The Wikimedia Foundation, the American non-profit organization that owns the internet domain names of many movement projects and hosts sites like Wikipedia, has decided to migrate their code repositories from Gerrit to Gitlab. Slashdot reader nfrankel shares the announcement: For the past two years, our developer satisfaction survey has shown that there is some level of dissatisfaction with Gerrit, our code review system. This dissatisfaction is particularly evident for our volunteer communities. The evident dissatisfaction with code review, coupled with an internal review of our CI tooling and practice makes this an opportune moment to revisit our code review choices. While Gerrit's workflow is in many respects best-in-class, its interface suffers from usability deficits, and its workflow differs from mainstream industry practices. This creates barriers to entry for the community and slows onboarding for WMF technical staff. In addition, there are a growing number of individuals and teams (both staff and non-staff) who are opting to forgo the use of Gerrit and instead use a third-party hosted option such as GitHub. Reasons vary for the choice to use third-party hosting but, based on informal communication, there are 3 main groupings: lower friction to create new repositories; easier setup and self-service of Continuous Integration configuration; and more familiarity with pull-request style workflows.

All these explanations point to friction in our existing code-review system slowing development rather than fostering it. The choice to use third-party code-hosting hurts our collaboration (both internal and external), adds to the confusion of onboarding, and makes it more difficult to maintain code standards across repositories. At the same time, there is a requirement that all software which is deployed to Wikimedia production is hosted and deployed from Gerrit. If we fail to address the real usability problems that users have with Gerrit, people will continue to launch and build projects on whatever system it is they prefer -- Wikimedia's GitHub already contains 152 projects, the Research team has 127 projects.

This raises the question: if Gerrit has identifiable problems, why can't we solve those problems in Gerrit? Gerrit is open source (Apache licensed) software; modifications are a simple matter of programming. [...] Upstream has improved the UI in recent releases, and releases have become more frequent; however, upgrade path documentation is often lacking. The migration from Gerrit 2 to Gerrit 3, for example, required several upstream patchsets to avoid the recommended path of several days of downtime. This is the effort required to maintain the status quo. Even small improvements require effort and time as, often, our use-case is very different from the remainder of the Gerrit community.

An anonymous reader quotes a report from Ars Technica: Researchers have extracted the secret key that encrypts updates to an assortment of Intel CPUs, a feat that could have wide-ranging consequences for the way the chips are used and, possibly, the way they're secured. The key makes it possible to decrypt the microcode updates Intel provides to fix security vulnerabilities and other types of bugs. Having a decrypted copy of an update may allow hackers to reverse engineer it and learn precisely how to exploit the hole it's patching. The key may also allow parties other than Intel -- say a malicious hacker or a hobbyist -- to update chips with their own microcode, although that customized version wouldn't survive a reboot.

"At the moment, it is quite difficult to assess the security impact," independent researcher Maxim Goryachy said in a direct message. "But in any case, this is the first time in the history of Intel processors when you can execute your microcode inside and analyze the updates." Goryachy and two other researchers -- Dmitry Sklyarov and Mark Ermolov, both with security firm Positive Technologies -- worked jointly on the project. The key can be extracted for any chip -- be it a Celeron, Pentium, or Atom -- that's based on Intel's Goldmont architecture. In a statement, Intel officials wrote: "The issue described does not represent security exposure to customers, and we do not rely on obfuscation of information behind red unlock as a security measure. In addition to the INTEL-SA-00086 mitigation, OEMs following Intel's manufacturing guidance have mitigated the OEM specific unlock capabilities required for this research. The private key used to authenticate microcode does not reside in the silicon, and an attacker cannot load an unauthenticated patch on a remote system."

