Tabnine, an AI code completion assistant, now gives developers long snippet suggestions and focused line code completions directly in Visual Studio Code or IntelliJ Idea IDEs.
In addition, Tabnine announced today that it has raised an additional $15.5 million in funding from investors including Qualcomm, Samsung Next Ventures and TPY Capital, bringing the company's total funding to date to $32 million. The infusion of funds will be used to add support for additional AI models and programming languages later this year, the company said.
Autocomplete solutions such as Tabnine reuse an organization's common coding patterns to reduce unnecessary developer toil, said Jason English, analyst at Intellyx. Applied AI use cases are already present within the software industry for accelerating high-speed data enrichment, code refactoring and process automation, so it is no surprise to see AI advancing into the developer's IDE, he said.
AI is the future of development
With the vast number of patterns and repetitiveness in software development, Tabnine believes that AI is the future of development, said Dror Weiss, Tabnine's CEO. One reason for the success of AI-powered assistants is that the life of a developer is harder today, he said, because developers must tackle limitless tools instead of living in the universe of one specific language.
An AI assistant puts code writing on rails, making suggestions for best code writing practices and possibly preventing a developer from writing code that doesn't adhere to best practices, Weiss said. Adding more precise AI capabilities that tackle specific languages enables developers to explore a new world of possibilities, he said.
In addition to providing a guiding hand for code writing, AI allows a developer to eliminate mundane tasks and focus on more meaningful tasks like analysis, Weiss said, which makes a developer's job more meaningful and more interesting.
Tabnine stands out from the crowd
Tabnine's competitors formerly included Israeli startup Codota, which acquired competitor Tabnine in late 2019 and combined the two similar models into one product under the Tabnine name.
While Kite Team Server runs on a company's GPU-equipped servers instead of a laptop's CPU, Tabnine's revamp means that its versatile AI can run in any environment, including on an individual developer's machine or in the cloud, Weiss said.
Another thing that makes Tabnine stand out is its goal to democratize AI model training and make it available to everybody, according to Weiss. Developers aren't limited to the AI models provided by Tabnine, he said, because anyone is allowed to train their own models by connecting to GitLab, GitHub or Bitbucket, and then training the AI model that reflects the best practices for a specific project.
Allowing developers to train their own AI brings Tabnine a step ahead of their competitors, said Diego Lo Giudice, vice president and principal analyst at Forrester Research. "This allows development leaders to scale the good internal coding practices and be under more control of where the source code is coming from," Lo Giudice said, "especially when this is raised as a concern from their executives."