A developer on the Chinese platform Juejin shared a detailed retrospective of using TRAE Work, an AI-powered development assistant, to cut a routine task from four hours down to seven minutes—a nearly 40x improvement. The post breaks down the workflow redesign, showing how breaking a large task into smaller, AI-executable steps with clear validation checkpoints made the difference. Beyond the specific tool, the case illustrates a broader shift: AI coding assistants are evolving from autocomplete features into autonomous agents that can handle multi-step engineering chores. For teams evaluating AI tooling, this provides a concrete benchmark of what is achievable with proper prompt structuring and workflow design. The author also notes pitfalls, such as the need for human review at critical stages, which adds practical value for adopters.
A Chinese developer reports a dramatic 40x efficiency improvement in a real project by adopting TRAE Work, an AI coding assistant. The case highlights how structured AI workflows can replace manual repetitive tasks. This signals a growing trend of AI tools moving from code completion to full task automation.