The constant switching between AI models is a recurring issue, causing frustration as users lose their accumulated work. This problem stems not from the models themselves but from the lack of a cohesive workspace. Current AI coding setups consist of isolated tools like chat windows and IDE extensions, with no central repository for decisions or past findings. Critical context, such as rejected approaches or conclusions from previous sessions, is lost when applications are closed or models are changed. While code files are shared, the thought processes surrounding them are not, making it difficult for new models to pick up where previous ones left off. A true AI workspace would offer shared memory, making learnings persistent across different agents and models. It would provide visible, accessible records of tasks and decisions, preventing redundant work. Such a workspace would allow multiple agents to collaborate on a single project, all accessing the same source of truth. Crucially, it would ensure continuity, allowing users to resume their work seamlessly. These features are independent of specific models, which are expected to continue evolving. The envisioned workspace aims to preserve the user's work, ensuring that progress is maintained, not lost, with each model update or shift.