I am about to cry. I have been working on a project that actually made me feel I was bringing a new approach until I found this thread with an alarming number o

I am about to cry. I have been working on a project that actually made me feel I was bringing a new approach until I found this thread with an alarming number of similar tools.. I guess many of us used the same LLMs to help us building our "unique" solutions. My approach was to remove completely docker and start using podman as rootless daemonless alternative. It also has a good solution to user-namespace mapping when I mount my repositories inside the sandbox. I let the ai agent make changes but then I review them and push them from my host. As an alternative, you can create a ssh key pair on the host and load it into a dedicated ssh-agent, so you expose that agent socket to the agent container. For API Tokens and similar credentials, I solved it by putting a credential broker between the podman agent container and the LLM provider. To avoid doing any internal firewalling in the container I run a proxy as a podman container that enforces the egress policy (only https on allowed domains); so no network capabilities are required, the proxy itself handles the filtering. Because I had the idea of using the sandboxing framework to allow specific communication inside the network (for example for pentesting), it has also (apart from the proxy mode) a "routed"-mode implemented where a podman gateway is started; this does the network filtering without holding any kernel capabilities. nftables is configured using an ephemeral container with net_admin in the same network space (one-shot network initializer). The framework orchestrates the different required networks and Podman containers. I haven't tried the Docker solution yet, so I'll avoid comparing them.