⚡ Why Java Still Wins in 2026 (Performance Reality) Is Python really “too slow” for modern systems? We’ve all heard: • Python is easier • Python has better libraries • Developer speed > machine speed That’s true… until you hit production scale. 💥 At scale: • Latency becomes visible • Infrastructure costs increase • Concurrency becomes a real bottleneck This is where Java still has a strong edge. 🧠 JVM optimizations (like JIT compilation) allow Java to handle high-load systems far more efficiently — sometimes dramatically so, depending on the workload. That said — Python is still the right choice in many scenarios (especially AI, data, and rapid prototyping). The real question isn’t “Which is better?” 👉 It’s “When should you use which?” I break this down in detail here: https://lnkd.in/dVjP3x4S Curious — what are you using in production today? #Java #Python #SoftwareEngineering #Backend #SystemDesign

Switched from Java to Python for DSA practice. First thing that broke my code? The modulo operator. Not because I didn't know how it works — but because it works differently depending on the language, and I had no idea. In Java/C++, % follows the sign of the dividend: -7 % 3 → -1 In Python, % follows the sign of the divisor: -7 % 3 → 2 I was solving a prefix sum problem. Logic was right, approach was right, wrong answers. Took me embarrassingly long to find it. Once I did, I started noticing more: → Division: Java's 7/2 = 3 (integer). Python's 7/2 = 3.5 (decimal). Use // for integer division. → Floor vs truncate: Java truncates toward zero. Python floors toward negative infinity. -7 // 2 → Java: -3 | Python: -4 Same symbols. Different contracts. No errors thrown. Fewer lines of code. More silent assumptions waiting to bite you. #Python #Java #DSA #LearningInPublic #CompetitiveProgramming

I created a Java binding for Google's Magika ONNX file-type detector. Maintains byte-for-byte parity with the upstream Python binding on every fixture. ~2 ms steady-state inference per file on M-series Mac. (cold first-call ~100s of ms) https://lnkd.in/eTjACKzf #java #magika #opensource #onnx

🚀 DSA Journey — LeetCode Practice 📌 Problem: Same Tree 💻 Language: Java 🔹 Approach: To solve this problem, I used Recursion to compare both binary trees node by node. • If both nodes are null → return true • If one node is null and the other is not → return false • If node values are different → return false • Recursively compare left subtrees • Recursively compare right subtrees • If both left and right comparisons return true → both trees are identical This approach works because each subtree is itself a smaller version of the same problem, making recursion a natural fit. ⏱ Time Complexity: O(n) 🧩 Space Complexity: O(h) (where h = height of tree, worst case O(n)) 📖 Key Learning: This problem strengthened my understanding of tree traversal, recursion, and base case handling. It also reinforced how breaking a problem into smaller subproblems makes tree problems much easier to solve 💡 #DSA #Java #LeetCode #ProblemSolving #CodingJourney #LearningInPublic #BinaryTree #Recursion

Your Kafka clusters are running, producers are producing, and consumers are consuming. Everything seems to be working fine until your Python service needs to talk to your Java service and suddenly, you begin to see type mismatches between what Python service sends to Kafka and what your Java service can consume.. explore schema registry for enforcing types https://lnkd.in/eRJbSCe6

Every insight Potpie delivers about your codebase starts with parsing. Our context engine builds a complete map of your codebase: its structure, its components, its relationships into a knowledge graph that agents can use to navigate and query code faster. Currently, it supports more than 15+ languages including python, typescript, java etc. We understand that parsing large repositories is inherently time-consuming, but it shouldn't slow you down. That's exactly why we rebuilt this critical functionality in Rust: to make understanding your codebase faster, without any performance compromise. Our benchmarks show approximately 30% faster parsing for repositories with 1M+ lines of code, with the performance gap expected to grow significantly for larger codebases. Looking ahead, we plan to introduce parallelization of the parsing pipeline to use multi-core threading for processing files simultaneously without Python's GIL constraints. This allows us to handle enterprise-scale repositories with sub-minute indexing times.

