Hristo Borisov, the co-founder and CEO at Payhawk, a firm that issues “next-generation” company cards with automated expense management, says that he’s happy to see “another strong story and exit from the diaspora.”
Borisov notes that “serving developers is one of the most rewarding experiences, and DeepCode have built a truly amazing product. Kudos to the team and their investors.”
Borisov’s comments have come in response to a recent announcement that DeepCode, an AI for code startup (founded Martin Vechev, Veselin Raychev and Boris Paskalev in 2016), has been acquired by Snyk, a “developer-first” security firm that shares the DeepCode values and mission statement.
Snyk was recently valued at $2.6 billion, Vechev confirms.
“Thanks to btov Partners (Florian Schweitzer, Jan-Hendrik Buerk) capital300 (Peter Lasinger) Earlybird Venture Capital (Andre Retterath) for believing in the DeepCode vision and investing in us. I am looking forward to further successes and impact with the Snyk team led by Peter McKay (CEO) and Guy Podjarny.”
As mentioned in a release, the “decisive” advantage that distinguishes the ETH spin-off DeepCode is that it reportedly created the first artificial intelligence system that’s able to learn from billions of computer program lines of code quickly, allowing AI-enhanced detection of security and reliability problems or issues in the source code. DeepCode is a good example of a new AI system that’s able to learn from data, program or write certain code as needed, and also stay transparent and “interpretable for humans.”
By joining Snyk, a security tools firm that allows computer programmers to quickly identify source code vulnerabilities, DeepCode will be able to integrate its AI-enhanced capabilities into existing Snyk products, “in turn moving closer to its original goal of impacting millions of users worldwide.”
Although DeepCode was established in 2016, the research for the project itself began at ETH Zurich in 2013 when Veselin Raychev, a doctoral student of Vechev (at that time), and Martin Vechev, Professor at the Secure, Reliable and Intelligent Systems Lab of the Department of Computer Science, worked with several others to lay the foundation or groundwork for the initiative.
They developed the very first prototypes of AI-powered systems that were able to “learn” from code by “showing how to combine data-driven machine learning methods with semantic static code analysis methods based on symbolic reasoning.”
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