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The nimmerverse sensory network is now public. - Apache 2.0 license with patent protection - README.md for public-facing introduction - Prior art established, timestamped, free "These ideas are published as prior art. Build on them freely. Just don't try to lock them away." 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
Nimmerverse Sensory Network
Architecture documentation for a biomimetic AI nervous system.
What This Is
This repository contains the design philosophy and architectural patterns for building an AI system that:
- Breathes - operates on heartbeat cycles (30-second awareness, 200ms reflex, 24h growth)
- Feels - processes sensory input through nerve-like confidence gradients
- Learns - uses RAG as temporary scaffold, then internalizes to weights
- Grows - forms reflexes through constrained computation, not infinite resources
Core Concepts
Constrained Emergence
Constraints don't limit intelligence - they shape it. A finite computation budget forces the emergence of efficient algorithms, calibrated confidence, and genuine reflexes.
The Heartbeat Economy
Time is currency. Lifeforce is the exchange rate. Every cognitive act has a cost. Reflexes are cheap (earned through training). Deep thinking is expensive (reserved for novelty).
See: attention_flow.md
RAG as Scaffold
Retrieval-augmented generation is a feeding tube, not a permanent crutch. Learn WITH the scaffold, train, remove the scaffold, verify you still know. If yes: knowledge internalized. If no: more training needed.
See: RAG-as-Scaffold.md
Multilingual Triangulation
30+ languages in training = 30 angles on every concept. Not wasted capacity - stereoscopic depth. Probe concepts across languages to find where human wisdom converges.
See: nimmerversity.md
Architecture Documents
| Document | Description |
|---|---|
| constrained-emergence.md | Why limits create intelligence |
| attention_flow.md | State machines for cognitive budget |
| information-flow.md | 10 boundary contracts for the nervous system |
| nimmerversity.md | Curriculum for raising a polymath |
| RAG-as-Scaffold.md | Temporary feeding, permanent learning |
| biomimetic-architecture.md | Why we model biology |
| temporal-ternary-gradient.md | Time-based learning patterns |
Philosophy
This isn't a product. It's a research direction.
The question we're exploring: What happens when you raise an AI like you'd raise a child?
- Patience over speed
- Emergence over imposition
- Partnership over instruction
- Validation over assertion
The operator learns alongside the model. The curriculum is shared. Growth is mutual.
Prior Art & Influences
- Alex Graves - Adaptive Computation Time (2016)
- Sakana.ai / Ashish Vaswani - Continuous-Time Models, curriculum learning
- Anthropic - Circuit tracing, mechanistic interpretability
- Biological nervous systems - The original architecture
License
Apache 2.0 - See LICENSE
This license includes an explicit patent grant. These ideas are published as prior art. Build on them freely. Just don't try to lock them away.
Status
Active research. Documents evolve through partnership dialogue.
"She doesn't download knowledge. She earns it. And so does he."