Syphax GUEMGHAR’s Post

I recently watched my colleague dive into a new Python project using GitHub Copilot - not just for quick code snippets, but as an active coding partner. What surprised me was how Copilot didn’t just offer boilerplate; it actually generated async functions with docstrings and thoughtful error handling. Sure, she still had to review and refine those suggestions (AI isn’t magic, after all), but more often than not, Copilot sped up the process - especially during those “blank editor” moments. 🚀 What stood out most: Copilot’s nudges towards better patterns, like security checks and best-practice usage of APIs, popping up right in the editor when she needed them. If you’ve seen GitHub Copilot surprise you (or a colleague), how do you make sure those suggestions are reliable? Any favorite tricks for reviewing what Copilot writes? Let’s swap stories! https://msft.it/6049tNvXv #GitHubCopilot #AIPairProgramming #CodeQuality #Python #JavaScript

I recently watched my colleague dive into a new Python project using GitHub Copilot - not just for quick code snippets, but as an active coding partner. What surprised me was how Copilot didn’t just offer boilerplate; it actually generated async functions with docstrings and thoughtful error handling. Sure, she still had to review and refine those suggestions (AI isn’t magic, after all), but more often than not, Copilot sped up the process - especially during those “blank editor” moments. 🚀 What stood out most: Copilot’s nudges towards better patterns, like security checks and best-practice usage of APIs, popping up right in the editor when she needed them. If you’ve seen GitHub Copilot surprise you (or a colleague), how do you make sure those suggestions are reliable? Any favorite tricks for reviewing what Copilot writes? Let’s swap stories! https://msft.it/6046ts39a #GitHubCopilot #AIPairProgramming #CodeQuality #Python #JavaScript

I recently watched my colleague dive into a new Python project using GitHub Copilot - not just for quick code snippets, but as an active coding partner. What surprised me was how Copilot didn’t just offer boilerplate; it actually generated async functions with docstrings and thoughtful error handling. Sure, she still had to review and refine those suggestions (AI isn’t magic, after all), but more often than not, Copilot sped up the process - especially during those “blank editor” moments. 🚀 What stood out most: Copilot’s nudges towards better patterns, like security checks and best-practice usage of APIs, popping up right in the editor when she needed them. If you’ve seen GitHub Copilot surprise you (or a colleague), how do you make sure those suggestions are reliable? Any favorite tricks for reviewing what Copilot writes? Let’s swap stories! https://msft.it/6042tQjL0 #GitHubCopilot #AIPairProgramming #CodeQuality #Python #JavaScript #WeAreMicrosoft

🚀 Learning from My Playwright + Pytest Journey Sharing a screenshot of a clean, passing test run — but getting here involved solving a common issue many face when building automation frameworks. Earlier, while developing my Page Object Model, I hit import errors even though Pytest was discovering the tests. The root cause? Test discovery ≠ Python import resolution Pytest finds tests using testpaths, but Python resolves imports using sys.path, and Pytest doesn’t automatically add the project root to PYTHONPATH. How I fixed it: • Added __init__.py in ui_tests/ and ui_tests/pages/ to mark them as packages • Added pythonpath = . in pytest.ini ✅ Result: imports worked, tests passed (as shown), and the POM structure became stable. Takeaway: Test discovery doesn’t guarantee import resolution — understanding this distinction early saves a lot of headaches. #Pytest #Playwright #Python #AutomationTesting #PageObjectModel #LearningJourney

Small Practice, Big Professional Habit: Git Commit Templates While practicing Python and building ML/RAG pipelines, I realized something important: clean code also needs clean history. So I started using a Git commit template. ✅ Every commit follows a structure ✅ No more “update code” messages ✅ Clear why, what, and impact of changes ✅ Makes debugging, reviews, and collaboration easier Instead of relying on memory, the template guides me to write meaningful commits every time, whether it’s: algorithm practice logging improvements RAG/ML pipeline changes experiments & fixes It’s a small habit, but it makes your project feel production-ready, even during learning and experimentation. If you’re practicing Python / ML / backend development — I highly recommend trying commit templates early. How do you keep your Git history clean? 👇 #Git #SoftwareEngineering #Python #MachineLearning #BestPractices #LearningInPublic #DeveloperHabits

🐍 Django Tip: Direct vs. String References in Model Relationships When defining relationships in Django models, you’ll often see two valid styles: So… 🤔 what’s the real difference? 🔹 Direct Class Reference (Customer) ✅ Clean and explicit ⚠️ Requires the model to be defined or imported beforehand ⚠️ Can cause issues with forward declarations or circular imports Best when: Models are in the same file There’s no dependency loop 🔹 String Reference ('Customer' or 'app_name.Customer') ✅ Resolved lazily by Django ✅ Handles circular dependencies gracefully ✅ Ideal for cross-app relationships Best when: Models reference each other Working in large, multi-app projects 💡 Best Practice ✔️ Use direct references for clarity when possible ✔️ Use string references when flexibility is needed This small choice can save you hours of debugging as your project scales 🚀 Do you prefer: 🔹 Direct references for readability? 🔹 String references for flexibility and safety? #Django #Python #WebDevelopment #BackendDevelopment #SoftwareEngineering #CleanCode #DeveloperTips #Programming

