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Host_Coupling
ric-massey edited this page Jul 8, 2026
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Orrin treats the machine it runs on as part of its runtime context and learns that machine's normal behavior. The key idea: the same host metrics feed three deliberately separate mappings, so a small or busy machine is not mistaken for chronic distress.
flowchart LR
HM[Host metrics: disk / swap / memory / battery] --> R[Reflex: absolute floors]
HM --> S[Control signals: deviation from learned band]
HM --> C[Cadence: absolute capacity]
R --> RP[Pause heavy cycles]
S --> SP[Bias internal state]
C --> CP[Set cycle rate]
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Reflex (absolute floors). The autonomic
HostResourceGuard(supervisor/host_resources.py) watches disk/swap/memory below cognition and pauses heavy cycles at absolute safety floors — separate from the deliberative loop, because a thrashing loop can't be asked to rescue the substrate it runs on. - Control signals (deviation from a learned band). The resource self-monitoring layer feeds the same metrics into internal state on deviation — falling disk reads as constriction, rising swap as slowdown, a draining battery as a finite-horizon signal.
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Cadence (absolute capacity). A small machine simply runs at a slower cadence, not a
degraded one (
resource_cadence.py).
brain/cognition/infancy.py learns that machine's normal oscillation before the runtime trusts
its own deviation signals. A user-facing RAM budget slider (host_budget.py) feeds both the cadence
policy and the reported "100%".
For the watchdogs and supervisor side, see Host Coupling & Supervisor.
-
supervisor/host_resources.py— the reflex guard -
brain/cognition/host_resource_monitor.py,host_band.py,resource_cadence.py,infancy.py,host_budget.py
- Cognitive Loop
- Workspace & Ignition
- Binding & Writeback
- Thinking & Action Selection
- Learning & Adaptation
- Effect Ledger
- Quality Standard
- Expression Membrane
- Self-Code
- Native Language Model
- Existence & Lifecycle