We add:

You bought the GPUs. Maybe you've got a couple of NVIDIA A100s in a rack, some RTX 4090s under desks, or a Kubernetes cluster with mixed hardware. You've got the compute. Congratulations!

Now what?

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.

Until now, you ended up either wrapping ai.generate() calls by hand or writing ad-hoc helpers that ended up duplicated across flows. The new Genkit Middleware changes that. It introduces a first-class, composable middleware layer for the generate() pipeline, with hooks for the model, the tool execution, and the high-level generation loop, plus a small but very useful set of official middlewares published in the brand new @genkit-ai/middleware package.

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.

A very common pattern for applications built using a micro-services architecture is this: everything runs quite normally for a long time. The architecture looks clean, services appear healthy, CI/CD tests are green, and monitoring dashboards do not raise alarms.

The Problem: "Anonymous" Queries

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

On the surface they look very similar. In practice, they sit at different abstraction levels and embody different philosophies. This article puts them side by side using their official docs as the source of truth:

That architecture rests on assumptions that have held true for years. The services and clients making requests are finite and well understood. Traffic patterns are predictable. Call sequences are bounded. Retry behavior is controlled. These assumptions shaped every rate limit, every circuit breaker threshold, every idempotency decision, and every capacity plan across the system.

And still, the interviews go nowhere.

You're running a Java application in a Docker container on your M1 Mac. Everything works fine, but you notice something strange: The resident set size (RSS) keeps growing, even though your heap usage is stable. After hours of investigation, you find mysterious rwxp memory regions, each exactly 128 MB, accumulating in your process memory map.

What's causing this? Is it a memory leak? A JVM bug? Something else entirely?

This article explores the integration of AI technologies into Agile frameworks, focusing on large-scale applications such as the Scaled Agile Framework (SAFe). Beginning with personal experiences, the article discusses the synergistic potential of combining AI tools like Splunk and MuleSoft with Agile methodologies to enhance project velocity and foresight. 

It highlights the importance of maintaining human oversight to balance AI insights, mitigating risks through regular feedback loops. Drawing on cross-industry insights, particularly from logistics, the article demonstrates the potential improvements AI can bring to software release cycles. 

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.

I believed this. Genuinely. For a long time.

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.

Reliability engineering has historically relied on a predictable workflow. A monitoring system detects an anomaly, an alert is triggered, and an engineer investigates logs and metrics before applying a remediation step. This model works reasonably well for traditional applications where failures occur slowly and are relatively easy to diagnose. AI-driven systems behave differently.

Modern AI platforms are built on layers of interconnected services. A typical architecture may include data ingestion pipelines, feature generation systems, vector databases, inference services, and orchestration frameworks that coordinate agents or downstream automation workflows. Failures rarely occur in isolation. A minor delay in a retrieval service can increase inference latency, which then cascades into application-level instability. In high-throughput systems processing thousands of requests per minute, such instability can propagate across the entire system before engineers have time to investigate the initial alert.

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.

Over the past decade, microservices architecture has become a widely adopted approach for building scalable and flexible software systems. Many organizations modernizing legacy platforms are moving away from monolithic architectures toward smaller, independently deployable services.

The promise of microservices is appealing: independent deployments, improved scalability, and faster development cycles.