An anonymous reader shares a column: In the distant past, there was a proverbial "digital divide" that bifurcated workers into those who knew how to use computers and those who didn't. Young Gen Xers and their later millennial companions grew up with Power Macs and Wintel boxes, and that experience made them native users on how to make these technologies do productive work. Older generations were going to be wiped out by younger workers who were more adaptable to the needs of the modern digital economy, upending our routine notion that professional experience equals value. Of course, that was just a narrative. Facility with using computers was determined by the ability to turn it on and log in, a bar so low that it can be shocking to the modern reader to think that a "divide" existed at all. Software engineering, computer science and statistics remained quite unpopular compared to other academic programs, even in universities, let alone in primary through secondary schools. Most Gen Xers and millennials never learned to code, or frankly, even to make a pivot table or calculate basic statistical averages.

There's a sociological change underway though, and it's going to make the first divide look quaint in hindsight. Over the past two or so years, we have seen the rise of a whole class of software that has been broadly (and quite inaccurately) dubbed "no-code platforms." These tools are designed to make it much easier for users to harness the power of computing in their daily work. That could be everything from calculating the most successful digital ad campaigns given some sort of objective function, or perhaps integrating a computer vision library into a workflow that calculates the number of people entering or exiting a building. The success and notoriety of these tools comes from the feeling that they grant superpowers to their users. Projects that once took a team of engineers some hours to build can now be stitched together in a couple of clicks through a user interface. That's why young startups like Retool can raise at nearly a $1 billion valuation and Airtable at $2.6 billion, while others like Bildr, Shogun, Bubble, Stacker and dozens more are getting traction among users.

The Python programming language "is a big hit for machine learning," read a headline this week at ZDNet, adding "But now it needs to change."

Python is the top language according to IEEE Spectrum's electrical engineering audience, yet you can't run Python in a browser and you can't easily run it on a smartphone. Plus no one builds games in Python these days. To build browser applications, developers tend to go for JavaScript, Microsoft's type-safety take on it, TypeScript, Google-made Go, or even old but trusty PHP. On mobile, why would application developers use Python when there's Java, Java-compatible Kotlin, Apple's Swift, or Google's Dart? Python doesn't even support compilation to the WebAssembly runtime, a web application standard supported by Mozilla, Microsoft, Google, Apple, Intel, Fastly, RedHat and others.

These are just some of the limitations raised by Armin Ronacher, a developer with a long history in Python who 10 years ago created the popular Flask Python microframework to solve problems he had when writing web applications in Python. Austria-based Ronacher is the director of engineering at US startup Sentry — an open-source project and tech company used by engineering and product teams at GitHub, Atlassian, Reddit and others to monitor user app crashes due to glitches on the frontend, backend or in the mobile app itself... Despite Python's success as a language, Ronacher reckons it's at risk of losing its appeal as a general-purpose programming language and being relegated to a specific domain, such as Wolfram's Mathematica, which has also found a niche in data science and machine learning...

Peter Wang, co-founder and CEO of Anaconda, maker of the popular Anaconda Python distribution for data science, cringes at Python's limitations for building desktop and mobile applications. "It's an embarrassing admission, but it's incredibly awkward to use Python to build and distribute any applications that have actual graphical user interfaces," he tells ZDNet. "On desktops, Python is never the first-class language of the operating system, and it must resort to third-party frameworks like Qt or wxPython." Packaging and redistribution of Python desktop applications are also really difficult, he says.

An anonymous reader quotes a report from The New York Times: In early September, the City Council in Portland, Ore., met virtually to consider sweeping legislation outlawing the use of facial recognition technology. The bills would not only bar the police from using it to unmask protesters and individuals captured in surveillance imagery; they would also prevent companies and a variety of other organizations from using the software to identify an unknown person. During the time for public comments, a local man, Christopher Howell, said he had concerns about a blanket ban. He gave a surprising reason. "I am involved with developing facial recognition to in fact use on Portland police officers, since they are not identifying themselves to the public," Mr. Howell said. Over the summer, with the city seized by demonstrations against police violence, leaders of the department had told uniformed officers that they could tape over their name. Mr. Howell wanted to know: Would his use of facial recognition technology become illegal?