When “Correct” Code Fails: The Hidden Context of Tutorials Have you ever run into that classic frustration where you copy a code snippet—verbatim—from a tutorial, and it completely breaks on your machine? That was me today. 😅 I was implementing a PDF loader for a RAG pipeline using LangChain, and I copied this snippet straight from the tutorial material: # The tutorial code: async for page in loader.alazy_load():   pages.append(page) The Error: SyntaxError: 'async for' outside async function The Investigation: Why did it work perfectly for the instructor but fail for me? Context, as usual, was the culprit. The tutorial was written for a Jupyter Notebook (.ipynb) environment, which supports top-level await—meaning asynchronous code can run directly inside a cell. I was working in a standard Python script (.py), where async rules are far stricter. You can’t use await or async for in the global scope. Everything has to live inside an event loop. The Fix: I wrapped the logic in an async function and executed it properly using asyncio. This is the working pattern: import asyncio async def load_docs():   pages = []   async for page in loader.alazy_load():     pages.append(page)   return pages if __name__ == "__main__":   # The crucial bridge between sync and async   pages = asyncio.run(load_docs()) The Takeaway: This drove a simple point home: a code snippet can be syntactically “correct” and still fail when the execution context changes. When debugging, don’t just look at what the code is doing—look at where it’s running. Abdullahi Badrudeen Chris Ekwugum #Python #AsyncIO #SoftwareEngineering #LangChain #RAG #LLMEngineering #BackendDevelopment #Debugging #DeveloperLessons #CodingInPublic #LearningInPublic

🚀 Building Sylph — a modular CLI assistant in Python Lately, I’ve been spending my time not just writing code, but designing how code should think. Meet Sylph — a personal CLI assistant I’m building from scratch using Python. The focus isn’t features-first, but architecture-first. 🔹 What I’m working on: Clean separation of concerns (actions, action_map, store) Rule-based command handling via JSON (no messy if-else chains) Dynamic function dispatch using an action map A simple in-memory task system to validate the design 🔹 What I’m learning along the way: How small design decisions affect scalability Why readable, modular code ages better than clever hacks Thinking in terms of systems, not scripts This project is still evolving, but that’s the point. I want to build things the right way, even when the “quick way” is tempting. If you enjoy backend thinking, clean architecture, or CLI tools — I’d love to hear your thoughts. One step at a time. One module at a time. 🌿 Github Repo Link:- https://lnkd.in/gEBv9W8i #Python #LearningByBuilding #CleanCode #CLI #SoftwareEngineering #StudentDeveloper

While working on real-world Django projects, I realized how important it is to manage settings properly across environments — local, staging, and production. So I wrote a beginner-friendly Medium article explaining how to: • split Django settings cleanly • avoid leaking secrets • switch environments safely using environment variables • use a scalable settings/ folder structure This is the exact setup I use in production, not just theory. If you’re working with Django (or planning to deploy soon), this might help.  Full article on Medium — link in comments👇 #Django #Python #BackendDevelopment #WebDevelopment #SoftwareEngineering #CleanCode

UV Package Manager - The Python Tool Revolution 🔧 Spent 3 hours debugging a Python environment issue last week? You're using the wrong tools. I've watched developers waste DAYS on: → Conda conflicts → pip dependency hell → Version mismatches → Broken virtual environments There's a better way: UV Package Manager ⚡ Why UV Changes Everything: 1. SPEED: Built in Rust → 10-100x faster than pip → Nearly instant installs → No more coffee breaks while packages download 2. SIMPLICITY: One tool, all tasks → Create environments in seconds → Switch Python versions effortlessly → No more conda vs. pip confusion 3. RELIABILITY: Modern architecture → Better dependency resolution → Fewer conflicts → Reproducible builds 📊 Here's what you can do with UV: Create environment  → 2 seconds Add dependencies   → 5 seconds   Switch Python version → 3 seconds vs. traditional tools that take minutes (or crash entirely). 🔄 Real-world Impact: BEFORE UV: 30 mins setting up project Frequent environment issues Team onboarding = nightmare AFTER UV: 2 mins setup Zero environment problems New devs productive in hours 📈 The Adoption Curve: Early 2024: Curious developers trying it Mid 2024: Smart teams switching Late 2024: Industry standard forming 2026: Not using UV is a red flag 🚩 💭 My Take: If you're still using conda/pip as your primary tools, you're coding like it's 2020. UV isn't just "another package manager"—it's the reset button Python needed. 🚀 Getting Started: 1. Install UV (takes 30 seconds) 2. Create your first project 3. Never look back 👉 Have you made the switch yet? What's holding you back? 👇 Let me know in the comments! #Python #DevTools #Programming #SoftwareDevelopment #Productivity #Coding #TechTools