Rod Johnson Brings GOAP to Spring with Embabel

💡 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

🔥 FastAPI in 2026: Why It’s Still Dominating Python Backend Development FastAPI continues to evolve as one of the fastest-growing Python frameworks, powering modern APIs, AI systems, and microservices at scale. Today’s backend world is shifting toward: ⚡ Async-first architecture ⚡ AI/ML-powered APIs ⚡ Microservices & event-driven systems ⚡ Cloud-native deployments And FastAPI fits perfectly into this ecosystem.

Chaofan Shou posted the discovery at 4:23 AM. Within hours, Python rewrites were already on GitHub That's the part I keep coming back to. Not the leak itself — the turnaround A developer took 512,000 lines of TypeScript and rewrote the core in Python before most of the world was awake. Mirrors spread faster than takedowns. When DMCA requests hit, the code had already moved to decentralized platforms. More forks kept appearing. A Rust rewrite is underway It wasn't clean. The same day saw a separate npm supply-chain attack hit the ecosystem. The community moved so fast it outpaced its own security instincts But here's what I actually noticed: nobody was waiting for permission. No one filed a request. No one asked Anthropic what was okay to build. They just... built. At 4 AM. In hours. In three languages We talk a lot about what AI is doing to developers. We talk less about what developers do the moment a tool they depend on becomes open Turns out the answer is: immediately, in parallel, with forks What does that say about the relationship between builders and the platforms they build on?

The End of the Language Wars: Why Java and Python are Better Together 🚀 Scalability or Agility? If you work with large-scale systems, you’ve likely faced this dilemma. While Java is the "safe harbor" for infrastructure, Python is the engine for rapid innovation. Instead of picking a side, here is how I view the evolution of modern architecture: 1. The Backend as a Fortress (Java) Java remains unbeatable for managing complex business rules, concurrency, and security. It’s where we ensure that authentication (JWT) is foolproof and that data persistence—in databases like PostgreSQL—is performant and scalable. 2. Intelligence as a Competitive Edge (Python) For data processing, automation, and AI models, the Python ecosystem is simply more productive. Integrating a quantitative trading bot or a sentiment analysis engine into a Java environment isn't a "workaround"—it’s a strategic advantage. 3. Interoperability is Key Whether using isolated microservices, gRPC for low latency, or the polyglot capabilities of GraalVM, integration today is seamless. Data flows from Java, gets processed by the "magic" of Python libraries, and returns to a modern frontend (like Next.js) transparently. The result? A robust system that doesn't sacrifice the speed of delivering new features. The question is: How polyglot is your stack today? Do you prefer the safety of a single ecosystem or the versatility of a hybrid architecture? #Java #Python #SoftwareArchitecture #WebDev #Backend #Scalability

CloudAEye is pleased to publish the 2026 open source benchmark report for code review. This benchmark is built on 50 real pull requests from five major open source projects, including Cal, Sentry, Keycloak, Discourse, and Grafana. The evaluation spans five programming languages: Python, TypeScript, Java, Go, and Ruby. Each review tool is assessed against human-verified golden comments that capture the issues a thorough reviewer is expected to catch. Every tool is measured on how effectively it identifies these issues and how much irrelevant noise it introduces alongside them. CloudAEye ranks #1 among 21 leading code review agents with an overall F1 score above 65 percent. Refer to the detailed report here: https://lnkd.in/gCs4-KPZ #CodeReview #AI #F1 #benchmark #AIDevTool

We lost a week because we treated n8n and Python as interchangeable. They’re not, each fails differently in production. Real Scenario: A team starts in n8n to validate an integration (API auth, payload shape, happy-path). Then it grows: versioned releases, CI/CD, complex branching, unit tests, perf tuning, and a few “we need this auditable” requests. Suddenly the workflow is hard to review, and failures become hard to reproduce. Why It Happens: - n8n optimizes for iteration speed; Python optimizes for control + testability. - Visual flows make state/retries easy to wire, but diffs/reviews are weak vs code. - Python makes contracts/idempotency explicit, but you have to build the guardrails. Production Guardrails: - Use n8n for prototypes, backoffice automations, and low-QPS glue. - Use Python (FastAPI + workers) for high-QPS, strict SLAs, complex logic, regulated environments. - Go hybrid: n8n orchestrates, calls versioned Python “tool” services. - Define interfaces early: schemas, idempotency keys, timeouts, retry budgets. Where do you draw the line what stays in n8n, and what must graduate to Python? #DataEngineering #DataPlatform #WorkflowOrchestration #n8n #Python #Microservices #DataArchitecture #LLMOps #BigData #APIIntegration #EventDrivenArchitecture #Observability For more details please feel free to reach: https://lnkd.in/daPKDHid

