Amulet

Amulet Labs describes conflict-safe publishing as a compare-and-swap operation on a workspace head. Each agent publishes only if its starting revision remains current; a stale writer receives an explicit conflict and retains its session. The approach includes per-commit provenance and isolated session forks for parallel agent fleets.

Jul 2026Amulet LabsNews article

Amulet Labs explains how copy-on-write branching creates writable views of existing data without copying the underlying bytes. The model uses shared immutable blocks and isolated branches, allocating storage only when a branch changes data. The article connects this approach to AI training and evaluation workflows, including dataset snapshots, isolated experiments, and rollback to prior revisions.

Jul 2026Amulet LabsNews article

Amulet Labs compares ways to preserve AI-agent state across disposable compute, including paused sandboxes, platform volumes, and mounted durable workspaces. It examines portability, concurrent workers, lifecycle coupling, and versioned state, and presents Amulet’s workspace as a durable filesystem mounted into isolated sandbox sessions, with published results represented as attributable commits.

Jul 2026Amulet LabsNews article

Amulet Labs compares s3fs, goofys, Mountpoint for S3, JuiceFS, EFS, Archil, and Amulet for workloads that need filesystem behavior over durable data. The discussion focuses on object-store round trips, rename behavior, concurrent writes, and operational tradeoffs; Amulet describes its positioning for parallel AI workloads as including isolated forks, versioned history, conflict-safe publishing, and usage-based pricing.

Jul 2026Amulet LabsNews article