AI & ML on JAVAPRO International

Solving Spring AI's UI Challenge with AG-UI's Java SDK

Thu, 16 Jul 2026 07:00:01 +0000

Introduction: The Missing Link in Java AI Development

Ask any Java developer who has tried to build an AI feature in a production application and they’ll tell you the same thing: the backend isn’t the hard part anymore. Spring AI makes it easy to invoke an LLM, define prompts, and create tools. But turning these interactions into a smooth, interactive user experience? That’s where the struggle begins.

Most developers solve this by inventing custom message formats, WebSocket events, and ad-hoc rules for how the UI should render tool calls. Each application reinvents the bridge between frontend interactions and agent intelligence. The result? A fragile, inconsistent layer that must be rebuilt for every project.

LangChain4j Agentic Workflows: From AI Calls to Multi-Agent Systems in Java

Wed, 08 Jul 2026 07:00:02 +0000

You have built AI features into your Java application. Your model is wrapped in a service, RAG is feeding it context, tools are wired, and calls are flowing. It works. Then requirements evolve. A single prompt-and-response is no longer enough. You need steps that follow each other, branches based on decisions, retries when things fail, and multiple actions running concurrently. The question shifts from “how do I call an LLM?” to “how do I orchestrate multiple LLM-driven tasks into a coherent system?”

BoxLang’s First 11 Months: Explosive Growth, Developer Empowerment, and the Return of Dynamic Language!

Fri, 03 Jul 2026 12:00:01 +0000

11 months. 13 releases. Production workloads already running. If you haven’t evaluated BoxLang yet, now is the time.

https://www.ortussolutions.com/blog/boxlangs-first-11-months-explosive-growth-developer-empowerment-and-the-return-of-dynamic-languages

At Into the Box 2026, Ortus Solutions pulled back the curtain on something that deserves serious attention from every technical leader building on the JVM: BoxLang is not a hobbyist language experiment. It is a production-grade, actively evolving development platform with enterprise adoption already underway and a runtime footprint that now reaches places Java simply cannot go.

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.

From Prototype to Production: Building Safe and Reliable AI Agents with Spring AI and MCP

Thu, 25 Jun 2026 07:00:01 +0000

The AI agent demo works perfectly in the development environment. Your prototype handles natural language queries, executes tool calls, and generates sensible responses. But when stakeholders ask ‘Can we deploy this?’, reality sets in. That question exposes the massive gap between a functional prototype and a Spring AI production system ready for real traffic, unexpected failures, and business-critical operations.

That question reveals the massive gap between a working prototype and a production system. The demo doesn’t handle failures gracefully. It has no guardrails against harmful outputs. There’s no visibility into what the agent is actually doing. Cost per request? Unknown. Error rates? No idea. Recovery strategy when the LLM provider goes down? Haven’t thought about it.

BoxLang 1.14.0 : Sets, Ranges, Inner Classes, and a Runtime That Talks Back

Wed, 24 Jun 2026 12:00:01 +0000

BoxLang has never stood still, but version 1.14.0 is something different. This is the release where the language stops filling gaps and starts defining what a modern dynamic JVM language looks like on its own terms. Sixty-five issues closed. Four innovative language features. A formatter that has grown up. And a companion module - bx-mcp - that fundamentally changes how you operate a running BoxLang application with AI.

This could have easily been a major release for the team. This has been a really amazing effort by everybody at Ortus, and we’ve received great feedback from our clients migrating to BoxLang and coming up with innovative ideas for this platform. We have only just begun!

Introducing bx-jwt: Enterprise-Grade JSON Web Tokens for BoxLang

Tue, 23 Jun 2026 12:00:02 +0000

JWT authentication is everywhere. But rolling it correctly — with proper algorithm enforcement, key management, clock skew handling, JWE encryption, and zero security footguns — is anything but trivial. Today, we’re shipping bx-jwt, a production-ready JWT/JWE module for BoxLang that handles all of it out of the box, so you can focus on building, not fighting cryptography.

bx-jwt is part of the BoxLang+ and BoxLang++ subscription tiers — our enterprise-grade module collection built for teams that take security seriously.

