How uv Boosted My Docker Build Time by 60%

I've open-sourced the Copilot instructions and prompts I use daily for a mission-critical production app in the aerospace industry. Nothing fancy, just the actual setup that helps me get: • Properly documented code • Consistent formatting and alignment • Type hints/safety in Python & TypeScript • Code that's readable & understandable Includes instruction files for Python, TypeScript/JS, and Vue.js, plus some utility prompts for generating diagrams, documentation, and a custom teacher agent. It's opinionated; Built around: KISS, DRY, YAGNI, and single responsibility principle. The kind of stuff ...that just makes code easier to maintain. If you use VS Code - Copilot and want a starting point for your own instructions setup, feel free to give it a try 🙏 https://lnkd.in/gQcfWc9q

Making Authentication Boring in Flask (And Why That’s Good) Flask works best when treated like a micro-web architecture, which is best for people getting started with Python development, and that mindset shapes how I approach things like authentication. There are two ways of implementing auth in your Python web apps, and I’ve implemented both: manual sessions for full control, and Flask-Login for consistency and scale. Manual sessions are great for staying close to the request–response cycle, but in production and real-world applications, they often become copy-paste security and a matter of crossed fingers. The other approach: Flask-Login, which keeps auth boring, and that’s a good thing. Clean and predictable with @login_required, current_user, and well-defined session handling. The rule is simple: no need to reinvent authentication wheels that have already survived production traffic! #flask #python #backenddevelopment #webdevelopment #softwareengineering #flasklogin #microwebarchitecture

I just published a technical deep-dive on reverse engineering legacy Python codebases. Here's what I learned: The challenge: You inherit a 3-year-old Django app with no documentation, no type hints, and 15 undocumented API endpoints. Traditional approach? Spend 2-3 weeks manually documenting, then hope nothing breaks during modernization. I built SpecFact CLI to solve this using AST (Abstract Syntax Tree) analysis - not LLM guessing, but actual code parsing. The result? Reverse engineered 19 features and 49 stories in 10 minutes, then added runtime contracts that prevented 4 production bugs during refactoring. The technical approach: ✅ AST analysis extracts actual code structure (95%+ accuracy) ✅ Deterministic parsing (works offline, no API calls) ✅ Symbolic execution (CrossHair) finds edge cases mathematically ✅ Runtime contracts prevent regressions automatically In the article, I walk through the step-by-step process: from installing the CLI to extracting features, generating contracts, and analyzing gaps. Real example with actual code. If you're working with legacy Python code, this might save you weeks of manual documentation work. What's your biggest challenge with undocumented legacy code? I'd love to hear your thoughts. Read the full technical deep-dive: https://lnkd.in/ekSgrZN2 #Python #LegacyCode #SoftwareEngineering #CodeQuality #AST #ReverseEngineering

I spent hours today debugging a function that was ironically bug-free. I am currently building a FastAPI service. I wrote the logic, checked the syntax, and ran the code. It crashed. I refactored the code. It crashed again. After digging deep, I realized the culprit wasn't my code at all. It was the latest version of a dependency I had just installed. It introduced a breaking change that wasn't compatible with the rest of my stack. The Fix: I downgraded the package to a stable version, and everything worked instantly. The Lesson: "Newer" isn't always "Better." This experience reinforced why tools like uv (which creates lockfiles to pin versions) are so essential for production. Stability > Bleeding Edge features. ❓ When you hit a bug like this, what is your first move? Do you check the GitHub Issues page or start ripping apart your own code? #Python #Debugging #FastAPI #DevOps #DataScience

Remember staring at your Python process chugging along on a beast of a machine, wondering why it acts like it's stuck in single-file traffic? That Global Interpreter Lock has been the ultimate party pooper for years, capping your multi-core dreams at a measly 10% utilization on anything with more than a couple brains. Then Python 3.14 drops in October, and boom: free-threaded mode is no longer experimental. It's baked in, GIL-free, with the adaptive interpreter humming along and single-threaded code only taking a 5-10% hit. Suddenly, your scripts can actually use all those cores without you rewriting everything for concurrency nightmares. Web dev's making a comeback too, but data folks? You're about to feel like you leveled up overnight. Rust's sneaking into a quarter of new PyPI natives for speed kicks, and uv is the tool everyone's whispering about for project management that just works faster. Python's shedding its slow skin. Who's firing up 3.14t first? What are you threading next? Drop your wildest perf story below. #Python314 #FreeThreading #PythonDev #uvTool #RustPython

