Abhishek Kulkarni’s Post

Most developers are still writing code. The best ones are starting to orchestrate it. In my latest article, I break down the AI-first IDE landscape in 2026 — and why this shift changes what it means to be a Java developer today. We’re moving from coding → reviewing → guiding AI agents. And that changes the game. The real advantage now isn’t syntax. It’s how well you: * frame problems * guide AI * make trade-offs If you’re a Java developer thinking about your next step, this is a shift you can’t ignore. Link in the first comment 👇 Are you still coding… or already orchestrating? #Java #SoftwareEngineering #AI #DeveloperExperience #AIEngineering #IDE

Most Java + AI tutorials are teaching how to get working code in minutes without explaining why any of those steps exist. They start with abstractions. ChatClient.builder() “Add advisors” “Enable function calling” Oops, tool calling by bad! So you get working code yes, but not the real understanding, that you need for building real-world systems that will scale with AI. And t𝗵𝗮𝘁'𝘀 definitely 𝗻𝗼𝘁 𝗵𝗼𝘄 𝗝𝗮𝘃𝗮 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿𝘀 𝗹𝗲𝗮𝗿𝗻. We don't just want code that works. We want to understand why it works. We want to know what problem each abstraction solves. We want to understand the design decisions so we can make our own when the tutorial doesn't cover our use case. 𝘚𝘰𝘧𝘵𝘸𝘢𝘳𝘦 𝘪𝘴 𝘥𝘦𝘵𝘦𝘳𝘮𝘪𝘯𝘪𝘴𝘵𝘪𝘤. 𝘎𝘪𝘷𝘦𝘯 𝘴𝘰𝘮𝘦 𝘪𝘯𝘱𝘶𝘵, 𝘪𝘧 𝘺𝘰𝘶 𝘳𝘶𝘯 𝘵𝘩𝘦 𝘱𝘳𝘰𝘨𝘳𝘢𝘮 𝘢𝘨𝘢𝘪𝘯, 𝘺𝘰𝘶 𝘨𝘦𝘵 𝘵𝘩𝘦 𝘴𝘢𝘮𝘦 𝘰𝘶𝘵𝘱𝘶𝘵. 𝘈 𝘥𝘦𝘷𝘦𝘭𝘰𝘱𝘦𝘳 𝘩𝘢𝘴 𝘦𝘹𝘱𝘭𝘪𝘤𝘪𝘵𝘭𝘺 𝘸𝘳𝘪𝘵𝘵𝘦𝘯 𝘤𝘰𝘥𝘦 𝘵𝘰 𝘩𝘢𝘯𝘥𝘭𝘦 𝘦𝘢𝘤𝘩 𝘤𝘢𝘴𝘦. But AI models are probabilistic. And production AI agents need memory, context, and the ability to take actions. You can't just call chatModel.call(prompt) and ship it. In This Series, I'm Going Deeper with the 'Why's For every abstraction Spring AI provides, I'll dissect it: What problem does it solve? (The production gap it addresses) How does it work? (The code and what's happening under the hood) Why do we need it? (What breaks without it) How do we work with it? (Practical patterns and best practices) This isn't a tutorial series. It's a systems thinking series. By the end, you won't just know how to build an agent. You'll understand the architecture that makes production agents possible. You'll know which abstractions to use, when to use them, and — most importantly — why they exist in the first place. #Java #SpringAI #AI #EnterpriseArchitecture #SoftwareEngineering #BackendDevelopment

I use AI tools every day. They help me write faster, explore ideas faster, and get unstuck faster. That part is real. But I think many senior developers feel the other side of it too: the work did not disappear. It moved. In enterprise Java, AI often gives you code that looks clean, plausible, and production-ready. It compiles. It boots. It even passes more checks than it probably should. And that is exactly why the human cost is still there. Maybe even more than before. Less typing. More supervision. More semantic review. More mental context switching. More carrying half-finished judgment into the evening. I wrote about that here, from a Java and Main Thread perspective: https://lnkd.in/duhPeeft #Java #EnterpriseJava #SoftwareEngineering #ArtificialIntelligence #DeveloperProductivity #Leadership

🚀 Everyone agrees "Python" is leading the AI wave right now.  But writing off "Java"? That’s a mistake. Python is amazing for experimentation, research, and rapid prototyping. No doubt. But when it comes to real world AI systems running at scale, Java is quietly stepping up. 💡 Here’s why Java is becoming powerful in the AI era: ⚡ Performance & efficiency (JVM handles large-scale workloads better, which matters when AI costs scale) 🏗️ Enterprise backbone (Most real-world systems (banking, logistics, e-commerce) already run on Java) 🔗 Strong integration (Connecting AI with existing systems is where Java shines) 🤖 Evolving AI ecosystem (Tools like Spring AI and LangChain4j are making AI integration easier than ever) 👥 Massive community (Decades of support, stability, and battle-tested frameworks) 👉 The pattern is clear: Python = build & experiment Java = scale & production And in the AI age, production is where the real impact happens. 💭 My take:  The future isn’t Python vs Java.  It’s Python + Java working together. One drives innovation.  The other powers it at scale. #AI #Java #Python #Backend #SoftwareEngineering #SpringBoot #TechTrends

