How Python and Rust Boost AI Performance

Python now powers nearly HALF of all new AI repositories GitHub's 2025 Octoverse data reveals Python's commanding 50.7% year-over-year growth, cementing its position as the undisputed backbone of AI development—from model training to production deployment. But here's what caught my attention: while Jupyter Notebook still dominates exploratory work (403k repos), the explosive growth in Python codebases signals a major shift from prototyping to production-ready AI systems. The supporting cast tells an equally interesting story: JavaScript remains the bridge to real-world applications (88k repos, +24.8% YoY), powering the dashboards and integrations that make AI accessible to end users. TypeScript is the fastest-growing frontend language for AI projects (86k repos, +77.9% YoY), reflecting the demand for type-safe, production-grade API clients and SDKs. Shell scripts saw a massive 324% growth despite smaller absolute numbers (9k repos), highlighting the rise of automation in ML pipelines and deployment workflows. C++ continues its steady climb (7.8k repos, +11% YoY), proving indispensable for performance-critical inference engines and hardware-optimized runtimes. The takeaway? The AI ecosystem is maturing rapidly. We're moving from "can we build it?" to "can we ship it reliably at scale?" #python #ai #machinelearning #automation

⚡ 𝐁𝐞𝐧𝐜𝐡𝐦𝐚𝐫𝐤𝐞𝐝 𝐏𝐲𝐭𝐡𝐨𝐧 𝐯𝐬 𝐆𝐨 𝐟𝐨𝐫 𝐋𝐋𝐌 𝐀𝐏𝐈 𝐜𝐚𝐥𝐥𝐬 𝐮𝐥𝐭𝐫𝐚-𝐟𝐚𝐬𝐭 𝐢𝐧𝐟𝐞𝐫𝐞𝐧𝐜𝐞. While Go demonstrated superior speed and consistency, the choice between languages isn't always straightforward. Python still excels for rapid prototyping and experimentation, while Go shines in production environments with strict performance requirements. Bottom line: the right tool depends on your specific use case, team expertise, and scaling needs. Read the full technical breakdown here 👇 https://lnkd.in/gy_5bAyB #AI #LLMEnginnering #GenAI #SoftwareEngineering #Performance #LLM #Benchmark

I’ve often heard people say “Python is slow” or “Python isn’t made for production.” But in reality, Python is far more than just a programming language, it’s an entire ecosystem. 🐍 It empowers developers to move from learning fundamentals to building impactful solutions whether in AI, data science, automation, or full-stack development. What truly makes Python stand out is its clarity, simplicity, and versatility. Its syntax reads almost like natural language, allowing teams to prototype faster, collaborate better, and scale ideas into production with confidence. Python has become the foundation of modern innovation a language capable of shaping any environment, from intelligent AI systems to scalable digital experiences that drive businesses forward. That’s why it remains one of the most widely adopted and loved languages worldwide. 💬 I’m curious to know how are you leveraging Python in your current projects? Let’s connect and share ideas that push the boundaries of what Python can do. #Python #Technology #AI #MachineLearning #WebDevelopment #SoftwareEngineering #Developers #Innovation

Why an AI agent framework for Rust? 🤔 The truth is that you don't have to use Rust for AI if you don't want to. Despite me being passionate about it, it's a pretty hard sell no matter what if they aren't already using Rust. To be honest, the truth is that both TS and Python do the job adequately using Mastra/AI SDK and Langchain/Langgraph, respectively. If you're already deep in either stack, there is no real reason to switch to Rust. What I've found however is that more and more companies who are using Rust don't really want to context switch to Python (and spend hours fixing runtime issues!) for AI agents. These companies also often have extremely competent engineers who are working on mission critical infrastructure, who also need to serve AI-related services. Whether it's an AI chatbot, a simple automated workflow that uses LLMs to deal with non-deterministic data or something else, you'll end up needing an AI-related library if you don't want to manually set up API calls yourself. That's where Rig and similar libraries come in. They make it super easy to focus on the engineering work rather than needing to fumble around with trying to remember how to automate agentic loops again or implementing memory from scratch. If you're building with Rig or wanna talk shop, feel free to reach out. More than happy to do so 😄

🧠 Machine Learning: Python or C#? Which one should we choose — and why? As AI and ML continue growing fast, developers often face this question when starting a new project or product: 👉 Should we build in Python or C#? Here’s a quick, practical comparison: 🐍 Python — The ML Powerhouse Built for experimentation & research Massive ecosystem: #TensorFlow, #PyTorch, #Scikit-Learn, #HuggingFace, #OpenCV Huge community support and tutorials Easy to prototype models quickly ✅ Best for: Data Scientists, ML Research, Prototyping, Fast Iteration ⚙️ C# — The Production & Enterprise Choice Strong with enterprise apps and backend systems Integrates great with .NET ecosystem, #Azure ML, and enterprise #APIs Stable, type-safe, scalable for large teams Good when ML must run inside production software ✅ Best for: Enterprise applications, real-time systems, ML deployment at scale 🎯 So the pattern is: Python → Build, experiment, learn, prototype C# → Integrate, optimize, deploy into real products Most companies start in Python, finish in C# or production-grade Java/C++, depending on infrastructure. 💬 Your Turn I’m curious — which one do YOU prefer, and why? Drop your answer below 👇 🟦 #TeamPython 🟧 #TeamCSharp Or maybe… both? 🤝 #MachineLearning #Python #CSharp #DotNet #AI #SoftwareEngineering #DataScience #ProgrammingCommunity #TechDebate #DeepLearning #Data #Programming

