Muse + Vilix AI: Every AI I Use Now Shares One Memory
I use too many AI tools. ChatGPT for brainstorming, Claude for writing and code, Cursor when I am deep in a repo, and Muse on my phone when I am away from my desk. Each one is good at its job. The problem was never the tools. The problem was that none of them knew what I had told the others. Every switch meant re-explaining. Every new chat meant pasting context I had already written somewhere…
Muse and Vilix AI have collaborated to create a solution that unifies AI tools by creating a shared memory layer, allowing context to be easily accessible across different applications. This innovation addresses the issue of each AI tool having no knowledge of the information shared with others, resulting in repetitive explanations and context reiteration. The solution, Vilix AI, utilizes MCP (Message Control Protocol) as an open standard to connect and share information between tools.
To set up the connection, one must create a Vilix AI API key, ask Muse to connect with Vilix AI, paste the key through Muse's secure connector flow, and verify the connection by answering a question only accessible through memory. The connection process is straightforward, taking approximately two minutes. Once connected, the assistant can pick up conversations across tools, continue planning, and follow predefined rules set in Vilix memory.
This creates a seamless experience for users across devices, eliminating the need for re-explaining or pasting context into new chats.
The project and task state can be managed through Vilix, with connected AI tools having access to active, blocked, or done tasks. This ensures phone-to-laptop continuity, allowing the assistant to work from the same memory, regardless of the device being used. While Muse is a good app, the essence of this development lies in the concept of shared memory over MCP for AI tools, enabling context to compound instead of resetting with each new tool or device.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.