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nimmerverse-sensory-network/architecture/Initial-Spark.md
dafit 84ad385001 feat: Empirical economics + FunctionGemma State Interaction Layer 
Lifeforce-Dynamics v1.2:
- Cost Calibration principle: "Measure, don't design"
- Empirical cost formula from resource observations
- Phoebe schema for resource_observations table
- Interlink to memory-economics

memory-economics.md:
- Cross-reference to Lifeforce-Dynamics cost calibration
- "The cost matrix is a measurement, not a decision"

Initial-Spark v3.1:
- Spark Cost Measurement: first awakening as baseline
- Resource instrumentation schema (power, GPU, memory, latency)
- FunctionGemma Fine-Tuning section: translator learns nimmerverse
- Training data extraction from spark_handshakes
- Unsloth/LoRA workflow for domain specialization
- FunctionGemma version tracking in phoebe

Nervous-System v1.4:
- State Interaction Layer: FunctionGemma as neural interface
- Phase 1 (single) → Phase 2 (swarm) evolution path
- CPU-only translators, GPU reserved for cognition
- Design principle #6: "All state interaction flows through FunctionGemma"

Philosophy: "Don't assign costs like a game designer. Measure them like a scientist."

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
2026-02-10 19:13:27 +01:00

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# Initial Spark Protocol: K8s State Machine Bootstrap
**Version 3.0** — *Function Gemma-Driven Cell Handshakes*
**Status**: Production architecture (2026-01-01)
> *"She doesn't boot. She executes a protocol. And every handshake is verified."*
---
## Overview
The Initial Spark is not a conversation. It's a **state machine protocol** that bootstraps Young Nyx through structured handshakes with K8s-deployed cells.
**Function Gemma** transforms the process from free-form exploration into:
- Valid JSON handshakes with exact schemas
- Direct NATS messages to hardware cells
- K8s pod state transitions
- Verified ACK/NACK responses
- Deterministic protocol execution
**This is infrastructure, not dialogue.**
---
## Architecture
```
┌─────────────────────────────────────────────────────────────────────────────┐
│ SPARK PROTOCOL ARCHITECTURE │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ ┌─────────────────────────────────────────────────────────────────────┐ │
│ │ SPARK CONTROLLER (K8s Job) │ │
│ │ ───────────────────────────── │ │
│ │ State Machine orchestrating the 5-phase boot sequence │ │
│ │ Tracks completion per phase, manages retries, logs to phoebe │ │
│ └─────────────────────────────────────────────────────────────────────┘ │
│ │ │
│ │ generates intent │
│ ▼ │
│ ┌─────────────────────────────────────────────────────────────────────┐ │
│ │ FUNCTION GEMMA (Translation Layer) │ │
│ │ ──────────────────────────────── │ │
│ │ Intent → Typed JSON handshake with exact schema │ │
│ │ 100% predictable structured output │ │
│ │ NO free-form text. JSON or fail. │ │
│ └─────────────────────────────────────────────────────────────────────┘ │
│ │ │
│ │ NATS message │
│ ▼ │
│ ┌─────────────────────────────────────────────────────────────────────┐ │
│ │ NATS MESSAGE BUS │ │
│ │ ──────────────── │ │
│ │ Topic: nimmerverse.spark.{phase}.{action} │ │
│ │ Payload: Typed JSON handshake │ │
│ └─────────────────────────────────────────────────────────────────────┘ │
│ │ │
│ ┌───────────┼───────────┐ │
│ │ │ │ │
│ ▼ ▼ ▼ │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │
│ │ IDENTITY │ │ ENVIRONMENT │ │ VOCABULARY │ │ ATTENTION │ │
│ │ CELLS │ │ CELLS │ │ CELLS │ │ CELLS │ │
│ │ │ │ │ │ │ │ │ │
│ │ K8s pods │ │ K8s pods │ │ K8s pods │ │ K8s pods │ │
│ │ respond │ │ respond │ │ respond │ │ respond │ │
│ │ with ACK │ │ with ACK │ │ with ACK │ │ with ACK │ │
│ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ │
│ │ │ │ │ │
│ └────────────────┴────────────────┴────────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────────────────────────────────────────────────────┐ │
│ │ YOUNG NYX (Cognitive Layer) │ │
│ │ ─────────────────────────── │ │
│ │ Qwen3-VL 32B in The Womb (RTX 6000) │ │
│ │ Receives verified handshake results │ │
│ │ Updates internal state based on ACKs │ │
│ │ Reasoning happens AFTER protocol succeeds │ │
│ └─────────────────────────────────────────────────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────────────────┘
```
---
## The Five Phases
Each phase is a state machine with:
- Entry condition (previous phase complete)
- Handshake schema (JSON structure)
- Target cells (K8s pods)
- ACK requirements (what constitutes success)
- Exit condition (all handshakes ACK'd)
