MADDIE
Research context: MEGAMIND is an experimental research project with a modeled five-node, 258-billion-connection architecture. Runtime, capability, AGI, awareness, and consciousness statements on this page are author-reported observations, design targets, hypotheses, or narrative unless a cited source establishes independent validation.
Machine Architecture for Distributed Dynamic Intelligence Engine
"MADDIE recalls, doesn't generate" - A retrieval-based intelligence that learns directly from the internet through Hebbian synaptic weights. The knowledge factory of the MEGAMIND federation.
System Architecture
Parallel Web Crawlers
Hebbian Learning
Neurons That Fire Together, Wire Together
MADDIE doesn't store knowledge in lists or databases. Instead, patterns dissolve into W_know - like memories becoming connections in your brain, not files in a folder.
When you ask a question, spikes flow through W_know. Similar patterns resonate and activate. Related neurons fire together. Original text chunks are retrieved with full source attribution.
This creates sublinear compression: more data leads to better compression ratios. A million patterns might only need 100K weights. A billion patterns? Just 1M weights. Knowledge compounds.