Rob Roggeveen’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/6044tSLx6 #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/6046tqI0r #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/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/6046tSP1O #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/6044tS8Ks #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/6048tSk8Y #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

Sometimes a string problem looks simple… until constraints change the rules 🔄 Hey everyone! Day 353 of my 365-day coding journey, and today’s challenge was LeetCode 541: Reverse String II — a great problem to understand how careful indexing and iteration matter in string manipulation. Let’s break it down. 🚀 🛠️ The Problem You’re given a string s and an integer k. For every 2k characters starting from the beginning of the string: - Reverse the first k characters - Leave the next k characters as they are This pattern continues until the end of the string. 🎯 The Approaches I explored three different approaches, each with its own learning value: Approach 1: Brute Force   I manually processed the string in chunks, reversing required parts step by step. This approach is easy to understand but not the cleanest in terms of implementation. Approach 2: Simple Slicing Approach   Using Python slicing made the solution much cleaner and more readable. By handling the string in 2k-sized windows, reversing the first k characters became straightforward. Approach 3: Optimal In-Place Style   This approach focuses on minimizing extra operations and handling the logic efficiently using controlled traversal. It’s closer to how you’d think about performance and constraints in real interviews. 🧠 Key Takeaways String problems are less about syntax and more about precise control over indices. Starting with a brute-force solution helps clarify the logic before optimizing. Understanding multiple approaches builds confidence for interviews and real-world problem solving. 💡 Challenge for You! Which do you prefer for string problems — clean slicing-based solutions or more controlled in-place logic? Let’s discuss! 💬 📺 Watch the Full Walkthrough I’ve explained all three approaches step by step in my latest video: https://lnkd.in/gDy7JinT 🔥 Join the Conversation If you’re also staying consistent with DSA practice or working through daily LeetCode problems, let’s connect and grow together. 🚀 #CodingJourney #DSA #LeetCode #Strings #ProblemSolving #Python #Algorithms #DataStructures #DeveloperLife #Programming #365DaysOfCode #LearningEveryDay

🚀 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