We're halfway through the year already? Madness. Even with all the craziness of 2020 going on, lots of games still managed to get out of the door. I know, I can't believe 2020 isn't over yet either. Thankfully there's plenty of games to take our minds off everything from murder hornets to COVID19 and more. Now we're at the halfway point, let's think about some of the top Linux releases of 2020 so far. This list is extremely subjective of course, this is just my personal pick on the top 15. Think of it as a starting point for good games to look at if you're stuck for something. In no particular order, going up to June 30 and I'm cheating just a little bit by including some Early Access titles too.

MergeGames, together with developers Red Blue Games have now released their action-adventure Sparklite on Linux along with a fresh content update. Originally released towards the end of 2019, Sparklite is an action-adventure set in the whimsical and ever-changing land of Geodia. With gorgeous pixel art and a top-down perspective, you battle foes using an arsenal of gadgets, guns, and gear. If you played and enjoyed Moonlighter, you would probably feel right at home with Sparklite too.

When it comes to video gaming, racing is the most popular genre, whether it is mobile gaming, pc gaming, or on any other gaming console. Racing games on Android have so much competition between them and the genre is crowded with tons of racing games. Every racing game has its own unique features and every gamer has his or her own preferences. In this genre, there is a large number of excellent free-to-play and paid games available for mobile users. This article covers the best racing games, in a variety of settings and with many different features for each unique user’s needs.

While this version is a release-randidate (with an expected full release within 3 months) it is the most stable and complete free COBOL compiler ever available.

This is arguably Rule Zero for a secure Git repository. As a project maintainer, whether you started it yourself or you’ve adopted it from someone else, it’s your job to know the contents of your own repository. You might not have a memorized list of every file in your codebase, but you need to know the basic components of what you’re managing. Should a stray file appear after a few dozen merges, you’ll be able to spot it easily because you won’t know what it’s for, and you’ll need to inspect it to refresh your memory. When that happens, review the file and make sure you understand exactly why it’s necessary. [...] Third-party libraries are no exception to this rule. While it’s one of the many benefits of open source that you can freely re-use and re-distribute code you didn’t write, there are many good reasons not to house a third-party library in your own repository. First of all, you can’t exactly vouch for a third party, unless you’ve reviewed all of its code (and future merges) yourself. Secondly, when you copy third party libraries into your Git repo, it splinters focus away from the true upstream source. Someone confident in the library is technically only confident in the master copy of the library, not in a copy lying around in a random repo. If you need to lock into a specific version of a library, either provide developers with a reasonable URL the release your project needs or else use Git Submodule.

Diversity and open source can help fix the software developer skills gap, argued Scala contributor and Carnegie Mellon Assistant Professor Heather Miller in a keynote talk at the virtual Open Source Summit North America. Miller examined the IT and computer-related skills shortage from a US perspective. "The Department of Labor statistics show that in 2017 there were over 500,000 computing-related jobs open in the US that were not filled. They project that this number is going to get a lot higher. If this trend continues, it's obvious that there's no way these posts can be filled by computer science graduates." There are, however, many new people coming into the profession, not necessarily computer science graduates, and a notable point of recent StackOverflow research is the large number of respondents who consider themselves professional and have been coding for less than five years – 39.6 per cent in the latest survey. "The years of experience of professional software engineers, that is going down," said Miller.

It seems GitHub is planning to rename the default brach for newly created repositories from "master" to "main". It's incredible how much positive PR you can get with a one line configuration change, while still working together with the ICE. However, this post is not about bashing GitHub. Changing the default branch for newly created repositories is good. And you also should do that for the ones you create with git init locally. But what about all the repositories out there? GitHub surely won't force-rename those branches, but we can! Ian will do this as he touches the individual repositories, but I tend to forget things unless I do them immediately…

To address the first point, WPE founding engineer, Žan Doberšek enabled software rasterizing support in WPE and its FDO backend. This is great because it allows WPE to run on machines without GPU (like continuous integration builders, test bots) but also “in the cloud” where machines with GPU are less affordable than bare metal! Following up, I enabled this feature in GstWPE. The source element caps template now has video/x-raw, in addition to video/x-raw(memory:GLMemory). To force swrast, you need to set the LIBGL_ALWAYS_SOFTWARE=true environment variable. The downside of swrast is that you need a good CPU. Of course it depends on the video resolution and framerate you want to target. On the latency front, I decided to switch from RTMP to WebRTC! This W3C spec isn’t only about video chat! With WebRTC, sub-second live one-to-many broadcasting can be achieved, without much efforts, given you have a good SFU. For this demo I chose Janus, because its APIs are well documented, and it’s a cool project! I’m not sure it would scale very well in large deployments, but for my modest use-case, it fits very well. Janus has a plugin called video-room which allows multiple participants to chat. But then imagine a participant only publishing its video stream and multiple “clients” connecting to that room, without sharing any video or audio stream, one-to-many broadcasting. As it turns out, GStreamer applications can already connect to this video-room plugin using GstWebRTC! A demo was developed by tobiasfriden and saket424 in Python, it recently moved to the gst-examples repository. As I kind of prefer to use Rust nowadays (whenever I can anyway) I ported this demo to Rust, it was upstreamed in gst-examples as well. This specific demo streams the video test pattern to a Janus instance. Adapting this Janus demo was then quite trivial. By relying on a similar video mixer approach I used for the first GstWPE demo, I had a GstWPE-powered WebView streaming to Janus.

