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Java 22 and 23 Features
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Manage episode 437877038 series 2469611
Java 22 and 23 new features overview, including unnamed variables with underscore, multi-source file launching, G1 region pinning, Foreign Function & Memory API finalization, Markdown Javadoc support, ZGC generational collector by default, discussion on Java installation and beginner-friendliness, debate on proper use of LTS terminology for Java releases, potential for Java in AI/ML space with new vector APIs and native performance, comparison of Java to python for AI workloads, challenges and opportunities for Java adoption in data science and machine learning domains, importance of specialized AI models vs general models for enterprise use cases, trade-offs between developer experience and operational efficiency for different languages and runtimes, potential future directions for Java in high-performance computing and AI acceleration, previously, Nicolai appeared on "#300 Object-Oriented Programming (OOP) vs. Data-Oriented Programming (DOP) in Java"
Nicolai Parlog on twitter: @nipafx
312 episoder
Fetch error
Hmmm there seems to be a problem fetching this series right now. Last successful fetch was on September 10, 2024 17:40 ()
What now? This series will be checked again in the next day. If you believe it should be working, please verify the publisher's feed link below is valid and includes actual episode links. You can contact support to request the feed be immediately fetched.
Manage episode 437877038 series 2469611
Java 22 and 23 new features overview, including unnamed variables with underscore, multi-source file launching, G1 region pinning, Foreign Function & Memory API finalization, Markdown Javadoc support, ZGC generational collector by default, discussion on Java installation and beginner-friendliness, debate on proper use of LTS terminology for Java releases, potential for Java in AI/ML space with new vector APIs and native performance, comparison of Java to python for AI workloads, challenges and opportunities for Java adoption in data science and machine learning domains, importance of specialized AI models vs general models for enterprise use cases, trade-offs between developer experience and operational efficiency for different languages and runtimes, potential future directions for Java in high-performance computing and AI acceleration, previously, Nicolai appeared on "#300 Object-Oriented Programming (OOP) vs. Data-Oriented Programming (DOP) in Java"
Nicolai Parlog on twitter: @nipafx
312 episoder
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