We are happy to announce the first preview version of the new major release: Kotlin 1.4-M1.
A few months ago, we published an announcement of what to expect in Kotlin 1.4. As the release approaches, we’re offering you a preview in which you can try some of the new things for yourself.
In this post, we’ll highlight the following new features and key improvements available in 1.4-M1:
- A new, more powerful type inference algorithm is enabled by default.
- Contracts are now available for final member functions.
- The Kotlin/JVM compiler now generates type annotations in the bytecode for Java 8+ targets.
- There’s a new backend for Kotlin/JS that brings major improvements to the resulting artifacts.
- Evolutionary changes in the standard library: completing deprecation cycles and deprecating some additional parts.
You can find the complete list of changes in the change log. As always, we’re really grateful to our external contributors.
We highly encourage you to try the preview, and we will appreciate any feedback you provide in our issue tracker.
Posted in EAP, Releases
The new edition of Kotlin Census is here! By the end of 2019, more than 4 million people had used Kotlin. This is a great number of people, and of course, they have a great number of use cases behind them. We care deeply about making sure Kotlin delivers a great experience for everyone who uses it. We want to improve it to help everyone in the community bring their ideas to life with Kotlin. This is why your feedback is so important to us. Please share with the Kotlin team how you’ve used the language over the last year. What did you like about it? What challenged you?
We also invite those who don’t use Kotlin to respond. Please share with us your reasons for not using it. Your responses are just as important to us and very much appreciated.
If you filled out the survey last year, thank you! To make sure we are keeping our information up to date, it would be really helpful for us if you could, please, do it again this year. We’ve added many new questions about the features and products that appeared in 2019. We’ve also added questions about how smooth your experience with Kotlin and its ecosystem was in 2019.
One more important thing – answering the Census gives you a chance to win a free ticket to KotlinConf or a special Kotlin T-shirt. We will raffle off the prizes among the respondents after the Census closes.
This year at KotlinConf 2019, Roman Belov gave an overview on Kotlin’s approach to data science. Now that the talk is available for everyone to see, we decided to recap it and share a bit more on the current state of Kotlin tools and libraries for data science.
How does Kotlin fit data science? Following the need to analyze large amounts of data, the last few years has brought a true renaissance to the data science discipline. All this renaissance of data science couldn’t be possible without proper tools. Before, you needed a programming language designed specifically for data science, but today you can already do it with general-purpose languages. Of course this requires general-purpose languages to make the right design decisions, not to mention getting the community to help in. All this made certain languages, such as Python, more popular for data science than others.
With the concept of Kotlin Multiplatform, Kotlin aims to replicate its developer experience and extend its interoperability to other platforms as well. The major qualities of Kotlin by design include conciseness, safety, and interoperability. These fundamental language traits make it a great tool for a wide variety of tasks and platforms. Data science is certainly one of these tasks.
The great news is that the community has already begun adopting Kotlin for data science, and this adoption is happening at a fast pace. The brief report below outlines how ready Kotlin is for data science, including the Kotlin libraries and Kotlin tools for data science.