PHP is used extensively on the web. How new features, security fixes, and bug fixes make their way into a release is important to understand. Likewise, understanding what can be expected in community support for previous releases is even more important. Since PHP-based sites are typically exposed to the Internet, keeping up-to-date is not something a security-minded administrator can afford to ignore. PHP has not always had a formal release process and corresponding time frame for support; the official policy the project has now wasn't adopted until 2011. Before then, the decisions of when to make releases and how long to support them were both made less formally by key members of the community. Let's start with PHP versioning, where the project is more or less dependable. The versioning of PHP releases aims to follow Semantic Versioning. Major releases such as 3.0 and 4.0 always come with backward-compatibility breaks. Minor versions, such as 4.1 and 4.2, fix bugs and add new features that are backward-compatible in relation to the major release. Patch releases, such as 4.1.1, tend to be strictly for important bug fixes and should never break backward compatibility.

Following Intel publishing the initial Advanced Matrix Extensions (AMX) documentation at the end of June, the open-source/Linux bring-up has continued for these new CPU instruction set extensions set to premiere with Sapphire Rapids next year.

Intel's open-source oneDNN library, which was formerly known as MKL-DNN and DNNL for this deep neural network library now living under the oneAPI umbrella, continues working on some big performance advancements for its 2.0 release. Intel on Thursday released oneDNN 2.0 Beta 7 and with it comes more Intel CPU performance optimizations around convolutional neural networks, binary primitive performance for the broadcast case, BFloat16 and FP32 weights gradient convolutions, INT8 convolutions with 1x1 kernel and spatial strides, and a variety of other specific areas within this deep learning library seeing optimizations.

A step by step, detailed video tutorial showing how to make Ubuntu look like macOS. Perfect example of the customization power of Linux desktop.

The wheel of software turns, and apps come and go. But the end of development does not always mean the end of usefulness. Sometimes, programs stubbornly remain around, offering a complete experience that can withstand the test of time. Several weeks ago, we talked about how you can preserve old applications with snaps. Today, we would like to expand on this concept and talk about Shutter, a feature-rich screenshot application that was rather popular several years ago. Its development has stalled in recent years, and it has become more difficult to install and run it on newer versions of various Linux distributions. But Shutter has gained a new life as a snap.

Do you have a big data center? Do you have terabytes of confidential data stored in that data center? Are you worried that your data might be exposed to malicious attacks? One of the most prominent security features of storage solutions is encryption at rest. This blog will explain this in more detail and how it is implemented in Charmed Ceph, Canonical’s software-defined storage solution.

Following the discussions last month over restricting access to dmesg / kernel logs on Ubuntu in matching the behavior of other Linux distributions for better security practices, Ubuntu 20.10 indeed is moving forward with these plans where dmesg access would require root privileges. In recent times more Linux distributions have been restricting access to dmesg over the possibility of kernel addresses being leaked or other potentially sensitive bits while as it stands now on Ubuntu there is free reign on multi-user systems to have unprivileged users read dmesg output.

Conference tickets are available on our registration page. We hope to see lots of you at the conference from July 23-26. Rest assured that we’ll make this a great event again — even within the limitations of running the conference online.

Flask web applications are highly customizable by developers thanks to the framework's extension-based architecture, but that flexibility can sometimes lead to more errors when you run the application due to rough edges between the libraries. Reporting errors is crucial to running a well-functioning Flask web application, so this tutorial will guide you through adding a free, basic Sentry configuration to a fresh Flask project.

PyCharm EAP #3 is out and it’s almost releasing time!! If you are like us you are also looking forward to the end of the month! We have been talking about new features for the last month and today we will take a deeper look into two very exciting ones. For the full list, check our release notes.

In this episode, we return to the homeschool application that I’m building. I’m in the final stretch of changes that need to happen to make the product minimally viable. We worked on a template, wrote some model methods, and did a bunch of automated testing. We started by adding students to the context of the students index page. With the students in the context, we updated the index page to display the list of students. After the students were available, we had to check their enrolled status in a school year. That logic doesn’t belong in the template so we worked out the changes needed for the view.

As you might have noticed, for two weeks we haven’t made our usual newsletter mailouts. But we definitely weren’t wasting any time. CheckiO team was actually preparing some important updates, which we want to share with you. That’s a common knowledge that CheckiO originated from the idea of practical learning through shared solutions. This means that in our portals you can learn not only by solving the coding tasks, but also by checking out and analyzing the solutions made by other users. In view of this, our next step became a logical continuation of this ideology. Since the creation of CheckiO, we’ve gathered nearly half a million of different solutions. Now, using the Solution Search feature, which becomes available from the 2nd Level, you can easily find any solution you need. Like you can look for the usage examples of an itertools.groupby function. You just need to enter it into the search field and you’ll see multiple solutions. Or you can type ‘itertools’ and you’ll be presented with all of the solutions where this module had been used. It’s fast, efficient and quite handy. The feature is still in the beta testing mode though.

Kubeflow Pipelines are a great way to build portable, scalable machine learning workflows. It is a part of the Kubeflow project that aims to reduce the complexity and time involved with training and deploying machine learning models at scale. For more on Kubeflow, read our Kubernetes for data science: meet Kubeflow post. In this blog series, we demystify Kubeflow pipelines and showcase this method to produce reusable and reproducible data science. In Part 1, we covered WHY Kubeflow brings the right standardization to data science workflows. Now, let’s see HOW you can accomplish that with Kubeflow Pipelines. In Part 2 of this blog series, we’ll work on building your first Kubeflow Pipeline as you gain an understanding of how it’s used to deploy reusable and reproducible ML pipelines.