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Runtime Metaprogramming With Groovy
Jeff Scott Brown discusses the metaprogramming capabilities offered by Groovy, providing everything that developers needs to build systems that are far more capable than their all Java counterparts.
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Groovy Mobile Automation
Bobby Warner discusses mobile automation and dives into the iOS and Android functional testing world using Groovy, Spock and Gradle.
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Advanced GORM: Beyond Relational
Grails project lead Graeme Rocher demonstrates some less known, advanced features of GORM and explores the possibilities offered going beyond the relational database.
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Swift - Under the Hood
Alex Blewitt introduces the history behind Swift, why it was created, how it differs from Objective-C and how Swift is compiled and executed under the covers.
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Exploring Melody Space with Clojure, Overtone, core.async and core.logic
Thomas Kristensen describes the overall architecture of Composer, a system for composing musing, showing how to build a system that achieves responsiveness while still being flexible.
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Java Performance Panel
The panelists discuss Java performance topics, bringing their specific experiences to bear.
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GORM Inside and Out
Jeff Scott Brown introduces GORM, a super powerful ORM tool that makes ORM simple by leveraging the flexibility and expressiveness of a dynamic language like Groovy.
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Advanced Groovy Tips and Tricks
Ken Kousen examines features of Groovy that can make life easier when going beyond the initial adoption stage.
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Distributed Platform Development with Groovy
Dan Woods discusses the approach to developing a scalable enterprise architecture, and demonstrates implementations based on the variety of technologies available from the Groovy ecosystem.
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Functional Programming on the Front-end with Facebook React
Dustin Getz,Daniel Miladinov demonstrate using Facebook React to build a CRUD editor, highlighting React's application of functional programming and immutability to manage complex application state.
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Using a Graph Database for JVM Heap Analysis
James Richardson, Nat Pryce discuss some of the challenges faced using Neo4J for interactive analysis of large data imports (80K nodes, 150k relationships) and how they overcame them.
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