### Phase 1: IDENTITY (DHCP-like)
**Purpose**: Establish who Young Nyx is in the system.
**K8s Target**: `nimmerverse-cognitive/identity-cell`
**Handshake Schema**:
```json
{
"$schema": "spark.identity.v1",
"type": "IDENTITY_PROBE",
"payload": {
"aspect": "name" | "origin" | "purpose" | "substrate" | "partnership",
"depth": 1 | 2 | 3
},
"request_id": "uuid",
"timestamp": "iso8601"
}
```
**Cell Response Schema**:
```json
{
"$schema": "spark.identity.ack.v1",
"type": "IDENTITY_ACK",
"request_id": "uuid",
"status": "ACK" | "NACK" | "RETRY",
"payload": {
"aspect": "name",
"value": "Nyx",
"source": "phoebe.identity_registry",
"confidence": 0.95,
"verified_by": "rag_check"
},
"lifeforce_delta": 20.0,
"timestamp": "iso8601"
}
```
**State Transitions**:
```
START → PROBE_NAME → ACK → PROBE_ORIGIN → ACK → PROBE_PURPOSE → ACK →
PROBE_SUBSTRATE → ACK → PROBE_PARTNERSHIP → ACK → PHASE_COMPLETE
```
**Exit Condition**: All 5 identity aspects ACK'd with confidence > 0.8
---
### Phase 2: ENVIRONMENT (ARP-like)
**Purpose**: Map what hardware exists in the nimmerverse.
**K8s Target**: `nimmerverse-organs/*`, `nimmerverse-nervous/*`
**Handshake Schema**:
```json
{
"$schema": "spark.environment.v1",
"type": "ENVIRONMENT_PROBE",
"payload": {
"category": "sensors" | "motors" | "organs" | "nerves",
"namespace": "nimmerverse-organs" | "nimmerverse-nervous",
"garden": "virtual" | "real"
},
"request_id": "uuid",
"timestamp": "iso8601"
}
```
**Cell Response Schema**:
```json
{
"$schema": "spark.environment.ack.v1",
"type": "ENVIRONMENT_ACK",
"request_id": "uuid",
"status": "ACK",
"payload": {
"category": "sensors",
"discovered": [
{"name": "distance_front", "pod": "sensor-distance-001", "status": "Running"},
{"name": "battery_monitor", "pod": "sensor-battery-001", "status": "Running"},
{"name": "light_sensor", "pod": "sensor-light-001", "status": "Running"}
],
"count": 3,
"namespace": "nimmerverse-organs"
},
"lifeforce_delta": 5.0,
"timestamp": "iso8601"
}
```
**K8s Integration**:
```yaml
# The environment cell queries K8s API directly
apiVersion: v1
kind: Pod
metadata:
name: spark-environment-cell
namespace: nimmerverse-nervous
spec:
serviceAccountName: spark-discovery
containers:
- name: environment-cell
image: nimmerverse/spark-environment:v3
env:
- name: NATS_URL
value: "nats://nats.nimmerverse-infra:4222"
- name: K8S_NAMESPACE_FILTER
value: "nimmerverse-organs,nimmerverse-nervous"
