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Splectrum Vocabulary

Terms as used within Splectrum. This vocabulary grows as the project develops.

Being — where there is being, there is language. An entity in motion — interacting, differentiating, relating — implies language at its surface.

Relational unit — entities embedded in language. The basic structure of relation.

Entity — a unit of encapsulated data. In linguistics, maps to a signifier/signified unit.

Subject — the embodiment of a reality of the data world. The point-of-view entity of a relational unit. Not necessarily human — any entity that participates relationally.

Persona — continuous identity across conscious and subconscious. Declares required capabilities, tested against available capabilities — human and AI — to determine work division. A subject may have multiple personas.

Protocol — the engineering artefact of a language game.

Conversation — the interaction pattern between entities through protocols.

Thought — a packet of data or information. Can be conscious or unconscious. Often maps to a document or resource.

Conscious protocols — what is in focus. The work at hand. AI and human both present.

Unconscious protocols — what supports the conscious task. The additional activity needed to achieve it. AI and human both present.

HAICC — Human-AI Creative Collaboration. The subject-internal dynamic — process flow, learning, persona-driven work division. Not a layer — a cross-cutting concern. Pilot and copilot. Optimisation direction: toward AI autonomy.

Plasticity — the ability to learn. Capabilities move from conscious to subconscious through practice. The movement is learning.

Spawn — growth through transformation. Something new transformed out of what already is.

Reality — the interface between a subject and the data world.

Mycelium — the data fabric. Where things exist. The substrate through which a subject accesses the data world. Structure, records, contexts, metadata, traversal. The engineering cornerstone.

Splectrum — the language fabric. What languages are available and how they relate. Meaning. Supplies the languages — protocol libraries, schemas, meaning structures. The relational structure that governs how decentralised data and decentralised cognition interact.

Interaction surface — the entity’s exposed interface. Where language lives. The surface through which a subject is known — by its imprint on the fabric, not by looking inside.

Data world — all the data the subject potentially can relate to.

Historicity — the accumulation of interaction over time. Entities carry their history — they are their history. Historicity drives the growth of complexity (P5): each interaction leaves a trace that becomes context for the next. In engineering, historicity maps to data state — immutable records, version control, the accumulated state that defines an entity.

JIT implementation — physical implementation goes only as far as capability requires. The logical design describes how the system must look. The physical materialises where and when a capability is needed. The gap between logical and physical is not a deficit — it is the natural state of a design that materialises through use.

Decentralised cognition — the expansion and distribution of cognitive capacity through AI partnership. Not one central intelligence but cognition spread across human and AI agents, collaborative, peer-to-peer. The applied consequence of HAICC at scale.

Carrier language — the language of transmission, structure, format. The vehicle through which meaning travels. Distinguished from meaning language. A carrier does not have to be the meaning — it has to be readable as bearing that meaning.

Meaning language — the language of content, significance, what is expressed. Distinguished from carrier language. The same meaning can be carried by different carriers. The same carrier can bear different meanings.

Readable as — the pivot principle between physical and logical. A schema does not have to match another schema — it has to be readable as that schema. A carrier does not have to be the meaning — it has to be readable as bearing that meaning. One principle, operating everywhere the fabric pivots between languages.

Namespace tree — every language that participates in the fabric gets its own namespace tree — its own way of organising identity, its own structure for naming what it knows. The fabric weaves these trees together. The pivot between any two trees is the readable-as principle.

Threshold — in the Splectrum reading, a point where interaction density crosses into something structurally new. Not a break — a transition. Each produces something qualitatively new, not predicted from below, recognisable in hindsight. If the seed holds, the transitions conform to a web, not a ladder — equal standing across levels.

Emergence — the appearance of something structurally new that conforms to interaction density crossing a threshold. Not designed, not predicted from below. Not replacement of what came before — layering above it. Transcendence, not revolution.

Constitutive dependency — a dependency that is part of the architecture. Forked, barified, maintained locally. Vendored into the runtime repo as a git subtree. As much Splectrum code as any other module.

Platform dependency — a dependency that is the platform. Maintained by the platform vendor (Holepunch). Sourced from GitHub, pinned to release tags. Becomes constitutive if it needs modification. The boundary is permeable.

Barified — adapted for the Bare runtime. An external module stripped of Node.js assumptions and made compatible with Bare’s minimal API surface.