A tinkering programmer and author of "Spring Cloud Microservices in Action"
This article explains how enterprise architects can integrate ONNX‑based machine‑learning inference directly into Java applications, covering tokenizer integration, GPU acceleration, deployment patterns, and lifecycle management to achieve secure, scalable, and observable AI services without relying on Python runtimes.
This guide explains why Java struggles with high‑performance or data‑intensive workloads, introduces GPU acceleration with CUDA, compares integration options such as JCuda, JNI, and JNA, walks through a practical encryption use case with performance benchmarks, and provides production‑grade best practices for memory, threading, testing, security, and deployment.
This tutorial demonstrates how to integrate multiple large language models from different providers into a Spring Boot application using Spring AI, configure primary, secondary, and tertiary models, and implement a fallback mechanism with Spring Retry to ensure high availability of the chatbot.
This article explains how Netflix’s engineering teams shifted incident handling from a centralized SRE function to a company‑wide, engineer‑driven practice by selecting the right tooling, standardizing processes, and reshaping culture, enabling rapid, reliable responses for hundreds of millions of viewers.
This article explores how developers can evaluate the reliability of AI‑generated code by examining three key dimensions—probability of error, impact of mistakes, and detectability—and offers a structured approach to balance speed with safety.
Learn how to prevent duplicate execution of Spring Boot @Scheduled tasks in multi-instance deployments by integrating ShedLock, including Maven dependencies, H2 database setup, LockProvider configuration, and annotation usage to ensure each job runs only once across all nodes.
This article reviews the major enhancements in Spring Boot 4 and Spring Framework 7, covering baseline requirements, performance boosts, native image support, modularization, new annotations, API versioning, declarative HTTP clients, resilient method support, multiple TaskDecorators, and null‑safety improvements, with practical code examples to help developers plan their migration.
At the recent CodeBuddy developer meetup, Vibe Coding’s design and product leads highlighted how AI reshapes design workflows and expands developer skill sets, while practitioners shared practical experiences with AI‑driven CLI tools, cloud integration, and the preference for Claude models in AI coding.
A recent Stanford study of ADP payroll data reveals that AI is already reducing employment rates for early‑career workers aged 22‑25 in high‑exposure jobs, while older employees see gains, and highlights that AI that augments rather than automates work may not cut entry‑level positions.
Spring Boot 4, built on Spring Framework 7, introduces elegant API versioning, streamlined Bean registration, null‑safety annotations, simplified HTTP proxy creation, upgraded SPEL, GraalVM native support, Jackson 3.x, newer Servlet/WebSocket versions, and numerous other enhancements, offering developers more powerful and convenient tools.