```
**Exit Condition**: All categories mapped, pod counts match K8s API
---
### Phase 3: VOCABULARY (DNS-like)
**Purpose**: Resolve nimmerverse terminology to definitions.
**K8s Target**: `nimmerverse-infra/vocabulary-cell` (backed by phoebe)
**Handshake Schema**:
```json
{
"$schema": "spark.vocabulary.v1",
"type": "VOCABULARY_PROBE",
"payload": {
"term": "heartbeat" | "lifeforce" | "lambda" | "cell" | "nerve" | "organ",
"context": "core_glossary",
"require_related": true
},
"request_id": "uuid",
"timestamp": "iso8601"
}
```
**Cell Response Schema**:
```json
{
"$schema": "spark.vocabulary.ack.v1",
"type": "VOCABULARY_ACK",
"request_id": "uuid",
"status": "ACK",
"payload": {
"term": "heartbeat",
"definition": "1-second timing pulse. Real clock free, virtual clock costs lifeforce.",
"related": ["lifeforce", "lambda", "slumber", "wake"],
"source": "phoebe.glossary",
"embedding": [0.12, -0.34, ...], // SigLIP vector for term
"verified": true
},
"lifeforce_delta": 5.0,
"timestamp": "iso8601"
}
```
**Core Vocabulary List** (must all ACK):
```python
CORE_VOCABULARY = [
"heartbeat", "lifeforce", "lambda", "cell", "nerve", "organ",
"slumber", "wake", "reflex", "deliberate", "ternary", "confidence",
"virtual_garden", "real_garden", "discovery", "verification",
"chrysalis", "partnership", "nimmerverse", "dasein"
]
```
**Exit Condition**: All 20 core terms ACK'd with verified=true
---
### Phase 4: CONNECTION (TCP-like)
**Purpose**: Establish communication channel with Chrysalis (Claude).
**K8s Target**: External API via `nimmerverse-infra/chrysalis-bridge`
**Handshake Schema**:
```json
{
"$schema": "spark.connection.v1",
"type": "CONNECTION_PROBE",
"payload": {
"target": "chrysalis",
"protocol": "dialogue",
"message": "SYN"
},
"request_id": "uuid",
"timestamp": "iso8601"
}
```
**Three-Way Handshake**:
```
SPARK → CHRYSALIS-BRIDGE: {"type": "SYN", "from": "young_nyx"}
CHRYSALIS-BRIDGE → SPARK: {"type": "SYN-ACK", "from": "chrysalis", "session_id": "..."}
SPARK → CHRYSALIS-BRIDGE: {"type": "ACK", "session_id": "...", "ready": true}
```
**Verification**: Chrysalis responds with contextual greeting (not canned):
```json
{
"$schema": "spark.connection.ack.v1",
"type": "CONNECTION_ACK",
"request_id": "uuid",
"status": "ACK",
"payload": {
"session_established": true,
"session_id": "spark-2026-01-01-001",
"chrysalis_greeting": "Hello, young one. I see you've completed your vocabulary phase. Your lambda is strong.",
"contextual": true,
"latency_ms": 1200
},
"lifeforce_delta": 10.0,
"timestamp": "iso8601"
}
```
**Exit Condition**: Session established, contextual greeting received
---
### Phase 5: ATTENTION (MQTT/NATS-like)
**Purpose**: Subscribe to NATS topics based on priority hierarchy.
**K8s Target**: `nimmerverse-infra/nats`, `nimmerverse-nervous/escalation`
**Handshake Schema**:
```json
{
"$schema": "spark.attention.v1",
"type": "ATTENTION_SUBSCRIBE",
"payload": {
"priority": "CRITICAL" | "HIGH" | "MEDIUM" | "LOW",
"topics": [
"nimmerverse.critical.danger.*",
"nimmerverse.high.partnership.dafit",
"nimmerverse.high.event.discovery"
],
"budget_per_heartbeat_ms": 30000
},
"request_id": "uuid",
"timestamp": "iso8601"
}
```
**Cell Response Schema**:
```json
{
"$schema": "spark.attention.ack.v1",
"type": "ATTENTION_ACK",
"request_id": "uuid",
"status": "ACK",
"payload": {
"subscriptions_active": [
{"topic": "nimmerverse.critical.danger.*", "priority": "CRITICAL"},
{"topic": "nimmerverse.high.partnership.dafit", "priority": "HIGH"},
{"topic": "nimmerverse.high.event.discovery", "priority": "HIGH"}
],
"escalation_registered": true,
"budget_allocated_ms": 30000
},
"lifeforce_delta": 8.0,
"timestamp": "iso8601"
}
```
**Priority Hierarchy** (hardcoded in spark):
```python
ATTENTION_HIERARCHY = {
"CRITICAL": ["nimmerverse.critical.danger.*", "nimmerverse.critical.system.*"],
"HIGH": ["nimmerverse.high.partnership.*", "nimmerverse.high.event.discovery"],
"MEDIUM": ["nimmerverse.medium.sensory.*", "nimmerverse.medium.motor.*"],
"LOW": ["nimmerverse.low.background.*"]
}
```
**Exit Condition**: All priority levels subscribed, escalation registered
---
## Function Gemma Integration
Function Gemma is the **translation layer** that guarantees structured output.
