Language Selection

English French German Italian Portuguese Spanish

The NEW PCLinuxOS Magazine January 2010 Issue

Filed under
PCLOS

The NEW PCLinuxOS Magazine staff is pleased to announce the release of the January 2010 issue of the PCLinuxOS Magazine.

In the January 2010 issue:

2009: A Look Back
Game Zone: Modern Warfare 2
Behind The Scenes: A Chat With Hootiegibbon
Sproggy's Glass Panel Tutorial
Command Line Interface Intro: Part 4
Double Take & Mark's Quick Gimp Tip
Forum Foibles
Ms_meme's nook
Book Worms Unite: An E-Book Explosion
Archiving Movies With dvd::rip - An Update
Computer Languages A to Z: Guile
Gadgets & Gear: Official PCLinuxOS Store Opens
Screenshot Showcase
and much, much more!

This month's cover features snowy winter pictures captured by magazine staff member ms_meme.

Download the PDF (8.3 MB)

Visit the HTML Version




More in Tux Machines

France: ‘tax source code will be made public’

France’s tax department is willing to make the source code available for its income tax software system, says Axelle Lemaire, minister responsible for Digital Affairs. However, preparation takes time, she told April, France’s free software advocacy group, last month. Read more

Simplicity Linux 15.7 Comes at the End of July with Linux Kernel 4.0

David Purse from the development team of Simplicity Linux, a distribution derived from LXPup and built around the LXDE desktop environment, has announced the release of the first Beta build towards the final version of Simplicity Linux 15.7. Read more

Linux Kernel 3.14.46 LTS Has ARM and ARM64 Improvements, Updated Drivers

After announcing the release of the Linux kernel 4.1.1, Linux kernel 4.0.7, and Linux kernel 3.10.82 LTS, Greg Kroah-Hartman also published details about a new maintenance release of the Linux 3.14 kernel branch. Read more

Google open-sources its software for making trippy images with deep learning

The deepdream project is now available on GitHub. The project relies on the open-source Caffe deep learning framework. Deep learning involves training artificial neural networks on a large pile of data — for example, pictures of geese — and then throwing them a new piece of data, like a picture of an ostrich, to receive an educated guess about it. Read more