Why Switching AI Feels Like Restarting the Project

Switching artificial intelligence models can feel like starting a project from scratch. Even though the files, repository, and previous work remain intact, the new AI often asks questions that have already been answered, re-checks verified work, and proposes options that were previously dismissed. To resume a project effectively, the new AI needs a concise representation of the current state, including the active task, completed work, verified evidence, approved decisions, and the immediate next step.

This information should be machine-readable and easily accessible, rather than relying on a lengthy transcript or vector database. The author created an open-source project called Resume the Scene to preserve the verified execution point of a project, independent of chat history or model vendor. The project focuses on storing only the essential project-memory layer required to resume work, without becoming another chat archive or requiring a vector database.

By providing the necessary project-memory files and a structured representation of the current state, the next worker can seamlessly continue the work without having to rediscover the execution state from history. The key distinction lies in separating conversation memory (what was said), knowledge memory/knowledge retrieval (what information exists), and project memory (what is true about the work and where to continue).

While the first two are valuable, the third is crucial for a smooth transition when switching AI models. The author recommends identifying the minimum state needed to trust another AI to continue the task without rereading the entire history.