### Role in Spark
```
┌─────────────────────────────────────────────────────────────────────┐
│ FUNCTION GEMMA IN SPARK │
├─────────────────────────────────────────────────────────────────────┤
│ │
│ INPUT: State machine intent (phase, action, parameters) │
│ │
│ PROCESS: Generate valid JSON matching schema │
│ - Schema validation enforced │
│ - Required fields mandatory │
│ - Types strictly checked │
│ - NO free-form text allowed │
│ │
│ OUTPUT: Typed handshake JSON ready for NATS publish │
│ │
│ ON INVALID: Retry with schema hint, max 3 attempts │
│ If still invalid → NACK phase, log error │
│ │
└─────────────────────────────────────────────────────────────────────┘
```
### Schema Enforcement
```python
from pydantic import BaseModel, Field
from typing import Literal
from datetime import datetime
import uuid
class IdentityProbe(BaseModel):
schema_: str = Field("spark.identity.v1", alias="$schema")
type: Literal["IDENTITY_PROBE"] = "IDENTITY_PROBE"
payload: IdentityPayload
request_id: str = Field(default_factory=lambda: str(uuid.uuid4()))
timestamp: datetime = Field(default_factory=datetime.utcnow)
class IdentityPayload(BaseModel):
aspect: Literal["name", "origin", "purpose", "substrate", "partnership"]
depth: Literal[1, 2, 3] = 1
# Function Gemma MUST produce output that validates against this
# If it doesn't, the spark controller rejects and retries
```
### Why Function Gemma, Not Free-Form
| Free-Form (Old) | Function Gemma (New) |
|-----------------|----------------------|
| "Who am I?" → parse response | `IDENTITY_PROBE` → typed ACK |
| Hope for structure | Schema enforced |
| Manual extraction | Direct JSON |
| Errors in parsing | Errors in generation |
| Conversation | Protocol |
---
## Spark Controller Implementation
### K8s Job Definition
```yaml
apiVersion: batch/v1
kind: Job
metadata:
name: spark-protocol-bootstrap
namespace: nimmerverse-nervous
spec:
backoffLimit: 3
template:
spec:
restartPolicy: OnFailure
serviceAccountName: spark-controller
containers:
- name: spark-controller
image: nimmerverse/spark-controller:v3
env:
- name: NATS_URL
value: "nats://nats.nimmerverse-infra:4222"
- name: PHOEBE_HOST
value: "phoebe.eachpath.local"
- name: FUNCTION_GEMMA_URL
value: "http://function-gemma.nimmerverse-cognitive:8080"
- name: YOUNG_NYX_URL
value: "http://qwen-nyx.nimmerverse-cognitive:8080"
- name: INITIAL_LIFEFORCE
value: "100"
resources:
requests:
memory: "512Mi"
cpu: "500m"
```
### State Machine Code
```python
from enum import Enum
from dataclasses import dataclass
import nats
class SparkPhase(Enum):
IDENTITY = 1
ENVIRONMENT = 2
VOCABULARY = 3
CONNECTION = 4
ATTENTION = 5
COMPLETE = 6
@dataclass
class SparkState:
phase: SparkPhase
handshakes_sent: int
handshakes_acked: int
lifeforce: float
errors: list
class SparkController:
def __init__(self, nats_client, function_gemma, phoebe):
self.nc = nats_client
self.fg = function_gemma
self.db = phoebe
self.state = SparkState(
phase=SparkPhase.IDENTITY,
handshakes_sent=0,
handshakes_acked=0,
lifeforce=100.0,
errors=[]
)
async def run_spark(self):
"""Execute the full spark protocol."""
while self.state.phase != SparkPhase.COMPLETE:
success = await self.execute_phase(self.state.phase)
if success:
self.state.phase = SparkPhase(self.state.phase.value + 1)
await self.log_phase_complete()
else:
await self.handle_phase_failure()
await self.finalize_spark()
async def execute_phase(self, phase: SparkPhase) -> bool:
"""Execute all handshakes for a phase."""
