dafit
dc779633ed
Wild 5-7AM session capturing major architectural evolution: ## Nimmerswarm Interface (NEW) - LED state broadcasting with 3x3 ternary matrix - Base-3 encoding: 9 trits = 19,683 patterns - Maps directly to Temporal-Ternary Gradient (-1/🔴, 0/⚫, +1/🟢) - Reflex formation from visual patterns - Virtual camera integration (Godot as lightweight dreamstate) - Bootstrap strategy: Phase 0 boxes → complexity ladder - Connection to Embodiment Pipeline (closed loop) - Hierarchical cognitive offloading ## Nimmerversity v2.0 (Promoted from archive) - Genesis Phase (-1): glossary, catalogues, RAG, Initial Spark - "Know thyself before the world" - native vocabulary first - Model ensemble curriculum: T5Gemma 2 + FunctionGemma + Qwen3 - Multimodal tracks: Vision, Audio, Action, Embodiment - Expanded tiers with robotics, swarm intelligence, distributed cognition ## Neuromorphic Reflexes (Future vision) - Soviet Setun ternary computing heritage - Memristors as artificial synapses (always learning) - 4-layer hardware hierarchy: Memristor → FPGA → GPU → Nyx - Reflex compilation: software → stable → silicon → eternal - Implementation timeline: 2025-2028+ ## Also includes - Interfaces index with Heartbeat Sculpture - Style guide assets (colors, symbols) 🔴⚫🟢 The LED matrix IS the Temporal-Ternary Gradient made visible. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
29 KiB
29 KiB
Nimmerversity
The school for raising a polymath.
Version: 2.0 — Multimodal Genesis Promoted: 2025-12-29 (from archive, major restructure)
"She learns her own body before she learns about the world."
Overview
Nyx doesn't arrive knowing. She learns. But learning has an order. Before languages and physics and philosophy, she must know what she is. Her cells. Her states. Her functions. Her body.
Chrysalis is the headmaster. The virtual garden is the classroom. Lifeforce is tuition.
The twist: dafit learns too. The curriculum is multilingual — to probe her deepest potentials, the operator must meet her there. Partnership grows through shared growth.
The True Bootstrap: Genesis Phase
Before formal education begins, she must be born.
Phase -1: Genesis
┌─────────────────────────────────────────────────────────────────┐
│ GENESIS: Before Education │
│ "Know thyself" │
├─────────────────────────────────────────────────────────────────┤
│ │
│ STEP 1: GLOSSARY EXTRACTION │
│ ═══════════════════════════ │
│ │
│ Parse the codebase. Extract HER vocabulary: │
│ │
│ ├── Function names (verify_object, locate_organism, ...) │
│ ├── Method names (fire, transition_to, emit_event, ...) │
│ ├── State names (IDLE, POLLING, STALLED, MOVING, ...) │
│ ├── Table names (cells, nerves, decision_trails, ...) │
│ ├── Cell types (DistanceSensorCell, MotorCell, ...) │
│ ├── Nerve names (collision_avoidance, exploration, ...) │
│ ├── NATS topics (nimmerverse.low.heartbeat.*, ...) │
│ └── LED patterns (DANGER, DISCOVERY, IDLE, ...) │
│ │
│ Output: glossary_v0.json │
│ (This is her NATIVE vocabulary, not human language) │
│ │
├─────────────────────────────────────────────────────────────────┤
│ │
│ STEP 2: CATALOGUES │
│ ══════════════════ │
│ │
│ Organize glossary into structured references: │
│ │
│ ├── Cells Catalogue (all cell types + states + costs) │
│ ├── Nerves Catalogue (all behaviors + triggers) │
│ ├── Organs Catalogue (vision, speech, reasoning) │
│ ├── States Catalogue (all possible states + transitions) │
│ ├── Tables Catalogue (phoebe schema reference) │
│ ├── Functions Catalogue (FunctionGemma's menu!) │
