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Git Cola is a sleek and powerful graphical user interface for Git. Git Cola is free software and written in Python (v2 + v3). Git Cola uses QtPy, so you can choose between PyQt6, PyQt5 and PySide2 by setting the QT_API environment variable to pyqt6, pyqt5 or pyside2 as desired. qtpy defaults to pyqt6 and falls back to pyqt6 and pyside2 if pyqt5 is not installed. Git Cola enables additional features when the following Python modules are installed. send2trash enables cross-platform "Send to Trash" functionality. ...

...After cd-ing into your new project and creating (or activating) an isolated development environment (with virtualenv, conda or your preferred tool), you can do the usual editable install. All configuration can be done in setup.cfg like changing the description, URL, classifiers, installation requirements and so on as defined by setuptools. That means in most cases it is not necessary to tamper with setup.py.

...You can download and install the latest version of nbstripout from PyPI, the Python package index. Strip output from IPython / Jupyter / Zeppelin notebook (modifies the file in-place). Usually, nbstripout is installed per repository so you can choose where to use it or not. You can choose to set the attributes in .gitattributes and commit this file to your repository, however there is no way to have git set up the filters automatically when someone clones a repository. This is by design, to prevent you from executing arbitrary and potentially malicious code when cloning a repository.

...Deletion is a sharp knife that requires care. While git reflog would have your back in most cases, git-delete-merged-branches takes safety seriously. git push is used with --force-with-lease so if the server and you have a different understanding of that branch, it is not deleted. There is no use of os.system or shell code to go wrong.

...It treats repositories and developers as a graph of interactions and applies large-scale matrix factorization to model affinities, with Apache Spark providing the distributed data processing. The project focuses on implicit feedback—stars, watches, and other engagement metrics—so it can build useful recommendations without explicit ratings. A reproducible setup and Makefile-driven workflow streamline tasks like spinning up services, loading datasets, training models, and generating candidate lists. Because it’s built around Spark’s scalable primitives, Albedo can experiment on substantial snapshots of GitHub metadata rather than toy corpora. ...