Java Enters AI Space with Spring AI and LangChain4j

A step-by-step guide on how to create a team of AI Agents that can analyze, translate, and test legacy code into modern Python code. Building a Multi-Agent AI System to Modernize Legacy Code. https://lnkd.in/gTtfc7A3 #AI #AIAgents #MultiAgentSystem #MAS #ModernizeLegacy

I keep wondering… why is almost every AI tool built on Python? It doesn’t really make sense at first. C++ is faster Rust is safer Java is built for scale So why did Python win? The answer is surprisingly simple. Because AI isn’t just an engineering problem. It’s an experimentation problem. When you’re building models, you’re not optimizing code first. You’re trying ideas. Breaking things. Testing again. Iterating constantly. Python just makes that easy. Less boilerplate Faster to write Easier to read A massive ecosystem ready to plug into And here’s the part most people miss. When you run an AI model, Python isn’t doing the heavy lifting. Underneath, it’s all highly optimized C++, CUDA, and hardware acceleration. Python is just the glue that holds everything together. So in a way, Python didn’t win because it’s the fastest. It won because it gets out of your way. And maybe that’s the bigger lesson beyond AI. Sometimes the best technology isn’t the most powerful one. It’s the one that lets more people build, faster. Curious how you see it. Do you think Python will still dominate AI in the long run, or are we heading toward something else? #ArtificialIntelligence #Python #MachineLearning #DataScience #SoftwareEngineering #TechLeadership #Innovation #AI #Programming #FutureOfWork

What if you could turn any Python function into an AI-powered one with just one line of code? Marvin makes that possible. One of the biggest shifts in AI development right now is simplicity. Marvin is a lightweight library that lets you add AI capabilities to ordinary Python functions with almost no extra code. No complex pipelines. No heavy frameworks. Just natural Python. Instead of building elaborate integrations, you describe what you want the function to do, and Marvin handles the language model interaction behind the scenes. What makes it interesting: - You can turn regular functions into AI-powered ones - Minimal setup and clean syntax - Works naturally with existing Python code - Great for quick prototypes and automation tasks - Removes a lot of boilerplate around LLM calls It feels less like “using an AI framework” and more like upgrading Python itself. Tools like this are lowering the barrier to building intelligent applications. You don’t need massive architectures anymore. Sometimes one well-designed abstraction is enough. #machinelearning #ai #datascience #data

👉 PYTHON FOR AI Python didn’t become the default for AI because it’s easy. It became default because it fits into the entire AI lifecycle. 👉 AI is not just about training a model. It’s about moving data, invoking models, handling outputs, and integrating systems. That’s where Python becomes critical. 👉 What makes Python critical in AI systems: • Interface layer → Interacts with models, APIs, and external services • Data layer → Handles preprocessing, transformations, and pipelines • Control layer → Manages workflows, decisions, and orchestration 👉 Most discussions stop at frameworks. But in real-world systems, Python is doing much more: • Structuring inputs before they reach the model • Managing responses after the model generates output • Connecting AI with applications, databases, and tools 👉 Key Insight: Python doesn’t just build models — it connects models to real-world systems. #Python #PythonForAI #AIEngineering #SystemDesign #LearningInPublic #GenAIJourney

In this article, you will learn how to build a local, privacy-first tool-calling agent using the Gemma 4 model family and Ollama. Topics we will cover include: An overview of the Gemma 4 model family and its capabilities. How tool calling enables language models to interact with external functions. How to implement a local tool calling system using Python and Ollama. https://lnkd.in/d6Wa86Gx

🧠 Released Axis-HFE v0.1.0! A Python library that transforms LLM reasoning from selecting an answer to creating one. Multiple hypotheses evolve across a 6-dimensional evaluation space — the best answer emerges through nonlinear synthesis. ✅ Ollama (local/free) / OpenAI / Anthropic ✅ pip install axis-hfe 📦 PyPI: https://lnkd.in/gEz4wWr6 🐙 GitHub: https://lnkd.in/gzCkVptv #Python #AI #LLM #Reasoning #OSS #OpenSource

Why Python For ML? Python wasn't designed for ML. But it accidentally became the king of AI. Here's the unusual story. Day 3 of 60 → Why does EVERY ML engineer use Python? Python was created in 1991 for general programming. Nobody planned it for AI. But here's what happened:  ·      scikit-learn — made ML accessible with clean APIs ·      NumPy — made fast math possible ·      pandas — made data manipulation human-readable ·      matplotlib — made visualizations easy ·      TensorFlow + PyTorch — made deep learning reachable The community built the tools. The tools built the ecosystem. The ecosystem became impossible to ignore. Today, most of the ML engineers use Python as their primary language. It's not the fastest language. It's not the most efficient. But it's the most learnable, most readable, and most supported. For ML, that's everything. If you're just starting: Python IS the answer. #Python #MachineLearning #DataScience #Programming #60DaysOfML #AI

#NeuralScript++ — The Road Ahead The #Python Superset #NeuralScript++ makes Python better today. Not by force, but through natural evolution. As more of your codebase adopts pipe operators, pattern matching, and domain-specific shorthand, it may gradually diverge from vanilla Python—and that’s perfectly acceptable. What's coming: Gradual typing that actually works — not mypy bolted on, but type inference built into the transpiler. Your code gets type-safe incrementally, without annotation burden. Async-first AI pipelines — training, data loading, and inference stages run concurrently by default. No asyncio boilerplate. The language handles parallelism. Auto-migration tooling — point it at a Python project and it suggests #NeuralScript++ rewrites that reduce code volume while preserving behavior. Accept one at a time. No big-bang rewrite. And #Python interop gets even deeper — seamless calling between #NeuralScript++ and #NeuralScript core, so you can gradually move performance-critical paths to the full DSL while keeping Python for glue code. The on-ramp gets smoother. The destination gets more compelling. 🔗 https://lnkd.in/dTE6SYeK 🌐 neuralecosystems.com demo: https://lnkd.in/dXUw7rDu #NeuralEcosystems - Let the world unite to explore the universe together! #AI #MachineLearning #DeepLearning #Python #OpenSource #GPU #StartupLife #Engineering #NeuralEcosystems #NeuralOS #NeuralSCRIPT #NeuralSCRIPT++ #NeuralCPU #NeuralGPU  #NeuralFUSE #NeuralRV #NeuralEDGE #NeuralDB #NeuralPIPE  #NeuralSENSE #NeuralAUTO #NeuralFUZZY #NeuralIP #NeuralSDR  #NeuralMESH #NeuralUI #NeuralZONE #NeuralGAURD #NeuralSHARE #NeuralGHOST #NeuralBIO #NeuralHEALTH #NeuralNAV #NeuralWEB  #UAE #Innovation

This isnt a Golang vs Python tiff. When you are building for scale a small tech decision can make or break your finances. Especially when the margins are very thin. RapidaAI

🚀 Python is no longer just a programming language — it’s the backbone of the AI revolution. From building LLM-powered applications to creating scalable data pipelines, Python continues to dominate across: • Generative AI & LLM integrations • Data Engineering & ETL pipelines • Backend APIs & microservices • Automation at scale What’s interesting is how fast the ecosystem is evolving: ➡️ Frameworks like LangChain are simplifying AI app development ➡️ RAG architectures are becoming the new standard ➡️ Developers are shifting from “coding features” to “orchestrating intelligence” The question is no longer “Should you learn Python?” It’s “How deep are you going with it?” #Python #AI #GenAI #LLM #DataEngineering #TechTrends #SoftwareDevelopment