A real Linux kernel and userland running alongside Windows — architecture, integration, and daily use.
A complete walkthrough running several separate Linux distros side by side under WSL — installing additional distros, switching between them, and setting a specific one as your default.
A complete walkthrough switching between WSL2's default NAT networking and mirrored networking mode — and how to verify which one actually solves your specific reachability problem.
A complete walkthrough installing and running a graphical Linux application directly on your Windows desktop — no separate X server setup, no remote desktop session, just a window that opens like any other.
A complete walkthrough editing and debugging code that lives inside your WSL distro, using a Windows-installed VS Code, without ever copying files to the Windows side or fighting cross-filesystem performance.
A complete walkthrough of the global .wslconfig file — setting memory, CPU, swap, and networking behavior for every WSL2 distro on your machine from one central configuration file.
Running notepad.exe from a Bash prompt, or a Linux tool from Windows' own command line, works because of a specific interop layer translating between two completely different executable formats and process models.
WSL2 doesn't borrow a distro's kernel — Microsoft maintains its own fork, patched specifically for the virtualized environment WSL2 runs in, and ships it independently of both Windows and any Linux distro's own kernel.
Before WSL let Windows run real Linux binaries, Microsoft was building a bridge to run Android apps on Windows 10 Mobile instead. That project was shelved in February 2016 — clearing the way for what came next.
Eighteen months after its first public reveal, WSL stopped being an experimental preview feature — gaining full Microsoft support, multi-distro installs via the Microsoft Store, and Windows Server compatibility.
Microsoft announced GPU-accelerated compute support for WSL2 at Build 2020, followed by an NVIDIA CUDA preview that let machine learning frameworks like PyTorch and TensorFlow use a physical GPU from inside WSL2.