I got tired of re-explaining myself to AI every day, so I built a self-hosted AI brain that works with any MCP-compatible assistant.

This started out of pure frustration.
Every day I’d open a new chat and have to explain the same things all over again: who I am, what I’m working on, how my home setup works, and how I like things done.
I wanted my AI assistant to remember me—not just inside one app, but everywhere.
So I built **AIcortex**.
It’s a self-hosted MCP server that acts as a persistent brain for AI assistants. Any MCP-compatible client can connect to it. The underlying model becomes interchangeable, while the memory, tools, skills, and identity stay with me on my NAS.
A small example that shows why I built it:
The other day I was lying on the couch and wondered whether my 3D print had finished. I asked my assistant from my phone, and it simply checked the printer and told me the current status. No dedicated app. No getting up. And I didn’t have to explain which printer I meant—it already knew.
Since then, it has become the same assistant everywhere.
Whether I’m on my MacBook or my phone, it already knows my projects, my home setup, and the way I work. Every conversation starts with context instead of a blank slate.
More importantly, it can actually *do* things.
It can print a PDF on the printer downstairs, scan documents directly into my archive, check on my 3D printer, control services in my smart home, or interact with anything else I expose through MCP.
The part I’m honestly happiest about is that none of this is tied to an AI provider.
Whether I’m using Claude, GPT, Gemini, or a local Ollama model through an MCP bridge doesn’t really matter. The model is replaceable. My memory, tools, skills, and data stay on my own hardware.
The assistant stays **mine**.
One design decision I’m particularly happy with is how the system grows.
New capabilities aren’t added by deploying new code. They’re just data.
A new note.
A new memory.
A new skill.
A small service configuration.
The assistant simply gains new abilities without requiring another deployment. It feels much more like teaching it something than installing software.
Because this system has access to my home network and stores credentials, I also tried to take security seriously.
Authentication is fail-closed (without logging in, only localhost is allowed), sign-in uses OAuth with my own OIDC provider, SSRF protection blocks private and metadata IP ranges unless you explicitly allow your LAN, and the encrypted vault refuses to store secrets in plaintext.
That said, I’m a hobbyist—not a security engineer. There are certainly rough edges, and right now it’s still single-user, meaning every authenticated user has full access.
If you decide to try it, I’d genuinely appreciate feedback, issues, or pull requests. I’d much rather improve it together with the community than assume I’ve thought of everything.
The project is MIT licensed, still in its early stages, and I’d love to hear what you think.

**Repo:** https://github.com/IkarusMK/AIcortex