Configure your desktop as you want with Budgie, the 18th in our series on open source tools that will make you more productive in 2019.
Budgie Desktop
There are many, many desktop environments for Linux. From the easy to use and graphically stunning GNOME desktop (default on most major Linux distributions) and KDE, to the minimalist Openbox, to the highly configurable tiling i3, there are a lot of options. What I look for in a good desktop environment is speed, unobtrusiveness, and a clean user experience. It is hard to be productive when a desktop works against you, not with or for you.
Linux has come a long way from its original offering. But, no matter how often you hear how easy Linux is now, there are still skeptics. To back up this claim, the desktop must be simple enough for those unfamiliar with Linux to be able to make use of it. And, the truth is that plenty of desktop distributions make this a reality.
No Linux knowledge required
It might be simple to misconstrue this as yet another “best user-friendly Linux distributions” list. That is not what we’re looking at here. What’s the difference? For my purposes, the defining line is whether or not Linux actually plays into the usage. In other words, could you set a user in front of a desktop operating system and have them be instantly at home with its usage? No Linux knowledge required.
Believe it or not, some distributions do just that. I have five I’d like to present to you here. You’ve probably heard of all of them. They might not be your distribution of choice, but you can guarantee that they slide Linux out of the spotlight and place the user front and center.
The very philosophy of Elementary OS is centered around how people actually use their desktops. The developers and designers have gone out of their way to create a desktop that is as simple as possible. In the process, they’ve de-Linux’d Linux. That is not to say they’ve removed Linux from the equation. No. Instead, what they’ve done is create an operating system that is about as neutral as you’ll find. Elementary OS is streamlined in such a way as to make sure everything is perfectly logical. From the single Dock to the clear-to-anyone Applications menu, this is a desktop that doesn’t say to the user, “You’re using Linux!” In fact, the layout itself is reminiscent of Mac, but with the addition of a simple app menu (Figure 1).
Figure 1: The Elementary OS Juno Application menu in action.
Another important aspect of Elementary OS that places it on this list is that it’s not nearly as flexible as some other desktop distributions. Sure, some users would balk at that, but having a desktop that doesn’t throw every bell and whistle at the user makes for a very familiar environment — one that neither requires or allows a lot of tinkering. That aspect of the OS goes a long way to make the platform familiar to new users.
And like any modern Linux desktop distribution, Elementary OS includes and App Store, called AppCenter, where users can install all the applications they need, without ever having to touch the command line.
Deepin not only gets my nod for one of the most beautiful desktops on the market, it’s also just as easy to adopt as any desktop operating system available. With a very simplistic take on the desktop interface, there’s very little in the way of users with zero Linux experience getting up to speed on its usage. In fact, you’d be hard-pressed to find a user who couldn’t instantly start using the Deepin desktop. The only possible hitch in that works might be the sidebar control center (Figure 2).
Figure 2: The Deepin sidebar control panel.
But even that sidebar control panel is as intuitive as any other configuration tool on the market. And anyone that has used a mobile device will be instantly at home with the layout. As for opening applications, Deepin takes a macOS Launchpad approach with the Launcher. This button is in the usual far right position on the desktop dock, so users will immediately gravitate to that, understanding that it is probably akin to the standard “Start” menu.
In similar fashion as Elementary OS (and most every Linux distribution on the market), Deepin includes an app store (simply called “Store”), where plenty of apps can be installed with ease.
You knew it was coming. Ubuntu is most often ranked at the top of most user-friendly Linux lists. Why? Because it’s one of the chosen few where a knowledge of Linux simply isn’t necessary to get by on the desktop. Prior to the adoption of GNOME (and the ousting of Unity), that wouldn’t have been the case. Why? Because Unity often needed a bit of tweaking to get it to the point where a tiny bit of Linux knowledge wasn’t necessary (Figure 3). Now that Ubuntu has adopted GNOME, and tweaked it to the point where an understanding of GNOME isn’t even necessary, this desktop makes Linux take a back seat to simplicity and usability.
Figure 3: The Ubuntu 18.04 desktop is instantly familiar.