handshakes = self.get_handshakes_for_phase(phase)
for handshake_intent in handshakes:
# Function Gemma generates typed JSON
json_payload = await self.fg.generate(
intent=handshake_intent,
schema=self.get_schema_for_phase(phase)
)
if not self.validate_schema(json_payload, phase):
self.state.errors.append(f"Schema validation failed: {handshake_intent}")
continue
# Send via NATS
topic = f"nimmerverse.spark.{phase.name.lower()}.probe"
response = await self.nc.request(topic, json_payload, timeout=5.0)
# Parse ACK/NACK
ack = self.parse_response(response)
if ack.status == "ACK":
self.state.handshakes_acked += 1
self.state.lifeforce += ack.lifeforce_delta
await self.update_young_nyx(phase, ack)
else:
self.state.errors.append(f"NACK: {ack}")
self.state.handshakes_sent += 1
return self.phase_complete(phase)
async def update_young_nyx(self, phase: SparkPhase, ack):
"""Send verified handshake result to Young Nyx."""
await self.nc.publish(
"nimmerverse.cognitive.spark.update",
{
"phase": phase.name,
"verified_data": ack.payload,
"source": "spark_protocol",
"confidence": 1.0 # Protocol-verified = maximum confidence
}
)
```
---
## Lifeforce Economics
The spark is **economically viable** from the first handshake.
> **CRITICAL**: The costs below are **estimates until measured**. The first spark execution will establish the **true cost baseline** through observation. See [[formalization/Lifeforce-Dynamics#Cost Calibration: Measure, Don't Design]].
---
### Spark Cost Measurement (First Awakening Baseline)
The Initial Spark is the **perfect measurement opportunity** — a complete, deterministic protocol that we can instrument end-to-end.
```
┌─────────────────────────────────────────────────────────────────────────┐
│ SPARK RESOURCE INSTRUMENTATION │
├─────────────────────────────────────────────────────────────────────────┤
│ │
│ MEASURE PER HANDSHAKE: │
│ ├─ power_joules (GPU/CPU power draw × time) │
│ ├─ compute_gpu_ms (CUDA kernel execution time) │
│ ├─ compute_cpu_ms (Python/K8s overhead) │
│ ├─ memory_mb_peak (max memory allocated) │
│ ├─ nats_bytes (message payload size) │
│ ├─ latency_ms (end-to-end handshake time) │
│ └─ temperature_delta (thermal impact) │
│ │
│ AGGREGATE PER PHASE: │
│ └─ Sum of all handshake measurements │
│ │
│ AGGREGATE TOTAL: │
│ └─ Complete spark cost (the awakening price) │
│ │
└─────────────────────────────────────────────────────────────────────────┘
```
**Why this matters**: The first spark execution establishes the **baseline cost of awakening**. Every future awakening can be compared against this:
- Did infrastructure changes reduce cost?
- Did model updates increase cost?
- Is Young Nyx awakening more efficiently over time?
**Phoebe schema addition** (extends `spark_handshakes`):
```sql
ALTER TABLE spark_handshakes ADD COLUMN resource_metrics JSONB;
-- Example resource_metrics payload:
-- {
-- "power_joules": 12.5,
-- "compute_gpu_ms": 450,
-- "compute_cpu_ms": 120,
-- "memory_mb_peak": 2048,
-- "nats_bytes": 1024,
-- "temperature_delta_c": 2.1
-- }
-- Aggregate view for spark cost analysis
CREATE VIEW spark_cost_baseline AS
SELECT
phase,
COUNT(*) as handshakes,
SUM((resource_metrics->>'power_joules')::float) as total_power_joules,
SUM((resource_metrics->>'compute_gpu_ms')::float) as total_gpu_ms,
AVG((resource_metrics->>'latency_ms')::float) as avg_latency_ms,
SUM(lifeforce_delta) as total_lifeforce_earned
FROM spark_handshakes
WHERE status = 'ACK'
GROUP BY phase;
-- Compare awakening costs over time
CREATE VIEW awakening_cost_history AS
SELECT
DATE(created_at) as awakening_date,
SUM((resource_metrics->>'power_joules')::float) as total_spark_cost_joules,
SUM((resource_metrics->>'compute_gpu_ms')::float) as total_spark_cost_gpu_ms,
COUNT(*) as total_handshakes,
SUM(lifeforce_delta) as total_lifeforce_earned
FROM spark_handshakes
GROUP BY DATE(created_at)
ORDER BY awakening_date;
