Contents
JCON
▪️Engineering Intelligence Live: JAVAPRO Benefits for JCON 2026
CORE JAVA
▪️Building Low-Latency Event Pipelines With Java 25
▪️Achieving Microsecond Latencies With Java: Practical Techniques for Building Ultra-Fast Systems
API & FRAMEWORKS
▪️API Development: Implementing POST and SSE Events in Jakarta REST
▪️Open J Proxy: A Modern Open-Source Approach to Database Connectivity for the Java Ecosystem
▪️From Tables to JSON: Handling Unstructured Data in RDBMS Systems With Spring Boot
▪️Java 26 and Spring Boot 4: From Certification Skills to Production-Ready Applications
▪️From Cron to Carbon and Cost-Aware: Modernizing Java Background Jobs
▪️Why Struts 1.3 Still Matters in 2026
▪️The Jakarta Cliff: Why Modern Java Leaves Struts 1 Behind
AI & ML
▪️Automating JVM Thread Dump Analysis With AI: Practical Observability for Java on Amazon ECS and EKS
▪️Enterprise-Grade AI for Java Developers: Raising the Bar in the AI Revolution
IDE & TOOLS
▪️Data-Driven Projections With Kotlin Notebooks
PROJECT MANAGEMENT
▪️Avoiding Unsustainable Responsibility
▪️Where Is the Logic of My Application?
WEB DEVELOPMENT
▪️When Developers Meet Designers: Lessons From Vibe Coding and the Art of Creative Expression
JVM LANGUAGE
▪️Scripting on the JVM With Java, Scala, and Kotlin
TESTING & QUALITY
▪️From Fragile to Fearless
HUMAN FACTORS
▪️Anchored in the Chaos: Managing Cognitive Load, AI Uncertainty, and Well-Being as a Developer
Details
Page count: 144
Authors: Bhupendra Mishra, Stefan Angelov, Buhake Sindi, Rogerio Robetti, Kush Sharma, Vincent Vauban, Ronald Dehuysser, Steve Poole, Sascha Möllering, Camille Nigon, Maarten Vandeperre, Tim Schraepen, Andreas Monschau, Andres Sacco, Csilla Szántó, Haoyi Li, Wouter Bauweraerts, Simon Verhoeven
Editorial
The Great Shift: From Syntax to Semantics
Since the first Java magazines reached developers in the mid-1990s, our industry has undergone repeated tectonic shifts: the rise of Java EE, the emergence of Spring’s dependency injection, the microservices movement, and the transition to cloud-native architectures. Each wave introduced new abstractions to manage growing complexity. Yet these tools remained instruments—we translated human intent into machine-executable code. We were the authors; the compiler was our editor.
Today, the situation is fundamentally different. We have entered the AI Age. Artificial Intelligence is no longer confined to research; it shapes our daily workflow. It operates within our IDEs, generates boilerplate, reviews pull requests, and increasingly participates at runtime. Amid the enthusiasm for “AI-driven development,” a more demanding question emerges: What does it mean to build systems when writing code is no longer the constraint? This issue explores the answer: the rise of Engineering Intelligence.
The Productivity Illusion: Why Code Is Cheap, but Judgment Is Expensive
There is a persistent misconception that faster code generation automatically results in better systems. In practice, the opposite is often true. A Large Language Model can produce hundreds of lines of syntactically correct Java within seconds—implementing a REST controller or data mapping layer with remarkable speed. Yet it does not understand the system. It detects patterns, but it cannot reason about intent. It cannot evaluate trade-offs such as low latency versus eventual consistency. It generates output, but it cannot ensure that this output is robust, maintainable, or secure within the constraints of a legacy enterprise environment.
As code generation accelerates, the bottleneck in software engineering shifts. Writing is no longer the constraint—judgment is. Critical evaluation, architectural integration, and long-term ownership now determine whether a system succeeds or accumulates catastrophic technical debt. Generated code is not a solution; it is a liability. It becomes part of a complex system that must remain coherent for years. This is where true engineering begins. And when the system fails under peak load, responsibility still rests with us—the developers and architects.
Architecture as a Stability Mechanism in the Storm
In an era where code can be summoned with a prompt, software architecture becomes more critical than ever. Architecture must act as a living system of constraints that stabilizes applications while implementation speed increases. Whether building ultra-low-latency services or handling unstructured data in relational systems with Spring Boot, structural decisions determine longevity.
Modern Java systems are distributed, layered, and interconnected. If every developer—or every AI—contributes code without alignment, architectural entropy emerges. This form of debt is more dangerous than isolated defects because it raises the cognitive load for everyone who maintains the system. Discipline becomes essential. If AI accelerates production, architecture must introduce the necessary friction to preserve coherence. We must move from “Move Fast and Break Things” to “Move Fast with Stable Structures.”
Testing as a Strategy for Continuity
Acceleration without safeguards leads to instability. This is why testing must be reconsidered. In the AI Age, tests are not merely verification artifacts—they are continuity mechanisms. They define what must remain invariant while implementations evolve.
When machines draft code, validation must focus on externally observable behavior, not internal structure. As we approach Java 26 and Spring Boot 4, production systems demand more than certification-level knowledge; they require resilient test strategies that secure long-term evolution. If implementations can be generated rapidly, the decisive capability becomes confident refactoring. High velocity is only sustainable when protected by durable, behavior-driven safeguards.
Observability and the Human Constraint
Complex systems fail in non-linear ways. Distributed services under load exhibit emergent behavior that static code cannot reveal. If we are to remain accountable for the systems we design, runtime transparency must be intentional. In this issue, we explore automated JVM thread dump analysis with AI—an example of applying AI for the engineer to achieve actionable observability on platforms such as Amazon ECS and EKS.
At the same time, we must confront human cognitive limits. Modern development demands navigating expanding stacks and constant information flow. Structural inconsistency and weak conventions act as attention drains. Engineering intelligence therefore includes designing for human clarity—actively managing cognitive load and developer well-being. Reducing unnecessary complexity is not merely stylistic; it is a strategic architectural choice and an act of responsibility toward our colleagues and our future selves.
Conclusion: Sharpening the Craft
The AI Age does not replace software engineering—it intensifies its demands. As routine tasks fade, what remains are the essential challenges of system design. In this issue, we examine these dimensions across the Java ecosystem: low-latency event pipelines with Java 25, microsecond architectures, the debate around Struts 1.3 in 2026 and the “Jakarta Cliff,” carbon-aware background processing, and the creative convergence of developers and designers in “Vibe Coding.”
These are not isolated themes. They converge on a central question: How do we build systems that remain reliable, comprehensible, and maintainable when generating code is no longer the constraint? This is the defining challenge of our time. It is also an opportunity—our role evolves from writing code to designing systems and curating intent.
Welcome to Engineering Intelligence!