Goodbye GIL. You will not be missed. 🍷🐍 Python 3.14 is removing the Global Interpreter Lock (GIL)… after 30 years of acting like that one coworker who must hold the whiteboard marker at all times. For decades, Python could technically run on multiple cores, but the GIL said: “No. Only one thread at a time. Everyone else sit down.” But now… • Real multi-core parallelism • Threads that actually thread • Async that doesn’t break down crying • AI pipelines that don’t require spiritual forgiveness and message queues For Data & ML folks, this means: • Faster data processing • More scalable inference + agents • CPU-heavy workflows without “fine, I’ll rewrite it in Rust” energy This is not just a performance upgrade. This is Python getting on steroid 🏋️ (Note: Python 3.14 ships with both the standard build and the new free-threaded build — so adoption will roll in gradually.) #MLEngineering #Pyhton #AI #DataEngineering

AI is here, and it is rewriting the JD for every engineer. The latest GitHub Octoverse data shows that the shift is happening faster than we think, and it is impacting data engineering as much as application development. 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝘁𝗵𝗲 𝘁𝘄𝗼 𝘀𝗶𝗴𝗻𝗮𝗹𝘀 𝘁𝗵𝗮𝘁 𝗺𝗮𝘁𝘁𝗲𝗿 𝗺𝗼𝘀𝘁: 💡𝗧𝗵𝗲 𝗡𝗲𝘄 𝗦𝗲𝗻𝗶𝗼𝗿 𝗥𝗼𝗹𝗲: For leaders and seniors, your job has fundamentally changed. It is less about writing the hardest code and more about architecture, validation, and debugging. You must master judging the code, not just producing it. 💡𝗦𝗮𝗳𝗲𝘁𝘆 𝗡𝗲𝘁 𝗼𝘃𝗲𝗿 𝗟𝗼𝘆𝗮𝗹𝘁𝘆: Typed languages provide the guardrails needed to quickly and safely validate AI-generated code, so TypeScript is taking over Python (developer's choice). Python remains the king in the DS/ML space, but optimization in its ecosystem is required for maximum AI leverage. #AI #FutureOfWork #SoftwareDevelopment #TechLeadership #DataEngineering #TypeScript #Python

DataSpear vs Python: The Future of Cognitive Data Python built the digital world we know a language that powered data science, machine learning, and automation across every major industry. Its libraries NumPy, Pandas, Scikit-learn, and PyTorch became the foundation for billions in innovation. But today, the world no longer needs code that just executes. It needs data that understands. That’s where DataSpear emerges not as a rival, but as the next evolution. While Python is designed for programmatic control, DataSpear is built for data orchestration a living, reflective ecosystem that adapts, reasons, and collaborates. In the DataSpear ecosystem, pipelines become conversations. Models don’t just learn they reflect. Every operation carries context, ethics, and adaptive intelligence at its core. Python was built to program machines. DataSpear is built to awaken systems. The future of AI isn’t about writing more code it’s about crafting languages that think. #DataSpear #Python #NeuraSpear #AIRevolution #CognitiveEcosystem #DataOrchestration #MachineLearning #NextGenAI #EthicalAI #Innovation #TechPhilosophy

The Great Journey of Python 😀 🐍 Why Python is no longer just - a language — it’s the foundation of modern AI, automation and data-driven impact. In 2025, Python’s value goes far beyond “easy to learn”. It’s about: • Versatility at scale — one language powering web apps, AI models, automation scripts and data pipelines. • Readability + speed of iteration — meaning faster prototyping, cleaner collaboration and less maintenance overhead. • A mature eco-system of libraries — from TensorFlow/PyTorch for ML, through Django/FastAPI for web-services, to automation and DevOps tools. • Career and real-world relevance — if you’re working with AI, Deep Learning, RAG, data science or building custom tools (like you are), Python is the bridge between research and production. So here’s my suggestion takeaways for my network: ✨ If you’re building agentic AI, fine-tuning models, creating pipelines or automating tasks — Python isn’t just optional. It’s strategic. ✨ If you’re showcasing projects (like your license-plate recognition work or your AI-Powered Code Assistant), calling out Python as your backbone helps signal both practical skill and modern relevance. ✨ And if you’re mentoring, teaching or collaborating — choosing Python helps you bring others along quickly, share code, and scale ideas faster. #Python #Programming #AI #MachineLearning #DataScience #Automation #CareerGrowth

Learning Python: you don’t have to build everything from scratch When I started with Python, I tried to code everything from the ground up. Reality check: in real projects especially AI, you rarely do that. You compose tools and glue them together. Think OpenAI API, LangChain, LangGraph, vector stores, web frameworks… you don’t need to reinvent them. Your job is to connect, configure, and ship something that works reliably. What you do need to know: - Core Python: loops, functions, f-strings, data structures, modules, virtual envs - Reading code > memorizing: understand what a snippet does and why - Glue & integration: APIs, env vars, secrets, error handling, retries, timeouts - Debugging & safety: logs, tests, exceptions, input validation, basic security - Architecture sense: where to put logic, how components talk, what to cache My shift in mindset: AI can already generate code. Your edge is to use AI well, review its output, improve it, and wire everything together (API → business logic → storage → monitoring). Be the engineer who turns snippets into a working product. How I practice now: Pick a tiny goal (e.g., call an LLM and store results). Use existing libs (OpenAI/LangChain/LangGraph) + write the glue code. Add logs/tests, containerize with Docker, and document trade-offs in a README. Personal note: I’m also using Jupyter Notebooks for AI prototyping, fast experiments, quick visual checks, and repeatable notebooks I can turn into services later. Build less from scratch. Ship more value. #Python #AI #OpenAI #LangChain #LangGraph #Jupyter #DevOps #Cloud #Automation