Twitter recently buzzed over the Ralph Wiggum loop, a simple pattern for keeping AI agents coding autonomously. A developer revealed they’ve been using a similar approach since mid-2025, shipping two products with it. The pattern uses a bash script loop, Git commits, and a markdown task list to persist work across agent sessions.

The core idea is straightforward: an AI agent reads a plan, picks a task, implements it, commits, and sleeps. This mirrors the open-source Taskmaster tool, which offers more scaffolding like dependency tracking and Docker sandboxes. The debate isn't about which is better, but which stage of development you're in. Simple experiments favor Ralph's clarity; complex, multi-agent projects require Taskmaster's coordination layer.

For solo experiments with straightforward tasks, Ralph's 500-line implementation wins for its simplicity. However, when running multiple agents on interdependent features, you need explicit dependencies to prevent collisions. Taskmaster’s system parses a PRD, scores task complexity, and breaks work into subtasks with machine-readable dependencies. This coordination is what enables parallel development without merge conflicts over months of work.