Artur Yusupov’s Post

Ditch Python: 5 JavaScript Libraries for Machine Learning: The article delves into the evolving landscape of machine learning and highlights the rise of JavaScript as a viable alternative to Python for various ML tasks. As organizations continue to integrate machine learning into their DevOps practices, leveraging JavaScript libraries can significantly streamline development processes while maintaining performance. Key libraries such as TensorFlow.js and Brain.js are discussed, emphasizing their ability to run directly in browsers, making ML more accessible for web developers. Moreover, the article explores the advantages of using JavaScript for machine learning, such as real-time data processing and the ability to create interactive visualizations. This capability allows DevOps teams to collaborate more effectively, enabling quicker iterations and feedback loops in their projects. The author also points out that while Python remains a popular choice for its extensive libraries, JavaScript's agility and integration with existing web technologies make it a strong contender for developing AI-driven applications. Embracing these JavaScript libraries not only benefits developers but also aligns with modern DevOps methodologies that prioritize rapid deployment and continuous integration. In summary, the transition from Python to JavaScript for machine learning represents a significant shift in how developers approach AI projects, encouraging innovation and efficiency in the ever-evolving tech landscape. Read more: https://lnkd.in/gGqjG7K7 🌟 Be part of the DevOps revolution! Join our community and collaborate with industry leaders.

🚀 Building a Python Web App Without Writing Code Reflex just launched Reflex Build, an AI-powered environment that lets you create and deploy full-stack Python apps simply by describing what you want. Everything runs in the browser: code editor, project menu, live preview, and one-click deployment. You can start with plain language instructions like “create a user dashboard with authentication and database access” and Reflex automatically generates the structure, logic, and integrations. I took a dive into this amazing product, so check out some thoughts here: 1.🔸Lowering the barrier to entry: For someone with domain expertise (business, product, data) but minimal engineering background, building a web app used to mean hiring devs, managing infrastructure, setting up CI/CD. With Reflex Build, that overhead dramatically shrinks. 2.🔸Focus on logic, not plumbing: Instead of spending a week configuring user authentication, database schemas, deployment pipelines, you can spend your time on what your app does. The mechanics become “solved.” 3.🔸Still valuable to know code: While you don’t have to code, knowing Python (or knowing you can open the editor and tweak) gives you freedom. You’re not boxed in. Even if you stick to auto-generated code, you’ll still understand the structure. 4.🔸Scale thoughtfully: Building quickly is great, but I’d still treat this like a prototype or MVP. When you scale (more users, more security, high SLAs), you need to understand the underlying stack, watch performance, manage secrets, etc. The great thing is Reflex Build appears to give those tools (database integration, secrets manager) out of the box. 5.🔸Iterative mindset wins: Because deployment is one click and preview is immediate, you can adopt a rapid build-test loop: “Does this concept work?”, “What UX tweaks do my users need?”, “Can I swap in a different data flow?” This is powerful for product-led growth. 6.🔸Technical trust matters: Even though you’re “coding less”, you’re still shipping an app. So you should still care about things like code quality, maintainability, security. Having access to the editor + full download gives confidence you’re not locked into a black box. This approach lowers the barrier to launching prototypes or internal tools while still teaching how real Python apps are built. It is a bridge between no-code simplicity and engineering depth. If you have an idea worth testing, Reflex lets you turn it into a live app in minutes. Give it a try and share your experience with the community at large! #Reflex #NoCode #PythonApps

At www.jaiinfoway.com we are inspired by innovations like Reflex Build that make Python app development accessible to everyone. Imagine creating and deploying a full-stack web app just by describing it in plain language. Reflex bridges the gap between no-code simplicity and full engineering control enabling rapid prototyping and one-click deployment. At www.jaiinfoway.com we believe this approach empowers businesses to focus on functionality creativity and user experience while reducing development time. Our team integrates such AI-driven tools to accelerate digital transformation with precision and scalability. #Jaiinfoway #NoCode #Python #AI #Automation #WebDevelopment #Innovation #DigitalTransformation #FutureTech #AppDevelopment