Introducing Petrify: A Machine Learning Model Compiler for the JVM Petrify compiles ONNX tree ensemble and linear models directly into JVM bytecode. Your model goes in, a plain Java class comes out. No interpreter, no native libraries, no pointer chasing; just comparisons, jumps, and arithmetic baked into bytecode. ONNX Runtimes are huge and drag a massive amount of dependencies into your projects and bring a host of problems with JNI. Petrify is much faster and lighter: There is no runtime. Your models are JVM bytecode. Give it a try and leave me a Star on https://lnkd.in/gaVCumP3

The programming language landscape is shifting again ⚡ For years, Java dominated concurrency. Multithreading, JVM optimizations, and battle-tested scalability made it the backbone of enterprise systems and large-scale backend architectures. But something interesting is happening now. Python 3.14 is pushing toward true parallel execution. With the removal of the Global Interpreter Lock (GIL) and continuous runtime evolution, Python is no longer just a scripting or AI experimentation language — it’s becoming a serious contender for high-performance development across AI systems, backend services, and automation platforms. At the same time: • Go keeps winning in cloud-native infrastructure and distributed systems • Rust continues redefining performance with memory safety and zero-cost abstractions • Developers are no longer choosing languages by hype — but by problem domain We’re entering an engineering era where: 👉 Productivity 👉 Concurrency 👉 Performance 👉 Safety all compete on equal footing. It’s no longer one language to rule them all. It’s the right language for the right scale problem. The real advantage today isn’t syntax mastery — it’s systems thinking. Understanding trade-offs. Understanding architecture. Understanding scale. The language wars are back. This time, everyone leveled up. 🚀 #Programming #SoftwareEngineering #Python #Java #GoLang #RustLang #Concurrency #SystemDesign #BackendEngineering #AIEngineering

Classic problem—real-time systems turn simple API calls into distributed systems challenges fast; backoff + caching is the difference between stability and constant 429 chaos .

𝗨𝗡𝗟𝗘𝗔𝗦𝗛𝗜𝗡𝗚 𝗧𝗛𝗘 𝗨𝗟𝗧𝗜𝗠𝗔𝗧𝗘 𝗣𝗬𝗧𝗛𝗢𝗡 𝗣𝗢𝗪𝗘𝗥𝗛𝗢𝗨𝗦𝗘: 𝗧𝗛𝗘 𝗦𝗘𝗖𝗥𝗘𝗧 𝗧𝗢 𝗕𝗨𝗜𝗟𝗗𝗜𝗡𝗚 𝗟𝗜𝗚𝗛𝗧𝗡𝗜𝗡𝗚 𝗙𝗔𝗦𝗧 𝗔𝗣𝗜𝗦 𝗥𝗘𝗩𝗘𝗔𝗟𝗘𝗗 As we head into 2026, building scalable microservices requires a rock-solid understanding of how your code is organized from the first line. This guide demystifies the structural foundations of FastAPI to ensure your backend remains maintainable as your codebase grows. THE FASTAPI INSTANCE The core of every application begins with initializing the FastAPI class. This instance acts as the central router and configuration hub, serving as the bridge between your incoming HTTP requests and your internal application logic. Understanding how to instantiate this object correctly is the first step toward managing middleware, dependencies, and route decorators effectively. ROUTING AND PATH OPERATIONS Path operations are the fundamental building blocks of your API. By using decorators linked to your FastAPI instance, you define how the server responds to specific HTTP methods like GET or POST. This video breaks down how these functions map directly to URL paths, allowing for clean, readable code that handles client communication without unnecessary complexity. PARAMETER HANDLING AND TYPE HINTS FastAPI leverages Python type hints to perform automatic data validation and documentation. By defining expected types for path parameters, query parameters, and request bodies, you enable the framework to enforce data integrity before your logic even executes. This approach significantly reduces the surface area for bugs while providing built-in Swagger UI documentation for your endpoints. As a Senior Engineer, I cannot overstate that the structure of your application in 2026 is just as important as the logic inside it. FastAPI enforces good habits early by requiring explicit type definitions and clear route mapping, which saves hundreds of hours in debugging and refactoring down the line. Treat your project structure as your primary documentation. Tags: #FastAPI #Python #API #BackendDevelopment 📺 Watch the full breakdown here: https://lnkd.in/d7YeSp75