Enterprise-grade AI for Java Developers: Behind the Bar of the AI Revolution

Wed, 17 Jun 2026 16:45:24 +0000

There is a moment many Java developers experience today. After years spent building reliable systems, modernizing monoliths, shaping APIs, orchestrating containers, and keeping distributed applications alive, someone suddenly asks whether you can “add a bit of AI.” At first it sounds innocent. You try a model endpoint, run a quick experiment in Quarkus, watch a few responses appear in your terminal, and feel the spark of possibility. It looks easy. Almost too easy.

Automating JVM Thread Dump Analysis with AI: Practical Observability for Java on Amazon ECS and EKS

Thu, 11 Jun 2026 07:00:01 +0000

Imagine the following scenario: a Java service that ran flawlessly yesterday suddenly starts consuming 90% CPU, barely responding to user requests. Users encounter timeouts, and the operations team is under immediate pressure to triage the incident. In these situations — whether the application appears stuck or CPU saturation has occurred — one of the most powerful diagnostic assets available is the thread dump. A thread dump captures the state of all JVM threads at a specific point in time, showing execution states, stack traces, and lock contention — the raw material needed to understand what the runtime is actually doing.

Beyond UML: Clean Software Architecture in the Age of AI‑Generated Code

Wed, 10 Jun 2026 07:00:02 +0000

Abstract

Today we write more code in less time than ever before. AI systems act like permanently available junior programmers: They produce thousands of lines of code in minutes – but who takes responsibility for the software architecture?

The real problem is therefore often not “bad AI code,” but the growing difficulty for people to keep an overview and make sound architectural decisions.

This article discusses an idea for quickly building a mental model of existing code, spotting architectural problems early, and making targeted changes – without having to maintain static documentation in parallel.

BoxLang AI 3.2.0 — Image Generation, Web Search, Fluent Audio, Agent Registry & MCP Observability

Fri, 05 Jun 2026 12:00:01 +0000

BoxLang AI 3.2.0 is here, and it’s a landmark release. We’re shipping five major features: image generation, web search, a fluent audio builder API, a centralized agent registry, and deep MCP observability along with a suite of analytics improvements and a critical bug fix. Let’s dig in. 🎉

🖼️ Image Generation — `aiImage()`

You can now generate images directly from BoxLang using any provider that supports text-to-image generation. The aiImage() BIF follows the same fluent, chainable philosophy as the rest of bx-ai then act on the result with expressive method calls.

Introducing skills.boxlang.io — The Open Agent Skills Ecosystem for BoxLang & the Ortus World

Wed, 03 Jun 2026 12:00:03 +0000

Today we’re launching something we’ve been quietly building for months: skills.boxlang.io — a public, agent-agnostic directory for AI skills covering BoxLang, ColdBox, TestBox, CommandBox, and the entire Ortus ecosystem.

If you’ve ever pasted a 400-line system prompt into yet another AI agent, watched two of your bots drift onto subtly different versions of the same coding standard, or spent half a Friday afternoon trying to convince an LLM that BoxLang is not Java and is not CFML, or how to code for Modern CFML; this launch is for you. 🎯

Virtual Threads Meet AI: Java Concurrency in the Age of Intelligent Systems

Tue, 02 Jun 2026 07:00:00 +0000

AI workloads change how Java applications scale and behave. This article explains how Java 26 and virtual threads simplify concurrency in AI-heavy systems, replacing complex reactive setups with clearer and more efficient designs. It shows when virtual threads outperform traditional async models and how concurrency choices impact system architecture.

Introduction: Java, AI, and the New Concurrency

The rapid growth of AI-driven systems is reshaping how we design and scale Java APIs. RAG systems, LLM inference, and recommendation pipelines bring thousands of simultaneous requests with heavy network I/O, vector databases, and external services.

JCON EUROPE 2026: Why the Java Community Still Meets in Person

Mon, 01 Jun 2026 07:00:00 +0000

Every year, when we start planning JCON EUROPE, one question always guides us:

How do we create a conference that developers genuinely enjoy attending?

Not just another event with slides and sponsor booths. Not a place where people passively consume information and leave. We wanted to create an environment where developers actively connect, discuss, challenge ideas, and solve real-world problems together.

Looking back at JCON EUROPE 2026, I believe this spirit was visible everywhere.

