Core Java Track at Devnexus: JVM Internals and Performance

☕ Java mastery starts under the hood. The Core Java Track at #Devnexus is all about JVM internals, performance, testing, and real-world production behavior. 🔗 https://lnkd.in/eHcfmqtK 💥 Here’s what’s coming in the Core Java Track: • To Java 26 and Beyond! — Billy Korando, from Oracle • JUnit 6 + Exploring the Testing Ecosystem — Jeanne Boyarsky, from CodeRanch.com • Java Performance: Beyond Simple Request Latencies — John Ceccarelli & Simon Ritter from Azul • Beyond Default Settings: Optimizing Java on K8s with AI-Driven Performance Tuning — Stefano Doni, from Akamas • Peek Inside Production JVMs for Full Insights — Bruno Borges, from Microsoft • The OffHeap Podcast: Devnexus Edition (Now with AI Agents) — Freddy Guime • Scotty, I Need Warp Speed: Ways to Improve JVM Startup — Gerrit Grunwald, from Azul • The Self-Cleaning Castle: How Garbage Collection Works in Java — Renette Ros, from Entelect  • Just-in-Time Compilation Isn’t Magic — Doug Hawkins, from Datadog • Java’s Asynchronous Ecosystem — Daniel Hinojosa • Zero to C-Speed with Only Java — David Vlijmincx, from JPoint This is one of 11 tracks at Devnexus, built for engineers who care about performance, correctness, and the long-term evolution of Java. 🚀 Don’t just write Java — understand how it works at its core. 👉 Secure your ticket: devnexus.com Sign up to stay up to date with all conference news: https://atlj.ug/LICTA #CoreJava #Java #JVM #JavaDevelopers #SoftwareEngineering #PerformanceEngineering #CloudNative #Kubernetes #TechConference #DevCommunity

Here’s a trending, up-to-date LinkedIn post based on what’s happening right now in the Java ecosystem (Java 26 release + AI + modern stack evolution): Java is evolving faster than most people realize. With the recent release of Java 26, along with ongoing updates in Spring and cloud-native frameworks, the ecosystem is clearly shifting toward performance, scalability, and modern architecture. But what’s more interesting is how Java is evolving: • Focus on performance over syntax – Modern Java is less about language debates and more about how systems behave under scale and failure. • AI integration becoming real – Java is increasingly being used to integrate AI capabilities into enterprise systems. • Cloud-native dominance – Frameworks like Spring and Quarkus are optimizing Java for Kubernetes and distributed systems. • Faster startup & efficiency – Innovations like AOT and modern JVM improvements are making Java more lightweight and production-ready than ever. What this means for developers: It’s no longer enough to just “know Java.” The real value comes from understanding: ✔ System design in distributed environments ✔ Performance optimization ✔ Cloud and AI integration ✔ Building resilient, production-ready services Java isn’t just surviving — it’s adapting and becoming even more relevant in modern engineering. The developers who evolve with it will stay ahead. #Java #SpringBoot #Microservices #AI #CloudNative #SoftwareEngineering #TechTrends

🚀 Java 26 is here — and it clearly shows that Java is not just evolving, but engineering its future deliberately. With every release, Java is refining both developer experience and runtime performance, and Java 26 continues that trend with some powerful advancements: 🔧 Key Technical Highlights: ✔️ Project Loom (Virtual Threads – further maturity) Lightweight threads are changing how we design concurrent systems. Writing high-throughput, scalable applications is now simpler without complex thread management. ✔️ Structured Concurrency (incubating → evolving) A more readable and maintainable way to manage concurrent tasks — making parallel programming less error-prone. ✔️ Scoped Values (Preview) A modern alternative to ThreadLocal — Safer and more efficient data sharing in concurrent environments. ✔️ Pattern Matching Enhancements Cleaner, more expressive code with improved type checks and deconstruction — reducing boilerplate significantly. ✔️ Foreign Function & Memory API (Project Panama) Better interaction with native libraries - enabling high-performance computing use cases without JNI complexity. ✔️ JVM & GC Improvements Continuous tuning in garbage collectors (like G1, ZGC, Shenandoah) ensuring lower latency and better memory efficiency for large-scale systems. 💡 Java in the AI Era — Underrated but Powerful While Python dominates prototyping, Java excels where it matters most: 🔹 Production-grade AI systems 🔹 High-throughput data pipelines 🔹 Scalable backend services supporting ML models 🔹 Strong integrations with distributed systems and cloud platforms Frameworks and tools in the Java ecosystem are increasingly bridging the gap between AI experimentation and enterprise deployment. 🌍 Why Java Still Stands Strong Java’s real strength lies in its balance: 👉 Stability + Performance + Continuous Innovation It’s not chasing trends — it’s building foundations that last. 📌 My Takeaway: Java is no longer just a “backend language.” It’s becoming a modern, concurrency-first, cloud-ready platform that fits perfectly even in an AI-driven world. Curious to hear your thoughts — Do you see Java gaining more relevance with these changes? #Java #Java26 #ProjectLoom #Concurrency #BackendDevelopment #SoftwareEngineering #AI #Cloud #JVM

