Java Web Development (JSP/Servlets) Services

Artificial intelligence (AI) is becoming increasingly pervasive. As an Enterprise Java developer, you might be wondering what value AI can add to your business applications, what tools Java provides to easily do that, and what skills and knowledge you might need to learn. In this article, we equip you with the basic knowledge and skills that you need to start exploring the capabilities of AI to build intelligent and responsive Enterprise Java applications.

When we talk about AI in this article, we mean getting responses from a large language model (LLM) based on a request that the Java application sends to the LLM. In our article’s example, we create a simple chatbot that customers can ask for planetary tourist destination recommendations, and then use to book a spaceship to visit them. We demonstrate using Java frameworks like LangChain4j with Quarkus to efficiently interact with LLMs and create satisfying applications for end-users.

So, I've been deep in the trenches with JNI lately (yeah, that Java Native Interface stuff) while working on a project where we had to plug a C++ AI assistant into our Android app. At first, it felt like stepping into a weird twilight zone — half Java, half C++, and all these random edge cases you never think about until you hit them. I remember staring at the stack trace for what felt like hours, realizing that one tiny missed DeleteLocalRef was enough to crash the whole app. Thought I'd share what actually tripped me up, what worked, and some ways to make life a little less miserable if you ever have to do this.

What the Hell Is JNI Anyway?

JNI is basically the bridge that lets Java (or Kotlin) talk to C/C++ code and vice versa. On Android, it’s the only real way to get heavy lifting done efficiently or access low-level APIs that Java/Kotlin just can't reach. Honestly, the first time I tried to wrap my head around it, I felt like I was learning a new language on top of Java and C++ at the same time.

It's been a while since my first article dedicated to Spring Data Elasticsearch usage as a NoSQL database was published. A couple of articles with configuration changes or hints followed the first article. Therefore, the main goal of this article is to define a new baseline for the full Elasticsearch setup.

Note: All previous articles are listed at the end.

There’s something that every backend or API developer faces sooner or later: the need for good fake data.

Whether you’re testing a new API, populating a database for demos, or simply trying to make your unit tests less “boring”, fake data is part of your daily routine. The problem? Most fake data feels… fake. You end up with “John Doe” and “123 Main Street” repeated over and over, which doesn’t look great when showing a prototype to your team or client.

If you are a developer, regardless of the technologies you use, at some point, you will face the challenge of handling large Excel files.

Most commonly, you will see the "out of memory" error when handling these files.