gathering is almost a sure-fire guarantee of eventual software product failure: If you don't know your product's requirements at the outset, your outcome will almost certainly not meet users' approval. Is requirements gathering an important, or even practical, step in the software process?

The series about the dark corners of the Python built-in super continues. In this installment, I discuss an ugly design wart: unbound super objects.

With a set of metrics, Coverity's new product aims to provide managers a true measure of risk in a code base. In an interview with Artima, Coverity CTO Ben Chelf explains why traditional code metrics alone don't provide sufficient insight into the riskiness of code.

super is perhaps the trickiest Python construct: this series aims to unveil its secrets

A non-Web-2.0 way of looking at it.

In the first part I have discussed the new features of metaclasses in Python 3.0, in particular the usage of the __prepare__ classmethod to intercept the class attributes before class creation. In this second part, I will show an example of the things you can do with metaclasses, by implementing a clever record system.

In the first installment of a two-part series, Michele Simionato discusses what's new for metaclasses in the upcoming version of Python.

Many discussions about caching focus on specific caching tools and APIs. In a recent blog post, Dhananjay Nene offers a tool-agnostic overview of designing an enterprise application for good cache performance.

Agile methodologies seemed like a good idea to this software development team. But when the company doesn't sincerely accept the change in work style, the result is just a buzzword for "project hell."