A recent Coding Dojo report reveals the top languages, frameworks, and databases at the top 25 unicorn companies in the US
Coding Dojo analyzed data from Indeed.com, CB Insights, PitchBook, and Stackshare to learn more about technology stacks at the top 25 unicorn companies in the US. The unicorns analyzed by Coding Dojo have valuations that range from $4B to $47B and include companies such as WeWork, Airbnb, SpaceX, Stripe, Palantir, and Coinbase.
Both Kotlin and Go are surprisingly more popular among unicorns than within the general developer community. Kotlin is used at 8 of the 25 unicorns, but is only ranked 40th in the global TIOBE Index of most popular programming languages. Google added support for Kotlin on Android in 2017, likely driving its adoption at large technology companies. Go is used at 11 of the top 25 unicorns, despite being ranked 15th in the TIOBE Index. Go is often one of the most popular languages that developers say they would like to adopt in the future, according to the most recent State of the Developer Ecosystem Survey by JetBrains.
Some things in software development change more quickly than others. ReactJS, a frontend framework that has risen to popularity in just a few short years, is now used at 24% of the top unicorns. Database technologies tend to be dominated by more mature stalwarts: MySQL and Redis, both more than a decade old, are the most popular database technologies, both used at 23% of the top unicorns.
For developers learning to code, the analysis provides a rough guide of what languages and technologies might be worthwhile to study. Most unicorns used between three and six languages, so developers will likely need to be well-rounded. Backend engineers ought to know the most mature technologies, while frontend engineers may have to contend with a fluctuating ecosystem.
TabNine adds powerful deep learning model to code suggestions
TabNine is a popular all-language autocompleter that has recently added deep learning models to its service to enable smarter code suggestions. The project was developed by a computer science student at the University of Waterloo who created the program as an alternative to less sophisticated code completion tools. TabNine uses a deep learning text-generation algorithm called GPT-2, which was designed by the AI research company OpenAI. Founded by Elon Musk and Sam Altman, OpenAI conducts extensive research in the field of AI and often makes its patents and research open to public.
TabNine is trained on two million public files on GitHub and uses that open source data to offer powerful code suggestions. Given its training data set, TabNine is best suited for writing commonly structured or boilerplate code—not exploratory code—that has been written before by other developers in the community. The program costs $49, but according to the creator, "TabNine will save you at least 1 second per minute. If you value your time above $1.40/hour, it will pay for itself in less than a year."
TabNine is not alone in its pursuit of more intelligent time-saving code recommendation systems. Microsoft offers IntelliCode, its AI-assisted developer productivity extension that uses machine learning to make better code suggestions. Kite, a startup based in San Francisco, offers a plugin that uses machine learning to give developers code completion functionality, but is available for Python only. TabNine is unique in that it offers wide language support and is capable of suggesting longer, more complex code snippets.
The creator of TabNine also noted that he is working to allow enterprises to fine-tune the deep learning models using internal source code. Not only will developers be able to draw from the developer community, they will be able to write more uniform code within the codebase of their own company. The effects of smarter code suggestions compound rapidly over time: developers can write code faster, thereby adding more data to the deep learning models more quickly. Furthermore, more uniform code cuts down time developers spend trying to understand confusing code, allowing them to focus on the parts of software development that matter most.
Amazon finally releases an AWS Toolkit for Visual Studio Code, playing catch-up to Microsoft and Google
Amazon made its AWS Toolkit extension generally available last week to help developers more easily build against AWS cloud services with Visual Studio Code. The extension helps developers test code locally in a Lambda-like environment, deploy applications to AWS, and invoke Lambda functions locally or remotely. To use the new toolkit, developers will also need to install the AWS CLI, AWS Serverless Application Model CLI, and Docker, in addition to the AWS Toolkit extension from the Visual Studio Code Marketplace.
Most surprising about Amazon’s announcement is that the cloud provider does not have AWS toolkit extensions for any other code editor or IDE (aside from Microsoft’s other wildly popular IDE, Visual Studio). Google, too, which only recently released its Cloud Code extension to assist developers in building cloud-native Kubernetes applications, does not have a cloud extension for any other major editor or IDE. The AWS toolkit has racked up more than 26k installs, while Cloud Code has only 13k installs despite being generally available for several months longer. Even so, one result is clear: Visual Studio Code is quickly becoming the most popular editor for cloud development, according to both users and cloud providers.
Microsoft has released a few extensions over the past few years that are working to more tightly connect code editors to cloud environments. Microsoft’s Docker and Kubernetes extensions connect to a number of cloud providers (Azure, AWS, GCP, and more). Azure Functions is an extension that helps deploy Azure functions from VS Code. Microsoft even built a set of remote development extensions that seamlessly connect VS Code to cloud environments. Other Microsoft extensions make it easy to develop with Terraform on Azure, Azure Pipelines, Azure Data Lakes, and Azure Blockchain—just to name a few.
The battle for cloud supremacy is being waged on—of all places—local machines and editors. The release of extensions from GCP and AWS indicate that both Google and Amazon give credence to this new local-first strategy, but it remains to be seen if Microsoft’s head start will materialize into greater developer interest.
- Fast software, the best software [CRAIGMOD]
- Object-oriented programming: the trillion dollar disaster [CODEIQ]
- When a rewrite isn’t: rebuilding Slack on the desktop [SLACK ENGINEERING]
- The war for the soul of open source [CHANGELOG]
- How I found my dream job by contributing to open source projects [FREECODECAMP]
- The ultimate strategy to preparing for the coding interview [THE STARTUP]
- No CS degree: inspiring interviews with successful self-taught and bootcamp developers [NOCSDEGREE]
- Cube.js is an open source modular framework to build analytical web applications [CUBE.DEV]
- Fork is a fast and friendly git client for Mac and Windows [FORK.DEV]
- Super Tiny Icons are miniscule SVG versions of your favourite website and app logos (each under 1KB) [GITHUB]
- FirmAI curated a list of applied machine learning and data science notebooks and libraries across different industries [FIRMAI]
- A to Z Resources for Students is a curated list of resources for anyone looking for tools to help learn a new coding language [GITHUB]
- Coding Interview University is a complete computer science study plan to become a software engineer [GITHUB]
- Your First Year in Code is a pay-what-you-want book about starting a career in programming [LEANPUB]
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