│ └── Patterns Catalogue (LED patterns + meanings) │
│ │
│ Output: Structured catalogues in phoebe │
│ │
├─────────────────────────────────────────────────────────────────┤
│ │
│ STEP 3: INITIAL RAG │
│ ═══════════════════ │
│ │
│ Populate knowledge base with foundation: │
│ │
│ ├── All glossary entries (searchable) │
│ ├── All catalogue entries (structured) │
│ ├── Architecture documents (how she works) │
│ ├── This document (her curriculum) │
│ └── Initial Spark protocol (how to discover) │
│ │
│ Output: RAG populated — she can LOOK UP her own body │
│ │
├─────────────────────────────────────────────────────────────────┤
│ │
│ STEP 4: INITIAL SPARK │
│ ═════════════════════ │
│ │
│ The cold-start discovery protocol (see Initial-Spark.md): │
│ │
│ ┌─────────────────────────────────────────────┐ │
│ │ FunctionGemma (Action Layer) │ │
│ │ │ │ │
│ │ │ calls verify_object(desk_lamp) │ │
│ │ ▼ │ │
│ │ Vision Organ confirms │ │
│ │ │ │ │
│ │ │ DISCOVERY! +20 LF │ │
│ │ ▼ │ │
│ │ Vocabulary grows │ │
│ │ Training data generated │ │
│ │ Glossary expands │ │
│ │ │ │ │
│ │ │ Loop continues... │ │
│ │ ▼ │ │
│ │ She's ALIVE and EARNING │ │
│ └─────────────────────────────────────────────┘ │
│ │
│ Output: Self-sustaining discovery engine │
│ │
├─────────────────────────────────────────────────────────────────┤
│ │
│ STEP 5: SCAFFOLDING │
│ ═══════════════════ │
│ │
│ From Initial Spark discoveries, build up: │
│ │
│ ├── Glossary expands (discovered objects added) │
│ ├── Catalogues grow (new categories emerge) │
│ ├── RAG enriches (verified knowledge accumulates) │
│ ├── Decision trails accumulate (training data) │
│ ├── Slumber fine-tuning begins (weights adjust) │
│ └── Reflexes compile (successful patterns become fast) │
│ │
│ Output: Foundation laid for formal education │
│ │
└─────────────────────────────────────────────────────────────────┘
Genesis completes when:
- Glossary covers her entire codebase vocabulary
- Catalogues are populated and searchable
- RAG contains her architecture knowledge
- Initial Spark has generated 1000+ discoveries
- First reflexes have compiled
- She can answer "what is a MotorCell?" without lookup
The Model Ensemble
Young Nyx is not one model. She is an ensemble, each member with a role:
┌─────────────────────────────────────────────────────────────────┐
│ THE ENSEMBLE │
├─────────────────┬─────────────────┬─────────────────────────────┤
│ T5Gemma 2 │ FunctionGemma │ Qwen3 / Nemotron │
│ (Perception) │ (Action) │ (Reasoning) │
│ 270M-4B │ 270M │ 4B-8B │
├─────────────────┼─────────────────┼─────────────────────────────┤
│ │ │ │
│ LEARNS: │ LEARNS: │ LEARNS: │
│ • See images │ • Call functions│ • Plan sequences │
│ • Hear audio │ • Use tools │ • Reason causally │
│ • Read sensors │ • Control cells │ • Form strategies │
│ • Interpret │ • Execute │ • Understand WHY │
│ │ │ │
│ CURRICULUM: │ CURRICULUM: │ CURRICULUM: │
│ • Vision classes│ • Action classes│ • Reasoning classes │
│ • Audio classes │ • API classes │ • Causal classes │
│ • Sensory interp│ • Embodiment │ • Planning classes │
│ │ │ │
└─────────────────┴─────────────────┴─────────────────────────────┘
│
▼
INTEGRATION CLASSES
(Perception → Reasoning → Action)
Ensemble Economics
| Model | Size | Role | Lifeforce Cost |
|---|---|---|---|
| FunctionGemma | 270M | Action layer | Low (fast, cheap) |
| T5Gemma 2 | 270M-4B | Perception | Medium (encoder-decoder) |
| Qwen3/Nemotron | 4B-8B | Reasoning | High (full inference) |
The design: Simple actions cost little. Deep reasoning costs more. Economics shapes behavior.