Will AI Replace Me? Understanding Our Value in the AI Era

Wed, 20 May 2026 07:00:00 +0000

If you tried to keep up with all the content that’s flooding the internet on the topic of “Will AI replace software developers?”, you’d probably found a new full-time job. Social media posts, vlogs or entire channels, even CTO keynotes… not a day goes by without new content.

This article cannot predict the future. But it can offer some orientation in a messy debate by structuring it and making its implicit (and often false) assumptions visible. Because the biggest fallacy may not be overestimating AI - but underestimating what software engineering actually is about.

Building Production-Ready AI Agents with Java and Spring AI

Thu, 30 Apr 2026 07:00:01 +0000

Java developers have been building enterprise applications for decades, but when it comes to AI, the conversation has been dominated by Python. Spring AI changes this situation. It brings the same patterns we know from Spring - dependency injection, auto-configuration, portable abstractions - to AI development.

In January 2026 we built a sample agent application, and we want to share what we learned. The sample AI agent handles conversations with memory, answers questions from internal knowledge bases, uses external APIs, and integrates with existing microservices - all running on Amazon Bedro ck. If you want to try it yourself, the Building AI Agents with Java and Spring AI workshop [1] walks you through typical challenges of Generative AI models and provides step by step solutions with code available on GitHub [2].

Building an AI-Powered RPG - to Learn Enterprise AI Integration

Tue, 21 Apr 2026 07:00:02 +0000

This article shares insights from my hackathon project “Runes of Reason” - an AI-powered RPG - and uses it to explain concepts for AI integration with Spring AI. Along the way, the game demonstrates how LLMs can fundamentally reshape our whole product design. The RPG is an entertaining project for learning, but many insights can be transferred to real-world enterprise software use cases (summarized in the “Lessons Learned” sections).

Note: The code examples use the Spring AI milestone release 2.0.0-M2 - the API might change. The article also assumes a basic understanding of LLMs and Spring. If it feels too fast-paced, JAVAPRO offers a Spring AI fundamentals tutorial as well as many other great articles that can help you getting started with the topic.

Don’t Miss: New AI Award & AI Highlights at JCON 2026

Mon, 20 Apr 2026 13:08:00 +0000

JCON EUROPE 2026 is about to kick off – and anticipation is building. The conference opens its doors today in Cologne, once again bringing together the international Java community. Just ahead of the event, new high-profile AI highlights have been announced, making it one of the most important meeting points for AI and Java experts in Europe.

Focus on AI: Highlights on Wednesday

A special focus is placed on AI on Wednesday, April 22. The day begins with a high-level keynote:

How to Develop AI Agents Using BoxLang AI: A Practical Guide

Mon, 20 Apr 2026 12:00:01 +0000

AI agents are transforming how we build software. Unlike traditional chatbots that just answer questions, agents can reason about what tools they need, decide when to use them, chain multiple actions together, and remember what happened earlier in a conversation.

In this tutorial, I’ll show you how to build a real-world AI agent using BoxLang AI — the official AI framework for the BoxLang JVM language. We’ll build SupportBot, an e-commerce customer support agent that can look up orders, check inventory, issue refunds, and answer questions grounded in your knowledge base.

BoxLang AI Deep Dive — Part 7 of 7: MCP — The Protocol That Connects Everything 🔌

Fri, 17 Apr 2026 12:00:01 +0000

BoxLang AI 3.0 Series · Part 7 of 7

The AI ecosystem has a tool problem. Every framework has its own way of defining tools, every agent has its own way of calling them, and every integration requires custom code on both sides. An agent built in Python can’t easily use tools built in Java. An MCP server written for Claude Desktop can’t easily be consumed by a BoxLang agent without a custom adapter.

BoxLang AI Deep Dive — Part 6 of 7: Memory Systems & RAG — Building AI That Remembers 🧠

Thu, 16 Apr 2026 12:00:01 +0000

BoxLang AI 3.0 Series · Part 6 of 7

A chatbot with no memory isn’t a conversation — it’s a series of isolated queries. Every message starts from scratch. The user has to re-explain who they are, what they’re working on, and what was just said. It’s exhausting, and it signals that the AI isn’t really listening.

Memory is what separates a useful AI application from a toy. BoxLang AI ships with one of the most comprehensive memory systems in any AI framework — 20+ memory types across two major categories, vector embedding support for semantic retrieval, 30+ document loaders for RAG pipelines, and a per-call identity routing system that makes multi-tenant applications safe by default.

