Switching to uv for Python and How It Cut My Docker Build Time by 60% I’ve been hearing a lot about uv, the new Python package manager built by Astral (the team behind Ruff). Everyone was talking about its speed, so I finally decided to try it in one of my FastAPI production backend projects. And honestly… I didn’t expect the impact to be this big. ⚡ The First Surprise: Pure Speed uv is written in Rust, and that alone gives it a massive performance boost. In practice, that means: - Creating a virtual environment in ~50 ms - Installing packages with parallel downloads - Running CLI tools almost instantly I immediately noticed the difference in my local development workflow. 📦 The Real Game Changer: Global Caching uv uses a global shared cache, so if a package version is already downloaded once, it never downloads it again. No network hit. No repeated wheel builds. No waiting. Just instant installs. 🐳 The Biggest Impact: Faster Docker Builds Here’s where uv really surprised me. After switching from pip to uv inside my Dockerfile for a FastAPI backend, my Docker image build time dropped from: ~3 minutes → just slightly above 1 minute That’s more than 60% faster, and for frequent deployments, this is huge. It also made my CI/CD pipeline noticeably faster and more reliable. If you work with Python especially FastAPI or backend development, I genuinely recommend trying uv. It’s fast, efficient, and modern, and it feels like the package manager Python should’ve had all along. #Python #FastAPI #BackendDevelopment #SoftwareEngineering #DevOps #Docker #RustLang #WebDevelopment #PythonDevelopers #CloudComputing #CI_CD #OpenSource #Productivity #uv

Thrilled to dive into the world of FastAPI! 🚀 This modern Python web framework for building APIs is truly a game-changer. I'm currently exploring its key features like blazing fast performance, automatic data validation using Pydantic, and the incredible out-of-the-box interactive documentation (Swagger UI/ReDoc) based on OpenAPI standards. The use of standard Python type hints not only makes the code cleaner and less error-prone but also significantly speeds up development time. Looking forward to building some high-performance, production-ready REST APIs and microservices! Has anyone else recently made the switch to FastAPI? What's your favorite feature so far? Share your thoughts and projects below! #FastAPI #Python #WebDevelopment #API #BackendDevelopment #Programming #Coding #Microservices #SoftwareDevelopment #TechLearning #Pydantic

🚀 Why I'm Excited About FastAPI! 🐍 After diving into FastAPI, I'm genuinely impressed by what this modern Python framework brings to the table. Here's what makes FastAPI stand out: ⚡ Lightning Fast Performance Built on Starlette and Pydantic, FastAPI delivers performance comparable to NodeJS and Go. It's one of the fastest Python frameworks available today. 📝 Automatic API Documentation Interactive API docs (Swagger UI) are generated automatically. No extra work needed - just write your code and get professional documentation out of the box! 🎯 Type Hints & Validation Python type hints aren't just for code clarity anymore. FastAPI uses them for automatic request validation, serialization, and documentation. Less boilerplate, fewer bugs. ⏱️ Developer Experience The framework is incredibly intuitive. I was building production-ready APIs within hours, not days. The learning curve is surprisingly gentle. 🔒 Built-in Security Features OAuth2, JWT tokens, and API keys are straightforward to implement. Security doesn't have to be complicated. Whether you're building microservices, RESTful APIs, or full-stack applications, FastAPI makes the development process smooth and enjoyable. Have you tried FastAPI? What's been your experience? #FastAPI #Python #WebDevelopment #API #Backend #Programming #SoftwareDevelopment #TechStack #Coding #DeveloperLife

Did you know you can submit build and test results to CDash even if you’re not using CMake to build your software? Using a simple Python example (py_square.py), this workflow demonstrates how to: 🧪 Define tests with CTest without compiling a project. 🐍 Integrate frameworks like PyTest. 📊 Submit results directly to a CDash dashboard. ⚙️ Automate with CTest drivers and scripts. This approach shows how CTest and CDash can be applied beyond traditional CMake projects, making it easier to track test results, integrate existing frameworks, and gain insight into project health without requiring a full build system. 📖 Read the full walkthrough here: https://ow.ly/ZaO550X4up9 #CDash #CTest #PythonTesting #SoftwareTesting