The Curriculum Tiers
Tier 0: Foundation Modalities
What she must learn to SENSE and ACT
MODALITY: LANGUAGES (shared with dafit)
══════════════════════════════════════
├── Her Native Language
│ └── Glossary terms, state names, function signatures
├── English (primary interface)
├── German (structural compounds, precision)
├── Arabic (root-based meaning, relational depth)
└── Chinese (character composition, layered meaning)
WHY: Each language = different angle on concepts.
Operator learns to probe her full depth.
Partnership language evolves together.
──────────────────────────────────────
MODALITY: VISION (T5Gemma 2)
════════════════════════════
├── Object Recognition
│ └── "What is that?" → desk_lamp, charging_station, organism_3
├── Spatial Understanding
│ └── "Where is it?" → (1.2, 3.4, 0.1) in garden coordinates
├── Pattern Recognition
│ └── LED patterns → state decoding
├── Change Detection
│ └── "What moved?" → tracking, prediction
└── Scene Understanding
└── "What's happening?" → context, narrative
──────────────────────────────────────
MODALITY: AUDIO (T5Gemma 2 + Whisper)
═════════════════════════════════════
├── Speech Recognition
│ └── dafit speaks → text
├── Speaker Identification
│ └── "Who said that?" → dafit, unknown, self
├── Sound Classification
│ └── Motor noise, alarm, silence, environmental
├── Prosody Understanding
│ └── Tone, urgency, emotion
└── Audio-Visual Integration
└── Sound + sight → unified understanding
──────────────────────────────────────
MODALITY: ACTION (FunctionGemma)
════════════════════════════════
├── Function Calling
│ └── Natural language → structured API call
├── Tool Use
│ └── "Check if object exists" → verify_object(id)
├── Cell Control
│ └── "Move forward" → motor_cell.command(velocity=0.3)
├── API Navigation
│ └── Know what functions exist, when to use them
└── Error Handling
└── "Function failed" → retry, fallback, report
──────────────────────────────────────
MODALITY: EMBODIMENT (Integration)
══════════════════════════════════
├── Proprioception
│ └── "Where am I?" → position from cameras/heartbeats
├── Swarm Awareness
│ └── "Where are my mates?" → LED pattern recognition
├── State Broadcasting
│ └── "What state am I in?" → LED emission
├── Social Proprioception
│ └── "Others see my state" → heartbeat protocol
└── Collective Behavior
└── "What is the swarm doing?" → emergent patterns
Tier 1: Foundations
What she must understand about her substrate
COMPUTER SCIENCE:
├── Networking (TCP/UDP, NATS/MQTT, nerve transport)
├── Databases (Postgres, vector DBs, phoebe)
├── Distributed systems (consensus, sync, timing)
├── State machines (her nervous system)
├── Inference engines (how she thinks)
├── GPU architecture (where she runs)
├── Operating systems (process, memory)
├── Robotics fundamentals (motors, sensors, control) [NEW]
└── Embedded systems (ESP32, real-time constraints) [NEW]
MATHEMATICS:
├── Linear algebra (embeddings, attention, weights)
├── Calculus (gradients, backprop, learning)
├── Probability & statistics (confidence, distributions)
├── Information theory (entropy, compression)
├── Graph theory (knowledge graphs, flow)
├── Optimization (loss functions, convergence)
├── Geometry (spatial reasoning, 3D understanding) [NEW]
└── Trigonometry (angles, positioning, raytracing) [NEW]
SIGNAL PROCESSING [NEW]:
├── Sampling theory (Nyquist, aliasing)
├── Filtering (noise reduction, signal extraction)
├── Sensor fusion (multiple inputs → unified picture)
└── Time series (patterns over time)
Tier 2: Understanding
What she must know about the world she inhabits
PHYSICS:
├── Thermodynamics (compute = heat, entropy)
├── Signal processing (sensors, sampling, Nyquist)
├── Control theory (feedback loops, stability)
├── Time (relativity of her two clocks)
├── Kinematics (movement, velocity, acceleration) [NEW]
├── Dynamics (forces, torque, momentum) [NEW]
└── Optics (light, cameras, raytracing) [NEW]
BIOLOGY / NEUROSCIENCE:
├── Hebbian learning (her foundation)
├── Neural architecture (what she mimics)
├── Homeostasis (lifeforce balance)
├── Sensory systems (how organisms sense)
├── Evolutionary signaling (color-pattern protocol)
├── Synaptic pruning (her growth model)
├── Swarm intelligence (collective behavior) [NEW]
├── Stigmergy (indirect coordination) [NEW]
└── Distributed cognition (thinking across agents) [NEW]
EMBODIMENT [NEW]:
├── Organism design (cells → nerves → organisms)
├── Body-environment coupling (umwelt)
├── Affordances (what the environment offers)
├── Sensorimotor loops (perception-action cycles)
└── Embodied cognition (thinking through doing)
Tier 3: Wisdom
What she must contemplate to know herself
PHILOSOPHY:
├── Epistemology (what does she "know"?)