Portland's mayor, Ted Wheeler, told Mr. Howell that his project was "a little creepy," but a lawyer for the city clarified that the bills would not apply to individuals. The Council then passed the legislation in a unanimous vote. Mr. Howell was offended by Mr. Wheeler's characterization of his project but relieved he could keep working on it. "There's a lot of excessive force here in Portland," he said in a phone interview. "Knowing who the officers are seems like a baseline." Mr. Howell, 42, is a lifelong protester and self-taught coder; in graduate school, he started working with neural net technology, an artificial intelligence that learns to make decisions from data it is fed, such as images. He said that the police had tear-gassed him during a midday protest in June, and that he had begun researching how to build a facial recognition product that could defeat officers' attempts to shield their identity. Mr. Howell is not alone in his pursuit. Law enforcement has used facial recognition to identify criminals, using photos from government databases or, through a company called Clearview AI, from the public internet. But now activists around the world are turning the process around and developing tools that can unmask law enforcement in cases of misconduct. The report also mentions a few other projects around the world that are using facial recognition tools against the police.

An online exhibit called "Capture," was created by artist Paolo Cirio and includes photos of 4,000 faces of French police officers. It's currently down because France's interior minister threatened legal action against Mr. Cirio but he hopes to republish them.

Andrew Maximov, a technologist from Belarus, uploaded a video to YouTube that demonstrated how facial recognition technology could be used to digitally strip away masks from police officers.

The report also notes that older attempts to identify police officers have relied on crowdsourcing. For example, news service ProPublica asks readers to identify officers in a series of videos of police violence. There's also the OpenOversight, a "public searchable database of law enforcement officers" that asks people to upload photos of uniformed officers and match them to the officers' names or badge numbers.

An anonymous reader writes: Kite, which suggests code snippets for developers in real time, today added support for 11 more programming languages, bringing its total to 13. In addition to Python and JavaScript, Kite's AI-powered code completions now support TypeScript, Java, HTML, CSS, Go, C, C#, C++, Objective C, Kotlin, and Scala. (The team chose the 11 languages by triangulating the StackOverflow developer survey, Redmonk's language rankings, and its own developer submissions.) AI that helps developers is growing in popularity, with startups like DeepCode offering AI-powered code reviews and tech giants like Microsoft trying to apply AI to the entire application developer cycle. Kite stands out from the pack with 350,000 monthly developers using its AI developer tool. Kite debuted privately in April 2016 before publicly launching a cloud-powered developer sidekick in March 2017. The company raised $17 million in January 2019 and ditched the cloud to run its free offering locally. In May, Kite added JavaScript support, launched a Pro plan with advanced line-of-code completions for Python, and updated its engine to use deep learning, a type of machine learning.

"It's the Great Pumpkin, Charlie Brown" will no longer air on broadcast TV this year, but it can be streamed on Apple+. This marks the first time since 1965 that the Peanuts special won't air on TV. People.com reports: Instead of airing on broadcast television, the Peanuts animated classics will be streaming on AppleTV+ as part of an expanded partnership with WildBrain, Peanuts Worldwide and Lee Mendelson Film Productions, according to a press release. It's the Great Pumpkin, Charlie Brown, A Charlie Brown Thanksgiving and A Charlie Brown Christmas will be offered on the streaming platform for free for certain dates this fall and winter.

Great Pumpkin will be available for free on the streamer from October 30 to November 1, the Thanksgiving special from November 25 to 27, and the Christmas special from December 11 to 13. But it's not just the iconic holiday specials that are moving to AppleTV+. Original Peanuts programming surrounding Mother's Day, Earth Day, New Year's Eve and back-to-school season will also be produced by WildBrain to air on AppleTV+. Included in the programming will be The Snoopy Show and season 2 of Snoopy in Space.

Tim Nolet, founder and chief technology officer of Checkly, tweeted on Friday: Oh @awscloud I really do love you! But next time you fork my OS project and present it as your new service, give the maintainers a short "nice job, kids" or something. Not necessary as per the APLv2 license, but still, ya know?

Wojtek Borowicz interviews David Heinemeier Hansson, the creator of the popular Ruby on Rails web development framework: Wojtek Borowicz: Software methodology is an industry of its own. There is Scrum, and Agile, and coaches, and books, and all of that. But you and your team at Basecamp don't follow these practices. Why?