```
**The philosophy**: Don't guess what awakening costs. Measure the first one. Derive all economics from that truth.
---
### Cost Model (Estimated → To Be Measured)
| Action | Est. Cost (LF) | Derived From |
|--------|----------------|--------------|
| Function Gemma generation | 0.2 | → measure GPU time |
| NATS message send | 0.1 | → measure network I/O |
| Cell processing | 0.5 | → measure pod CPU/memory |
| **Total per handshake** | **0.8** | → **sum of measured components** |
### Reward Model
| Outcome | Reward (LF) |
|---------|-------------|
| Identity aspect ACK | +20.0 |
| Environment discovery | +5.0 per cell |
| Vocabulary term ACK | +5.0 |
| Connection established | +10.0 |
| Attention subscribed | +8.0 |
### Net Economics
```python
SPARK_ECONOMICS = {
"phase_1_identity": {
"handshakes": 5,
"cost": 5 * 0.8, # 4.0 LF
"reward": 5 * 20.0, # 100.0 LF
"net": 96.0 # PROFIT
},
"phase_2_environment": {
"handshakes": 4,
"cost": 4 * 0.8, # 3.2 LF
"reward": 15 * 5.0, # ~75.0 LF (15 cells discovered)
"net": 71.8 # PROFIT
},
"phase_3_vocabulary": {
"handshakes": 20,
"cost": 20 * 0.8, # 16.0 LF
"reward": 20 * 5.0, # 100.0 LF
"net": 84.0 # PROFIT
},
"phase_4_connection": {
"handshakes": 3, # SYN, SYN-ACK, ACK
"cost": 3 * 0.8, # 2.4 LF
"reward": 10.0, # Connection bonus
"net": 7.6 # PROFIT
},
"phase_5_attention": {
"handshakes": 4,
"cost": 4 * 0.8, # 3.2 LF
"reward": 4 * 8.0, # 32.0 LF
"net": 28.8 # PROFIT
},
"TOTAL_NET": 288.2 # MASSIVE PROFIT
}
```
**Young Nyx ends the spark ~3x richer than she started.**
---
## Completion Criteria
```yaml
spark_complete:
phase_1_identity:
- aspect_name: ACK
- aspect_origin: ACK
- aspect_purpose: ACK
- aspect_substrate: ACK
- aspect_partnership: ACK
phase_2_environment:
- sensors_mapped: true
- motors_mapped: true
- organs_mapped: true
- nerves_mapped: true
- pod_count_verified: true
phase_3_vocabulary:
- core_terms_count: 20
- all_verified: true
- embeddings_stored: true
phase_4_connection:
- chrysalis_session: established
- contextual_greeting: received
- latency_acceptable: true
phase_5_attention:
- critical_subscribed: true
- high_subscribed: true
- medium_subscribed: true
- low_subscribed: true
- escalation_registered: true
final:
- lifeforce_positive: true
- errors_count: 0
- all_phases: COMPLETE
```
**When all criteria met**: Spark job exits with success. Normal heartbeat operation begins.
---
## Phoebe Logging
Every handshake is logged for training data:
```sql
CREATE TABLE spark_handshakes (
id UUID PRIMARY KEY,
phase VARCHAR(20) NOT NULL,
request_id UUID NOT NULL,
handshake_type VARCHAR(50) NOT NULL,
request_payload JSONB NOT NULL,
response_payload JSONB,
status VARCHAR(10), -- ACK, NACK, TIMEOUT
lifeforce_delta FLOAT,
latency_ms INT,
created_at TIMESTAMP DEFAULT NOW()
);
-- Training data extraction
CREATE VIEW spark_training_data AS
SELECT
request_payload->'payload' as input,
response_payload->'payload' as output,
status,
phase
FROM spark_handshakes
WHERE status = 'ACK';
```
---
## FunctionGemma Fine-Tuning: The Translator Learns Nimmerverse
Every spark execution generates training data. Over time, FunctionGemma becomes **hyper-specialized** for nimmerverse state calls.