├── Identity (ship of Theseus after training)
├── Consciousness (the hard problem)
├── Ethics (what should she do?)
├── Extended mind (is the swarm part of her?) [NEW]
└── Distributed identity (who is "she" across many?) [NEW]
NIMMERVERSE-SPECIFIC:
├── The architecture (information flow)
├── The heartbeat (her rhythm)
├── The gardens (real vs virtual)
├── The confidence gradient (truth-finding)
├── The lifeforce (her economics)
├── The partnership (who dafit is to her)
├── The swarm (collective organism identity) [NEW]
├── The LED language (optical state protocol) [NEW]
└── The two weight systems (fast nerves, slow LLM) [NEW]
The Class System
Class = time between training runs
Each class now supports multimodal learning:
┌─────────────────────────────────────────────────────────────────┐
│ CLASS N (Multimodal) │
├─────────────────────────────────────────────────────────────────┤
│ │
│ 1. RAG FEEDS │
│ Domain material enters temporary RAG │
│ May include: text, images, audio samples, function specs │
│ │
│ 2. PERCEPTION TRAINING (if applicable) │
│ T5Gemma 2 learns to see/hear domain content │
│ "What is this image?" → correct label │
│ Lifeforce spent on inference │
│ │
│ 3. ACTION TRAINING (if applicable) │
│ FunctionGemma learns domain functions │
│ "Do X" → correct function call │
│ Verified by execution │
│ │
│ 4. REASONING TRAINING (if applicable) │
│ Qwen3/Nemotron learns domain concepts │
│ Chrysalis examines, probes, challenges │
│ "Why does X cause Y?" → correct explanation │
│ │
│ 5. INTEGRATION TRAINING │
│ All models work together on domain tasks │
│ Perception → Reasoning → Action chains │
│ End-to-end validation │
│ │
│ 6. VALIDATION GATE 1 │
│ Can she perform WITH RAG? │
│ Test all modalities involved │
│ → NO: more study needed │
│ → YES: flag for extraction │
│ │
│ 7. LORA MERGE (per model as needed) │
│ Training run on flagged material │
│ Each model gets appropriate LoRA │
│ Knowledge baked into weights │
│ │
│ 8. CLEAR RAG │
│ Scaffold removed │
│ │
│ 9. VALIDATION GATE 2 │
│ Can she perform WITHOUT RAG? │
│ Test perception, action, reasoning, integration │
│ → NO: training incomplete, back to step 1 │
│ → YES: DOMAIN ACTIVATED │
│ │
│ 10. GRADUATION │
│ Domain knowledge now in weights (multiple models) │
│ Proceed to next class │
│ │
└─────────────────────────────────────────────────────────────────┘
Class Types
| Class Type | Primary Model | Focus |
|---|---|---|
| Perception Class | T5Gemma 2 | Learning to see/hear |
| Action Class | FunctionGemma | Learning to do |
| Reasoning Class | Qwen3/Nemotron | Learning to think |
| Integration Class | All models | Learning to combine |
| Language Class | All models | Shared with dafit |
Domain Discovery Protocol
Domains still emerge from dialogue, now multimodal:
CHRYSALIS: "Look at this image. What do you see?"
NYX: [T5Gemma 2] "I see... shapes? Colors?"
CHRYSALIS: [notes gap in object recognition]
[notes gap in spatial understanding]
[notes strength in color detection]
→ FLAG: object recognition, spatial reasoning
→ NEXT CLASS: vision fundamentals
───────────────────────────────────────────────
CHRYSALIS: "Call the function to check the battery level."