Java is not just evolving, it's reinventing itself for the AI era. From legacy systems to AI-powered cloud-native architectures, the journey of Java is nothing short of incredible: 🔹 Java 8 (2014) The foundation era ✔️ Lambdas ✔️ Streams ✔️ Functional programming shift 🔹 Java 11–17 (LTS) The productivity boost ✔️ Records ✔️ var keyword ✔️ Modern HTTP client 🔹 Java 21–25 (LTS) The performance revolution ✔️ Virtual Threads (Project Loom) ✔️ Structured Concurrency ✔️ Better scalability with lower memory footprint 🔹 Java 26 (2026) The AI & Cloud-native era ✔️ AI/ML interoperability ✔️ HTTP/3 support ✔️ Advanced native integration (FFM API) ✔️ AOT optimizations 💡 What does this mean for developers? Java is no longer just “enterprise backend.” It’s becoming a high-performance, AI-ready, cloud-native powerhouse. If you're still treating Java like it's stuck in 2014… you're already behind. 🔥 The real question is: Are you upgrading your skills as fast as Java is evolving? #JavaDeveloper #JavaInterviewQuestions #Java26 #JavaWithAI #JavaCareers #CoreJava #JavaInterview #JavaVacancy #Java26Features #InterviewPreparation #JavaJobs #JavaPreparations #AIJobs #JavaProgramming #LearnWithGaneshBankar

⚡Java isn’t making a comeback — it’s accelerating. What we’re seeing today is not revival… it’s evolution at scale🚀 Here are 4 major shifts redefining Java: • Virtual Threads (Project Loom) → Massive concurrency with simple code • AI Integration→ Spring AI & LangChain4j bringing intelligence into backend systems • Faster Spring Boot → Better startup time & memory efficiency with modern Java • Modernization Push → Moving from Java 8/11 → 17/21/25 using tools like OpenRewrite 🧠 What this means: ✔️ Less complexity, more productivity ✔️ Better performance without rewriting systems ✔️ AI becoming a native part of backend development 💡 The real shift isn’t a new framework… It’s simplicity + performance + intelligence Java is becoming: ✅ Easier to write ✅ Faster to run ✅ Smarter with AI And honestly… this is just the beginning 🔥 What trend are you seeing in your projects? #Java #SpringBoot #BackendDevelopment #AI #Java21 #Microservices #SoftwareEngineering #TechTrends

Coding under a tree… reflecting on JVM evolution. One thing I’ve started noticing about Java’s journey is this: It rarely evolves loudly — but it evolves deeply. With the release of Java 26, the platform continues its quiet but powerful transformation across performance, concurrency, and distributed system readiness. Not an LTS release.But definitely a directional one. Here are a few updates that genuinely stood out to me: What’s interesting in Java 26 🔹 HTTP/3 support in HttpClient   A strong step toward modern networking stacks required by distributed systems. 🔹 G1 GC throughput improvements   Subtle runtime optimizations that make a significant impact at scale. 🔹 Ahead-of-Time Object Caching   Startup performance continues to become a critical factor in cloud-native architectures. 🔹 Structured Concurrency (Preview)   The long-term vision initiated by pioneers like Brian Goetz continues to reshape how developers approach parallelism. 🔹 Vector API evolution   Java is steadily strengthening its position in performance-sensitive computing domains. 🔹 Removal of legacy Applet API   Platform maturity often means intentional simplification. 🔹 Stronger immutability direction in JVM Indicates deeper optimization potential in the future evolution of JVM runtime design. What stands out to me: Java’s innovation model is not disruption-driven.  It is ecosystem-driven. Decades later, the platform continues to power mission-critical systems globally — thanks to the sustained efforts of the broader OpenJDK engineering community. Educators and advocates like Trisha Gee continue to play a vital role in helping the developer community understand where the platform is heading. Curious to hear how others in the ecosystem see Java evolving over the next few years. Still learning. Still building. #Java #Java26 #JVM #BackendEngineering #SoftwareEngineering #CloudNative #DistributedSystems #Concurrency #OpenJDK #DevCommunity