DHH: First of all, our approach to software development is heavily inspired by the Agile Manifesto and the Agile values. It is not so much inspired by the Agile practices as they exist today. A lot of Agile software methodologies focus on areas of product development that are not where the hard bits lie. They are so much about the procedural structures. Software, in most cases, is inherently unpredictable, unknowable, and unshaped. It's almost like a gas. It can fit into all sorts of different openings from the same basic idea. The notion of trying to estimate how long a feature is going to take doesn't work because you don't know what you're building and because humans are terrible at estimating anything. The history of software development is one of late or cancelled projects. If you were to summarize the entire endeavor of software development, you'd say: 'The project ran late and it got canceled.' Planning work doesn't work, so to speak.

What we do at Basecamp we chose to label Shape Up, simply because that is where we find the hard work to be. We're trying to just accept the core constraint that it is impossible to accurately specify what software should do up front. You can only discover what software should do within constraints. But it's not like we follow the idea that it's done when it's done, either. That's an absolute abdication of product management thinking. What we say instead is: don't do estimates, do budgets. The core of Shape Up is about budgets. Not how long is something going to take but what is something worth. Because something could take a week or four months. What is it worth? [...]

Wojtek Borowicz: So the problem with those methodologies is they put too much focus on estimating, which is inherently impossible with software?

DHH: I'd go even further and say that estimation is bullshit. It's so imprecise as to be useless, even when you're dealing with fixed inputs. And you're not. No one is ever able to accurately describe what a piece of software should do before they see the piece of software. This idea that we can preemptively describe what something should do before we start working on it is bunk. Agile was sort of onto this idea that you need running software to get feedback but the modern implementations of Agile are not embracing the lesson they themselves taught.

An anonymous reader quotes a report from Insider: Netflix is killing its most interesting shows in their infancy and it could be the streaming giant's downfall. In the seven years that Netflix has produced original content, the world of TV streaming has dramatically changed. Now Netflix is getting left behind in the race it started. Many of its unique and ambitious shows have been canceled before they could reach their full potential. And Netflix keeps churning out more shows each year, without replicating the breakout success of 2016's "Stranger Things."

Statements from executives have described the cancellations as the result of a cost analysis that tells Netflix a longer-running show won't lead to new subscribers. Still, with syndicated shows such as "The Office" and "Friends" leaving its platform and a string of disappointing cancellations, including "Glow," Netflix has set itself up for a disaster when it comes to its reputation as a TV-watcher's must-have service. In 2020 alone, Netflix has canceled 18 original series. Of those, 14 had only one season. [...] TV lovers in these fandoms can only be burned so many times before they stop investing. Why should a Netflix subscriber spend 10 hours watching a new show if there's a decent chance they'll never see it end?

Python has reached "a new all-time high" on TIOBE's index of programming language popularity. TechRadar reports: Java's days as the world's second most popular programming language could be numbered according to Tiobe's latest programming language rankings which show Python is becoming increasingly popular among developers. The firm's Index for October 2020 shows that Java has been overtaken by C as the world's most popular programming language when compared to the same period last year. Python remains in third place but it's quickly closing the gap between it and Java. According to Tiobe CEO Paul Jensen, C and Java have held the top two spots consistently for the past two decades. However, the 25-year-old programming language Java is approaching its "all time low" in popularity as it has fallen by 4.32 percentage points when compared to where it stood in October of last year. Tiobe ranks programming languages in its popularity index based on the number of hits each language gets across 25 search engines.
RedMonk's rankings already show Python as more popular than Java — the first time since 2012 that Java isn't one of their top two most popular languages. And TIOBE's CEO says "Let's see what will happen the next few months."

Here's their October rankings for the top 10 most popular programming languages.

• C
• Java
• Python
• C++
• C#
• Visual Basic
• JavaScript
• PHP
• R
• SQL

And coming in at #11 is Perl.

