Jonathan Vila López on JAVAPRO International

The 5 Knights of the MCP Apocalypse

Wed, 01 Jul 2026 07:00:01 +0000

Let’s talk about that new MCP (Model Context Protocol) Server your team is using to connect to your real data services. It’s awesome, right? It’s the “magic box” that gives your AI Agent access to the real world—live databases, internal APIs, and all your tools.

But here’s the catch: you don’t own the code.

It’s a vendor product, an open-source project, or another team’s platform. You can’t just change its code when you find a security hole, unless you have the code and recompile it and deploy it.

7 Habits of Highly Effective AI Java Coding

Thu, 12 Mar 2026 07:00:01 +0000

From AI User to AI Pro

Let’s be real, AI coding tools are everywhere now. They’re no longer some shiny new toy—they’re a part of our daily grind as developers, just like our morning coffee.

For us Java devs, whether we’re wrestling with a giant legacy app or juggling a bunch of microservices, these tools look like a huge win for getting stuff done faster.

But here’s the catch: just coding faster isn’t the whole story. If you’re not careful, it can actually lead to bigger problems down the road.

Building Local LLM AI-Powered Applications with Quarkus, Ollama, and Testcontainers

Tue, 09 Sep 2025 07:00:01 +0000

Traditionally, many AI-powered applications rely on cloud-based APIs or centralized services for model hosting and execution. While this approach has its advantages, such as scalability and ease of use, it also introduces challenges around latency, data privacy, and dependency on third-party providers.

This is where local AI models shine. By running models directly within your application’s infrastructure, you gain greater control over performance, data security, and deployment flexibility. However, building such systems requires the right tools and frameworks to bridge the gap between traditional software development and AI model integration.

Code Reviews with AI: a Developer Guide

Wed, 30 Jul 2025 07:00:01 +0000

Code reviews are a cornerstone of software development. They’re where we share knowledge, catch bugs early, and ensure our code meets the highest standards.

But let’s be honest…

Traditional code reviews can be time-consuming and tedious and sometimes even miss subtle yet critical issues. Enter the age of AI-powered code review, a game-changer that addresses these challenges and elevates code quality to new heights.

This article dives into the common pitfalls of code reviews and explores how AI tools can revolutionize each phase of the development lifecycle.

I will discuss the use and impact of AI on the different phases of the SDLC from the 2 main perspectives of the developer and the reviewer. I will also talk specifically about the use of AI in the Code Review and how to implement it productively. I will provide examples of tools used in the different phases: Github Copilot, SonarQube, Qodo, and IntelliJ.

Top Security Flaws hiding in your code right now - and how to fix them

Tue, 29 Apr 2025 07:00:07 +0000

In 2019, a significant breach in Fortnite, the popular game, reportedly exposed millions of players to malware risk. The incident underscored the importance of securing SQL databases properly.

But this is not an isolated issue.

Multiple attacks involving SQL injection have occurred, like the one Tesla experienced in 2018. In that case, another SQL injection attack affected Tesla’s Kubernetes console, resulting in financial losses due to unauthorized cryptocurrency mining activities.

But this is not only about SQL Injection.