Legacy Java Modernization with AI at Code Remix Summit

API versioning used to be an implementation problem. Now it’s an architecture problem. With tools like IBM Bob, you can generate versioned APIs in Quarkus in seconds. Path versioning, headers, media types — all done automatically. But here’s the catch: if your strategy is wrong, you just created technical debt faster than ever. I broke down the 3 approaches and when they actually make sense 👇 https://lnkd.in/dUs8iMb4 #Java #Quarkus #SoftwareArchitecture #API #BackendDevelopment #AI

🚀 Exciting Update: Solving the Latest Spring Boot Bugs with Scenario-Based AI Contexts! If you are building AI-driven applications in Java, managing runtime contexts effectively is crucial. I’ve just pushed a significant update to my open-source project: Scenario-Based Runtime Context for AI. In our latest update, I’ve added detailed descriptions and concrete evidence demonstrating how this framework successfully resolves some tricky bugs encountered in the latest Spring Boot releases. 🐛🔨 By utilizing a scenario-based approach, we can handle dynamic AI runtime contexts much more smoothly without clashing with Spring Boot's latest lifecycle or dependency injection changes. Curious to see how it works? Check out the updated repository and the new use cases here: 🔗 https://lnkd.in/gUnnVG5y I'd love to hear your thoughts! If you find it helpful, a ⭐️ on GitHub is always appreciated. Feedback and PRs are welcome! #SpringBoot #Java #AI #OpenSource #GitHub #SoftwareEngineering #DeveloperTools

What gets me is the AI execution layer angle. Most teams are still debating Python vs Java for ML pipelines when Java’s JVM is quietly becoming the most reliable runtime for inference at scale.

Most backend problems we deal with today are not new. But the way we solve them is changing. This is how we should start to look at Java systems 👇 → Logs are no longer just logs → they can be analyzed → APIs are not just fast → they can be smart → Rules are not fixed → they can be adaptive → Systems are not reactive → they can be predictive The interesting part is: None of this requires replacing Java or rewriting systems. 👉 It’s about adding an AI layer on top of existing architecture Clean microservices + AI integration = systems that don’t just process data, but understand it Still exploring this space and learning how to design it better. #Java #BackendDevelopment #AI #Microservices #SystemDesign #SoftwareEngineering

We are launching a new series of technical articles where we open the hood on the actual architecture behind our AI projects. First up: building an Agentic AI system on the JVM and Rust. Here's what you can find inside: 🔹 Model Context Protocol (MCP): Why we use TypeScript just as a proxy, while Apache Pekko secures the heavy domain core. 🔹 The reality of ADK-Java: We show why we had to manually build task state machines (submitted -> working) and pay the polyglot serialization tax between Scala (Circe) and Java (Jackson / JsonNode). 🔹 A2A Architecture: Why we deliberately kicked Kafka out of this project for direct HTTP/JSON-RPC routing. Link to the article in the comment. 👇 #Backend #Scala #Rust #Java #AgenticAI #SoftwareArchitecture #Scalac

