have been placed along the bridge recently on February 13, 2013 in New York City. The phenomenon has gained followers in recent years as couples seek to publicly mark weddings, engagements, and anniversaries in a permanent way. Besides New York,"love locks" can be found on public monuments and bridges in Venice, St. Petersburg and Paris amongst other cities. Everything is automated.
Where we now see this process happening is in cloud-native environments with a new and different set of tools. MongoDB is a company known for its developer data platform, a database with an integrated set of related services and the company is now working with Amazon Web Services, Inc. to optimize Amazon CodeWhisperer.See The Moon ‘Kiss’ Venus As Orion’s Belt Returns: The Night Sky This Week
MongoDB is working with AWS in this vein to provide enhanced suggestions for application development and modernization on MongoDB itself. While Amazon CodeWhisperer already provided support for building applications on MongoDB, developers can now get enhanced suggestions based on highly curated training data and evaluations from MongoDB to use best practices. The companies think this will enable software developers to ‘ideate’ , prototype new features and accelerate their work in general.
“More and more developers are realizing the potential of generative AI-powered coding companions to transform how work gets done, giving them more time to focus on solving hard problems,” said Deepak Singh, VP of next-gen developer experience at AWS. “Amazon CodeWhisperer already provides an optimized experience when working on common coding tasks and with AWS APIs. By collaborating with MongoDB, we are extending those capabilities to millions of MongoDB developers.
These optimizations are available for five of the most common programming languages used to build with MongoDB, including C#, Go, Java, JavaScript and Python, while also allowing developers to take advantage of core Amazon CodeWhisperer features, including built-in security scanning and a reference tracker that provides information about the origin of code suggestions when it resembles open source training data.
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