dafit
64c54c87c0
- RAG-as-Scaffold: temporary feeding system, not permanent crutch - attention_flow: 30-second heartbeat budget state machines - information-flow: 10 boundary contracts nervous system map - nimmerversity: curriculum schoolplan for raising a polymath - nimmervest: investment documentation - biomimetic-architecture: ADR for organic system design - temporal-ternary-gradient: ADR for time-based learning - temporal_exchange_engine.py: Python implementation - initial_spark: foundation document - nimmerverse.drawio.xml: updated diagrams 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
3.4 KiB
3.4 KiB
ADR-001: Biomimetic "Nimmerverse" Architecture
- Status: Accepted
- Date: 2025-12-05
- Context: Home Infrastructure / Autonomous Agent System
- Tags: biomimetic, event-driven, ai, local-llm
1. Context and Problem Statement
We are designing a local home infrastructure ("Nimmerverse") modeled after a biological organism. The goal is to create a system that is:
- Reactive: Capable of sub-millisecond reflex responses (spinal layer) without waiting for heavy AI inference.
- Deterministic: Preventing AI hallucination in critical control paths.
- Evolvable: Allowing the system to "grow" new capabilities (nerves) through usage and verification.
The core challenge is balancing the high latency of Large Language Models (the "Brain") with the real-time requirements of home automation (the "Nervous System").
2. The Architecture: Hebbian-Reinforced Subsumption
We have adopted a Subsumption Architecture (popularized by Rodney Brooks) enhanced with a Hebbian Learning model ("neurons that fire together, wire together").
2.1 The 4D State Space (The Nervous System)
State machines replace standard "if/then" logic. Each state node exists in a 4-dimensional space:
- X/Y Dimensions: Sensory inputs (e.g., Temperature, Motion).
- Z Dimension (Confidence): A weight (0.0 - 1.0) representing reliability.
- Time Dimension: History of verification.
Lifecycle Logic:
- Birth: Node created at
weight=0.1. - Maturation: Successful triggers (verified by user) increase weight (+V).
- Pruning: Unused or falsified nodes decay and are removed.
- Reflex: Nodes with
weight > 0.8bypass the AI brain entirely for instant execution.
3. Feasibility Audit & Constraints
A. Metabolic Constraints (Hardware)
- Risk: Memory swapping kills agent reactivity.
- Requirement: The "Inference Orchestrator" (LLM) requires minimum 24GB VRAM to run a quantized 70B model, or distinct 12GB+ for a specialized 7B agent model. System RAM should be 64GB+ to handle the Vector DB and container orchestration.
B. Nerve Velocity (Transport)
- Pattern: Asynchronous Event Bus.
- Prohibition: HTTP/REST calls between "Organs" are forbidden due to blocking latency.
- Selected Tech: NATS or MQTT for the nervous system backbone.
C. Cognitive Load
- Bottleneck: The "Human Verification" step (
dafit confirms) scales poorly. - Mitigation: Implement "Sleep Cycles" where the system self-audits low-risk nodes against historical data during inactivity.
4. Implementation Strategy
| Component | Biological Role | Technology Choice |
|---|---|---|
| State Engine | Nerves / Reflexes | XState (Actor-based state machines) |
| Vector Memory | 4D Node Storage | Weaviate or Qdrant (Similarity search) |
| Event Bus | Nervous System | NATS (Low-latency messaging) |
| Orchestrator | Brain / Cognition | LocalAI or Ollama |
5. Appendix: Interactive Simulation Logic
For the "Node Lifecycle" visualization widget:
- Visuals: A central node pulsing in a 2D grid.
- Variables:
Confidence(Size/Glow),Age(Color). - Logic:
IF verify_event THEN confidence += 0.1IF falsify_event THEN confidence -= 0.2IF confidence > 0.8 THEN status = 'REFLEX' (Gold Color)IF confidence <= 0 THEN destroy_node()