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

🚀 DSA Journey — LeetCode Practice 📌 Problem: Binary Tree Preorder Traversal (LeetCode 144) 💻 Language: Java 🔹 Approach: To solve this problem, I used Depth-First Search (DFS) with Recursion to perform preorder traversal of the binary tree. • Visit the root node first • Store the root value in the result list • Recursively traverse the left subtree • Recursively traverse the right subtree • Follow Preorder pattern: Root → Left → Right This approach works efficiently because recursion naturally follows the preorder traversal structure. ⏱ Time Complexity: O(n) 🧩 Space Complexity: O(n) (including recursion stack) 📖 Key Learning: This problem strengthened my understanding of DFS, recursion, and tree traversal patterns. It also reinforced the preorder pattern (Root → Left → Right), which is a fundamental concept used in many tree-based problems 💡 #DSA #Java #LeetCode #ProblemSolving #CodingJourney #LearningInPublic #BinaryTree #DFS #Recursion #TreeTraversal

Running Python inside the JVM is cool. 𝙄’𝙢 𝙣𝙤𝙩 𝙘𝙤𝙣𝙫𝙞𝙣𝙘𝙚𝙙 𝙞𝙩’𝙨 𝙪𝙨𝙚𝙛𝙪𝙡. 𝙔𝙚𝙩. I tried Project Detroit, and technically — it delivers. You can call Python from Java. Python can call back into Java. Same process. No network hop. That part works. But the thing that actually matters right now? 𝗡𝗼 𝗽𝗶𝗽. 𝗡𝗼 𝗡𝘂𝗺𝗣𝘆. 𝗡𝗼 𝗣𝘆𝗧𝗼𝗿𝗰𝗵. 𝗡𝗼 𝗿𝗲𝗮𝗹 𝗠𝗟 𝘀𝘁𝗮𝗰𝗸. 𝘿𝙚𝙩𝙧𝙤𝙞𝙩 𝙞𝙨𝙣’𝙩 𝙨𝙤𝙡𝙫𝙞𝙣𝙜 𝘼𝙄 𝙞𝙣𝙩𝙚𝙜𝙧𝙖𝙩𝙞𝙤𝙣 𝙩𝙤𝙙𝙖𝙮. It’s redefining what the boundary between runtimes could look like. And that raises some uncomfortable questions: 𝘋𝘰 𝘸𝘦 𝘢𝘤𝘵𝘶𝘢𝘭𝘭𝘺 𝘸𝘢𝘯𝘵 𝘵𝘰 𝘳𝘦𝘮𝘰𝘷𝘦 𝘵𝘩𝘦 𝘱𝘳𝘰𝘤𝘦𝘴𝘴 𝘣𝘰𝘶𝘯𝘥𝘢𝘳𝘺? 𝘖𝘳 𝘥𝘰𝘦𝘴 𝘵𝘩𝘢𝘵 𝘣𝘰𝘶𝘯𝘥𝘢𝘳𝘺 𝘦𝘹𝘪𝘴𝘵 𝘧𝘰𝘳 𝘨𝘰𝘰𝘥 𝘳𝘦𝘢𝘴𝘰𝘯𝘴 — 𝘪𝘴𝘰𝘭𝘢𝘵𝘪𝘰𝘯, 𝘴𝘤𝘢𝘭𝘪𝘯𝘨, 𝘧𝘢𝘪𝘭𝘶𝘳𝘦 𝘤𝘰𝘯𝘵𝘳𝘰𝘭? Because once everything runs in one process, you don’t just gain performance… You also inherit each other’s problems. Right now: 𝘯𝘰𝘵 𝘱𝘳𝘰𝘥𝘶𝘤𝘵𝘪𝘰𝘯-𝘳𝘦𝘢𝘥𝘺. 𝘕𝘰𝘵 𝘦𝘷𝘦𝘯 𝘤𝘭𝘰𝘴𝘦. But if they crack the Python ecosystem problem, this stops being a demo and starts becoming architecture.

Spring creator wants Java’s type system to tame agentic AI Embabel treats LLMs as participants in strongly typed workflows — not black boxes — and the Spring creator Rod Johnson says that gives Java developers an edge Python can't match. #devops #operations