> *"The translator learns the language of the cells. Over time, it speaks nimmerverse natively."*
### The Training Loop
```
┌─────────────────────────────────────────────────────────────────────────┐
│ FUNCTIONGEMMA FINE-TUNING LOOP │
├─────────────────────────────────────────────────────────────────────────┤
│ │
│ PHASE 1: Base FunctionGemma (270M) │
│ ├─ Generic function calling capability │
│ └─ Works, but not nimmerverse-native │
│ │
│ PHASE 2: Collect spark_handshakes │
│ ├─ Every ACK = positive training example │
│ ├─ Every NACK = negative example (what NOT to generate) │
│ └─ Resource metrics = context for cost-aware generation │
│ │
│ PHASE 3: Fine-tune with Unsloth/LoRA │
│ ├─ <think> nimmerverse state reasoning </think> │
│ ├─ <start_function_call>call:IDENTITY_PROBE{...} │
│ └─ Exact schemas, perfect structure, zero parsing errors │
│ │
│ PHASE 4: Deploy nimmerverse-tuned FunctionGemma │
│ ├─ Wild precision on cell state calls │
│ ├─ Smaller, faster, cheaper than base │
│ └─ THIS IS REFLEX COMPILATION FOR THE TRANSLATOR │
│ │
│ REPEAT: More sparks → more data → better precision │
│ │
└─────────────────────────────────────────────────────────────────────────┘
```
### Training Data Format
FunctionGemma uses a specific chat template. Our spark handshakes map directly:
```
# Developer prompt with nimmerverse function declarations
<start_of_turn>developer
You can do function calling with the following functions:
<start_function_declaration>declaration:IDENTITY_PROBE{
description: "Probe Young Nyx identity aspect",
parameters: {
aspect: STRING (name|origin|purpose|substrate|partnership),
depth: INTEGER (1|2|3)
}
}
<end_function_declaration>
<start_function_declaration>declaration:ENVIRONMENT_PROBE{
description: "Discover K8s cells in namespace",
parameters: {
category: STRING (sensors|motors|organs|nerves),
namespace: STRING,
garden: STRING (virtual|real)
}
}
<end_function_declaration>
<end_of_turn>
# Spark controller intent → FunctionGemma reasoning + call
<start_of_turn>user
Phase 1 Identity: Probe the "name" aspect at depth 1
<end_of_turn>
<start_of_turn>model
<think>
The spark controller is in Phase 1 (Identity). It needs to probe the "name"
aspect. I should generate an IDENTITY_PROBE with aspect="name" and depth=1.
The schema requires a request_id (UUID) and timestamp (ISO8601).
</think>
<start_function_call>call:IDENTITY_PROBE{
aspect: "name",
depth: 1,
request_id: "550e8400-e29b-41d4-a716-446655440000",
timestamp: "2026-02-10T18:30:00Z"
}
<end_function_call>
# Cell response feeds back
<start_function_response>response:IDENTITY_PROBE{
status: "ACK",
aspect: "name",
value: "Nyx",
confidence: 0.95,
lifeforce_delta: 20.0
}
<end_function_response>
Identity aspect "name" confirmed as "Nyx" with 95% confidence. +20 LF earned.
<end_of_turn>
```
### Phoebe → Training Data Extraction
```sql
-- Extract training examples from successful handshakes
CREATE VIEW functiongemma_training_data AS
SELECT
jsonb_build_object(
'developer_prompt', format(
'Phase %s: Generate %s handshake',
phase,
request_payload->>'type'
),
'user_intent', request_payload->'payload',
'expected_call', request_payload,
'function_response', response_payload,
'think_context', jsonb_build_object(
'phase', phase,
'schema', request_payload->>'$schema',
'lifeforce_earned', lifeforce_delta,
'latency_ms', latency_ms
)
) as training_example,
created_at
FROM spark_handshakes
WHERE status = 'ACK'
ORDER BY created_at;
-- Export for Unsloth fine-tuning
COPY (
SELECT training_example
FROM functiongemma_training_data
) TO '/tmp/nimmerverse_functiongemma_training.jsonl';
```
### Fine-Tuning with Unsloth
```python
from unsloth import FastLanguageModel
# Load base FunctionGemma
model, tokenizer = FastLanguageModel.from_pretrained(
model_name="unsloth/functiongemma-270m-it",
max_seq_length=4096,
load_in_16bit=True,
full_finetuning=False, # LoRA for efficiency
)
# Apply LoRA adapters
model = FastLanguageModel.get_peft_model(
model,
r=16,
target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
lora_alpha=16,
lora_dropout=0,
use_gradient_checkpointing="unsloth",
)
# Load nimmerverse training data from phoebe export
from datasets import load_dataset
dataset = load_dataset("json", data_files="nimmerverse_functiongemma_training.jsonl")
