Yogesh Chavan’s Post

I got tired of catching the same Java mistakes in code reviews. String concatenation in loops. Mutable state leaking through getters. What was missing was a consistent layer that actually understands context. So I built one. It's called the 𝗝𝗮𝘃𝗮 𝗕𝗲𝘀𝘁 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 𝗖𝗵𝗲𝗰𝗸𝗲𝗿. It's an agentic skill that plugs into your editor, and from that point on your AI agent knows Java best practices from modern Java 21–24 features down to JVM internals, collections, thread safety, and design patterns. It works across 𝙑𝙎 𝘾𝙤𝙙𝙚, 𝘾𝙪𝙧𝙨𝙤𝙧, 𝙒𝙞𝙣𝙙𝙨𝙪𝙧𝙛, and any other agentic editor. The idea behind agentic skills is simple but underappreciated. Instead of relying on your AI to guess what good code looks like, you give it a precise, versioned definition of your standards. It sits at the center of your development environment and applies that knowledge consistently across every file, every session, every teammate. For teams this is meaningful. Your best engineer's instincts stop being a bottleneck and start being infrastructure. The skill is live at https://lnkd.in/e5gnAW-v feel free to try it. Would love to hear what you think. #Java #SoftwareEngineering #AI #DeveloperTools #OpenSource

💡 Spring AI isn't the only way to bring AI into a Java application. And depending on what you're building, it might not be the best one either. Andrew B compared two serious alternatives - LangChain4j and Semantic Kernel for Java - across the features that actually matter when you're making a framework decision: model support, RAG capabilities, ease of integration, and maturity. If you're a Java developer evaluating your options, this one saves you the research. 👉 Read the full comparison: https://lnkd.in/drsZRnKd __ At Grape Up, we begin every engagement by asking 'Why?' "Thinking out loud" series is our way of making those answers visible - our engineers and consultants writing about what they've actually learned, based on real projects and real trade-offs. #ThinkingOutLoud #GrapeUp #SpringAI #JavaApplication

🚀 Day 3 – Deeper into Multithreading: How Java Powers High-Scale & AI Systems Hi everyone 👋 Continuing my backend journey, today I went deeper into multithreading and concurrency in Java, focusing on how it actually works in real-world systems. 📌 What I explored: 🔹 Thread Lifecycle & Execution Control Understanding how threads move across states and how CPU scheduling impacts performance. 🔹 Synchronization & Race Conditions - Why multiple threads accessing shared data can cause inconsistency - Using "synchronized" to ensure thread safety 🔹 Executor Framework (Important 🔥) - Managing threads using thread pools instead of creating threads manually - Better performance and resource utilization 🔹 Future & Callable - Handling asynchronous tasks - Getting results from background threads 📌 Why this matters in real systems: In production systems, creating a new thread for every request is expensive. 👉 Thread pools help reuse threads and handle high traffic efficiently. 💡 AI System Example: Consider an AI-powered backend handling multiple requests like: - User sends a query - Backend calls an ML model/API - Fetches additional data from DB - Processes and returns response Using concurrency: 👉 Multiple requests are handled in parallel 👉 External API calls don’t block the system 👉 Overall latency reduces significantly Without proper multithreading: ❌ Requests get queued ❌ Response time increases ❌ System fails under load 📌 Key Takeaway: Efficient concurrency using tools like ExecutorService is critical for building scalable backend systems, especially when dealing with AI workloads and high request volumes. 📌 Question: 👉 What is the difference between "synchronized" and "ReentrantLock", and when would you use each? #Day3 #Java #Multithreading #Concurrency #BackendDevelopment #SystemDesign #AI #LearningInPublic

☕ Why I Still Choose Java in the Age of AI In a world buzzing with Python and AI frameworks, some ask: "Is Java still relevant?" Absolutely. Here's why: 🔹 Enterprise Backbone – 90% of Fortune 500 companies run on Java. AI doesn't replace infrastructure; it enhances it. 🔹 AI Integration – From Deeplearning4j to Spring AI, Java is evolving. We're not just writing code; we're building intelligent systems. 🔹 Performance & Scale – When your AI model needs to serve millions of requests, Java's JVM optimization and concurrency handling become your superpower. 🔹 Write Once, Run Anywhere – Still true after 28 years. Deploy AI-enhanced applications anywhere. The mindset that matters: "Don't fear AI taking your job. Fear the developer who uses AI with Java better than you." Every NullPointerException taught me resilience. Every Stream API taught me elegance. Java isn't just syntax—it's a philosophy of robust engineering. To fellow Java developers: The language is mature, but our applications are becoming smarter. Keep learning. Keep building. The JVM is your launchpad, not your limit. #Java #AI #SoftwareEngineering #TechLeadership #Programming #DeveloperLife #JVM #ArtificialIntelligence #CodeNewbie #100DaysOfCode

Java 21+ and AI: Virtual Threads or CompletableFuture? If you're integrating LLMs (OpenAI, Gemini, Claude) into your Java application, this decision matters. The rule is simple: → Need to make multiple parallel calls to an AI API? Virtual Threads. Sequential code, automatic scaling, no callback hell. → Need to compose a pipeline (classify → summarize → translate)? CompletableFuture. Chaining, fallback, fine-grained error control. The most common mistake? Using CompletableFuture for a simple call. Virtual Threads do the same with half the code. What's your approach? Drop it in the comments! #java #java21 #virtualthreads #ai #springai #microservices #backend #developer