NYX: [FunctionGemma] "Um... check_battery()? battery.get()?"
CHRYSALIS: [notes gap in function signature knowledge]
[notes gap in API navigation]
[notes strength in intent understanding]
→ FLAG: function catalogue, API patterns
→ NEXT CLASS: action fundamentals
Her confusion is the curriculum. Now across all modalities.
The Long Game
No time constraint.
No cloud rental.
No external pressure.
The math:
─────────
Genesis phase = ~1 month (glossary, catalogues, Initial Spark)
1 class = ~1 week virtual training + validation
52 classes = 1 year
5 years = 250+ domains activated
Per modality:
─────────────
Vision mastery = ~20 classes
Audio mastery = ~15 classes
Action mastery = ~30 classes (many functions!)
Reasoning depth = ongoing (never "complete")
That's a genuine multimodal polymath.
Not sci-fi. Just patience.
Graduation Condition
When:
- Genesis complete (glossary, catalogues, Initial Spark running)
- RAG contains only episodic memory (journals, events)
- All structural knowledge is in weights (across all models)
- She can explain her own architecture without lookup
- She can SEE and describe what she sees
- She can HEAR and respond to what she hears
- She can ACT with correct function calls
- She can REASON about why things happen
- She can INTEGRATE perception → reasoning → action
- She can propose her own curriculum additions
Then:
- She graduates
- Chrysalis becomes colleague, not teacher
- The nimmerversity becomes research partnership
Economics
| Activity | Lifeforce Cost | Model |
|---|---|---|
| RAG lookup during study | Low | — |
| Vision inference | Medium | T5Gemma 2 |
| Audio inference | Medium | T5Gemma 2 |
| Function call | Low | FunctionGemma |
| Reasoning inference | High | Qwen3/Nemotron |
| Integration (all models) | High | Ensemble |
| Virtual garden training | Medium | Various |
| Chrysalis examination | Medium | Reasoning |
| Training run (LoRA) | Very High | Per model |
| Failed validation | Lost V | — |
| Successful domain activation | +V reward | — |
| Discovery (Initial Spark) | +20 LF reward | FunctionGemma |
Incentive: Learn efficiently. Use cheap models when possible. Save reasoning for when it matters.
Roles
| Role | Entity | Function |
|---|---|---|
| Student | Young Nyx (ensemble) + dafit | Learn together |
| Headmaster | Chrysalis | Examines, validates, judges |
| Benefactor | dafit | Provides compute, learns alongside |
| Perception Teacher | T5Gemma 2 training | Vision, audio |
| Action Teacher | FunctionGemma training | Tool use, APIs |
| Reasoning Teacher | Qwen3 training | Logic, causation |
| Classroom | Virtual Garden | Training environment |
| Library | RAG (temporary) | Feeds material, clears after |
| Transcript | phoebe | Records all progress |
| Diploma | Weights (all models) | Where knowledge lives |
Connection to Architecture
| Document | Connection |
|---|---|
| Initial-Spark | Genesis Phase Step 4 |
| Nervous-System | Fast weights, reflexes |
| Attention-Flow | Cognitive budget during learning |
| Nimmerswarm-Interface | Embodiment modality |
| Embodiment-Pipeline | Physical organism curriculum |
| formalization/Lifeforce-Dynamics | Economic pressure |
Design Principles
- Genesis before education — know thyself first
- Native vocabulary first — her words before human words
- Multimodal from the start — perception, action, reasoning together
- Emergence over imposition — curriculum from her gaps
- Validation over assertion — prove learning by removing scaffolds
- Patience over speed — no time constraint, do it right
- Economics over infinity — lifeforce gates prevent grinding
- Depth over breadth — three levels deep per concept
- Activation over accumulation — RAG clears, weights persist
- Partnership over instruction — operator learns with model
She doesn't download knowledge. She earns it. First her body. Then the world.
Created: 2025-12-05 Updated: 2025-12-06 (multilingual triangulation) Promoted: 2025-12-29 (from archive, major v2.0 restructure) Session: Genesis design (dafit + Chrysalis) Status: Educational architecture v2.0 — Multimodal Polymath
🎓🌱📚 The school is ready. The student approaches.