Spring Boot’s Actuator and Micrometer provide rich metrics that can be scraped by Prometheus and visualized in Grafana. This guide covers configuring a Spring Boot application to expose Prometheus-formatted metrics, writing custom metrics, and setting up Prometheus and Grafana for monitoring.

We cover installing Prometheus, writing a configuration to scrape your application, importing Grafana dashboards, and crafting PromQL queries and alerting rules. We also discuss Prometheus best practices, including metric naming conventions, label cardinality, and retention settings. Security considerations, troubleshooting tips, and the performance impact of metrics collection are also included.

The problem wasn't a lack of monitoring. It was a lack of structure.

How Angular Treats 200 as Success

Angular’s HTTP layer is intentionally aligned with HTTP semantics a request is represented as an Observable and failures in the HTTP layer are emitted on the Observable error channel. Angular documents three broad categories of request failure network/connection failure, timeout and backend error responses and states that HttpClient captures these errors as an HttpErrorResponse returned through the Observable’s error channel. When an API responds with a non success HTTP status, the error channel is used and HttpErrorResponse provides the HTTP layer context. 

1. Event Production with Spring Boot and Kafka
2. AI-Driven Processing in Kafka Consumers
3. Real-Time WebSocket Delivery to the Frontend

Event Production with Spring Boot and Kafka

The first step is capturing an event and publishing it to Kafka. By offloading work to Kafka the application can respond immediately to the user without waiting for processing. Spring Boot’s integration with Apache Kafka provides a KafkaTemplate to send messages to topics.

This article covers the four frameworks I have personally used to ship Java AI applications: Genkit Java, Spring AI, LangChain4j, and Google ADK Java. Each one represents a meaningfully different bet on what a Java AI framework should be, and understanding those differences will save you from picking the wrong tool.

This is not a benchmark post. Tokens per second and latency numbers go stale within weeks. Instead, this is a developer experience and architecture comparison, the kind of thing that matters when you’re deciding what framework will carry your product through 2026 and beyond.

I’ve decided to dedicate this week to exploring data flow in SwiftUI. In this article, we’ll discuss the differences between the @StateObject, @EnvironmentObject, and @ObservedObject property wrappers. From my experience, this is often the most confusing topic for developers just starting out with SwiftUI.

Why do we need property wrappers in SwiftUI?

SwiftUI uses immutable struct types to describe the view hierarchy. Every view provided by the framework is inherently immutable. This is why SwiftUI provides a specific set of property wrappers to handle data changes.

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.

Additionally, industry-specific insights from healthcare and retail demonstrate significant efficiency improvements, while technical implementations such as AI-enhanced CI/CD pipelines and story point estimations offer tangible advantages. However, challenges like the skills gap and lack of standardized methodologies highlight areas for growth and development. The article underscores the importance of a balanced approach, leveraging both AI and human insight for sustainable innovation.

Flutter 3.41 continues improving the framework’s efficiency, rendering pipeline and developer tooling. But even with these improvements, developers still need to follow certain best practices to ensure that their applications remain responsive and efficient on real devices.

Challenges in Kafka Consumer Fault Tolerance

Kafka consumers usually operate in an at least once delivery mode, which means a message might be delivered multiple times if not acknowledged properly. Transient errors can cause message processing failures. Without proper handling, such failures might lead to data loss or duplicate processing. If a consumer fails after processing a message but before committing the offset, Kafka will resend that message to another consumer, leading to a duplicate delivery. A fault tolerant consumer design addresses these scenarios by:

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

Architecture Overview

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

 Here's something nobody tells you when you're selecting an LLM: the decision isn't about which model is "best." It's about which model's failures won't kill your business.

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.

Something is wrong, but the metrics say everything is fine.

We have spent billions of dollars making AI content classifiers faster, more accurate, and more scalable. And yet, the fundamental accountability problem in content governance remains unsolved. When a machine flags content for review, it tells a human reviewer: "This is a problem." What it almost never tells them is: "here is why" — in terms a human can verify, challenge, or build upon.

That gap — between machine classification and human accountability — is where content governance systems fail. At enterprise scale, processing hundreds of millions of items quarterly, that failure is not a minor inefficiency. It is a structural problem.

Categorizing HTTP Status Codes

To provide a more granular and descriptive way of handling network events, I decided to categorize HTTP status codes into specific enums. This approach ensures that our logic is both type-safe and highly readable. By referencing the MDN Web Docs, I mapped out each response category to its own structure.