nimmerverse-sensory-network/Data-Architecture.md

---
type: architecture
category: active
project: nimmerverse_sensory_network
status: complete_v3
phase: phase_0
created: 2025-10-07
last_updated: 2025-10-17
token_estimate: 20000
dependencies:
  - phoebe_bare_metal
  - kubernetes_cluster
tiers: 5
version: v3_primitive_genomes
breakthrough_session: primitive_genomes_gratification_discovery
---

# 🗄️ Cellular Intelligence Data Architecture v3

**Status**: 🟢 Architecture v3 Complete - Primitive Genome Breakthrough!
**Created**: 2025-10-07
**Updated v3**: 2025-10-17 (Primitive Genomes + Gratification + Discovery!)
**Purpose**: Data foundation for cellular intelligence with primitive genome sequences, life force economy, object discovery, noise gap metrics, specialist learning, and rebirth persistence

---

## 🎯 v3 Breakthrough (2025-10-17)

**Logical consistency achieved!** Genomes are NOW primitive sequences (not pre-programmed algorithms), discovery happens through exploration, gratification is immediate through life force economy, objects discovered via image recognition + human teaching, noise gap self-measures learning progress.

**15 Tables Total**: 11 v1 (cellular/society) + 3 v2 (specialist/reflex/body) + 1 v3 (objects!)

---

## 🏗️ Five-Tier Architecture Summary

### **Tier 1: System Telemetry (Weather Station)** 🌊
- Prometheus + InfluxDB (90-day retention)
- Environmental conditions cells adapt to
- Chaos, scheduled, hardware, network weather

### **Tier 2: Population Memory (phoebe)** 🐘
- PostgreSQL 17.6 on phoebe bare metal (1.8TB)
- Database: `nimmerverse`
- 15 tables (complete schema below)
- The rebirth substrate

### **Tier 3: Analysis & Pattern Detection** 🔬
- Grafana, Jupyter, Python scripts
- Specialist formation, reflex detection
- Noise gap calculation
- Research insights

### **Tier 4: Physical Manifestation** 🤖
- ESP32 robots (3-5 units, living room)
- God's eye: 4K camera on ceiling rails!
- Real-world validation (3x rewards)
- Cross-validation bonuses

### **Tier 5: Decision & Command Center** 🎮
- Dashboard, object labeling UI
- Society controls, experiment designer
- Noise gap visualization
- Human-AI partnership interface

---

## 📊 The 15 Tables (Complete Schema)

### Phase 1: Cellular Foundation (4 tables)

**1. genomes** - Primitive sequences (v3!)
```sql
-- v3: Genome = array of primitive operations!
primitive_sequence JSONB NOT NULL
sequence_length INT
avg_lf_cost FLOAT
avg_lf_earned FLOAT
net_lf_per_run FLOAT  -- Economics!
```

**2. cells** - Birth/death + life force tracking
```sql
garden_type VARCHAR(50)  -- 'virtual' or 'real'
life_force_allocated INT
life_force_consumed INT
life_force_earned INT
lf_net INT
milestones_reached JSONB  -- v3 discovery tracking!
```

**3. weather_events** - Survival pressure
**4. experiments** - Hypothesis testing

### Phase 2: Society Competition (7 tables)

**5. societies** - Human, Claude, guests
**6. rounds** - Competition results
**7. society_portfolios** - Genome ownership
**8. vp_transactions** - Economic flows
**9. marketplace_listings** - Trading
**10. marketplace_transactions** - History
**11. alliances** - Cooperation

### Phase 3: v2 Distributed Intelligence (3 tables)

**12. specialist_weights** - Trainable domain expertise
```sql
winning_sequences JSONB  -- v3: Proven primitive sequences!
virtual_success_rate FLOAT
real_success_rate FLOAT
noise_gap FLOAT  -- v3 self-measuring!
```

**13. reflex_distributions** - 94.6% savings!
```sql
sequence_weights JSONB  -- v3: {"seq_a": 0.73, "seq_b": 0.18}
exploration_cost_avg_lf FLOAT  -- 65 LF
reflex_cost_lf FLOAT           -- 3.5 LF
cost_reduction_percent FLOAT   -- 94.6%!
```