BoxLang AI Deep Dive — Part 5 of 7: One API, 17 Providers — The Provider Architecture Deep Dive 🛡️

Wed, 15 Apr 2026 12:00:01 +0000

BoxLang AI 3.0 Series · Part 5 of 7

Vendor lock-in is the silent killer of AI projects. You pick OpenAI, build everything against the OpenAI API, and then GPT-5 launches at three times the price. Or a competitor launches a model that’s faster for your use case. Or you need to self-host for compliance. Or your client is on AWS and wants Bedrock.

Every time the answer to “can we switch providers?” is “it would take months,” something went wrong architecturally.

Petabyte-Scale AI Memory with Serverless Java

Wed, 15 Apr 2026 07:00:01 +0000

The current wave of Generative AI innovation is built on a paradox. While models are becoming more powerful and efficient, the infrastructure required to operate them at scale is becoming increasingly wasteful. Enterprises are investing millions into compute, storage, and energy, yet a significant portion of these resources remains idle. Modern vector databases, graph systems, and caching layers are based on a monolithic architecture run as always-on clusters, consuming CPU and RAM regardless of whether data is actively accessed. Studies and industry analyses consistently show that up to 80% of compute resources in such systems are effectively wasted on idle workloads.

BoxLang AI Deep Dive — Part 4 of 7: Middleware — The Missing Layer in Every AI Framework 🧵

Tue, 14 Apr 2026 12:00:01 +0000

BoxLang AI 3.0 Series · Part 4 of 7

Here’s the question every team eventually asks about their AI agents: how do we test these things?

Agents make live LLM calls. They invoke real tools. They have non-deterministic outputs. Standard unit testing approaches fall apart. You can’t mock every provider. You can’t replay a conversation from three weeks ago. You can’t confidently tell stakeholders that the agent you deployed today behaves the same way it did when you signed off on it.

BoxLang AI Deep Dive — Part 3 of 7: Multi-Agent Orchestration — Building AI Teams That Work 🌲

Mon, 13 Apr 2026 12:00:01 +0000

BoxLang AI 3.0 Series · Part 3 of 7

A single agent is useful. An orchestra of agents is powerful.

The problem with most multi-agent frameworks is that the orchestration layer is bolted on — you’re managing agent references manually, passing outputs between them by hand, and hoping you haven’t introduced a cycle. There’s no concept of hierarchy. No cycle detection. No way to ask “who’s in charge here?” or “how deep in the tree am I?”

BoxLang AI Deep Dive — Part 2 of 7: Building a Production-Grade AI Tool Ecosystem

Fri, 10 Apr 2026 12:00:00 +0000

BoxLang AI 3.0 Series · Part 2 of 7

Function calling is where most AI frameworks look deceptively simple on the surface and turn into a mess underneath. You define a tool, pass it to the LLM, and when the LLM calls it — who handles the lifecycle? Who fires observability events? Who serializes the result? Who resolves the tool by name when the only thing you have is a string?

BoxLang AI Deep Dive — Part 1 of 7: The Skills Revolution 🎓

Thu, 09 Apr 2026 12:00:02 +0000

Every AI framework eventually hits the same wall: your system prompts start drifting. Agent A has a slightly different version of the SQL rules than Agent B. The tone policy on your support bot is three weeks behind the tone policy on your documentation bot. Someone copy-pasted the wrong version. Nobody noticed.

This isn’t a discipline problem: it’s an architecture problem. System prompts are plain strings, and plain strings don’t have a source of truth.

Take Control of GenAI — Without Compromising Your Data

Thu, 09 Apr 2026 11:50:02 +0000

Most GenAI demos look impressive — until data protection becomes a blocker.

Suddenly, you’re dealing with external APIs, unclear data flows, and compliance risks that slow everything down or stop projects entirely.

But what if you could build GenAI systems without any of that?

In this hands-on JCON EUROPE 2026 workshop, “Building Secure, Self-Hosted RAG Systems in Java”, you’ll explore how a fully local, self-hosted RAG system can be implemented — using Java and technologies you fully control.

