AI Generated Code: Usable or Not?

You don’t need Python or TypeScript to build serious AI workflows. Using Java, it comes down to two building blocks: - A reliable, durable workflow execution engine like Temporal Technologies - Unified model access using Spring AI I put that into a repo: spring-temporal-ai-workflow-patterns. It includes these common AI workflow patterns: - Sequential processing - Parallel processing - Routing - Evaluator-optimizer - Orchestrator-worker The video shows Routing: a first classification step decides which model and prompt should run next. Production AI is often less about “one clever prompt” and more about orchestration, durability, observability and controlled execution paths. Especially in enterprise environments, that matters a lot more than hype. If you’re in a Java-heavy company, this stack is a very practical way to build AI systems without forcing a language detour.

The point is simple: Python is excellent for exploration, but production AI often stops being a script very quickly. Once the work needs jobs, APIs, auth, observability, failure handling, deployment, and team ownership, Java becomes much more interesting. Not because it is fashionable, but because it is built for systems that have to keep running. https://lnkd.in/d2kjTAT9 #Java #AI #SoftwareArchitecture

Exploring how Java is becoming a strong player in real-world AI applications. #AI #Java #SoftwareDevelopment #Backend #Tech 🤖 🧠 💻 ⚙️ 🧩 https://lnkd.in/dvN6gpwb

Loved the conversations coming out of AI4J. From context engineering to predictive AI and functional code, check out this recap to learn how Java continues to play a major role in modern AI systems. #Java #AI4J #AI #Engineering #Developers

Node.js for AI In 2026, we’re moving beyond the "Research Phase" and into the "Production Phase" of AI. That means swapping Python for languages built for scale: Java and Node.js. Why the shift? ☕ Java for Scalability: Python’s Global Interpreter Lock (GIL) is a bottleneck for high-traffic enterprise systems. Java’s multithreading and the JVM provide the speed and security needed for massive AI backends. 📜 Node.js for Efficiency: Why manage two stacks? Running AI on Node.js means a unified team, non-blocking I/O for real-time streaming, and lower server costs by running inference on the edge. The Strategy: Train in Python if you must, but implement in Java or JS. Lab tools are for experiments. Production tools are for products. 🏗️ #AI #NodeJS #Java #SoftwareEngineering #TechTrends #Coding

Writing Python code is easy. Building production-ready systems is not. This gap can be seen in most projects. Many people can write scripts, but designing scalable systems is still a challenge. Models get trained, but they are not always ready for real-world use. Code works well on a local machine, but issues come up during deployment. AI solutions are built, but they are not fully connected to business workflows. In Python and PyTorch development, the difference is mostly about approach. Some focus on writing code and getting quick results. Others focus on how the system will perform, scale, and run in a real environment. That difference makes a real impact. From experience, real value comes from systems that are stable, scalable, and work end-to-end. It is not just about code or models. It is about making sure everything works together in a reliable way. Developers write code. Engineers build systems. That’s where the real impact is. #CSV #Automation

Forget Python Or Java: What You’re Speaking Is Code The most important programming language in the AI era is English! Not Python. Not Java. Not JavaScript. But there is a catch: Natural language only becomes “code” when it is precise enough to guide machines. A vague prompt is not engineering. A clear specification is. As AI coding agents become more capable, the developer’s role is shifting from writing every line of code to defining intent, constraints, architecture, tests, and quality. That is the idea behind my recent Forbes article: https://lnkd.in/dWsX2a-8 My view: the future is not less engineering. It is better engineering. What do you think will matter most for developers in the next few years: coding, prompting, architecture, or product judgment?

The most powerful programming language in 2026 isn't Python. It isn’t Java. It’s what you’re speaking right now. For decades, the "translation layer" between human intent and machine execution has been the biggest barrier to innovation. We moved from machine code to high-level languages like C++ and Python, each step bringing us closer to human logic. But the gap was always there. Today, that gap is closing entirely. According to a recent Forbes feature, natural language is becoming the ultimate programming interface. Large Language Models (LLMs) have turned words into instructions, allowing us to move directly from intent to results. This shift is fundamentally changing what it means to be a "developer": Syntax is secondary: AI can handle the boilerplate and the libraries. What it can’t replace is clear thinking. The Spec is the Code: The ability to write a structured, logical specification is now more valuable than writing 100 lines of error-free syntax. Human-Machine Collaboration: The most competitive professionals will be those who can provide precise context, manage constraints, and guide intelligent systems through language. The future of technology isn't just about who can code; it’s about who can communicate. We are entering an era of "spec-driven development" where your clarity is your greatest technical asset. Are we witnessing the end of traditional coding, or just its ultimate evolution? #GenerativeAI #FutureOfWork #SoftwareDevelopment #Coding #TechTrends #AI #PromptEngineering #Innovation #ForbesTechCouncil

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

Python vs Node.js is not a language debate. It is a debate over workload and execution models. Python - Interpreted, synchronous-first (with async support) - Strong for CPU-intensive and data-heavy workloads - Dominates in AI/ML, data engineering, and automation - Prioritises readability and developer productivity Node.js - Single-threaded event loop with async I/O - Strong for high-concurrency, I/O-heavy workloads - Ideal for real-time systems and lightweight APIs - Fast iteration, especially with JavaScript/TypeScript teams The real difference is not “which is better?” It is where each runtime performs best. Python often wins in data-driven systems, AI pipelines, and backend logic. Node.js shines in event-driven services, BFFs, and real-time applications. Good engineering is choosing the right model for the workload.