If you’re a Python dev thinking of learning Go, this one’s for you! When I made the switch, a few things really stood out: 🔹 Assignment works differently In Python, everything in memory is by reference. So when you do a = b, you’re basically pointing to the same value. But in Go, it’s by value. An equal sign creates an exact copy. This tripped me up at first—be careful with it! (Yes, Go also gives you pointers if you want reference semantics—I personally prefer those in most cases.) 🔹 Goroutines & Channels = 💯 This has to be one of my favorite parts of Go. The simplicity and reliability of concurrency in Go feels like something Python really missed. Fan-in, fan-out, pipelines—suddenly multithreading isn’t a headache, it’s fun. 🔹 No Classes (but structs + methods) Coming from heavy OOP, the lack of classes was a shocker. But Go’s structs + methods combo (with interfaces) gives you almost the same power—just with a different mindset. 🔹 Error Handling (no try/except) Another big shift: instead of throwing and catching, you return errors explicitly. At first it feels awkward… then you realize how clean and predictable it is. 🔹 Defer = cleanup magic Go’s defer lets you schedule a function to run when the current scope ends. Perfect for things like defer file.Close(). In Python you’d usually rely on with blocks—this is Go’s way of keeping things tidy. 🔹 Type Definition Python is dynamically typed—you don’t declare types up front. Go is strongly and statically typed, so you explicitly define them. Strict at first, but it saves you from many bugs. And yes, Go has generics now, so you can write flexible, type-safe code too. 🔹 Compiled to a single binary Unlike Python’s venv/pip chaos, Go compiles into one clean binary. No dependency hell, just ship and run anywhere. Huge productivity win. 🔹 The fmt package 😅 Want to print something? You actually import fmt. Feels strange at first, but hey—production apps shouldn’t be full of print statements anyway! 👉 My takeaway: once you wrap your head around assignments, goroutines, structs, errors, and type definitions—Go becomes a breeze. Curious—what’s the one Go feature that impressed you the most when you first tried it?

You’ve finished the backend tutorials. You’ve built a calculator, a weather app, maybe even a Django blog. Yet every time you try to build something real, you hit a wall. Let’s talk about why — and how backend engineers overcome it 🧵 The truth is, you’ve learned backend like a student, not like an engineer. You’ve learned to follow instructions. Engineers learn to design systems. That’s the fundamental shift most backend learners never make. See, tutorials are designed to teach, not to prepare. They give you clean, linear examples: - “Here’s how to connect to a database.” - “Here’s how to make an API route.” But real backend work? It’s messy. It’s full of trade-offs, debugging, and system decisions. When you build a backend in the real world, you face questions like - How do I structure my code so it scales? - What happens if two users update the same data at once? - How do I cache results without breaking consistency? These are engineering problems, not tutorial exercises And this is why many backend developers never feel “ready.” They’re stuck in what I call tutorial paralysis — learning endlessly without applying anything in a realistic environment. You don’t grow by consuming knowledge. You grow by building systems that can fail. Backend engineering isn’t just writing endpoints. It’s connecting layers, the database, API, authentication, background jobs, caching, and deployment. When those layers finally click together, you stop being a “Backend learner” and start thinking like an engineer. Here’s a simple example: You’re building a task API A beginner thinks, “I just need CRUD endpoints.” An engineer thinks - How do I prevent duplicate tasks? - How should I handle concurrency? - Should I add pagination, filters & caching? That’s the mindset that gets you hired So how do you make the switch? By building real projects intentionally: - Where do you handle errors - Integrate databases - Deploy APIs - Understand what’s happening under the hood. That’s what gives you the confidence employers look for. That’s exactly why the “Become A Python Backend Engineer” course exists. It takes you beyond syntax into system design, real projects, and production-level backend thinking. You don’t just learn Python — you engineer with it. https://lnkd.in/d5tahN8C

🚀 Streamline Your Python Dev: uv Eliminates Tooling Headaches (and Trims Your Docs!) Let's face it: managing Python projects often feels like a multi-tool juggling act – pip, venv, pip-tools, pipx... It’s a lot to document, teach, and maintain. But a new era just dawned: meet uv Born from the ruff team and powered by Rust, uv is Python's answer to Rust's Cargo. It's not merely a faster package installer; it’s an integrated workflow powerhouse designed to replace your disparate toolchain. The result? Dramatic performance gains and a simplified, cohesive developer experience. 🐚 This means less cognitive load for you, and significantly leaner project documentation for your team! 🥳 Here’s why uv is a game-changer for your Python workflow: 1️⃣ All-in-One Project Control (Your Docs Just Got Shorter) uv takes command of your entire project lifecycle, collapsing multiple tools into single, intuitive commands. This means your "Getting Started" guides are about to shrink! 📍 Initialize: Set up new projects swiftly. 📍 Dependency Management: Effortlessly add, remove, and sync packages. 📍 Version Bumping: Update project versions with a single command. 📍 Build & Publish: From source to distribution, uv handles it seamlessly. Imagine replacing pages of multi-tool instructions with a single "Use uv for everything." 2️⃣ Flawless Isolation for Tools & Scripts (No More site-packages Nightmares) uv brings order to your global environment, managing external tools and temporary scripts with elegance: 📍 Isolated Tool Installs: uv tool install black deploys linters/formatters into isolated environments, preventing global dependency conflicts. 📍 On-the-Fly Script Execution: Run standalone scripts with uv run my_script.py, letting uv parse inline comments for dependencies and execute in a clean, ephemeral environment. Perfect for reproducible examples! 3️⃣ Unparalleled Speed & System Efficiency At its core, uv is engineered for speed and resourcefulness: 📍 Rust-Native Performance: Leverage compiled speed for dependency resolution and installation. 📍 Global Package Cache: Dependencies are shared intelligently across all your projects, saving vast disk space and making new virtual environments nearly instantaneous. 📍 Built-in Python Management: uv python install 3.11 provides a direct, simple way to fetch and manage specific Python versions. uv represents a monumental leap in Python tooling cohesion. It streamlines our stack, boosts efficiency, and makes our development lives, and especially our project documentation, profoundly simpler. Follow winston mhango for more Python insights!

