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HomeNewsTabnine, an AI Code Completion Startup, raises $15.5M to expand its AI

Tabnine, an AI Code Completion Startup, raises $15.5M to expand its AI

Tabnine, a startup creating AI Code, raises $15.5M funding in a round to expand its AI to 11 programming languages.

Tabnine will use its $15.5M funding to create an AI-powered assistant that autocompleted code for software developers. Qualcomm Ventures, OurCrowd, and Samsung NEXT Ventures co-led the funding round with Kholsa Ventures and Headline Ventures. The proceeds of this investment will improve developer experience, add new capabilities, and strengthen Tabnine’s offerings.

Tabnine was founded by Dror Weiss and Eran Yahav in 2012 as Codota, named later Tabnine around 2017. It employs AI to complete chunks of code after knowing the purpose. It uses trained tapping algorithms to understand the code models and then attempts to learn the developer’s practices. 

“Based on our previous work on code analysis and simulation, we realized that with the vast amount of commonality and standard patterns in code, it was inevitable that AI will be a critical part in the dev process. We set out and pioneered the AI code assistant category”, said Dror Weiss, CEO, and Co-founder of Tabnine.

Read More: Logy.ai introduces India’s First AI-based Cataract Screening Solution with Sharp Sight Eye Hospitals. 

Tabnine provides improvements on every keystroke. Developers can also have full-line recommendations within the development environments like VSCode, IntelliJ, Eclipse, Android Studio, and Webstorm. The platform provides ‘code-native’ AI models trained in specific programming languages. It supports Java, C++, PHP, Go, C#, Python, Ruby, JavaScript, Rust, Swift, TypeScript, Haskell, OCaml, Scala, Kotlin, Perl, SQL, CSS, HTML, and Bash.

Weiss also claims that Tabnine’s approach enables it to learn the patterns in code efficiently. He said, “Our models give customers the flexibility to run Tabnine either on our cloud or on their network, and the ability to train custom AI models that capture the specific patterns in their repositories.”

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Disha Chopra
Disha Chopra
Disha Chopra is a content enthusiast! She is an Economics graduate pursuing her PG in the same field along with Data Sciences. Disha enjoys the ever-demanding world of content and the flexibility that comes with it. She can be found listening to music or simply asleep when not working!

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