A Big Screen Experience for Java Developers & Architects

Wed, 08 Apr 2026 12:00:02 +0000

JCON EUROPE 2026 | April 20–23, 2026 | Cinedom Cologne

Anyone talking about modern software development today can hardly avoid Java. Few technologies have shaped the enterprise world as sustainably, and few communities are as vibrant, diverse, and innovation-driven. This is exactly where JCON comes in: as a meeting point, a platform, and an experiential space for developers from all over the world. With participants from more than 70 countries, JCON has long since become an international gathering. Openness, knowledge sharing, and a strong sense of community remain at its core.

High-Performance Vector-Search Grids with Java

Wed, 08 Apr 2026 07:00:02 +0000

The rapid rise of Generative AI has fundamentally changed how modern systems are designed. While much of the attention has focused on large language models and inference pipelines, the real differentiator in production systems lies elsewhere: in the infrastructure that delivers context to those models. Retrieval-Augmented Generation (RAG) has become the de facto pattern for enterprise GenAI. It relies on fast, scalable access to embeddings, metadata, and relationships. Yet for Java developers, building such systems at scale has remained a challenge. External vector databases introduce latency, fragmentation, and operational complexity. Distributed caches struggle with memory inefficiency and serialization overhead. Traditional databases cannot handle high-dimensional similarity search at the required speed.

BoxLang AI v3 released - Multi-Agent Orchestration, Tooling, Skills and so much more

Tue, 07 Apr 2026 13:33:36 +0000

It’s been a while since we’ve shipped something this big. BoxLang AI 3.0 is a ground-up rethink of how AI agents, models, and tools work in the BoxLang ecosystem — and it lands with ten major features at once.

The headline is the AI Skills system: a first-class implementation of Anthropic’s Agent Skills open standard that lets you define reusable knowledge blocks: coding styles, domain rules, tone policies, API guidelines once in a SKILL.md file and inject them into any number of agents and models at runtime. No more copy-pasting the same system-prompt boilerplate everywhere. Skills are versioned, composable, and come in two modes: always-on (full content in every call) and lazy (only a name + description until the LLM asks for more).

Java Performance Optimization with Agentic AI: Autonomous Diagnostics and Actionable Recommendations

Tue, 07 Apr 2026 07:00:01 +0000

Java performance diagnosis in production is manual, slow, and requires deep expertise. You get a Grafana alert, grab a thread dump, download a JFR recording, open it in JDK Mission Control, stare at flamegraphs, correlate with metrics — hours of work per incident. Most teams don’t have a dedicated performance engineer, so alerts get ignored or result in generic “add more replicas” responses.

We built a system that does this autonomously. When a monitoring alert fires, it collects profiling data and thread dumps, extracts runtime metrics, generates flamegraphs, and sends everything to an AI model that produces a structured performance report — including root cause analysis and concrete code fixes with file paths and line numbers from the actual source repository. The pipeline runs on Kubernetes, triggered by Grafana webhooks, with results stored in Amazon S3.

Build Vector Database Apps with Pure Java

Thu, 02 Apr 2026 07:00:01 +0000

The Java ecosystem is entering a new phase. Generative AI is no longer experimental; it is becoming a foundational capability in enterprise systems. Yet while frameworks for inference and orchestration have matured, one critical bottleneck continues to slow down adoption: data access for semantic retrieval.

At the heart of modern GenAI systems lies Retrieval-Augmented Generation (RAG). This paradigm depends on fast access to embeddings - high-dimensional vectors that represent meaning. For Java developers, integrating this capability has historically meant introducing an external vector database into the architecture. What appears to be a straightforward design decision quickly becomes a source of complexity, inefficiency, and architectural friction.

Architect Your Own Experience: Creating Your Individual JCON 2026 Journey

Tue, 31 Mar 2026 12:00:03 +0000

The JCON EUROPE 2026, taking place from April 20 to 23 at the Cinedom Multiplex Cinema in Cologne, consistently focuses on a format that offers participants maximum flexibility. Instead of a fixed schedule, the individual design of one’s own conference experience takes center stage.

With a wide range of sessions, formats, and networking opportunities, visitors can decide for themselves which content, conversations, and contacts they want to focus on.

The central foundation for this is the JCON Schedule: participants can create their personal agenda, bookmark relevant sessions, and organize targeted 1:1 meetings with speakers or exhibitors. This turns the conference into something that is not just consumed, but actively shaped—depending on individual interests, projects, and professional priorities.