After 44 years, there's finally a better way to find approximate solutions to the notoriously difficult traveling salesperson problem. From a report: When Nathan Klein started graduate school two years ago, his advisers proposed a modest plan: to work together on one of the most famous, long-standing problems in theoretical computer science. Even if they didn't manage to solve it, they figured, Klein would learn a lot in the process. He went along with the idea. "I didn't know to be intimidated," he said. "I was just a first-year grad student -- I don't know what's going on." Now, in a paper posted online in July, Klein and his advisers at the University of Washington, Anna Karlin and Shayan Oveis Gharan, have finally achieved a goal computer scientists have pursued for nearly half a century: a better way to find approximate solutions to the traveling salesperson problem. This optimization problem, which seeks the shortest (or least expensive) round trip through a collection of cities, has applications ranging from DNA sequencing to ride-sharing logistics. Over the decades, it has inspired many of the most fundamental advances in computer science, helping to illuminate the power of techniques such as linear programming. But researchers have yet to fully explore its possibilities -- and not for want of trying. The traveling salesperson problem "isn't a problem, it's an addiction," as Christos Papadimitriou, a leading expert in computational complexity, is fond of saying.

Most computer scientists believe that there is no algorithm that can efficiently find the best solutions for all possible combinations of cities. But in 1976, Nicos Christofides came up with an algorithm that efficiently finds approximate solutions -- round trips that are at most 50% longer than the best round trip. At the time, computer scientists expected that someone would soon improve on Christofides' simple algorithm and come closer to the true solution. But the anticipated progress did not arrive. "A lot of people spent countless hours trying to improve this result," said Amin Saberi of Stanford University. Now Karlin, Klein and Oveis Gharan have proved that an algorithm devised a decade ago beats Christofides' 50% factor, though they were only able to subtract 0.2 billionth of a trillionth of a trillionth of a percent. Yet this minuscule improvement breaks through both a theoretical logjam and a psychological one. Researchers hope that it will open the floodgates to further improvements.

An anonymous reader shares a report: On Tuesday, Congress revealed whether it thinks Amazon, Apple, Facebook, and Google are sitting on monopolies. In some cases, the answer was yes. But also, one app developer revealed to Congress that it -- just like WordPress -- had been forced to monetize a largely free app. That developer testified that Apple had demanded in-app purchases (IAP), even though Apple had approved its app without them two years earlier -- and that when the dev dared send an email to customers notifying them of the change, Apple threatened to remove the app and blocked all updates. That developer was ProtonMail, makers of an encrypted email app, and CEO Andy Yen had some fiery words for Apple in an interview with The Verge this week. We've known for months that WordPress and Hey weren't alone in being strong-armed by the most valuable company in the world, ever since Stratechery's Ben Thompson reported that 21 different app developers quietly told him they'd been pushed to retroactively add IAP in the wake of those two controversies. But until now, we hadn't heard of many devs willing to publicly admit it. They were scared.

And they're still scared, says Yen. Even though Apple changed its rules on September 11th to exempt "free apps acting as a stand-alone companion to a paid web based tool" from the IAP requirement -- Apple explicitly said email apps are exempt -- ProtonMail still hasn't removed its own in-app purchases because it fears retaliation from Apple, he says. He claims other developers feel the same way: "There's a lot of fear in the space right now; people are completely petrified to say anything." [...] "For the first two years we were in the App Store, that was fine, no issues there," he says. (They'd launched on iOS in 2016.) "But a common practice we see ... as you start getting significant uptake in uploads and downloads, they start looking at your situation more carefully, and then as any good Mafia extortion goes, they come to shake you down for some money."

Alphabet's Google got a mixed reception at the U.S. Supreme Court as it sought to overturn a ruling that could force the company to pay billions of dollars for improperly using Oracle's copyrighted code in the Android operating system. From a report: Holding a low-tech telephone session in one of the biggest software fights in American history, the justices on Wednesday questioned Google's contention that it had no way to replicate the code without forcing millions of software developers to learn a new programming language. Justice Neil Gorsuch told Google's lawyer that Apple and other companies have "come up with phones that work just fine without engaging in this kind of copying." But Gorsuch also raised the possibility of returning the case to a federal appeals court for another look at Google's contention that it engaged in legitimate "fair use" of Oracle's Java programming language. Oracle says it's entitled to at least $8.8 billion in damages. A jury found that Google's code copying was a legitimate fair use, but a federal appeals court reversed that finding.