Unlike Elementary OS, Ubuntu doesn’t hold the user back. So anyone who wants more from their desktop, can have it. However, the out of the box experience is enough for just about any user type. Anyone looking for a desktop that makes the user unaware as to just how much power they have at their fingertips, could certainly do worse than Ubuntu.
I will preface this by saying I’ve never been the biggest fan of Linux Mint. It’s not that I don’t respect what the developers are doing, it’s more an aesthetic. I prefer modern-looking desktop environments. But that old school desktop metaphor (found in the default Cinnamon desktop) is perfectly familiar to nearly anyone who uses it. With a taskbar, start button, system tray, and desktop icons (Figure 4), Linux Mint offers an interface that requires zero learning curve. In fact, some users might be initially fooled into thinking they are working with a Windows 7 clone. Even the updates warning icon will look instantly familiar to users.
Figure 4: The Linux Mint Cinnamon desktop is very Windows 7-ish.
Because Linux Mint benefits from being based on Ubuntu, it’ll not only enjoy an immediate familiarity, but a high usability. No matter if you have even the slightest understanding of the underlying platform, users will feel instantly at home on Linux Mint.
Our list concludes with a distribution that also does a fantastic job of making the user forget they are using Linux, and makes working with the usual tools a simple, beautiful thing. Melding the Budgie Desktop with Ubuntu makes for an impressively easy to use distribution. And although the layout of the desktop (Figure 5) might not be the standard fare, there is no doubt the acclimation takes no time. In fact, outside of the Dock defaulting to the left side of the desktop, Ubuntu Budgie has a decidedly Elementary OS look to it.
Figure 5: The Budgie desktop is as beautiful as it is simple.
The System Tray/Notification area in Ubuntu Budgie offers a few more features than the usual fare: Features such as quick access to Caffeine (a tool to keep your desktop awake), a Quick Notes tool (for taking simple notes), Night Lite switch, a Places drop-down menu (for quick access to folders), and of course the Raven applet/notification sidebar (which is similar to, but not quite as elegant as, the Control Center sidebar in Deepin). Budgie also includes an application menu (top left corner), which gives users access to all of their installed applications. Open an app and the icon will appear in the Dock. Right-click that app icon and select Keep in Dock for even quicker access.
Everything about Ubuntu Budgie is intuitive, so there’s practically zero learning curve involved. It doesn’t hurt that this distribution is as elegant as it is easy to use.
Give One A Chance
And there you have it, five Linux distributions that, each in their own way, offer a desktop experience that any user would be instantly familiar with. Although none of these might be your choice for top distribution, it’s hard to argue their value when it comes to users who have no familiarity with Linux.
Learn more about Linux through the free “Introduction to Linux” course from The Linux Foundation and edX.
Some of us have been zipping files on Unix and Linux systems for many decades — to save some disk space and package files together for archiving. Even so, there are some interesting variations on zipping that not all of us have tried. So, in this post, we’re going to look at standard zipping and unzipping as well as some other interesting zipping options.
The basic zip command
First, let’s look at the basic zip command. It uses what is essentially the same compression algorithm as gzip, but there are a couple important differences. For one thing, the gzip command is used only for compressing a single file where zip can both compress files and join them together into an archive. For another, the gzip command zips “in place”. In other words, it leaves a compressed file — not the original file alongside the compressed copy. Here’s an example of gzip at work:
$ gzip onefile
$ ls -l
-rw-rw-r-- 1 shs shs 10514 Jan 15 13:13 onefile.gz
And here’s zip. Notice how this command requires that a name be provided for the zipped archive where gzip simply uses the original file name and adds the .gz extension.
Let’s Encrypt is a free and open certificate authority developed by the Internet Security Research Group (ISRG). Certificates issued by Let’s Encrypt are trusted by almost all browsers today.
In this tutorial, we will explain how to use the Certbot tool to obtain a free SSL certificate for Nginx on Debian 9. We’ll also show how to configure Nginx to use the SSL certificate and enable HTTP/2.
As we enter 2019, we asked some of our O’Reilly authors and training course instructors for their thoughts on what’s in store for established players and fast-growing languages.