Agentic AI Patterns for Enterprise Software

Tue, 31 Mar 2026 07:00:01 +0000

It is no surprise that software development is increasingly shaped by the integration of AI. As the technology matures, we are moving beyond simple, monolithic AI models or single shot API calls. Modern enterprise architectures are rapidly adopting agentic AI systems, which orchestrate multiple independent reasoning agents to accomplish complex, real-world tasks. This article explores these powerful patterns, covering the core concepts of agents, their coordination, orchestration models, and crucially how to implement them effectively in enterprise Java applications.

A2A Building Interoperable AI Agents with Java

Wed, 18 Mar 2026 07:00:02 +0000

Why agents need friends?

We’ve recently seen a boom in AI “agents”. AI Agents are usually described as a service that talks to an AI model to perform some kind of goal-based operation using tools and context it assumes.

But most of these agents are still working in an isolated environment. You build an agent into your application and it offers some capabilities, but that’s it. You’re basically building a monolith with AI in it.

AI without spaghetti: Clean architecture in the age of AI

Tue, 17 Mar 2026 07:00:01 +0000

When “Just ship it” turns into lasagna code

We start with a familiar scene: a small experiment that grows faster than its architecture can handle. This is exactly the kind of situation where clean architecture becomes essential. Like layering pasta without checking the recipe, we kept adding features until the structure began to wobble.

At first everything seems perfectly reasonable. A quick integration with an API, a prompt that produces surprisingly good results. Suddenly the prototype starts solving real problems. The codebase grows organically: a helper class here, a service there. Maybe a controller that does just a little bit more than it probably should.

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.

Bridging Java and Python for AI/ML in Production: The Case for GraalPy on GraalVM

Tue, 10 Mar 2026 07:00:01 +0000

Java with a dash of Python

Abstract

In the Java stack, tapping into Python’s powerhouse of NLP and AI/ML libraries often means messy inter-process plumbing—until GraalPy changed the game. With GraalVM’s Python runtime embedded directly in your JVM, you can import and run Python libraries like TextBlob for sentiment analysis straight from Java. This approach offers a viable alternative for the more complex solutions, such as HTTP, gRPC, or subprocesses.

Bring AI into your Jakarta EE apps with LangChain4J-CDI (formerly SmallRye-LLM)

Wed, 25 Feb 2026 07:00:01 +0000

Goal: This article will demonstrate how to add AI features to a Jakarta EE / MicroProfile application using LangChain4J‑CDI, with simple to implement examples that runs on Payara, WildFly, Open Liberty, Helidon, Quarkus or any CDI 4.x compatible runtime.

What is LangChain4J-CDI?

Langchain4J is a Java library that simplifies the integration of AI and LLMs easier, and with their feature of AI services it provides a declarative and type-safe API for developers to define interfaces that represent AI services, abstracting away the complexities of direct LLM communication

Can GenAI Help Reduce Energy Used by Java Code?

Fri, 13 Feb 2026 07:00:02 +0000

Some recent studies show that IT is emitting more CO2 than civil aviation! As software developers, there is something we can do: optimising our code. Making our code run more efficiently should reduce the carbon footprint of IT. It will consume less CPU, RAM, disk, and network. Hardware being powered by electricity, these optimisations should reduce our electricity bills and lower emissions due to generation of that electricity. Optimised software also extends hardware lifespan by reducing the need for upgrades, diminishing the embodied carbon from manufacturing and avoiding raw material extraction (e.g., rare earths mining).

Bridging Creativity and Code: Generative AI Video with Java and RunwayML

Thu, 05 Feb 2026 07:00:01 +0000

Introduction

Generative AI is revolutionizing creative workflows - from image synthesis and text-to-video to motion design and inpainting. While most of these applications live in the Python or web ecosystem, there’s a compelling case for integrating them into Java-based environments, especially in enterprise software or automation contexts.

This article explores how you can control RunwayML, a leading generative AI platform, from Java—turning static enterprise systems into engines of creativity. We’ll go beyond simple API calls and dive into video generation, frame processing, and end-to-end automation using smart, production-ready Java libraries.

JCON Meets Enterprise AI - From Basics to Production

Thu, 29 Jan 2026 12:00:01 +0000

From April 20–23, 2026, the European Java community will once again gather in Cologne for JCON EUROPE 2026. Taking place at the Cinedom multiplex cinema, the conference brings together developers, architects, and Java enthusiasts for four days of knowledge sharing, inspiration, and real-world experience — all on the big screen.

