Just like POST requests, the content-type header is important because it tells the server how to interpret the data we’re sending.

That's the problem Apereo CAS solves every day. CAS — the Central Authentication Service — is an identity and single sign-on platform that's been running in production for over 20 years. Its current incarnation is a Spring Boot 3.x application on Java 21+, and its codebase is one of the best real-world examples I've seen of Spring Boot engineering at scale.

Nowadays, there are quite a lot of AI coding assistants. In this blog, you will take a closer look at Claude Code, a terminal-based AI coding assistant. Since mid January 2026, Claude Code can also be used in combination with Ollama, a local inference engine. Enjoy!

Introduction

There are many AI models and also many AI coding assistants. Which one to choose is a hard question. It also depends on whether you run the models locally or in the cloud. When running locally, Qwen3-Coder is a very good AI model to be used for programming tasks. In previous posts, DevoxxGenie, a JetBrains IDE plugin, was often used as an AI coding assistant. DevoxxGenie is nicely integrated within the JetBrains IDEs. But it is also a good thing to take a look at other AI coding assistants. In a previous blog, Qwen Code was used; now it is time to take a look at Claude Code.

As modern applications require greater scalability, resilience, and responsiveness, traditional REST-based architectures are hitting their limits. This article looks into how Spring Boot developers can upgrade their APIs from synchronous REST calls to asynchronous, event-driven communication through an event mesh that utilizes technologies like Kafka, RabbitMQ, or NATS. 

It emphasizes important architectural differences, design patterns for decoupling services, and practical implementation strategies in Spring Boot. Readers will discover how to integrate event streams, manage eventual consistency, and achieve real-time responsiveness while ensuring observability and fault tolerance. The article also covers trade-offs, performance improvements, and best practices for moving enterprise APIs towards event-driven systems.

For engineers and technology leaders managing advanced IoT deployments, power systems, edge computing and integrated GIS solutions, the challenge is enabling real-time operational visibility while safeguarding critical enterprise systems.

The Goal

User asks in natural language → chatbot reads/searches PDFs → returns an answer.

In this article, you'll see how to configure request logging in Spring Boot and inspect request payloads and parameters.

In this article, we dive into the implementation details of the integration between the Bucket4j rate-limiting framework and Embedded Infinispan (not HotRod). This setup creates a data grid across different pods of a single application, allowing for seamless, distributed token management.

In my experience, these failures are rarely caused by tools. They come from how integration patterns are applied without considering real-world conditions like latency, retries, partial failures, and security boundaries.

Architecture Overview

The pipeline involves multiple components working together in sequence. Below is the event flow:

This is not a hypothetical.

Most APIs get secured after something breaks. A token leaks, an endpoint misbehaves, a pen test surfaces, an authorization gap. Suddenly, the team is patching a live system under pressure. That’s not a human failing — it’s an industry habit.

That’s why API-first development has become a foundation of modern application architecture. When teams adopt an API first approach, they treat APIs as products that are designed intentionally, documented clearly, versioned responsibly, secured rigorously, & governed consistently across teams. 

Architectural Inversion

SaaS-based AI doesn’t just introduce risk; it fundamentally inverts the traditional enterprise model. Instead of building internal capability, organizations now pay to improve external models. The learning power — the ability to generalize, adapt, and optimize — is centralized away from the data owner. You’re not just losing privacy; you’re losing leverage.

Audit data are recorded and authenticated using cryptographic algorithms, such as hash functions (e.g., SHA-256) and HMAC signatures, to guarantee integrity and non-repudiation. Scalability and fault tolerance, together with type safety and consistency, are achieved through asynchronous message processing via a message broker and standardized Pedantic data models, respectively. 

If you aren't running end-to-end tests in a continuous pipeline, you're essentially maintaining a safety net that only works in your backyard. CI/CD integration changes that. It turns testing from a manual chore into a mandatory, objective gate for your code quality.

The Eureka Moment at the Crossroads of Technology

It was one of those late nights at the Woodland Hills office, staring at an endless scroll of burn-down charts, drowning in caffeine. I had this moment of clarity — or perhaps it was a caffeine-induced epiphany — where I realized that the traditional Agile metrics weren't cutting it. We needed something more dynamic, more responsive. Enter AI, MuleSoft, and AWS, the trio that I believe can redefine the very core of SAFe. Over the years, I’ve dabbled in various roles — solution architect, project lead, and even a hands-on coder — and this perspective is born from my trenches of experience.

AWS Bedrock is Amazon’s answer to this problem. Rather than offering a single model or framework, Bedrock provides a managed platform where enterprises can access multiple foundation models, build retrieval‑augmented generation (RAG) pipelines, orchestrate agents, and deploy AI features without exposing sensitive data or managing infrastructure. In many ways, Bedrock represents a shift in how organizations will adopt AI over the next decade.

The author shares personal insights on integration challenges and the effectiveness of tools like Kubernetes and Docker, while also looking at future prospects with quantum computing and 5G.

But this is changing. Today, leading global retail platforms are moving to a more dynamic product road mapping path fueled by real-time data from distributed logistics systems. They can do a good, theoretically sound, and organically resilient product strategy by continuously tracking supply chain costs, delivery times, and stock levels.