Run the Sweep on Daytona. Keep the tapes.
Blog post from Paper Compute Company
Sweeper is an open-source tool for distributing bounded engineering tasks, such as lint fixes and documentation updates, among parallel AI agents running in Daytona sandboxes, but its author argues that deleting those ephemeral environments should not erase the work history generated within them. The proposed workflow uses tapes to preserve model exchanges alongside patches, validation results, telemetry, and spending data outside each sandbox, allowing later workers to learn from failed attempts, recurring errors, and successful strategies. Earlier sweeps demonstrated both the benefits and limitations of parallel execution: one resolved 842 lint errors in 54 minutes but introduced 61 downstream TypeScript errors, illustrating why final patches alone do not capture an agent’s reasoning or the reliability of its changes. The author envisions archiving session records before sandbox cleanup, then selectively using that evidence to improve prompts, diagnose whether failures stem from models, context, or environments, and reduce repeated effort in future sweeps.
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