I'm not sure why it took me this long to try NotebookLM, but here we are. I used it to generate a video on concurrency in Node.js vs Java and it genuinely made a tricky topic way easier to wrap my head around. Node.js = single-threaded, event-driven magic. Java = multi-threaded, fine-grained control. Java Virtual Threads (Java 21) = lightweight threads that make high-concurrency code in Java so much simpler — no more wrestling with thread pools for every async task. Same problem. Very different approaches. And Java is evolving fast. AI-powered learning is hitting different lately. This tool takes your topic and turns it into digestible content — no fluff, no wasted time. Video attached if you want to see how it turned out 👇 Have you tried NotebookLM? What are you using it for? #AI #NotebookLM #LearningInPublic #NodeJS #Java #VirtualThreads #Concurrency #TechTools

💡 Hot take: Java isn't legacy — it's leading the AI backend race. While everyone talks Python for AI, the JVM quietly shipped 👇 ☕ Java 26 with Vector API, Structured Concurrency & AOT caching — built for AI workloads 🌱 Spring AI hitting production maturity 🔗 LangChain4j + Google ADK for Java 1.0 now GA 🤖 Keycloak adding MCP (Model Context Protocol) support The pattern is clear: enterprises aren't rewriting 20 years of Java systems in Python. They're bringing AI to the JVM. If you're a Java dev, you're not behind the curve — you're exactly where the next wave is landing. Which side are you on — "Python for AI" or "JVM all the way"? 👇 #Java #SpringAI #JVM #AI #BackendEngineering

🚀 Java Deep Dive Series — Classes & Generics AI can generate classes. But designing the right type of class and writing type-safe code is what makes systems scalable. Today, I revisited: 👉 Classes & Generics in Java Here’s a quick breakdown 👇 🔹 Class Basics → What is a class, object, and Object class (java.lang.Object) 🔹 Types of Classes → Concrete, Abstract, Final, Enum, POJO 🔹 Class Relationships → Superclass & Subclass, inheritance basics 🔹 Nested Classes → Inner, Local, Anonymous, Static nested 🔹 Generics → Type safety using <T>, avoid typecasting 🔹 Advanced Generics → Bounded types, wildcards (?), multiple type params ⚙️ Deep dive covered: Generic classes & methods, inheritance with generics, raw types, type erasure, wildcards vs generics, singleton patterns (multiple approaches), immutable classes, and enum capabilities (fields, methods, interfaces). 💡 My Key Takeaway: Classes define structure, but mastering different class types and generics is what makes your design flexible and production-ready. 📘 I’ve documented detailed notes (with examples) here: 🔗 [https://lnkd.in/d43y3MPy] I’ll keep adding more topics as I go. If you're revising Java fundamentals or preparing for interviews, this might help 🤝 #Java #Generics #OOPS #LearningJourney #SoftwareEngineering #BackendDevelopment #AI

"async" is one of those concepts that looks similar across languages… until You look under the hood. Lately, I have been exploring how different languages handle async. At first glance, the answer seems simple: - Use async/await, threads, or some concurrency feature. But the deeper I go, the more I realise this: The syntax may look familiar, but the execution model underneath can be completely different. For example: - JavaScript uses an event loop with non-blocking I/O - Python uses asyncio for cooperative asynchronous programming - Java often relies on threads, executors, and reactive models - Go uses goroutines to make concurrency lightweight - C# provides a very mature Task-based async/await model All of them are trying to solve a similar problem: "How do we handle more work efficiently without blocking everything?" But the way they solve it changes a lot: - How does code feel to write - How systems scale - How errors behave - How debugging feels - How much complexity does the developer have to manage That is what makes backend engineering so interesting to me. Two languages can support “async,” but the model underneath can shape performance, scalability, developer experience, and even architecture decisions in very different ways. That is also why learning only syntax is never enough. - Knowing how to write async code is useful. - Knowing how it actually runs is what helps us design better systems. The more I learn, the more I feel this: "Good engineers do not stop at syntax." "They stay curious about the runtime model underneath." #SoftwareEngineering #AsyncProgramming #BackendDevelopment #Programming #SystemDesign #JavaScript #Python #Java #Go #CSharp

Java is quietly becoming one of the best platforms for Gen-AI — and most teams haven't noticed yet. We're not just building APIs and microservices anymore. The real shift is AI calls, LLM logic, and vector search living right inside Spring Boot services — and Java is evolving fast to support all of it. 🔥 Java 26 just dropped in March 2026, and Java 25 (LTS) is already solid in production: Pattern matching for primitives — cleaner, less boilerplate Smaller object sizes — better memory usage out of the box Here's what's changing on the ground right now: ->LangChain4j + Spring Boot → building AI search pipelines in Java is actually enjoyable now. ->LLM responses + Java's type system → less guessing, fewer bugs, cleaner code -> Stream Gatherers → process AI response streams in real time, the right way Java gives you speed, safety, and stability that Python just can't match at enterprise scale. It's not a Python vs Java debate anymore — it's about picking the right tool for production. 💬 Are you already using LLMs inside your Java services? What does your stack look like? #Java #Java25 #Java26 #GenerativeAI #SpringBoot #LangChain4j #BackendEngineering #LLM #GenAI