Recently attended a Backend Developer (Python) interview, it was a solid mix of Python, backend frameworks, Git, DevOps, and coding challenges. Sharing all the questions I faced so it helps others preparing for backend roles 👇 --- 🐍 Core Python Concepts 1️⃣ Explain Python memory management and garbage collection. 2️⃣ Difference between `is` and `==`. 3️⃣ What are shallow and deep copies? 4️⃣ Explain iterators, generators, and the use of the `yield` keyword. 5️⃣ What are `itertools` in Python? 6️⃣ How does Python handle multithreading (and what is the GIL)? 7️⃣ How does Python interpretation work internally? 8️⃣ What are keyword arguments and why do we need `*args` and `**kwargs`? 9️⃣ Are `is` and `==` similar to deep copy and shallow copy? ____ Backend Frameworks & Architecture 🔹 Explain Django architecture (MVT pattern). 🔹 Difference between Flask and FastAPI. 🔹 What is OAuth and why is it used? 🔹 What is a Bearer Token? 🔹 Difference between Authentication and Authorization. 🔹 Explain WebSockets and Webhooks — how are they different? 🔹 How does a request flow in Django or FastAPI (from client → response)? --- Git, Docker & CI/CD 🔸 What’s the use of `git stash`? 🔸 Difference between `git fetch` and `git pull`. 🔸 Best way to resolve merge conflicts in Git. 🔸 How does Docker help in backend deployment? 🔸 What is a CI/CD pipeline? 🔸 How would you automate builds and testing for a backend project? --- AI/ML Related Backend ⚙️ How to train AI models for higher accuracy? ⚙️ What’s the best way to get or generate quality training data? ⚙️ How to deploy an ML model using FastAPI? --- Coding & Problem-Solving - Group matching anagrams together. - Find the second largest element in an array. - Sort an array using bubble sort and quick sort (explain time & space complexity). - Implement linear and binary search (handle edge cases). - Move all zeros to the end of an array. - Find missing number in sequence 1…N. - Check if two strings are anagrams. - Find first non-repeating character in a string. It was an insightful experience covering everything from Python internals to real-world backend and ML integration. Every interview helps you grow — this one definitely sharpened my fundamentals 💪 What’s the most interesting backend or Python question you’ve faced recently? #BackendDevelopment #Python #Django #FastAPI #Git #Docker #CICD #AI #MachineLearning #CodingInterview #CareerGrowth #InterviewPreparation

Choosing the right backend technology can shape your project’s performance and reliability. Python remains a top choice thanks to its simplicity, scalability, and robust frameworks like Django and Flask-perfect for building production-grade systems. Read our blog on: Python Backend Development: https://lnkd.in/gN-4Avq6 #pythondevelopment

Loraine Lawson shares Laurie Lay’s insights on why JavaScript is emerging as a strong alternative to Python for machine learning, blending AI with frontend development.

Master Web Development in Python—Unlock Your Tech Potential. Are you ready to build powerful, scalable, and dynamic websites using one of the world’s most versatile programming languages? Whether you’re an aspiring developer, a tech professional upgrading your skills, or an organisation looking to train your team, it’s time to master web development in Python. 🌐 Learn how to: ✅ Build modern web apps with frameworks like Django & Flask ✅ Integrate APIs and databases seamlessly ✅ Optimise performance for real-world projects ✅ Gain the practical skills employers value most With Python leading the digital transformation wave, mastering it opens doors to limitless career opportunities in tech, AI, and automation. 📈 Don’t just code—create, innovate, and lead with Python. Start your journey today! 👉 #MasterWebDevelopmentInPython #PythonDevelopment #WebDevelopment #Programming #Coding #TechCareers #DigitalTransformation #PythonLearning #Upskill #CareerGrowth