You know that feeling when you ask ChatGPT about your architecture decision and it's like "That's a great approach!" even though you're about to shoot yourself in the foot?
I got tired of that.
So I built MIMIR — an AI that runs 2-4 models in parallel on every message. A Lead Analyst breaks down your problem. A Strategic Advisor thinks long-term. A Creative finds the angle you missed. Then a synthesis layer merges their thinking into one response.
Here's what's different:
When you float a bad idea, MIMIR tells you it's bad. When there's a tradeoff you're ignoring, it surfaces it. When you're optimizing for the wrong thing, it pushes back with a better path.
Not because I programmed it to be contrarian — but because multiple models thinking independently means blind spots get caught before they become your answer.
The stack:
Multi-model collective intelligence (2-4 models per response)
Persistent memory across sessions (not context window tricks)
Self-improving: learns from corrections in real-time
Can query your connected databases and services via MCP tools
1 comment
[ 3.1 ms ] story [ 10.3 ms ] threadI got tired of that.
So I built MIMIR — an AI that runs 2-4 models in parallel on every message. A Lead Analyst breaks down your problem. A Strategic Advisor thinks long-term. A Creative finds the angle you missed. Then a synthesis layer merges their thinking into one response.
Here's what's different:
When you float a bad idea, MIMIR tells you it's bad. When there's a tradeoff you're ignoring, it surfaces it. When you're optimizing for the wrong thing, it pushes back with a better path.
Not because I programmed it to be contrarian — but because multiple models thinking independently means blind spots get caught before they become your answer.
The stack:
Multi-model collective intelligence (2-4 models per response) Persistent memory across sessions (not context window tricks) Self-improving: learns from corrections in real-time Can query your connected databases and services via MCP tools