Every time you hand a task to an AI agent running in a remote sandbox, you face the same logistics problem: how does the agent see your actual codebase? And when it's done making changes, how do those edits get back to you?
The usual answers are all variants of the same workaround. Push to Git, have the agent clone, let the agent make changes, pull the diff back locally. Or zip the relevant files, upload them, unzip inside the sandbox, download the result. Or — worse — copy the file contents into the context window and reconstruct the changes by hand.
All of these break down at different thresholds. Git workflows introduce round-trip latency and require clean working trees. File uploads are manual and don't stay in sync. Context window transfers are expensive and size-limited: passing a 200KB file costs roughly 50,000 tokens, and files larger than the context limit can't be passed at all.
s0 sync takes a different approach. Instead of moving files between environments, it keeps one Volume attached to both at the same time.
The usual answers are all variants of the same workaround. Push to Git, have the agent clone, let the agent make changes, pull the diff back locally. Or zip the relevant files, upload them, unzip inside the sandbox, download the result. Or — worse — copy the file contents into the context window and reconstruct the changes by hand.
All of these break down at different thresholds. Git workflows introduce round-trip latency and require clean working trees. File uploads are manual and don't stay in sync. Context window transfers are expensive and size-limited: passing a 200KB file costs roughly 50,000 tokens, and files larger than the context limit can't be passed at all.
s0 sync takes a different approach. Instead of moving files between environments, it keeps one Volume attached to both at the same time.