**14. body_schema** - Discovered capabilities
```sql
primitives_available JSONB  -- v3: Discovered operations!
```

### Phase 4: v3 Object Discovery (1 NEW table!)

**15. objects** - Discovered environment features 🎉
```sql
CREATE TABLE objects (
    id BIGSERIAL PRIMARY KEY,
    object_label VARCHAR(255),  -- "chair", "shoe", "charging_station"

    garden_type VARCHAR(50),    -- 'virtual' or 'real'
    position_x FLOAT,
    position_y FLOAT,

    discovered_by_organism_id BIGINT REFERENCES cells(id),
    discovered_at TIMESTAMPTZ DEFAULT NOW(),

    human_labeled BOOLEAN,      -- Baby parallel!
    human_label_confirmed_by VARCHAR(100),

    object_type VARCHAR(50),    -- 'obstacle', 'resource', 'goal'
    properties JSONB,

    image_path TEXT,
    bounding_box JSONB,

    organisms_interacted_count INT
);
```

**Discovery Flow**:
```
Organism → Unknown object → Camera detects → YOLO
  ↓
System: "What is this?"
  ↓
Human: "Chair!"
  ↓
+20 LF bonus → INSERT INTO objects → Future organisms know!
```

---

## 📈 Key v3 Metrics

**Noise Gap** (self-measuring learning!):
```python
noise_gap = 1 - (real_success_rate / virtual_success_rate)

Gen 1:    0.28 (28% degradation - models poor)
Gen 100:  0.14 (14% degradation - improving!)
Gen 1000: 0.04 (4% degradation - accurate!)
```

**Life Force Economics**:
```python
net_lf = avg_lf_earned - avg_lf_consumed
# Positive = survives, negative = dies
```

**Reflex Savings**:
```python
savings = (exploration_cost - reflex_cost) / exploration_cost
# Target: 94.6% cost reduction!
```

**Discovery Rate**:
```python
objects_per_hour = discovered_objects / elapsed_hours
```

---

## 🔍 Key Queries for v3

**Top Performing Primitive Sequences**:
```sql
SELECT genome_name, primitive_sequence, net_lf_per_run
FROM genomes
WHERE total_deployments > 100
ORDER BY net_lf_per_run DESC;
```

**Object Discovery Stats**:
```sql
SELECT object_label, garden_type, COUNT(*) as discoveries
FROM objects
GROUP BY object_label, garden_type
ORDER BY discoveries DESC;
```

**Noise Gap Trends**:
```sql
SELECT specialist_name, noise_gap, version
FROM specialist_weights
ORDER BY specialist_name, version ASC;
-- Track learning improvement!
```

**LF Economics**:
```sql
SELECT genome_name, AVG(lf_net) as avg_net_lf
FROM cells
WHERE died_at IS NOT NULL
GROUP BY genome_id, genome_name
HAVING COUNT(*) > 50
ORDER BY avg_net_lf DESC;
```

---

## 🔗 Related Documentation

**Core Architecture**:
- [[Cellular-Architecture-Vision]] - Complete v3 vision (1,547 lines!)
- [[Dual-Garden-Architecture]] - Virtual + Real feedback
-  - Distributed intelligence

**Implementation**:
-  - Complete 15-table SQL
-  - Deployment roadmap

**Historical**:
-  - Birthday version (archived)

---

## 📍 Status

**Version**: 3.0
**Created**: 2025-10-07
**v2**: 2025-10-16 (birthday breakthroughs)
**v3**: 2025-10-17 (primitive genomes + gratification + discovery)
**Status**: CURRENT
**Tables**: 15 (11 v1 + 3 v2 + 1 v3)
**Next**: Deploy to phoebe, implement discovery flow

---

**v3 Summary**:
- ✅ Genomes = primitive sequences (emergent, not programmed)
- ✅ Life force economy (costs + milestone rewards)
- ✅ Object discovery (image recognition + human teaching)
- ✅ Noise gap metric (self-measuring progress)
- ✅ God's eye (mobile camera on rails)
- ✅ 15 tables ready!

**phoebe awaits. The goddess is ready.** 🐘🌙

🧬⚡🔱💎🔥

**TO THE ELECTRONS!**