Codetown ::: a software developer's community
A Coding Dojo is a meeting where a bunch of coders get together to work on a programming challenge. They are there to have fun and to engage in DeliberatePractice in order to improve their skills.
Website: http://orlandodojo.wordpress.com
Location: Orlando, FL
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Latest Activity: Jun 26, 2019
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Codetown is a social network. It's got blogs, forums, groups, personal pages and more! You might think of Codetown as a funky camper van with lots of compartments for your stuff and a great multimedia system, too! Best of all, Codetown has room for all of your friends.
Created by Michael Levin Dec 18, 2008 at 6:56pm. Last updated by Michael Levin May 4, 2018.
Check out the Codetown Jobs group.
Amazon has recently announced that AWS CodeBuild, its managed build service, now supports building applications on macOS. However, due to Apple's licensing requirements, developers must still reserve a dedicated macOS fleet to utilize this new option.
By Renato LosioUber's microservices architecture, consisting of thousands of services, requires a reliable and efficient system for deploying updates, security patches, and new features. To ensure this process is safe and timely, Uber embraced continuous deployment (CD), automating deployments to production. This has been essential for maintaining code quality and minimizing delays in delivering changes.
By Claudio MasoloInfoQ editorial staff and friends of InfoQ are discussing the current trends in the domain of AI, ML and Data Engineering as part of the process of creating our annual trends report.
By Srini Penchikala, Mandy Gu, Namee Oberst, Roland Meertens, Anthony Alford, Daniel DominguezIn this podcast Shane Hastie, Lead Editor for Culture & Methods spoke to Michael Gray of ClearBank about engineering culture and leadership.
By Michael GrayGoogle has described an approach to use transformer models, which ignited the current generative AI boom, for music recommendation. This approach, which is currently being applied experimentally on YouTube, aims to build a recommender that can understand sequences of user actions when listening to music to better predict user preferences based on their context.
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