We all have hard drives full of old node_modules and Python environments from weekend projects we haven't touched in months. I was losing gigabytes of space but avoided cleaning it up. Why? Because checking every folder manually is a pain and I was terrified that one wrong rm -rf command would accidentally wipe out code I actually needed. That friction led me to build sweepr. This is a smart, cross-platform CLI toolkit that intelligently finds, verifies and removes inactive dev dependencies safely in one command. Here's how it automates your workflow: 1. Smart Inactivity Detection: Instead of just checking folder dates, it analyzes the "Last Modified" time of your actual code files (ignoring logs/docs) to accurately identify dormant projects. 2. Multi-Language Intelligence: It detects both Node.js (node_modules) and Python (venv) environments structurally. For Python, it even auto-generates a requirements.txt receipt before deletion so you can rebuild later. 3. Safety First: It calculates exactly how much space you'll save and moves files to the System Trash by default (Soft Delete), so you never accidentally lose work. The goal is simple: Go from a cluttered drive to gigabytes of free space without the anxiety of running manual delete commands. It’s fully open-source and available on npm, Inc. right now. Do give it a try on your own machine! I’m actively looking for feedback, so feel free to reach out to me with any suggestions or ideas on how I can make it even better. Use it to clean your drive by: npm i -g sweepr Find the Source Code: https://lnkd.in/dFa9xa5s Get it on npm: https://lnkd.in/dMsX5KZm #Nodejs #Automation #OpenSource #CLI #Python #Productivity #DeveloperTools #SoftwareEngineering #npm

🌟 DAY 6 — URL Routing in Django URL routing defines how user requests are mapped to views. Today I learned how Django efficiently routes URLs to keep applications clean and scalable. 🔹 Routing Highlights: • urls.py → URL dispatcher • path() and re_path() for routing • App-level URL inclusion • Named URLs for better maintainability Clean routing makes applications easier to scale and debug. On to Day 7! 🚀 Official Docs 🔗 https://lnkd.in/dUE3m7Ch #Django #Python #WebDevelopment #Mixins #Decorators #CBV #FBV #CodeReuse #CleanCode #BestPractices #BackendDevelopment #Authentication #Permissions #DjangoDeepDive #PythonDevelopers #Maintainability #ScalableCode #OpenSource #FullStackDevelopment #DjangoTips #DjangoCommunity #PythonTips #CodeQuality #SoftwareDesign #DesignPatterns #ReusableCode #ScalableArchitecture #DeveloperProductivity #ProgrammingTips #BackendEngineer #TechBlogging

🐍 Python Backend Showdown: FastAPI vs. Django DRF Building your next API? Here’s the quick breakdown to help you choose: ⚡ FastAPI Focus: Speed & Modernity. Key Wins: Native Async support, automatic docs (Swagger), and incredible performance. Best for: Microservices and high-performance apps. 🛡️ Django REST Framework Focus: Stability & Power. Key Wins: "Batteries-included" (ORM, Admin Panel, Auth), mature ecosystem. Best for: Complex, data-heavy enterprise applications. 👇 Team Speed or Team Stability? Let me know your go-to framework in the comments! #Python #FastAPI #Django #BackendDeveloper #WebDevelopment #Coding

Creating a Flask application is exciting, but dealing with media uploads, storage, and optimization can be a hassle. Transform your media management from "tedious task" to "smooth and scalable." This updated guide offers Python developers a way to streamline the complete media lifecycle, including images, videos, and audio, allowing you to dedicate your efforts entirely to developing the features that delight your users. Check out the tutorial and enhance your Flask deployment: https://okt.to/vuBZ10