AI tools bind context directly to a model (GPT, Claude, Gemini, etc) which makes switching tools unproductive and collaboration on ideas non existent. We kept running into this internally, so we built a system where context is stored in a structured, model-agnostic layer and translated into model-specific prompts at runtime.
The core idea is treating modes as execution layers rather than the source of truth. This lets us switch models, share context across a team or project, and preserve context always.
Still early, but curious how others here think about context management across models, especially for collaborative workflows.
The core idea is treating modes as execution layers rather than the source of truth. This lets us switch models, share context across a team or project, and preserve context always.
Still early, but curious how others here think about context management across models, especially for collaborative workflows.