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Java Annotated Monthly – August 2025

Welcome to this month’s Java Annotated Monthly – your pit stop for all things related to Java, Kotlin, frameworks, and the ever-shifting tech scene. From helpful reads to red-hot takes, we’ve lined up a range of articles for curious devs and ambitious teams.

But wait, there’s more! As always, our AI corner is here, and we’ve sprinkled in some thought-provoking, non-tech gems on building better teams and smarter products.
Oh, and don’t miss the special feature by A N M Bazlur Rahman! He’s bringing you his top picks, personal reflections, and sharp takes on where the dev world’s heading. Trust us, it’s a ride worth taking.

Let’s go! 

Featured Content

A N M Bazlur Rahman

A N M Bazlur Rahman is a Java Champion, Jakarta EE Ambassador, and Software Engineer with over 12 years of specialized experience in Java and related technologies. He is the founder and moderator of the Java User Group in Bangladesh, and has been organizing educational meetups and conferences since 2013. Bazlur is a veteran editor at InfoQ and a contributing editor at Foojay.io. He has authored six books on Java programming, including Modern Concurrency in Java with O’Reilly, scheduled for release in September. He is also working on his seventh book, Build Smart Java Applications with Large Language Models, with his friend Shaaf Syed and Manning Publications. When not coding or writing, you can find him speaking at conferences and Java User Groups, sharing his passion for Java, Jakarta EE, and the integration of AI technologies into enterprise applications.

Java and AI integration takes center stage

As a Java developer with a recent interest in the AI integration space, I’m excited to share what I consider the most transformative period in Java’s 30-year history. The ecosystem has reached a tipping point where AI integration is no longer experimental. It’s production-ready, and Java developers are embracing it at an unprecedented pace. From my recent talk at GeeCON 2025 in Kraków to my hands-on experiments building AI-powered applications, I’ve witnessed firsthand how Java is becoming the go-to platform for enterprise AI development.

The foundation of this revolution lies in the language itself. JDK 25 is shaping up to be a landmark release, with features that directly benefit AI workloads. The game changer is JEP 519: Compact Object Headers, with Amazon reporting 22% less heap usage and 8% faster execution in production. However, what really excites me is JEP 505: Structured Concurrency (Fifth Preview), which I explored in depth in my article, Java’s Structured Concurrency: Finally Finding Its Footing. This feature simplifies concurrent programming by treating related tasks as single units, making them ideal for orchestrating multiple AI model calls. Combined with instance main methods becoming final in JDK 25, which provides a pleasing experience for beginners, Project Leyden and the evolving Vector API are positioning Java as a serious contender for AI workloads.

The framework landscape has me particularly excited. The general availability of Spring AI 1.0 streamlines AI app development with support for 20+ models and built-in RAG capabilities. LangChain4j’s 1.1.0 release introduces comprehensive guardrail support – something I’ve written about extensively in Building Robust AI Applications with LangChain4j Guardrails and Spring Boot, complete with plenty of code examples. Microsoft’s strategic partnership with LangChain4j has resulted in extensive security audits, making it truly enterprise-ready. However, the real showstopper is Rod Johnson’s Embabel agent framework, which he describes as “the most significant project since Spring Framework itself.” It brings goal-oriented action planning from gaming to create deterministic, explainable AI agents with strong typing. What’s equally exciting is seeing the integration of LangChain4j with MicroProfile and Jakarta specifications take shape, bringing AI capabilities to enterprise Java standards. 

To help developers navigate this landscape, my friend Shaaf Syed and I created the llm-jakarta GitHub project, which provides a progressive learning path through Jakarta EE and LLM integration, comprising 10 practical steps. We plan to add more examples combining Spring Boot and Quarkus soon. 
For those interested in GPU acceleration, I recommend checking out GPULlama3.java Brings GPU-Accelerated LLM Inference to Pure Java, which showcases how Java can leverage hardware acceleration for AI workloads.

Cloud providers are fully committed to Java’s AI future. Oracle launched generative AI with LangChain4j integration, and Google released their Agent Development Kit for Java, both of which are crucial for adopting modern AI features. Microsoft’s comprehensive Java and AI strategy includes native integration with Azure. 

I’ve been experimenting with these features extensively, documenting my findings in my newsletter, The Coding Café, where I share practical experiments, such as building FormPilot and creating an AI-powered flight tracker. Join me as we explore this exciting frontier where Java’s enterprise strengths meet cutting-edge AI capabilities!

Java News

Check out the recent Java news: 

Here are the JEPs targeted to JDK 25: 

Java Tutorials and Tips

Learn new things from our selection of Java tutorials and tips: 

Kotlin Corner

Don’t miss these Kotlin updates: 

AI

Stay up to date with AI news and insights: 

Languages, Frameworks, Libraries, and Technologies

Read about the most influential technologies and frameworks in the industry: 

Conferences and Events

Visit these Java events in August and watch some helpful recordings: 

Culture and Community

Be a great leader and a perfect team player: 

And Finally…

Don’t miss the most important updates from the IntellIJ IDEA team: 

That’s it for today! We’re always collecting ideas for the next Java Annotated Monthly – send us your suggestions via email or X by August 20. Don’t forget to check out our archive of past JAM issues for any articles you might have missed!

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