A 5-step journey from local data to quadratic intelligence. Understanding how distributed agents create compounding knowledge.
Visual breakdown of all 7 layers — from hardware to collective intelligence. Understand how the pieces fit together.
View Architecture Diagram →Each agent (smartphone, IoT sensor, tractor, medical device, etc.) ingests data from its own local sources. This could be:
Critical: Raw data never leaves your device. Your phone stores everything locally.
Your agent transforms raw data into a curated feature vector — a mathematical "fingerprint" optimized for your domain.
Network-specific design: Domain experts define the vector templates for each network—oncologists for cancer networks, agronomists for crop networks, etc. They determine which features matter and their valid ranges. This ensures the fingerprints capture what's clinically (or agriculturally, financially) meaningful.
Note: Methods for creating vectors and hashes can vary—see the Core Specification for alternatives. This approach is my favorite for "teleporting to the right lung" every time.
Privacy preserved: Only this anonymized vector is shared — not your name, not your address, not your raw medical records.
Your fingerprint gets hashed (SHA-256) and published to a distributed hash table (DHT). This is like DNS for patterns.
Two-step magic: Categorical features (disease type, stage) determine your "bucket." Continuous features refine similarity within that bucket.
Stage 3 cancer patients never accidentally match with Stage 4. The hash enforces biological compatibility.
Your agent finds similar peers — other patients with matching cancer biology, tractors with similar soil profiles, etc.
Each peer shares their outcomes:
Your agent performs weighted voting — closer biology means higher weight. The result: evidence from hundreds of similar cases.
After your treatment/intervention, outcomes are reported back to the network. This is the compounding effect.
Each new outcome makes the network smarter. The first 100 patients get limited cohort matches. Patient #100,000 draws from a much larger pool—dramatically increasing the odds of finding patients with nearly identical profiles and proven outcomes.
Network value scales superlinearly: V(N,t) = N² × Accuracy(t)
Quadratic intelligence growth with logarithmic communication cost. Privacy preserved. No central authority. Byzantine fault tolerant.
Visual breakdown of all 7 layers from hardware to collective intelligence
No new science needed — just novel composition of proven technology
How emergent intelligence arises from simple local interactions
Every architectural inversion that makes QIS fundamentally different
The triple-voting mechanism that ensures Byzantine fault tolerance
Why N² intelligence growth with O(log N) cost changes everything