🚀 𝗘𝘃𝗲𝗿𝘆𝗼𝗻𝗲 𝘁𝗮𝗹𝗸𝘀 𝗮𝗯𝗼𝘂𝘁 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀. Spring Boot. Micronaut. Quarkus. But the 𝗿𝗲𝗮𝗹 𝗿𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 happened inside the 𝗝𝗩𝗠. 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗧𝗵𝗿𝗲𝗮𝗱𝘀 𝗶𝗻 𝗝𝗮𝘃𝗮 𝟮𝟭. For years the model looked like this: 𝟭 𝗿𝗲𝗾𝘂𝗲𝘀𝘁 → 𝟭 𝗲𝘅𝗽𝗲𝗻𝘀𝗶𝘃𝗲 𝗢𝗦 𝘁𝗵𝗿𝗲𝗮𝗱 Which meant: • Limited scalability • Thread pool management • Complex async programming Now Java gives us a new model: 𝟭 𝗿𝗲𝗾𝘂𝗲𝘀𝘁 → 𝟭 𝗹𝗶𝗴𝗵𝘁𝘄𝗲𝗶𝗴𝗵𝘁 𝘃𝗶𝗿𝘁𝘂𝗮𝗹 𝘁𝗵𝗿𝗲𝗮𝗱 And the impact is huge. With Virtual Threads you can: • 𝗛𝗮𝗻𝗱𝗹𝗲 𝗺𝗶𝗹𝗹𝗶𝗼𝗻𝘀 𝗼𝗳 𝗰𝗼𝗻𝗰𝘂𝗿𝗿𝗲𝗻𝘁 𝘁𝗮𝘀𝗸𝘀 • 𝗪𝗿𝗶𝘁𝗲 𝘀𝗶𝗺𝗽𝗹𝗲 𝗯𝗹𝗼𝗰𝗸𝗶𝗻𝗴 𝗰𝗼𝗱𝗲 • 𝗔𝘃𝗼𝗶𝗱 𝗰𝗼𝗺𝗽𝗹𝗲𝘅 𝗿𝗲𝗮𝗰𝘁𝗶𝘃𝗲 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 • 𝗕𝘂𝗶𝗹𝗱 𝗰𝗹𝗲𝗮𝗻𝗲𝗿 𝗺𝗶𝗰𝗿𝗼𝘀𝗲𝗿𝘃𝗶𝗰𝗲 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀 This is one of the 𝗯𝗶𝗴𝗴𝗲𝘀𝘁 𝗰𝗼𝗻𝗰𝘂𝗿𝗿𝗲𝗻𝗰𝘆 𝗶𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁𝘀 in 𝗝𝗮𝘃𝗮 𝗵𝗶𝘀𝘁𝗼𝗿𝘆. Java isn’t slowing down. 𝗜𝘁’𝘀 𝗲𝘃𝗼𝗹𝘃𝗶𝗻𝗴. #Java #Java21 #VirtualThreads #JVM #BackendDevelopment #SoftwareArchitecture #Concurrency

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

What happens if you create 10,000 threads manually? 🔧 ⚙️ Each JVM thread allocates its own stack (often around ~1MB per thread by default, depending on -Xss and platform). Rough math: thousands of threads × ~1MB stack ≈ gigabytes of memory just for stacks — before your application data. That’s not all. The OS scheduler has to manage all of them. Context switching becomes expensive, throughput drops, and you may hit OS limits (you might see errors like “unable to create new native thread”). This is why thread pools exist: a fixed number of worker threads + a task queue — instead of “one thread per task. Concurrency isn’t “more threads = faster.” It’s controlled concurrency and predictable resource usage. #Java #Concurrency #Multithreading #BackendEngineering

🚀 I asked Codex to build a TypeScript → JVM compiler… and it did. That experiment grew into TSJ — a toolchain that can compile and run TypeScript on the JVM, with support for real JAR dependencies via --jar / --classpath. Today I published TSJ v0.2.0 🎉 This release pushes TSJ forward with stronger test coverage and key runtime/compiler improvements (including better async iteration support and runtime capabilities like Proxy/Reflect). If you’re JVM-first but enjoy TypeScript’s ergonomics, I’d love your feedback (especially on interop and “what should TSJ support next?”). 🔗 Repo: https://lnkd.in/d5sEmzTj 🏷️ Releases: https://lnkd.in/d37Hd8-4 #TypeScript #JVM #Compilers #Java #OpenSource #BuildInPublic #AI #DeveloperTools