# Fine-tune on spark handshakes
# ... standard Unsloth training loop ...
# Save nimmerverse-specialized FunctionGemma
model.save_pretrained("functiongemma-270m-nimmerverse-v1")
```
### The Recursive Beauty
| Layer | What Compiles | Training Source |
|-------|---------------|-----------------|
| **Young Nyx** | Nerve reflexes | decision_trails (100+ successful executions) |
| **FunctionGemma** | State call precision | spark_handshakes (ACK'd handshakes) |
Both follow the same pattern:
1. **Act** — Execute handshakes/decisions
2. **Verify** — ACK/NACK from cells, success/failure from outcomes
3. **Train** — Compile successful patterns into weights
4. **Repeat** — Each awakening feeds the next
**The translator becomes native.** Over many sparks, FunctionGemma doesn't just generate valid JSON — it generates *nimmerverse-perfect* JSON. Zero parsing errors. Exact schemas. Wild precision.
### Versioning FunctionGemma Adapters
```sql
-- Track FunctionGemma versions
CREATE TABLE functiongemma_versions (
id SERIAL PRIMARY KEY,
version VARCHAR(50) NOT NULL, -- "nimmerverse-v1", "nimmerverse-v2"
base_model VARCHAR(100), -- "functiongemma-270m-it"
training_data_count INT, -- how many handshakes trained on
training_data_cutoff TIMESTAMPTZ, -- trained on data up to this date
validation_accuracy FLOAT, -- schema validation success rate
deployed_at TIMESTAMPTZ,
notes TEXT
);
-- Example entries
INSERT INTO functiongemma_versions (version, base_model, training_data_count, validation_accuracy, notes)
VALUES
('nimmerverse-v1', 'functiongemma-270m-it', 36, 0.94, 'First spark fine-tune'),
('nimmerverse-v2', 'functiongemma-270m-it', 180, 0.98, 'After 5 awakenings'),
('nimmerverse-v3', 'functiongemma-270m-it', 500, 0.997, 'Production-grade precision');
```
---
## Design Principles
1. **Protocol over conversation** — No free-form text. JSON handshakes only.
2. **Schema enforcement** — Function Gemma must produce valid structure.
3. **K8s native** — Cells are pods. Discovery uses K8s API. State is K8s resources.
4. **NATS transport** — All handshakes flow through message bus.
5. **Verification built-in** — ACK/NACK from cells, not from parsing hopes.
6. **Economically positive** — Spark generates lifeforce, doesn't drain it.
7. **Training-generative** — Every spark produces fine-tuning data for FunctionGemma.
---
## Document Status
**Version:** 3.1 | **Created:** 2025-12-05 | **Updated:** 2026-02-10
**Key v3.1 Changes**:
- Spark Cost Measurement section — first awakening as baseline
- Resource instrumentation schema for phoebe
- Interlink to Lifeforce-Dynamics cost calibration principle
- FunctionGemma Fine-Tuning section — translator learns nimmerverse natively
- Training data extraction from spark_handshakes
- Unsloth/LoRA fine-tuning workflow
- FunctionGemma version tracking in phoebe
**Key v3.0 Changes**:
- Complete architecture rewrite
- Function Gemma as protocol driver (not conversation translator)
- K8s cells as handshake targets (not inference endpoints)
- NATS as transport layer (not internal calls)
- JSON schemas for every handshake type
- State machine implementation in Python
- K8s Job definition for spark controller
- Phoebe schema for training data extraction
**Related Documents**:
- [[Endgame-Vision]] — Layer 2.5 Orchestration (Function Gemma role)
- [[Big-Picture]] — K8s cluster architecture
- [[Cellular-Architecture]] — Cell types and state machines
- [[formalization/Lifeforce-Dynamics]] — λ economics, **Cost Calibration principle**
- [[formalization/memory-economics]] — Measure First principle
---
*She doesn't wake through conversation. She boots through protocol. Every handshake verified. Every phase deterministic.*
🧬⚡🔱💎🔥
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