Why your 16GB Heap is pausing your AI Agents (and the 2026 fix). ⏱️🗑️ If you are running a modern Spring Boot application, you are likely using the default G1 Garbage Collector. For 90% of microservices, G1 is fantastic. It balances throughput and latency nicely. But what happens when you enter the top 10%? In my Travel Agent RAG system, a single user query might trigger parallel calls to Ollama, parse megabytes of JSON responses, and map thousands of vector embeddings. This creates massive "Object Churn" in the young generation of the heap. Under this kind of load, G1 GC eventually has to clean up. When it does, it triggers a "Stop-The-World" pause. Your application completely freezes. Even a 200ms pause during an LLM streaming response feels like a massive lag spike to the user. The Fix: Generational ZGC. Introduced as a production-ready feature in Java 21, Generational ZGC is designed to give you sub-millisecond pause times, regardless of whether your heap is 1GB or 16 Terabytes. It does the heavy lifting of garbage collection concurrently alongside your application threads. The 2026 JVM Flags: Bash # ❌ THE DEFAULT (Prone to latency spikes under heavy AI load) java -XX:+UseG1GC -jar travel-agent.jar # ✅ THE HIGH-SCALE FIX (Sub-millisecond pauses) java -XX:+UseZGC -XX:+ZGenerational -jar travel-agent.jar Why this is a Senior Architectural Move: Predictable p99 Latency: Your API response times become entirely predictable because the JVM never freezes for more than a fraction of a millisecond. No Tuning Required: Unlike older collectors where you had to meticulously tune generation sizes and pause targets, ZGC is designed to be auto-tuning. You set the max heap size, and it handles the rest. If you are building Agentic workflows or high-throughput data pipelines, upgrading your GC is the highest ROI change you can make without touching a single line of business logic. Are you still riding with G1 GC, or have you benchmarked Generational ZGC in your production environment? Let’s swap metrics below. 👇 #Java #JVM #GarbageCollection #BackendEngineering #SystemDesign #SoftwareArchitecture #HighScale #PerformanceTuning

From Spring Boot to Helidon: is performance becoming the new priority in Java microservices? Cloud-native architectures are redefining how we build and scale applications and frameworks like Helidon are gaining momentum for a reason. This week’s Tech Tuesday explores how Helidon is reshaping Java microservices with: • Faster startup times • Lower memory footprint • Native compilation with GraalVM • Seamless AI integration with LangChain4J A shift from “one-size-fits-all” frameworks to performance-driven architectures is underway and hybrid approaches are becoming the new standard. 👀 We’re curious: would you prioritize performance over ecosystem maturity in your next microservices project? 👉 Read the complete article written by Claudia Grieco on our blog: https://lnkd.in/dbqakQhk #TechWithKineton #Java #Microservices #CloudNative #Helidon #SpringBoot #AI #SoftwareEngineering

GitHub Copilot— AI pair programmer — and like any collaborator, the quality of output depends on how you communicate. High-Impact Prompt Patterns —— 1. Be explicit about architecture “Create a microservice with controller, service, and repository layers using Spring Boot” 2. Add constraints “Optimize this method for O(n) complexity and avoid nested loops” 3. Define role/context “Act as a senior Java developer and refactor this code for scalability” 4. Ask for improvements, not just code “Identify performance bottlenecks and suggest improvements” 5. Generate tests & documentation “Write JUnit test cases with Mockito for this service class” Because in the AI era, how you ask matters more than how fast you type. #GitHubCopilot #AIforDevelopers #Productivity #SoftwareEngineering

🚀 Backend Learning | Event-Driven Architecture in Modern Systems While working on backend systems, I recently explored how systems communicate efficiently using event-driven architecture. 🔹 The Problem: • Tight coupling between services • Slow response when handling multiple dependent operations • Difficult to scale synchronous systems 🔹 What I Learned: • Event-Driven Architecture (EDA) allows services to communicate via events • Producers publish events, consumers react asynchronously • Tools like Kafka / RabbitMQ enable event streaming 🔹 Key Insights: • Improves scalability and flexibility • Reduces coupling between services • Enables asynchronous processing 🔹 Outcome: • Faster and more scalable systems • Better handling of high-volume events • Improved system decoupling Modern systems are not just request-response — they are event-driven. 🚀 #Java #SpringBoot #SystemDesign #BackendDevelopment #Microservices #Kafka #EventDriven #LearningInPublic

Christian Tzolov introduces the Spring AI roadmap at Spring I/O keynote: - 2.0 May 2026 : Spring Boot 4 baseline, Jackson 3, null safety, API refinements, MCP updates, focus on quality and consistency - 2.1 November 2026 : dedicated agentic API and annotations, durable workflow, sessions - 3.0 May 2027 : unified Virtual Thread friendly stack (no more blocking versus reactive dilemma) #spring #java #springio26