Java 26 just dropped — and the boring enterprise language narrative is officially dead ☕ 10 JEPs in one release. Structured Concurrency moves to its 6th preview (yes, it's getting close), the Vector API is still incubating but gaining traction, and Java is now actively positioning itself for AI and cloud-native workloads — not just legacy backends. The most underrated part? Garbage collection improvements that actually matter for latency-sensitive services. While everyone's distracted by LLMs, the JVM is quietly becoming one of the best runtimes for production AI infrastructure. Hot take: Java in 2026 is a better choice for AI backends than most people are willing to admit. Fight me. #Java #Java26 #JVM #Backend #SoftwareEngineering

Is #Java finally #cool again? In 2026, the conversation has shifted from "Will #Java #survive?" to "How fast and small can we make it? If you still think #Java is a "slow, memory-hungry beast," you’re missing the biggest #comeback in #software #history. The #Spring #Boot #Powerhouse: Still the king of the enterprise. With Spring AI now fully integrated, it’s the go-to for building LLM-orchestrated backends. It’s the "#Tesla" of frameworks—polished, powerful, and everywhere. If you’re running on #Kubernetes or #Serverless, #Quarkus is a cheat code. It’s reduced cloud bills by 30-50% for teams by slashing memory footprints and cold start times. It’s the "#SpaceX"—built specifically for the #modern #environment. Java isn't just surviving; it’s out-pacing the "modern" languages that tried to replace it. The future of Java is being defined by a fierce but #healthy #competition between the #Enterprise #Giant (Spring) and the #Cloud-Native #Speedster (Quarkus). #Java #SoftwareEngineering #SpringBoot #Quarkus #CloudNative #Programming #2026Tech

If your Spring Boot backend crashes during heavy file uploads, the problem isn't Java. It's your architecture. 🛑 This week, I’m building 'Aegis'—a Distributed Enterprise RAG Engine. To handle massive data ingestion without causing JVM memory spikes, I ripped out synchronous processing and implemented the Claim Check Pattern using MinIO and Apache Kafka. API latencies dropped from 32s to 12ms, and the system can now handle virtually infinite throughput. I wrote a deep-dive on exactly how I built this distributed Java architecture. Read the full breakdown here: https://lnkd.in/g7WYEG6Q (Check out the raw code on GitHub: https://lnkd.in/gdwJ_drr) #SystemDesign #Java #SpringBoot #Kafka 

Spring Boot handles your REST APIs. Spring Security locks them down. Spring Data talks to any database. Thymeleaf + HTMX covers your UI. Testcontainers tests the real thing. GraalVM compiles it all to a native binary. One language. One ecosystem. Zero context switching. While everyone's juggling 5 frameworks across 3 languages, Java developers ship end-to-end features without leaving their IDE. Stop sleeping on the stack that runs half the world's enterprise software. #Java #SpringBoot #FullStackDevelopment #SoftwareEngineering #BackendDevelopment

JVM Flags Matter More Than You Think Default JVM settings are not production settings. Example: -Xms512m -Xmx512m If not tuned: - Memory resizing overhead - Unpredictable GC behavior In containers: - JVM may misread available memory 💡 Real-world issue: App crashes even when “system has memory” Because: - Container limits ≠ JVM defaults 💡 Takeaway: JVM tuning is part of backend engineering — not DevOps only. #Java #JVM #DevOps #CloudNative

Most Spring Boot teams are still debugging distributed systems like it's 2019. Java 25 ships with native AI observability hooks baked into the JVM. Not a library. Not optional tooling. Built in. And 70% of Java workloads in finance are already migrating their observability stacks to support AI-driven routing. Your team? Probably still stitching together manual distributed traces across 40 microservices. Half a sprint just to reproduce a latency spike. (I was on that team six months ago.) Virtual threads already changed how we think about concurrency. AI-native monitoring is the next shift. The window to adopt it cleanly is right now. I watched a payments team spend 4 sprints retrofitting observability into services that were never instrumented for it. They ignored OpenTelemetry adoption in 2023. By 2027, that debt gets worse, not better. Teams that skip this now will burn cycles later. That's the trade-off nobody puts in the PR description. Is your team actively prepping for JVM-native AI hooks, or are you still treating observability as a nice-to-have you'll get to next quarter? #Java25 #SpringBoot #Observability #DistributedSystems #JavaDevelopment #AIDebugging #Fintech