Over the past few weeks, I’ve been experimenting with Claude Code for AI-assisted code changes in my Java projects—and honestly, it’s been eye-opening. Instead of spending hours refactoring or chasing down subtle bugs, I’ve been able to lean on MCP’s AI-driven suggestions to: Spot performance bottlenecks in JVM-heavy code paths Propose cleaner, more idiomatic Java constructs Auto-generate unit tests that actually catch edge cases I’d overlooked What surprised me most wasn’t just the speed—it was the quality of the changes. The AI doesn’t just patch code; it nudges me toward better design decisions. It feels less like a tool and more like a collaborative pair programmer. For Java developers, this is where the AI buzz gets real. It’s not about replacing us—it’s about amplifying our craft. With MCP, I’m spending less time firefighting and more time building features that matter.

AI-native IDEs are fundamentally changing how software engineering teams operate. But writing code faster is only half the battle. If your team is still bottlenecked by lengthy Java build and restart times, you're losing the efficiency AI gives you. We break down the pros, cons, and use cases of the Top 4 AI IDEs (Cursor, Windsurf, Kiro, Antigravity) for Java. Pro Tip: Combine these tools with JRebel for zero-latency hot reloads. Read the full comparison below and reach out to us to supercharge your DevSecOps pipeline!https://lnkd.in/gh5zKxmr #Java #AI #DevSecOps #Productivity #SoftwareEngineering #Dragonsoft

Over the past few years, I’ve found myself constantly coming back to one question: how do traditional software skills—like Java development—fit into the rapidly evolving world of Generative AI and Machine Learning? As someone who started with Java, I used to think of it primarily as a “backend language”—great for building scalable systems, APIs, and enterprise applications. And that’s still true. But what’s changed is where and how those systems are being used. Today, Java is quietly playing a significant role in AI-driven architectures. From integrating machine learning models into production systems, to handling high-throughput data pipelines, Java continues to be a strong backbone. Frameworks and tools have evolved, making it easier to connect Java applications with Python-based ML models, cloud AI services, and even real-time inference systems. What I’ve learned along the way is this: you don’t need to abandon your core skills to stay relevant—you need to extend them. Machine Learning, at its core, is about data, patterns, and decision-making. Generative AI takes it a step further—creating content, automating workflows, and reshaping user experiences. But none of this works in isolation. These models need robust systems around them—APIs, orchestration layers, monitoring, and scalability. That’s where strong engineering foundations, like Java, really shine. Here are a few practical shifts that have helped me: Moving from “just building services” to building intelligent systems Learning how ML models are trained, even if I’m not training them myself Understanding how to integrate AI services into real-world applications Focusing more on data flow, latency, and system design in AI contexts One important realization: AI is not replacing developers—it’s changing what we build and how we think. Instead of writing every line of logic, we’re increasingly designing systems that learn and adapt. That requires a different mindset—less about control, more about orchestration and evaluation. If you’re a Java developer wondering where to start with GenAI or ML, my advice is simple: Start small. Integrate an API. Experiment with a use case. Focus on understanding the ecosystem rather than mastering everything at once. The future isn’t Java or AI—it’s Java with AI.

🚀 AI is helping me write code faster… but here’s what actually made me a better developer 💭 Earlier, I used to focus only on writing code. ✨ Now I’m learning how to write better, production-ready code. Here’s what I explored recently: 🧠 Caching (Redis vs Caffeine) → Learned when to use in-memory vs distributed caching based on system design 🧪 Unit Testing & Code Coverage → Writing test cases using JUnit → Measuring code quality with JaCoCo 🤖 AI in Development (Cursor) → Used AI to write cleaner and faster code → Helpful in refactoring and improving code quality 🔥 Biggest Learning: Writing code is easy. Writing scalable, testable, and maintainable code is the real skill. 💬 Do you use AI while coding, or do you prefer writing everything manually? #Java #SpringBoot #AI #BackendDevelopment #CleanCode #LearningInPublic

🚀 You don’t need to switch to Python to use AI. You need to understand how to use AI effectively. Whether you’re working in Java, Python, or any other language — AI is becoming part of everyday development. Here’s what actually matters 👇 🔹 Prompting skills How you ask > what tool you use Clear context = better results 🔹 Understanding use-cases Where to apply AI: Code generation, debugging, documentation, automation 🔹 RAG (Retrieval-Augmented Generation) Don’t rely only on model knowledge Bring your own data → get accurate, context-aware responses 🔹 APIs & Integration Calling AI services from your backend (Spring Boot, etc.) 🔹 Validation mindset AI can be wrong — always verify before using in production 💡 Reality: AI is not replacing developers. It’s amplifying those who know how to use it well. 👉 Start simple: Ask better questions → build small use-cases → integrate gradually Follow for more on AI, Java & System Design 🚀 Want to discuss any topic? DM me 👍 #AI #ArtificialIntelligence #Java #Python #BackendDevelopment #SoftwareEngineering #Developers #Learning #Tech #Programming