Python Gains Ground in Concurrency, Java and Other Languages Adapt

💡 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

AI-assisted coding has made it incredibly easy to write code. But writing code was never the hard part. The real engineering begins when you ask: - How does this behave under load? - What do JVM metrics say when traffic spikes? - Should we go full concurrency, or controlled parallelism? - Do we scale horizontally or vertically? - What’s the cost trade-off of each decision? That’s where judgment matters. AI can generate threads, but it won’t decide the right level of concurrency for your system. It can suggest patterns, but it won’t own your production incidents. It can optimize snippets, but it won’t balance performance vs cost vs reliability. That’s still on us.(At least for now… unless someone builds an incredibly complex agent architecture that can debug production issues, tune performance, scale systems, and run cost analysis better than us. In which case, we might need to have a different conversation.) I’ve always enjoyed working with Java, especially its multithreading capabilities. But in real systems, even something as “simple” as threading becomes a series of trade-offs: - Throughput vs stability - Latency vs resource usage - Speed vs predictability The difference between a good engineer and a great one isn’t how fast they write code anymore. It’s how well they think through trade-offs. Code is easy. Decisions are hard. And that’s exactly where engineering lives. #SoftwareEngineering #AI #AICoding #SystemDesign #BackendEngineering #Java #JVM #Multithreading #Concurrency #Scalability #DistributedSystems Read “Multithreading in Java: Implementing Multithreading with Spring Annotations“ by Yash Paliwal on Medium: https://lnkd.in/g2qyD3JK

Most people think debugging is about finding the bug. It's not. It's about building the system that finds the next one faster. At my current role, when I joined, critical backend issues took about 48 hours to resolve. Not because people were slow — because there was no structure around how issues got triaged, reproduced, and traced through the pipeline. I didn't write a magic tool. I just built a repeatable debugging workflow. Structured logging in the right places. Clear escalation steps. A habit of writing down what broke and why after every incident. Resolution time dropped to about 12 hours. The lesson I keep relearning: the highest-leverage engineering work is often not building new features. It's making the system easier to understand when something goes wrong at midnight. That applies to every backend I've worked on — Java microservices, Python pipelines, LLM-integrated workflows. The stack changes. The need for structured observability never does. #SoftwareEngineering #BackendEngineering #Debugging #Python #ProductionSystems #AIEngineering #BuildInPublic

Coming from a Java background, most of my work has been around building APIs, backend services, and production systems, and that’s still where I’m strongest. At the same time, with how fast AI tools are evolving, Python has naturally become a big part of my workflow as well. I’ve been using Python quite a bit for automation, working with AI libraries, and building quick solutions where speed really matters. What I’ve realized is it’s not about replacing Java, but about complementing it. For building scalable and reliable systems, I still lean on Java. For fast iteration, data handling, and AI driven use cases, Python fits in perfectly. It’s no longer Java vs Python, it’s about using both where they bring the most value. #AI #SoftwareEngineering #Java #Python

Stop learning Python to "get into AI."   I've shipped more working software in the last 6 months with Claude Code than in my first 3 years writing Java microservices at Bell.   The bottleneck in 2026 isn't "can you write the code." It's: → Can you decompose a problem cleanly? → Can you write a precise specification? → Can you read a diff and catch what's wrong?   Those are engineering skills, not language skills. They transfer from any stack.   If you're a non-developer, you don't need to learn Python before you learn Claude Code. You need to learn how to think in systems. Claude Code will write the Python for you — and more importantly, it'll write the TypeScript, Go, SQL, and bash your solution actually needs.   If you're already a developer, the leverage is even bigger. Stop typing. Start architecting.   What's the last thing you tried to learn because you thought you "had to"?   #ClaudeCode #VibeCoding #AIAutomation

☕ 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

Why engineers are rewriting Python automation engines in Golang — and getting 10x throughput with the same logic. Here’s the technical breakdown 🧵 Python is great for automation. But when task volume scales — scraping, parsing, hitting APIs, pushing data — these cracks start to show: ❌ High memory usage under concurrent load ❌ The GIL blocking true parallelism ❌ Slow cold starts in containerized deployments ❌ Thread management becoming increasingly complex This is where Golang steps in. Same business logic. Same endpoints. Completely different performance profile. Here’s what changes: ✅ Goroutines vs ThreadPoolExecutor Spawning 10,000 goroutines costs almost nothing in Go. Python threads can’t compete at that scale. ✅ True concurrency, no GIL Golang utilizes all CPU cores natively. Python’s GIL prevents that by design. ✅ Memory footprint drops ~60% Go’s compiled binary vs Python’s interpreter overhead — the difference is significant at scale. ✅ Near-zero cold start time Critical when running bots on serverless or containerized infrastructure. ✅ Built-in race condition detection go build -race surfaces concurrency bugs that would silently corrupt data in production. Python is not the wrong choice. It remains the best tool for ML, Computer Vision pipelines, and rapid prototyping. But for high-throughput automation engines that need to scale — Golang is the stronger choice. The rule is simple: pick your language based on the problem, not habit. What’s your take — have you made a similar switch for performance reasons? 👇 #Golang #Python #Automation #SoftwareEngineering #RPA #BackendDevelopment #Performance #ComputerVision #MachineLearning

You ask AI to build a rate limiter in Java. One model replied in Python . 🧐 Both solutions were technically correct. But only one was actually usable. I was testing a real backend scenario: 👉 Design a thread-safe rate limiter (API Gateway style) 👉 Handle high concurrency 👉 Prevent abuse in production systems One model gave a proper Java implementation with concurrency handling. The other? Returned a clean solution… in Python. 🤔 That’s when it clicked for me: 👉 AI doesn’t always fail at logic. 👉 It fails at following constraints and context. And in real-world software development: Language matters System constraints matter Requirements matter Because you’re not just solving problems — you’re building systems that need to run in production. This experiment on VibeCode Arena taught me something important: AI can generate answers. But it’s still the developer’s job to ask: Is this usable? Does it match requirements? Can I deploy this? 🤔 Takeaway Correct code ≠ Correct solution Try it yourself I ran this duel on VibeCode Arena — you can explore it, test your own prompts, and compare models yourself - So get Ready for challange the AI Models: 👉 https://lnkd.in/gVfVfqjY Also curious to see what solution you’d prefer. Would you accept this in an interview or production system? #Java #BackendDevelopment #SystemDesign #Concurrency #SoftwareEngineering #Coding #AI #MachineLearning #VibeCoding #Developers #Programming #Tech #APIDesign #DistributedSystems

From Apps to Hardware — Why Python Dominates Testing Today Java was the default for test automation for years. That's shifted. Python is now the go-to — not because Java is bad, but because the ecosystem moved. Pytest, Playwright, and AI/ML libraries all speak Python natively. Most modern tools are Python-first. But it goes beyond application testing. Python is increasingly the preferred choice in network validation, embedded and hardware testing, and storage domain testing. Frameworks like Robot Framework, Scapy, and custom socket-level tooling are all Python-native. Teams across these domains are standardising on it. Hiring reflects this too. Whether you're building AI-assisted test frameworks or validating firmware, the skill that travels across domains is Python. Java still has its place. But Python is no longer just a web automation tool — it's the common language of modern quality engineering. --- `#TestAutomation #Python #QualityEngineering #SDET`