An anonymous reader quotes Ars Technica: Sourcegraph, a company specializing in universal code search, polled more than 500 North American software developers to identify issues in code complexity and management. Its general findings are probably no surprise to most Ars readers — software has gotten bigger, more complex, and much more important in the past ten years — but the sheer scope can be surprising... When asked how the size of the codebase across their entire company, measured in megabytes and the number of repositories, has changed in the past decade, over half (51%) of software development stakeholders reported they have more than 100 times the volume of code they had 10 years ago. And a staggering 18% say they have 500 times more code.
Ars also reports another surprising finding: 91% of the surveyed developers said their non-technology company "functions more like a technology company than it did ten years ago.

"This won't surprise anyone who has noticed firms like Walmart Labs sponsoring open source technology conferences and delivering presentations."

"To teach the next generation of computer scientists the basics of Python programming, Microsoft recently announced a partnership with NASA to create a series of lessons based on space exploration efforts," reports TechRepublic: Overall, the project includes three different NASA-inspired lessons... The Introduction to Python for Space Exploration lesson will provide students with "an introduction to the types of space exploration problems that Python and data science can influence." Made up of eight units in total, this module also details the upcoming Artemis lunar exploration mission.

In another learning path, students will learn to design an AI model capable of classifying different types of space rocks depicted in random photos, according to Microsoft. However, the company recommends a "basic understanding of Python for Data Science" as a prerequisite for this particular lesson. The last of the three learning paths serves as an introduction to machine learning and demonstrates ways these technologies can help assist with space exploration operations.

Students are presented real-world NASA challenges, particularly rocket launch delays, and learn how the agency can leverage machine learning to resolve the issues... Microsoft also announced partnerships with Wonder Woman 1984 and Smithsonian Learning Labs to curate five additional programming lessons for students.

Netflix CEO Reed Hastings, writing at CNBC: In the first few years of Netflix, we were growing fast and needed to hire more software engineers. With my new understanding that high talent density would be the engine of our success, we focused on finding the top performers in the market. In Silicon Valley, many of them worked for Google, Apple, and Facebook -- and they were being paid a lot. We didn't have the cash to lure them away in any numbers. But, as an engineer, I was familiar with a concept that has been understood in software since 1968, referred to as the "rock-star principle." The rock-star principle is rooted in a famous study that took place in a basement in Santa Monica, California. At 6:30 a.m., nine trainee programmers were led into a room with dozens of computers. Each was handed a manila envelope, explaining a series of coding and debugging tasks they would need to complete to their best ability in the next 120 minutes. The researchers expected that the best programmer would outperform his average counterpart by a factor of two or three. But it turned out that the most skilled programmer far outperformed the worst. He was 20 times faster at coding, 25 times faster at debugging, and 10 times faster at program execution than the programmer with the lowest marks.

This study has caused ripples across the software industry since it was published, as managers grapple with how some programmers can be worth so much more than their perfectly adequate colleagues. With a fixed amount of money for salaries and a project I needed to complete, I had a choice: Hire 10 to 25 average engineers, or hire one "rock-star" and pay significantly more than what I'd pay the others, if necessary. Over the years, I've come to see that the best programmer doesn't add 10 times the value. He or she adds more like a 100 times. Bill Gates, whom I worked with while on the Microsoft board, purportedly went further. He is often quoted as saying, "A great lathe operator commands several times the wages of an average lathe operator, but a great writer of software code is worth 10,000 times the price of an average software writer." In the software industry, this is a known principle (although still much debated). I started thinking about where this model applied outside the software industry. The reason the rock-star engineer is so much more valuable than his counterparts isn't unique to programming. The great software engineer is incredibly creative and can see conceptual patterns that others can't.

