As platforms grow, data starts arriving from many different systems through REST APIs, SOAP services, SFTP drops, database extracts, queues, and other interfaces. In many teams, these integrations are built one by one to solve immediate business needs. Over time, that creates a fragmented connector landscape where every source behaves a little differently.

Why Zoneless Became the Default

Angular’s official guidance frames Zone.js as a source of unnecessary synchronization. Zone.js uses DOM events and async tasks as indicators that the application state might have updated and triggers application synchronization to run change detection, while lacking insight into whether the state actually changed, so synchronization is triggered more frequently than necessary. The same guidance connects Zone.js to payload and startup overhead, debugging friction, and ecosystem compatibility risks that arise from patching native APIs, including the explicit note that some APIs cannot be patched effectively, such as async/await, which must be downleveled to work with Zone.js. 

"Do the simplest thing that could possibly work."

— Kent Beck, creator of Extreme Programming and pioneer of Test-Driven Development.

Have you ever needed to generate OpenAPI documentation directly from your code and, more importantly, do it in a way that fits cleanly into a CI pipeline? Swagger UI is commonly used in Spring Boot applications to visualize and test APIs from the browser. It can also expose the generated OpenAPI definition through a configurable endpoint, and that endpoint is exactly what we will use in this article.

Intelligent deployment patterns and practices are all about building applications that are not just easy to deploy, but also reliable, scalable, and efficient in production. This means moving away from traditional, manual deployment patterns and toward automated, container-based deployment practices.

By storing frequently accessed data in memory, APIs can handle dramatically more requests per second with much lower latency. As an example, one test showed that using Redis cut an expensive request’s response time from over 10 seconds down to under 1 second.

If you’ve built more than a couple of CRUD-heavy services, you’ve probably experienced the same pain points: repeating the same layers (entity, repository, service, controller), keeping consistent naming and structure across modules, and constantly adjusting boilerplate when requirements change. Spring CRUD Generator aims to reduce that overhead by letting you define your data model and project options once (in YAML) and generate a consistent project structure around it.

Whether you’re already using Swift’s async/await features or just starting to explore concurrency, this guide will give you a solid understanding of the mechanisms that keep your concurrent code safe and efficient.

For Java developers, the challenge is familiar: most computer vision examples live in Python notebooks, while the systems that actually need image classification run on the JVM. Bridging that gap usually means standing up a separate Python microservice, managing REST calls, and dealing with serialization overhead. That’s a lot of ceremony for what should be a single processing step.

The Problem: "Anonymous" Queries

When your database starts lagging (slow queries), you check the processlist in MySQL only to find a vague line:

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.