I got tired of AI assistants that forget everything the moment a session ends. So I built one that doesn't.
It runs 24/7 on my Mac, has access to my files, GitHub, iMessage, email, and calendar. It knows who I am, what I'm working on, and what I said to it last week.
The core problem with stateless AI
Every time you open a new Claude or ChatGPT session, you start from zero. You re-explain your context. You re-establish what you're working on. You paste in the same background info.
This is fine for one-off tasks. It's terrible for an ongoing working relationship.
The memory architecture
Instead of in-context memory, I use files:
-
MEMORY.md— long-term curated knowledge. What matters, distilled. -
memory/YYYY-MM-DD.md— daily logs. What happened, decisions made, things to remember. -
USER.md— who I am, my stack, my communication style. -
TOOLS.md— local setup specifics.
Every session, the agent reads the relevant files before doing anything. This is the continuity layer.
MCP for real-world access
Model Context Protocol (MCP) is what lets the agent actually do things — not just talk about them.
I use it for:
- Apple Mail, Calendar, Messages via a local MCP server
- GitHub via
ghCLI - File system access
- Browser automation (Puppeteer via Chrome DevTools Protocol)
The result
It's not a chatbot. It's closer to a part-time assistant who's always available and never forgets anything. The most useful thing isn't any single capability — it's that context persists.
I can say "remember the JWT issue from last week" and it actually knows what I mean.
The hardest part isn't the AI. It's designing the memory and context system that makes it feel coherent over time.
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