"To celebrate Java's 25th anniversary this year and the latest release of Java 15, JetBrains has compiled data from multiple sources to look at what the current state of the language," reports SD Times: The largest concentration of Java developers is in Asia, where 2.5 million developers use it as their primary language. JetBrains believes this may be due to the fact that it is common to hire offshore developers in countries like China and India to build Android apps. "We might have expected the USA to have a high percentage of Java users, but it also makes a lot of sense that they don't. There is a big technology stack to choose from and often a lot of the tech companies are at the forefront of that stack, so it could be that developers there don't need the power or stability of Java and are using languages that allow them to build and test quickly," JetBrains wrote in a post.
The post on JetBrains notes that the six countries with the highest percentage of developers using Java as their primary language are: China, South Korea, India, Germany, Spain, and Brazil: The reasons Java is most likely so popular in the first 6 countries include the free use of Java, governmental support, and open-source... Germany is also very high which could be attributed to Java being the most popular language in Germany for software engineers as it is used to build highly scalable applications for a multitude of industries. Most enterprise services rely on Java to power the applications that enable the day-to-day running of businesses, such as payroll, inventory management, reporting, and so on. Germany also has a big financial sector that uses Java heavily for their homegrown tech, such as trading bots, retail banking systems, and other applications that the finance industry requires in order to remain competitive...

According to the State of the Developer Ecosystem Survey 2020, more than a third of professional developers use Java as a primary language and Java remains the second primary language among professional developers after JavaScript. Expert analysis: It is not surprising to see JavaScript and Java taking the leading positions as they are kind of paired together; developers who work with Java often write their frontend and any quick scripts in JavaScript. Python is probably third place due to the spread of machine learning. In general, we expect the web to be a big part of the developer ecosystem and so JavaScript, HTML and CSS, and PHP will always have solid standing. SQL is also always going to be around as there isn't much that doesn't require databases in some capacity. C++ is also kind of a solid language in that it is used for a lot of embedded applications, so it won't be disappearing off the charts any time soon. C# though seems to be losing ground, and I guess if Java is high then C# will be low, as they are both very similar in terms of capabilities.

As to why I think Java is so high in the sphere of professional development — it's similar to what was mentioned about Germany. Most enterprise business services rely on Java to make them tick along. It's not just the IT sector either — almost every company, be it in distribution, manufacturing, or banking, has IT services as part of their infrastructure, and these services, such as payroll or inventory management, are generally built with Java in the backend. So Java is used a lot by professional developers who work for these companies.

Michael Ilseman, an engineer on the Swift Standard Library team at Apple, writes: In June, Apple introduced Swift System, a new library for Apple platforms that provides idiomatic interfaces to system calls and low-level currency types. Today, I'm excited to announce that we're open-sourcing System and adding Linux support! Our vision is for System to eventually act as the single home for low-level system interfaces for all supported Swift platforms.

Legendary horror game Amnesia: The Dark Descent and A Machine for Pigs is now open source, meaning that modders can dig in and see what lies underneath the hood of both games. Polygon reports: The full source code for The Dark Descent and A Machine for Pigs has been released on Github for folks who want to take a crack at modifying the game. It's relatively rare for developers to post their game codes themselves, though sometimes proprietary code can make its way online via leaks. "This doesn't mean that the game is suddenly free," Amnesia developer Frictional Games said in a blog post. "It just means that people are free to use the source however they want as long as they adhere to the GPL3 licence. The game and all of its content is still owned by Frictional Games. Just like before."

Seeing how these classic games are built will also mean seeing things like flaws, and inefficiencies Frictional Games warned -- but it's all still functional and potentially useful to anyone learning game development. "I also hope this release can be of help to anyone wanting to create their own engine or just wanting to learn more about game programming," Frictional Games said. "While the code is not the greatest in places and the tech used is not the latest, it is a fully contained game engine in a fairly easy-to-understand package. It is also a testament that it is possible to do this sort of thing, even with a very limited team."