Under the motto “Big screen. Big community. Big AI.”, JCON EUROPE 2026 offers an in-person program running daily from 9:00 to 18:00 (CEST), combining technical depth with a strong community focus.

Java 25 + GenAI: A New Era for Microservices in Finance

Thu, 22 Jan 2026 07:00:01 +0000

Java 25 and GenAI combine to build explainable, scalable microservices for finance — blending performance, transparency, and open-source innovation in one architecture.

Sarcasm-as-a-Service: Five Years Later

Wed, 14 Jan 2026 07:00:01 +0000

Rory Preddy, Author, Travelling

TLDR; Demo App https://aka.ms/ttsazure, Repo: https://github.com/roryp/ttsazure

I am 4 foot 1, and I have achondroplasia (dwarfism). Following spinal surgery, I was in a coma for three months and woke up unable to speak. I longed to tell my family that I loved them. That experience changed everything. A voice is not just sound; it is identity, emotion, and memory.

As a Developer Advocate at Microsoft, my role blends equal parts influencer and engineer, with a constant supply of questions from younger generations. When I first presented this talk in 2019, my career took off—audiences laughed at my demos, my jokes, and occasionally at me. Many assumed I was joking when I promised to revisit the project. Yet here I am, in a country where perpetual load shedding inspired this talk’s subtitle: “The power is out again. Fantastic.”

Building MCP Tools (for AI Agents) using Spring AI

Wed, 07 Jan 2026 07:00:02 +0000

Introduction

The innovation of AI Agents and Agentic AI systems has revolutionized the adoption of Generative AI. Agents empower systems to autonomously achieve goals by performing tasks. Tools are the key building blocks for AI agents that provide them with superior decision-making and implementation capabilities. Model Context Protocol (MCP) protocol has standardized the approach of building tools enabling faster, seamless integration. In this paper, we are going to talk about MCP, the underlying architecture. We will also provide a simple code example of building MCP tools in Java using Spring AI and integrating it with an AI Agent.

Code. Collaboration. Community.

Wed, 24 Dec 2025 07:00:02 +0000

JCON USA @ IBM TechXchange 2025 – where the spirit of Java found a new home in Orlando.

When the JCON team announced they were bringing their renowned Java conference series across the Atlantic, expectations were high. Known for its lively spirit and deep technical content, JCON Europe has become a must-attend event for Java developers. Its first U.S. edition – JCON USA @ IBM TechXchange 2025 – not only met those expectations but exceeded them, uniting Java professionals from around the world under one roof in Orlando.

AI-driven reverse engineering of java applications

Tue, 16 Dec 2025 07:00:02 +0000

Traditional reverse engineering

It is not an uncommon task to understand the innerworkings of an existing Java project, whether it is proprietary or open source. This can range from a simple task such as decompiling and reviewing the source code of an existing library to understanding how a large codebase is architected, built and deployed. In many cases developers are looking for proper documentation that describes in details and with examples the concepts implemented in the target project but quite often such a documentation is simply missing. In the case of decompilation there are tools like JD or IDE-specific decompiler plugins that do the job straight away. However if we consider the case a completely new and unknown code repository there a number of things we typically start with to understand how it is structured:

Solutions to achieve desired results from Generative AI Models

Wed, 10 Dec 2025 07:00:03 +0000

Rise of Generative AI

Generative AI (GenAI) has gained significant prominence in the last couple of years. This technological breakthrough has created the possibility of integrating various innovative use cases into applications across domains. Organizations of all sizes, small and large, have started exploring, experimenting and adopting GenAI. Tools, frameworks and platforms are rapidly evolving and allow application developers to easily integrate GenAI capabilities into their existing applications or to build new innovative applications leveraging GenAI. There are lots of options available – GenAI capabilities available as services, foundation models, open-source models and model provider platforms that allow hosting of models.

Why AI Agents Need a Protocol-Flexible Event Bus

Thu, 06 Nov 2025 07:00:01 +0000

Your AI agents are only as effective as the messaging infrastructure that ties them together.

