Python App Testing with Modern Tools

My article on writing, compiling and running C code from within your familiar Python dev environment just dropped on the Medium platform. If you have an account over there, you should go check it out. Article link is in the first comment.

CI/CD has improves code quality - and GitHub Actions makes it easier than ever to get started. I just built my first CI/CD pipeline with GitHub Actions! Check out my documentation for the step-by-step process 👇 ✅ Created a Python project with pytest tests ✅ Set up GitHub Actions workflow ✅ Configured automated testing on every push ✅ Watched my first green build pass Huge thanks to @NextWork for this awesome project guide. https://lnkd.in/gqvEJrP9 #CICD #GitHubActions #Python #DevOps #NextWork

Most of us learned Python package management the same way: - pip install ... - set up venv - manage requirements.txt It works... but it’s not always the smoothest experience, especially for beginners. I recently published a short article exploring pip vs uv, a newer tool that’s rethinking how Python developers manage dependencies and environments. Why it matters: - Much faster installs (written in Rust) - Built-in environment management - Simpler workflow (fewer moving parts) Read the full article here: https://lnkd.in/gipM8p7T Curious to hear from the community: Are you sticking with pip, or experimenting with tools like uv? #Python #SoftwareDevelopment #DeveloperTools #Programming #Tech

🐍📰 Integration Testing with pyVows and Django We will continue in this final article of the three part series to explore how pyVows can be used for integration testing django and testing of django models #python

🐍 Python Development Tools — On this page you will find articles that will help you get started on the road to mastering the most common tools used in the #Python ecosystem. Knowing how to use these tools will serve you well in your career. https://lnkd.in/g-e3xpA

🐍📰 How to Add Features to a Python Project With Codex CLI Learn how to use Codex CLI to add features to Python projects via the terminal. Master AI-powered coding without needing a browser or IDE plugins https://lnkd.in/gXQbyFF7

🐍📰 How to Add Features to a Python Project With Codex CLI Use Codex CLI to add features to Python projects via the terminal. Master AI-powered coding without the need of a browser or IDE plugins https://lnkd.in/gXQbyFF7

CI/CD has improves code quality - and GitHub Actions makes it easier than ever to get started. I just built my first CI/CD pipeline with GitHub Actions! Check out my documentation for the step-by-step process 👇 ✅ Created a Python project with pytest tests ✅ Set up GitHub Actions workflow ✅ Configured automated testing on every push ✅ Watched my first green build pass https://lnkd.in/g3b7jxaK #CICD #GitHubActions #Python #DevOps #NextWork

AI didn’t create itself - human intelligence did. Keep learning, keep building, and keep sharpening your mind every single day. #Programming #Coding #Tech #Learning #Developer

Production debugging is often less about the error itself and more about sequence. What failed first? What was a consequence? Which exception actually triggered the chain? That is why PEP 830 caught our attention. It brings timestamps into Python tracebacks, which can be especially useful in async flows and ExceptionGroup scenarios where several failures happen close to each other. Most teams already reconstruct this through logs, tracing, or external tools. The interesting part here is that Python itself is starting to expose more timing context directly at the traceback level. This will not replace observability. But it may reduce the amount of guesswork during failure analysis, especially when multiple exceptions are involved and timing is part of the story. For teams running Python in production, small changes like this can quietly improve debugging quality without changing application code. Curious how others see it: would you use this directly in Python, or would logs and observability tools still remain the only source of truth?