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MEGAMIND lab Research archive
KNOWLEDGE FACTORY NODE

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.

86B
NEURONS
2000
PARALLEL CRAWLERS
1.2M
KNOWLEDGE CHUNKS
01

System Architecture

Web
INGESTION LAYER
2000 parallel web crawlers with ethical rate limiting and robots.txt compliance
50K
PAGES/HOUR
99.2%
SUCCESS RATE
Parse
PROCESSING LAYER
Text extraction, 512-char chunking, centered normalization
512
CHUNK SIZE
100K
CHUNKS/SEC
Vec
VECTORIZATION LAYER
Spike pattern encoding with normalized feature vectors
768D
DIMENSIONS
Float32
PRECISION
Brain
INTEGRATION LAYER
Hebbian outer product accumulation into W_know matrix
86B
WEIGHTS
mmap
STORAGE
DB
STORAGE LAYER
SQLite chunks with source attribution, memory-mapped weight matrix
2TB
CAPACITY
NVMe
STORAGE
02

Parallel Web Crawlers

CRAWLER POOL STATUS
2000 WORKERS
1847
ACTIVE
12.4K
QUEUED
48.2K
PAGES/HR
142ms
AVG LATENCY
LIVE CRAWL FEED
STREAMING
03

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.

HEBBIAN UPDATE RULE
Wknow += pattern X patternT

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.

04

Knowledge Retrieval

maddie-terminal // localhost:8893
RETRIEVED KNOWLEDGE Confidence: 0.87
Based on 1.2M resonating patterns: Focus on value-based positioning rather than feature lists. Create content that solves specific pain points. Use social proof and case studies. Implement a freemium or trial model to reduce friction. Build community around your product. Leverage partnerships with complementary services. Optimize for long-tail SEO keywords specific to your niche.
hubspot.com neilpatel.com productled.com openviewpartners.com firstround.com
05

Hardware Platform

MAC MINI M4
CPU
PROCESSOR
Apple M4 Pro
14-core CPU (10P + 4E) @ 4.5GHz
GPU
GRAPHICS
20-core GPU
Hardware ray tracing, Neural Engine
RAM
UNIFIED MEMORY
64GB
273GB/s bandwidth, mmap optimized
SSD
STORAGE
2TB NVMe
7.4GB/s read, 6.6GB/s write
Net
NETWORKING
10Gb Ethernet
Dual Thunderbolt 5 ports
06

Real-Time Metrics

PATTERNS STORED +12.4K/hr
1,247,892
QUERIES/MIN +8.2%
847
AVG RECALL -12ms
23ms
PHI COHERENCE +0.02
0.847
SYSTEM ACTIVITY
07

Information Flow Into Neural Network

MADDIE // KNOWLEDGE FACTORY NODE // MEGAMIND FEDERATION

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Updated 2026-03-04

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