AI & ML on JAVAPRO International

Solving Spring AI's UI Challenge with AG-UI's Java SDK

Introduction: The Missing Link in Java AI Development

LangChain4j Agentic Workflows: From AI Calls to Multi-Agent Systems in Java

BoxLang’s First 11 Months: Explosive Growth, Developer Empowerment, and the Return of Dynamic Language!

The 5 Knights of the MCP Apocalypse

From Prototype to Production: Building Safe and Reliable AI Agents with Spring AI and MCP

BoxLang 1.14.0 : Sets, Ranges, Inner Classes, and a Runtime That Talks Back

Introducing bx-jwt: Enterprise-Grade JSON Web Tokens for BoxLang

Enterprise-grade AI for Java Developers: Behind the Bar of the AI Revolution

Automating JVM Thread Dump Analysis with AI: Practical Observability for Java on Amazon ECS and EKS

Beyond UML: Clean Software Architecture in the Age of AI‑Generated Code

Abstract

BoxLang AI 3.2.0 — Image Generation, Web Search, Fluent Audio, Agent Registry & MCP Observability

🖼️ Image Generation — aiImage()

Introducing skills.boxlang.io — The Open Agent Skills Ecosystem for BoxLang & the Ortus World

Virtual Threads Meet AI: Java Concurrency in the Age of Intelligent Systems

Introduction: Java, AI, and the New Concurrency

JCON EUROPE 2026: Why the Java Community Still Meets in Person

Will AI Replace Me? Understanding Our Value in the AI Era

Building Production-Ready AI Agents with Java and Spring AI

Building an AI-Powered RPG - to Learn Enterprise AI Integration

Don’t Miss: New AI Award & AI Highlights at JCON 2026

Focus on AI: Highlights on Wednesday

How to Develop AI Agents Using BoxLang AI: A Practical Guide

BoxLang AI Deep Dive — Part 7 of 7: MCP — The Protocol That Connects Everything 🔌

BoxLang AI Deep Dive — Part 6 of 7: Memory Systems & RAG — Building AI That Remembers 🧠

BoxLang AI Deep Dive — Part 5 of 7: One API, 17 Providers — The Provider Architecture Deep Dive 🛡️

Petabyte-Scale AI Memory with Serverless Java

BoxLang AI Deep Dive — Part 4 of 7: Middleware — The Missing Layer in Every AI Framework 🧵

BoxLang AI Deep Dive — Part 3 of 7: Multi-Agent Orchestration — Building AI Teams That Work 🌲

BoxLang AI Deep Dive — Part 2 of 7: Building a Production-Grade AI Tool Ecosystem

BoxLang AI Deep Dive — Part 1 of 7: The Skills Revolution 🎓

Take Control of GenAI — Without Compromising Your Data

A Big Screen Experience for Java Developers & Architects

High-Performance Vector-Search Grids with Java

BoxLang AI v3 released - Multi-Agent Orchestration, Tooling, Skills and so much more

Java Performance Optimization with Agentic AI: Autonomous Diagnostics and Actionable Recommendations

Build Vector Database Apps with Pure Java

Architect Your Own Experience: Creating Your Individual JCON 2026 Journey

Agentic AI Patterns for Enterprise Software

A2A Building Interoperable AI Agents with Java

Why agents need friends?

AI without spaghetti: Clean architecture in the age of AI

When “Just ship it” turns into lasagna code

7 Habits of Highly Effective AI Java Coding

From AI User to AI Pro

Bridging Java and Python for AI/ML in Production: The Case for GraalPy on GraalVM

Abstract

Bring AI into your Jakarta EE apps with LangChain4J-CDI (formerly SmallRye-LLM)

What is LangChain4J-CDI?

Can GenAI Help Reduce Energy Used by Java Code?

Bridging Creativity and Code: Generative AI Video with Java and RunwayML

Introduction

JCON Meets Enterprise AI - From Basics to Production

Java 25 + GenAI: A New Era for Microservices in Finance

Sarcasm-as-a-Service: Five Years Later

Building MCP Tools (for AI Agents) using Spring AI

Introduction

Code. Collaboration. Community.

AI-driven reverse engineering of java applications

Traditional reverse engineering

Solutions to achieve desired results from Generative AI Models

Rise of Generative AI

Why AI Agents Need a Protocol-Flexible Event Bus

🖼️ Image Generation — `aiImage()`