Python
Python’s incredible growth over the past decade shows no signs of slowing. In addition to maintaining its position as the most popular introductory language for students, scientists, and knowledge workers, Python will continue its widespread adoption in web development, DevOps, data analysis, and machine learning circles. Matt Harrison, who runs the Python and data science training and consulting company MetaSnake (and is a frequent instructor of Python courses on the O’Reilly online learning platform), offers his take:
Python has traditionally been more focused on small data, but I think that as other tools that enable big data—such as Dask and flexible Python solutions on top of Kubernetes—continue to improve, we will see Python dominate in big data as well. I’m continuing to see large companies that have traditionally used Java or proprietary languages replacing those with Python.
Do you want to do machine learning using Python, but you’re having trouble getting started?
In this post, you will complete your first machine learning project using Python.
In this step-by-step tutorial you will:
Download and install Python SciPy and get the most useful package for machine learning in Python.
Load a dataset and understand it’s structure using statistical summaries and data visualization.
Create 6 machine learning models, pick the best and build confidence that the accuracy is reliable.
If you are a machine learning beginner and looking to finally get started using Python, this tutorial was designed for you.
Let’s get started!
How Do You Start Machine Learning in Python?
The best way to learn machine learning is by designing and completing small projects.
Python Can Be Intimidating When Getting Started
Python is a popular and powerful interpreted language. Unlike R, Python is a complete language and platform that you can use for both research and development and developing production systems.
There are also a lot of modules and libraries to choose from, providing multiple ways to do each task. It can feel overwhelming.
The best way to get started using Python for machine learning is to complete a project.
It will force you to install and start the Python interpreter (at the very least).
It will given you a bird’s eye view of how to step through a small project.
It will give you confidence, maybe to go on to your own small projects.
It has been one year since the Xen Project introduced Unikraft as an incubator project. In that time, the team has made great strides in simplifying the process of building unikernels through a unified and customizable code base.
Unikraft is an incubation project under the Xen Project, hosted by the Linux Foundation, focused on easing the creation of building unikernels, which compile source code into a lean operating system that only includes the functionality required by the application logic. As containers increasingly become the way cloud applications are built, there is a need to drive even more efficiency into the way these workloads run. The ultra lightweight and small trusted compute base nature of unikernels make them ideal not only for cloud applications, but also for fields where resources may be constrained or safety is critical.
Unikraft tackles one of the fundamental downsides of unikernels: despite their clear potential, building them is often manual, time-consuming work carried out by experts. Worse, the work, or at least chunks of it, often needs to be redone for each target application. Unikraft’s goal is to provide an automated build system where non-experts can easily and quickly generate extremely efficient and secure unikernels without having to touch a single line of code. Further, Unikraft explicitly supports multiple target platforms: not only virtual machines for Xen and KVM, but also OCI-compliant containers and bare metal images for various CPU architectures.
Over the last year the lead team at NEC Laboratories Europe along with external contributors from companies like ARM and universities such as University Politehnica of Bucharest have made great strides in developing and testing Unikraft’s base functionality, including support for a number of CPU architectures, platforms, and operating system primitives. Notable updates include support for ARM64.
The Unikraft community continues to grow. Over the last year, we’ve seen impressive momentum in terms of community support and involvement:
Contributions from outside the project founders (NEC) now make up 25% of all contributions.
Active contributors rose 91%, from 2 contributors to 23.
The initial NEC code contribution was around 86KLOC: since then around 34KLOC of code have been added and/or modified.
An upcoming milestone for the project is the Unikraft v0.3 release, which will ship in February. This release includes:
Xenstore and Xen bus support
ARM32 support for Xen
ARM64 support for QEMU/KVM
X86_64 bare metal support
Networking support, including an API that allows for high-speed I/O frameworks (e.g., DPDK, netmap)
A lightweight network stack (lwip)
Initial VFS support along with an a simple but performant in-RAM filesystem
We are very excited about this coming year, where the focus will be on automating the build process and supporting higher-layer functionality and applications:
External standard libraries: musl, libuv, zlib, openssl, libunwind, libaxtls (TLS), etc.
Language environments: Javascript (v8), Python, Ruby, C++
Frameworks: Node.js, PyTorch, Intel DPDK
Applications: lighttpd, nginx, SQLite, Redis, etc.
