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Working with Code Assistants: The Skeleton Architecture
Prevent AI-generated tech debt with Skeleton Architecture. This approach separates human-governed infrastructure (Skeleton) from AI-generated logic (Tissue) using Vertical Slices and Dependency Inversion. By enforcing security and flow control in rigid base classes, you constrain the AI to safe boundaries, enabling high velocity without compromising system integrity.
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Why Most Machine Learning Projects Fail to Reach Production
In this article, the author diagnoses common failures in ML initiatives, including weak problem framing and the persistent prototype-to-production gap. The piece provides practical, experience-based guidance on setting clear business goals, treating data as a product, and aligning cross-functional teams for reliable, production-ready ML delivery.
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Virtual Panel - AI in the Trenches: How Developers Are Rewriting the Software Process
This virtual panel brings together engineers, architects, and technical leaders to explore how AI is changing the landscape of software development. Practitioners share their insights on successes and failures when AI is incorporated into daily workflows, emphasizing the significance of context, validation, and cultural adaptation in making AI a sustainable element of modern engineering practices.
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Spec Driven Development: When Architecture Becomes Executable
Spec-Driven Development inverts traditional architecture by making specifications executable and authoritative. It transforms declared intent into validated code through AI generation and provides architectural determinism. It eliminates drift through continuous enforcement, but demands new engineering discipline in schema design and contract-first reasoning.
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InfoQ Java Trends Report 2025
This report summarizes how the InfoQ Java editorial team and several Java Champions currently see the adoption of technology and emerging trends within the Java and JVM space in 2025. We focus on Java the language, as well as related languages like Kotlin and Scala, the Java Virtual Machine (JVM), and Java-based frameworks and utilities.
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Building a RAG Application with Spring Boot, Spring AI, MongoDB Atlas Vector Search, and OpenAI
The RAG paradigm redefines AI: it combines generative models and business data for accurate, contextualised responses. The article shows how to integrate Spring Boot, Spring AI, MongoDB Atlas and OpenAI into a powerful and flexible pipeline capable of transforming the way businesses access and create value from data, with applications ranging from finance and healthcare to customer service.
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Bringing AI Inference to Java with ONNX: a Practical Guide for Enterprise Architects
Java applications can now run transformer-based AI models directly within the JVM—without Python, REST wrappers, or microservices. This guide shows how to integrate ONNX-powered inference with tokenizer support, GPU acceleration, modular deployment, and observability, enabling architects in regulated domains to adopt AI without disrupting compliance or CI/CD workflows.
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/articles/kotlin-multiplatform-evaluation/en/smallimage/kotlin-multiplatform-evaluation-thumbnail-1756451872984.jpg)
Evaluating Kotlin Multiplatform: Benefits and Trade-Offs in Cross-Platform Development
KMP is emerging as an alternative for cross-platform development, offering a path to share code without sacrificing the performance and feel of a native application. KMP comes with its own set of trade-offs and this article dives deep into those. While it focuses primarily on Android and iOS, KMP can be used to build desktop, web, and server-side applications, all from the same shared logic.
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Infusing AI into Your Java applications
Equip yourself with the basic AI knowledge and skills you need to start building intelligent and responsive Enterprise Java applications. With the help of our simple chatbot application for booking interplanetary space trips, see how Java frameworks like LangChain4j with Quarkus make it easy and efficient to interact with LLMs and create satisfying applications for end-users.
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Backend FinOps: Engineering Cost-Efficient Microservices in the Cloud
Backend FinOps integrates financial discipline into microservices, crucial for cutting cloud costs. Challenges such as resource fragmentation and cold starts underscore the need for intelligent design, effective language choice, robust tagging, and automation. Implementing FinOps via IaC, CI/CD checks, and dynamic autoscaling (e.g., Karpenter) ensures sustained efficiency.
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Jakarta EE 11 Overview: Virtual Threads, Records, and the Future of Persistence
Jakarta EE 11 delivers enhancements that include support for Java 17 and 21, integration with Java records and virtual threads, and the introduction of the Jakarta Data specification for unified SQL and NoSQL persistence. This release simplifies enterprise Java and establishes the groundwork for Jakarta EE 12, which will advance capabilities in data management.
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Spring AI 1.0 Delivers Easy AI Systems and Services
AI is here to stay, and it represents a unique and wonderful opportunity for Java and Spring developers. For most people, “AI engineering” simply means calling an LLM-based service over HTTP. In this article, we’ll examine the new Spring AI 1.0 project and explore how it can be used to integrate AI more effectively.