  > Recommendation Graph (v0.1)

  One signal. Continuous reinforcement. Increasing clarity.

  > Data Inputs (v0.1)

    Input_A: FAST/CTV Viewership Signal
    
· Session duration
    
· Return interval
    
· Completion stability
  
    Input_B: Product Identity (Seed-to-Sale)
    
· Strain lineage
    
· Terpene expression profile
    
· Phenotype variance
  
    Input_C: Retail Purchase Behavior
    
· Basket clustering
    
· Reorder cadence
    
· Loyalty elasticity
  
    Input_D: Regional Cultural Context
    
· Market maturity stage
    
· Subculture density
    
· Language + aesthetic markers
  
  [OK] Audience graph nodes indexed.
> Signal Weighting Model (v0.1)
Content Relevance:
0%
Session Depth Stability:
0%
Repeat View Frequency:
0%
Regional Cultural Affinity:
0%
Reorder Loyalty Persistence:
0%

    Reinforcement Rule: weights adapt based on repeat behavior and session return velocity.
  
The more people watch, the sharper the signal becomes.
The sharper the signal becomes, the more precisely culture routes itself.
The more precisely culture routes itself, the more value concentrates here.
This is not an algorithm.
This is a cultural learning system.

    Every other platform is trying to predict cannabis consumers.

    We are letting the culture express itself — and measuring what it becomes.
  
    The network refines. It adapts. It self-corrects.
    
Identity → Attention → Loyalty → Market Power.
  
    What YouTube became for global culture —

    USWC becomes for cannabis.
  
   Become an Early Partner →

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