In Python, virtual environments are used to isolate projects from each other (if they require different versions of the same library, for example). They let you install and manage packages without administrative privileges, and without conflicting with the system package manager. They also allow to quickly create an environment somewhere else with the same dependencies.

Virtual environments are a crucial tool for any Python developer. And at that, a very simple tool to work with.

Pipenv is a Python packaging tool that does one thing reasonably well — application dependency management. However, it is also plagued by issues, limitations and a break-neck development process. In the past, Pipenv’s promotional material was highly misleading as to its purpose and backers.

In this post, I will explore the problems with Pipenv. Was it really recommended by Can everyone — or at least, the vast majority of people — benefit from it?

Gynvael Coldwind jest badaczem bezpieczeństwa pracującym w Google, który organizuje cotygodniowe livestreamy na tematy bezpieczeństwa i programowania po polsku i po angielsku). Częścią streamów są misje — w skrócie, zadania w stylu CTF-owym dotyczące inżynierii wstecznej. Wczorajsza misja była o elfickim — znaczy o Paint’cie — znaczy o programowaniu w Pythonie i jego bajtkodzie.

Setting up Python is usually simple, but there are some places where newcomers (and experienced users) need to be careful. What versions are there? What’s the difference between Python, CPython, Anaconda, PyPy? Those and many other questions may stump new developers, or people wanting to use Python.

On Monday, Apple announced some changes to its Mac lineup. All MacBooks (even the Air) got CPU upgrades, and the starting price of a MacBook Pro (13″, no Touch Bar) went down to US$1299. Which makes the 12-inch model effectively pointless.

A quick spec comparison reveals that the Pro comes with a much better CPU, GPU, screen, camera — the only drawback is the storage space.