Looking forward, in the first half of 2019 Unikraft will be concentrating its efforts towards supporting an increasing number of programming languages and applications and towards actively creating links to other unikernel projects in order to ensure that the project delivers on its promise. Stay tuned for what’s in store. If you want to take Unikraft out for a spin, to contribute or to simply find out more information about Unikraft please head over to the project’s website.
Also, if you are attending FOSDEM, February 2nd and 3rd, please stop by room AW1.121 for the talk “Unikraft: Unikernels Made Easy,” given by Simon Kuenzer. Simon, a senior systems researcher at NEC Labs and the lead maintainer of Unikraft, will be speaking all about Unikraft and giving a comprehensive overview of the project, where it’s been and what’s in store.
Want to learn more about Unikraft and connect with the Xen community at large? Registration for the annual Xen Project Developer and Design Summit is open now! Check out information on sponsorships, speaking opportunities and more here.
Despite the significant potential of blockchain, it is also difficult to find a consistent description of what it really is. A Google search for “blockchain technical papers” returns nothing but white papers for the first three screens; not a single paper is peer-reviewed.10 One of the best discussions of the technology itself is from the National Institute of Standards and Technology, but at 50-plus pages, it is a bit much for a quick read.9
The purpose of this article is to look at the basics of blockchain: the individual components, how those components fit together, and what changes might be made to solve some of the problems with blockchain technology. This technology is far from monolithic; some of the techniques can be used (at surprising savings of resources and effort) if other parts are cut away.
Because there is no single set of technical specifications, some systems that claim to be blockchain instances will differ from the system described here. Much of this description is taken from the original blockchain paper.6 While details may differ, the main ideas stay the same. …
While there are lots of different ways to implement a blockchain, all have three major components. The first of these is the ledger, which is the series of blocks that are the public record of the transactions and the order of those transactions. Second is the consensus protocol, which allows all of the members of the community to agree on the values stored in the ledger. Finally, there is the digital currency, which acts as a reward for those willing to do the work of advancing the ledger. These components work together to provide a system that has the properties of stability, irrefutability, and distribution of trust that are the goals of the system.
This is brief summary of parts of my master’s thesis and the conclusions to draw from it. This medium-story focuses on containerized application isolation. The thesis also covers segmentation of cluster networks in Kubernetes which is not discussed in this story.
Container orchestration and cloud-native computing has gained lots of traction the recent years. The adoption has increased to such level that even enterprises in finance, banking and the public sector are interested. Compared to other businesses they differ by having extensive requirements on information security and IT security.
One important aspect is how containers could be used in production environments while maintaining system separation between applications. As such enterprises uses private clouds powered by bare-metal virtualization, the separation loss upon migrating to a container orchestrated environment is not negligible. It is in this scope that my thesis is written –with the Swedish Police Authority as the target client.
The specific research question that the thesis explores is the following:
How can Docker and Kubernetes support the separation of applications for the Swedish Police Authority compared with virtual machines powered by the bare-metal hypervisor ESXi?
That question has a lot to unwrap. To break this down, let’s start by looking in to the common denominator — the applications.
AI is the fastest growing field in enterprise tech. Here’s how to get an AI job you will love.
AI job listings have become the fastest growing categoryon LinkedIn, and Indeed is packed with listings. But most job requisitions seek a computer scientist type with a PhD in neural networks or some other years-long study. The trick is to look past those, and you’ll find that what many companies need can’t be outsourced or given to a freshly minted college grad: an IT pro with enterprise-scale experience who also knows how to deliver on a machine learning project.
Machine learning is where the jobs are
Here’s the secret: There are plenty of AI-related jobs that aren’t advanced science but simply applying new machine learning features from cloud services giants to familiar IT environments. “Most ML jobs aren’t about advancing ML technology and algorithms,” says Ross Mead, founder and CEO of robotics software startup Semio and an industry consultant in AI with a PhD from the University of Southern California. “The money in AI for most companies is using ML for better business intelligence.”
That means using turnkey ML packages to analyze internal data—customer behavior, sales, etc.—to look for patterns that indicate likely business success. Machine learning is different from deep learning, the more esoteric field of AI that it is often confused with.