Java is not just evolving, it's reinventing itself for the AI era. From legacy systems to AI-powered cloud-native architectures, the journey of Java is nothing short of incredible: 🔹 Java 8 (2014) The foundation era ✔️ Lambdas ✔️ Streams ✔️ Functional programming shift 🔹 Java 11–17 (LTS) The productivity boost ✔️ Records ✔️ var keyword ✔️ Modern HTTP client 🔹 Java 21–25 (LTS) The performance revolution ✔️ Virtual Threads (Project Loom) ✔️ Structured Concurrency ✔️ Better scalability with lower memory footprint 🔹 Java 26 (2026) The AI & Cloud-native era ✔️ AI/ML interoperability ✔️ HTTP/3 support ✔️ Advanced native integration (FFM API) ✔️ AOT optimizations 💡 What does this mean for developers? Java is no longer just “enterprise backend.” It’s becoming a high-performance, AI-ready, cloud-native powerhouse. If you're still treating Java like it's stuck in 2014… you're already behind. 🔥 The real question is: Are you upgrading your skills as fast as Java is evolving? #JavaDeveloper #JavaInterviewQuestions #Java26 #JavaWithAI #JavaCareers #CoreJava #JavaInterview #JavaVacancy #Java26Features #InterviewPreparation #JavaJobs #JavaPreparations #AIJobs #JavaProgramming

Java 26 just dropped — here are the main highlights you should know ☕ • 10 JEPs in this release → steady, incremental innovation • Structured Concurrency (6th preview) → simpler, more maintainable concurrent code • Vector API (incubator) → improved performance for AI/ML and numerical workloads • Foreign Function & Memory API → better, safer interaction with native code • Garbage Collection upgrades → lower latency + improved throughput • JVM performance tuning → optimized for modern, cloud-native environments • Enhanced pattern matching & language refinements → cleaner, more expressive code • Continued focus on Loom (virtual threads ecosystem) → scalable concurrency model • Stronger positioning for AI workloads → better runtime efficiency for data-heavy tasks Big picture: Java is no longer just “enterprise backend” — it’s evolving into a high-performance, AI-ready platform. Follow Bhuvnesh Yadav for more content!! #Java #Java26 #JVM #Backend #SoftwareEngineering

Everyone is buzzing about "Agentic IDEs" like Cursor and Google Antigravity. But for Java developers, an AI agent is only as fast as the next build. If your IDE is smart but your feedback loop is slow, you’re missing the point of AI-driven development. We took a look at the shift from AI plugins to AI-native environments and what it means for the JVM ecosystem. Read it here: https://lnkd.in/gRgpERTU #Java #SoftwareDevelopment #AI #JRebel #Productivity #Agentic

Distributed Transactions are Hard. Here is how to master the SAGA Pattern. 🔄 In a microservices world, we can’t rely on a single BEGIN TRANSACTION and COMMIT because every service has its own database. So, how do we ensure that if a payment fails, the order is canceled and the inventory is restored? The answer is the SAGA Pattern—a way to manage distributed transactions by breaking them into a sequence of small, local transactions. 🏗️ Two Ways to Implement SAGA: 1️⃣ Choreography-Based (Event-Driven) 💃 Each service reacts to events from others without a central leader. Pros: Loose coupling; simple for smaller systems. Cons: Risk of circular dependencies or infinite event loops if not designed carefully. 2️⃣ Orchestration-Based (The Conductor) 🎼 A central "Orchestrator" tells every service exactly what to do and when. Pros: Centralized logic; easier to track and debug complex flows. Cons: The orchestrator can become a complex "God Object" in very large systems. 🛠️ The "Real World" Java Survival Kit To make SAGAs work in production, you need more than just a diagram. You need these three pillars: 📦 The Transactional Outbox Pattern: In Java, we use this to solve the "Dual Write" problem. It ensures that your Database update and your Message Broker (Kafka/RabbitMQ) publish happen atomically. No more lost events! 🛡️ Idempotency is Mandatory: Since events can be redelivered, your services must be idempotent. If your Payment Service receives the same "Charge" event twice, it should only process the payment once. 🔍 Distributed Tracing: For Orchestration, tools like Jaeger or Zipkin are vital. They allow you to attach a "Trace ID" to a transaction so you can visualize exactly where a failure happened across your entire mesh. 💡 The Takeaway: Eventual Consistency SAGA provides Eventual Consistency. Your data may be temporarily inconsistent, but through local transactions and compensating events (undoing changes on failure), it will settle into a correct state eventually. Java Developers: Are you using Spring State Machine for Orchestration or Axon for Choreography? Let’s talk about your stack in the comments! 👇 #Microservices #Java #SpringBoot #SystemDesign #SoftwareEngineering #SagaPattern #Backend #DistributedSystems #Kafka