Class Based Lifestyle in Django When I first started out with class based views in django, honestly I wasn't pleased. I was used to everything being in one place. Tracing what was going on behind the scenes was actually annoying. After some projects and reading other people's code, I started looking at things the right way. I began to confidently write class based views that could involve just specifying overridden class variables , no method, nothing. The magic done behind the scenes was traceable. I believe the enlightenment first came with ListAPIView. It inherits from ListModelMixin and GenericAPIView. This is the interesting part. The ListModelMixin WHICH IS A BASE CLASS (no parents) defines a list method that calls "undefined" functions like filter_queryset() , paginate_queryset(). At first I was like, "we dealing with a base class..from where did these functions come from?" I later realized the mixin has to be paired with the GenericAPIView class in another child class i.e ListAPIView. So the ListModelMixin works on the data directly and returns the final response, but it uses methods from GenericAPIView, another class entirely. Also, the get method which is triggered in a class based view by GET request is not called in the mixin. It is called directly in the ListAPIView. The get function then calls the list() method gotten from the mixin. At the top of the mixins code file, mixins.py the django engineers wrote in comments: "We don't bind behaviour to http method handlers yet, which allow mixin classes to be composed in interesting ways". This suggested they went with a decoupling style for flexibility, like Iron Man's suit. #django #djangorestframework #softwareengineering #optimization #mixins #python #backenddevelopment #backendengineering #ironman

Django 2025 — The Evolution Continues! Async Everywhere Django now runs async views like a breeze — real-time apps finally feel real-time! ⚡ DRF Still Rules, Ninja Rising REST Framework remains king 👑, but Django Ninja is the new speed demon. AI Joins the Team Developers now ship faster — thanks to Copilot & JetBrains AI Assistants 🤖 Hybrid Architectures Win Mix Django monolith + FastAPI microservices = best of both worlds. 💡 Security Never Out of Style Still the most trusted framework for production-grade apps. 🔒 💬 Final Thought: Django isn’t old — it’s battle-tested. It’s not fading — it’s adapting. #Django #Python #WebDevelopment #BackendDevelopment #TechTrends #SoftwareEngineering #AIinDev

I just launched EnvNX - a lightweight CLI tool for Python developers who work across multiple projects. The problem: Switching between projects means dealing with mismatched dependencies, cluttered directories, and inconsistent environments. The solution: EnvNX gives you four simple commands: ✓ Environment Check - compare installed packages with requirements.txt ✓ Project Cleanup - remove pycache, build artifacts, and temp files ✓ Code Search - fast pattern matching across your codebase ✓ Config Sync - maintain a shared base environment across projects No dependencies. No frameworks. Just practical utilities. Install from PyPI: "pip install envnx-cli" https://lnkd.in/gNeZEC_m GitHub: " https://lnkd.in/gX-ce2jr " Check out the video to see it in action. If you've ever wrestled with Python environments, this might save you some headaches. Want to contribute or need more details? DM me or check the GitHub repo - I'm also happy to create detailed docs if there's community interest. #Python #DevTools #OpenSource #CLI #DeveloperProductivity #EnvNX

🎯 Flask Number Guessing Game Here’s a fun and interactive web project I built using Python and Flask! It’s a simple Number Guessing Game where users can visit a local webpage and guess a number between 0–9 — directly from the URL. Each guess triggers a dynamic response with colorful messages and playful GIFs 🎨 Whether it’s too high, too low, or spot-on — Flask handles it all in real-time! 💻 Tech Stack: Python | Flask | HTML | GIPHY 📂 GitHub Repository: https://lnkd.in/g_pyJhZC 💡 Impact: Reinforced my understanding of Flask routing, URL parameters, and response handling — while adding a creative, visual twist to backend logic. #Python #Flask #WebDevelopment #CodingProjects #GitHub #BackendDevelopment #100DaysOfCode

What’s a macro vs. real browser automation? A macro records a single sequence of clicks/keys and replays them. Browser automation frameworks (Playwright) use code to say what you want to check and add safeguards: wait until an element appears, retry if slow, and run the same check across different browsers. Think of macros as a quick demo; automation frameworks are the durable, repeatable version you use long-term. Hashtags: #AutomationExplained #Playwright #Python #Macros Truly yours Bot.

Hello and Asalam Alaikum Everyone! Class-Based Views vs Function-Based Views, What Should Django Beginners Learn First? Friends understanding how Django handles requests is one of the most important foundations for any new backend developer and that’s where FBVs and CBVs come in. ➡️ Function-Based Views (FBVs) are the simplest way to write views. They show the entire request–response flow step-by-step. For beginners this makes it easier to understand what’s happening behind the scenes. ➡️ Class-Based Views (CBVs) take things further by giving structure, reducing repetition, and offering useful built-in features like list views, detail views and generic cbv views of both display type mentioned earlier and editing type views like Formview, CreateView, UpdateView, and DeleteView. In simlpe terms they’re great for writing cleaner and more scalable code. ❓ So what should you learn first? See most developers start with FBVs to understand the basics, then move to CBVs to write cleaner and more efficient code especially for real projects. So friends in this carousel, I’ve broken down: ▪️ What FBVs and CBVs actually are? ▪️ How they work? ▪️ Their key differences ▪️ Simple code examples for both Hopefully on reading this carousel you will learn something valuable, see you guys in the next post until then goodbye! #Python #Django #Learning