Contents
JCON
▪️JCON Meets Enterprise AI – From Basics to Production
CORE JAVA
▪️Best Practices for Writing Clean Code in Java
▪️Unlocking Developer Productivity with Java 25: Features You’ll Actually Use
▪️Pattern Matching in Java 25: Writing Cleaner, Safer, Faster Code
▪️Intelligent Test Automation in the Java 26 Era
SERVERSIDE JAVA
▪️Is Java Cloud Native?
▪️As Java EE 8 Runtimes Age: What Comes Next for Enterprise Java Applications?
API & FRAMEWORKS
▪️Bridging the Gap: Full-Stack Development Without the Headaches
▪️Another JVM Language?
▪️Strategic Domain-Driven Design: The Missing Link in Modern Java Projects
▪️How Java Changed My Life!
▪️LangChain4j Production-Ready Features with Quarkus
▪️Is It Worth Running Java on ARM? Trust Me, This Is an Interesting Question
▪️JVM Iceberg – The AI Edition
▪️Optimizing Spring Integration Tests at Scale
▪️Apache Causeway – An Introduction
▪️Apache Causeway – Going Further
AI & ML
▪️Where to Use AI for Development: A Practical Guide for Engineers and Managers
▪️Solutions to Achieve Desired Results from Generative AI Models
▪️Why AI Agents Need a Protocol-Flexible Event Bus
▪️AI-Driven Reverse Engineering of Java Applications
IDE & TOOLS
▪️Map Your Code – Master Your Architecture
PROJECT MANAGEMENT
▪️Grow Beyond Senior: Use Your Java Expertise to Gain Autonomy and Impact the World
SECURITY
▪️Top Security Flaws Hiding in Your Code Right Now – and How to Fix Them
▪️The Shai-Hulud NPM Worm: When Supply Chains Bite Back
Details
Page count: 210
Authors: Muaath Bin Ali, Mihaela Gheorghe-Roman, Dmitry Chuyko, Civardi Chiara, Loïc Magnette, Peter Verhás, Otavio Santana, Vipin Menon, Mohamed Ait Abderrahman, Artur Skowronski, Sergei Chernov, Dan Haywood, Steve Poole, Lokesh Chenta, David Kjerrumgaard, Martin Toshev, Richard Gross,
Bruno Souza, Jonathan Vila López,
Editorial
From the Edges to the Core
Artificial intelligence has quietly changed its position in modern software systems. It is no longer something that lives at the edges of applications, accessed through isolated APIs or experimental integrations. Instead, it is moving closer to the core, influencing workflows, coordinating actions, and participating directly in system behavior. This shift does not announce itself loudly, but its consequences are profound, because it changes how software is designed, reasoned about, and ultimately trusted.
Questions That Never Went Away
As AI becomes more autonomous, familiar questions return with new urgency. How do components communicate? How is behavior controlled and observed? How do systems remain understandable as complexity grows? These questions are not new to Java developers. They are the same questions that have shaped enterprise software for decades, now reappearing in a different context, with higher stakes and fewer margins for error.
Engineering Before Intelligence
Java’s long-standing strengths turn out to be particularly relevant here. Explicit structure, strong typing, mature concurrency models, and a culture of maintainability provide exactly the kind of foundation that agent-based systems require. Building intelligent systems is not only about models and prompts; it is about protocols, orchestration, boundaries, and trust. In that sense, agentic AI feels less like a disruption and more like a continuation of established engineering principles that have proven their value over time.
Understanding What Actually Runs
As software systems evolve, understanding what happens beneath the surface becomes increasingly important. AI-powered applications add new layers of behavior, but they do not remove the need for insight into runtime characteristics, memory usage, or architectural dependencies. If anything, they make that understanding more critical. AI can help create software, but it can also help analyze, document, and reason about the systems that already exist, a capability that is especially valuable in long-lived Java environments with years of accumulated complexity.
Progress Without Shortcuts
At the same time, Java itself continues to move forward. Improvements in language expressiveness, productivity, concurrency, and testing are not abstract advancements – they shape how confidently systems can be built and evolved. Intelligent behavior does not compensate for weak foundations. On the contrary, it amplifies both good and bad engineering decisions. Reliable tests, clear architecture, and disciplined code remain essential, particularly when systems begin to act with a degree of autonomy and independence.
A Convergence, Not a Competition
What emerges from this convergence is not an “AI-first” or “Java-first” narrative, but something more balanced. Intelligent systems are becoming part of everyday Java development, while modern Java provides the stability and clarity required to keep those systems understandable and maintainable. The focus shifts from experimenting with possibilities to designing systems that can be trusted, operated, and evolved over time.
Continuity as a Strength
Java has always been a platform for building complex software responsibly, at scale, and with a long-term perspective. That role does not disappear in the age of AI. It becomes even more relevant as intelligent components increasingly rely on strong foundations and clear structure – especially in environments where systems are expected to endure and evolve for many years.
The articles that follow explore how this responsibility translates into practice, across architecture, tooling, and everyday development.
A heartfelt thank you goes to all the fantastic authors of this issue who write with great passion for the Java community and share their expert knowledge.
Enjoy reading!
