Oracle has delivered a production release of GraalVM, a universal virtual machine for running applications written in any of many languages.

The technology has served as a just-in-time compiler and polyglot runtime for the JVM. GraalVM Version 1.0 provides high performance for individual languages as well as interoperability with no overhead in building polyglot applications, Oracle said.

If there is any language that is a known and proven quantity for developers, it’s Java. Enterprise developers, web developers, mobile developers, and plenty of others besides, have made Java ubiquitous and contributed to the massive culture of support around Java.

What’s more, the Java runtime, or Java Virtual Machine (JVM), has become a software ecosystem all its own. In addition to Java, a great many other languages have leveraged the Java Virtual Machine to become powerful and valuable software development tools in their own right.

Java development frameworks can help developers get an application up and running faster, offering code reuse and eliminating the task of building boilerplate code.

Developers have many choices of Java frameworks. Here are 15 prime choices for building Java platform applications, in alphabetical order.

Blade: lightweight MVC framework

This MVC framework is based on Java 8 and the Netty web server. Described as lightweight and simple, Blade offers a RESTful-style routing interface and has no invasive interceptors, documentation states. Blade’s source code is smaller than 500KB.

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Name your favorite open source project, and the odds are good—very good—that a small handful of contributors account for the vast majority of significant development thereof. The odds are just as good that most of those contributors work for just one or a few vendors. Such is open source today, and such has been open source for the past 20 years.

So, does that mean open source is really just commercial software by another name?

Sails, an MVC web framework for building Node.js applications also known as Sails.js, is now in Version 1.0 production status, featuring an async/await capability from the ECMAScript 2017 specification that promises to improve developer productivity.

Async/await promises sizable gains in productivity and fewer lines of code. It lets server-side JavaScript code be written with the await keyword, instead of using lower-level, more-fragile flow control functions such as nested callbacks and promise chains. “Callback hell,” the most common source of bugs and stability issues in Node.js and Sails applications, is ended with async/await. Developers also are freed from manual error handling after every database query.

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AI and machine learning are going to start making a lot more decisions. They probably still won’t be used in the near future to make “big” decisions like whether to put a 25 percent tariff on a commodity and start a trade war with a partner.

However, nearly anything you’ve stuck in Excel and massaged, coded, or sorted is a good clustering, classification, or learning-to-rank problem. Anything that is a set of values that can be predicted is a good machine learning problem. Anything that is a pattern or shape or object that you just go through and ”look for” is a good deep learning problem.

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Through the power of deep and machine learning, faster CPUs, and new types of sensors, computers can now see, hear, feel, smell, taste, and speak. All these senses take the form of some kind of sensor (like a camera) and some kind of mathematical algorithm, usually a supervised machine learning algorithm and a model.

Here is what is available.

See: image and facial recognition

Recent research into image and facial recognition lets computers not only detect object presence but detect multiple instances of similar objects. Facebook and Google have really been leading the way here, with multiple open source releases. Facebook has stated that it has a goal of detecting things in video.

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Devops may be one of the haziest terms in software development, but most of us agree that five activities make devops what it is: continuous integration, continuous delivery, cloud infrastructure, test automation, and configuration management. If you do these five things, you do devops. Clearly, all five are important to get right, but all too easy to get wrong. In particular, continuous integration and continuous delivery (CI/CD) may be the most difficult devops moves to master.

Continuous integration (CI) is a process in which developers and testers collaboratively validate new code. Traditionally, developers wrote code and integrated it once a month for testing. That was inefficient—a mistake in code from four weeks ago could force the developers to revise code written one week ago. To overcome that problem, CI depends on automation to integrate and test code continuously. Scrum teams using CI commit code daily at the very least, while a majority of them commit code for every change introduced.

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Normally, mainstream enterprises are slow to embrace cutting-edge technologies, with startups and other early adopters setting the pace on everything from public cloud to NoSQLs. Serverless computing, however, just might be different.

Serverless, first popularized by AWS Lambda, has seen “astonishing” growth of over 300 percent year over year, according to AWS chief Andy Jassy. Ironically, that growth may be driven by the “laggards,” as Redmonk analyst James Governor calls them, rather than the techno-hipsters.

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GitHub has open-sourced its Licensed tool, a Ruby gem that caches and verifies the status of license dependencies in Git repos.

Licensed has helped GitHub engineers who use open source software find potential problems with license dependencies early in the development cycle. The tool reports any dependencies needing review.