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proxy.py is made with performance in mind. By default, proxy.py will try to utilize all available CPU cores to it for accepting new client connections. This is achieved by starting AcceptorPool which listens on configured server port. Then, AcceptorPool starts Acceptor processes (--num-acceptors) to accept incoming client connections. Alongside, if --threadless is enabled, ThreadlessPool is setup which starts Threadless processes (--num-workers) to handle the incoming client connections....

...With a simple installer and systemd service setup, users can quickly deploy it on a Linux VPS as a tunneled access point, then connect from Windows, macOS, or Linux clients using auto-generated client packages. The project is useful for environments where standard VPNs or proxies are blocked, offering a combination of performance and stealth.

Jupyter Server Proxy lets you run arbitrary external processes (such as RStudio, Shiny Server, Syncthing, PostgreSQL, Code Server, etc) alongside your notebook server and provide authenticated web access to them using a path like /rstudio next to others like /lab. Alongside the Python package that provides the main functionality, the JupyterLab extension (@jupyterhub/jupyter-server-proxy) provides buttons in the JupyterLab launcher window to get to RStudio for example.

NNTP2NNTP Proxy allow you to use your NNTP Account from multiple systems, each with own user name and password. It fully supports SSL and you can also limit the access to proxy with SSL certificates. NNTP2NNTP Proxy is very simple and pretty fast. An nntp uploader is also included.