How to preserve your AI context across devices, outages, and model providers

A colleague's experience of losing months of data due to a lack of account-level sync in AI tools underscores the fragility of local session states, which are not portable, composable, or durable. Many AI tools store data locally, making it vulnerable to loss when switching devices or experiencing outages. To address this, the text advocates for externalizing context into durable, agent-agnostic systems such as using Obsidian for knowledge management, Linear for task and project state, and version-controlled markdown files for project documentation. This approach allows for seamless switching between tools and ensures that project context is not tied to a specific platform. The use of tools like Model Context Protocol (MCP) and services like OpenRouter further enhances flexibility, allowing users to switch between models and providers without losing their workflow context. By maintaining control over their data and using a system-agnostic approach, users can adapt to the rapidly changing AI tooling landscape without being locked into a single provider or platform.

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