Contents
JCON
▪️Architect Your Own Experience: Creating Your Individual JCON 2026 Journey
CORE JAVA
▪️The Foundations of Data-Oriented Programming in Java
▪️Lazy Constants in Java 26: Deferred Immutability and JVM Trust
▪️Final Is No Longer a Hint in Java 26
▪️Java 26 in Practice: How the JVM Is Changing the Way We Write Code
▪️Reverse Engineering Java: Backtracking Native Crashes
API & FRAMEWORKS
▪️Paketo Buildpacks: Ready for Java 26 & More!
▪️Stop Writing YAML: How to Define, Test, and Deploy Your Cloud in Pure Java
▪️Beyond REST: Rethinking APIs with GraphQL and Java
▪️Solving Spring AI’s UI Challenge with AG-UI’s Java SDK
AI & ML
▪️Virtual Threads Meet AI: Java Concurrency in the Age of Intelligent Systems
ARCHITECTURE & MICROSERVICES
▪️From Spaghetti to Hexagons: A Practical Guide to Clean Java Architecture
▪️Do You Still Write Code Without Hexagon?
DEVOPS
▪️From ‘Class Not Found’ to Calm: Practical Shading and Shadowing for Java Apps
IDE & TOOLS
▪️Meet APIdia: A New Approach to API Documentation Browsing
HUMAN FACTORS
▪️Will AI Replace Me? Understanding Our Value in the AI Era
▪️When Product Owners Commit Code: Small Steps, Big Impact
Details
Page count: 132
Authors: Atiq Amjad, Babneet Singh, Damiana Nascimento, Ravali Yatham, Gireesh Punathil, Anthony Dahanne, Wladislaw Mitzel, Vipin Menon, Pascal Wilbrink, Enrique Medina Montenegro, Marek Dominiak, Vadzim Prudnikau, Kamil Banach, Stefan Richthofer, Florian Sommer, Anshika Koul
Editorial
From Writing Code to Designing Systems
Software development has always evolved through shifts in abstraction. From object orientation to frameworks, from monoliths to microservices, from on-premise systems to cloud-native architectures — each step promised to reduce complexity while enabling us to build more powerful systems. Yet every abstraction we introduced caused new layers, demanding deeper understanding rather than less. What we are experiencing now is not just another step in that evolution. It is a structural shift.
Java is no longer just a language we use to write applications. It has become a platform that actively shapes how systems are designed, executed, and scaled. Modern garbage collectors have absorbed performance concerns, AOT techniques reshaped startup behavior, virtual threads redefine concurrency, and new APIs bring hardware capabilities closer to everyday development. Responsibilities that once lived in application code are steadily moving into the runtime.
At the same time, artificial intelligence accelerates another revolutionary transformation. Code generation is no longer the bottleneck. Boilerplate disappears, patterns are reproduced instantly, and entire workflows can be scaffolded in seconds. But this acceleration does not simplify software development. The challenge is no longer writing code. The challenge is understanding systems.
When Complexity Moves, Not Disappears
It is tempting to interpret these developments as simplification. Virtual threads make concurrency easier, buildpacks reduce deployment complexity, infrastructure becomes programmable, AI generates code on demand. But complexity has not vanished – it has shifted. When infrastructure becomes part of the language, we must reason about system boundaries rather than isolated components. When APIs become more dynamic, we must ensure that distributed systems remain coherent and predictable. When AI generates code, we must validate intent instead of syntax. Our effort moves from implementation to interpretation.
This JAVAPRO issue explores that shift from multiple perspectives. It examines how the JVM becomes a co-author, how AI reshapes concurrency and workflows, how modern Java encourages clearer models and stronger guarantees, and how platform capabilities increasingly replace handcrafted solutions.
Architecture as a Stability Mechanism
As the cost of producing code approaches zero, architecture becomes the primary mechanism of control. Without structure, accelerated development leads to accelerated chaos. Systems degrade not because they fail immediately, but because they become harder to understand and evolve. When both humans and AI contribute code, consistency must be enforced deliberately. Architectural patterns such as hexagonal architecture and domain-driven design are no longer optional refinements. They are stabilizing forces. They define boundaries, guide dependencies, and preserve long-term maintainability. They ensure that rapid iteration does not erode clarity. If the platform increases velocity, architecture must provide direction.
Understanding What Actually Runs
While Java raises the level of abstraction, it also reminds us that the underlying system still matters. Class loading, dependency conflicts, and runtime behavior do not disappear simply because they are hidden. When failures occur, they surface at the lowest levels, requiring us to bridge the gap between machine state and application logic. Debugging, observability, and a solid understanding of runtime mechanics remain essential skills. The more powerful our abstractions become, the more important it is to understand where they break — and how to recover when they do.
Systems Are Built by People
As technology evolves, so do teams. Roles become more fluid. Developers, product owners, and tools collaborate more closely. AI participates in implementation, but responsibility remains human. Software development has never been just about code. It is about communication, shared understanding, and ownership. As tools improve, these human factors become more critical. Clear structures reduce cognitive load, and good collaboration prevents fragmentation. Clarity and collaboration determine whether systems remain maintainable — or drift into complexity.
From Code to Understanding
What connects the articles in this issue is a common realization: We are no longer optimizing how we write code. We are learning how to design systems in an environment where code is abundant. Java’s transformation into a platform, the rise of AI, and the renewed importance of architecture all point in the same direction: The future of software development will not be defined by how fast we produce code, but by how well we understand the systems we build — and how responsibly we evolve them over time.
Enjoy reading!
