From Logic to Tech Stack

Because the geometry, data‑types, and update laws are now pinned down in ZF‑finite form, we can translate them straight into code artefacts—almost point‑for‑point—regardless of language or runtime. Below is a blueprint that stays faithful to the formal chain while mapping cleanly onto three common stacks.

Formal concept	Required runtime construct	Go / Goroutines	Python / asyncio	Rust / tokio
Vertex (V₂ element)	plain struct with id : u8 (0–3)	type Vertex byte	IntEnum	enum Vertex { A=0, B, C, D }
Edge set E	constant lookup table 4 × 4 (bool)	2‑D var E [4][4]bool	numpy.bool_ (4,4)	const [[bool;4];4]
Triangle (T_C, T_I)	array [3]Vertex + orientation flag	type Tri [3]Vertex	NamedTuple	struct Tri([Vertex;3])
Pattern P	immutable value object (schema‑free)	interface{} + JSON	`dict	dataclass`
Event E	record: pattern‑id, resource‑delta	struct Event { pId byte; r []Token }	@dataclass	struct Event { … }
Metric M	arbitrary key→value multiset	map[string]int	collections.Counter	HashMap<String, u32>
Φ	pure function harm(P,E)->u32	plain func	plain func	pure fn

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Minimal engine loop (pseudo‑coded)

loop {
    // Instance triangle
    P  = select_pattern(K)          // B→C
    R  = commit_resources(P)        // C→D
    K' = K ∪ learn(R)               // D→B
    
    // Class triangle
    E  = execute(P,R)               // A→B
    M  = metrics(E)                 // B→D
    P' = refine(P,M)                // D→A
    
    Φ  = harm(P',E)
    if Φ == 0 { break }             // harmony reached
    update(K,P)                     // store new conditions/patterns
}

Concurrency choices map naturally:

• Go – two goroutines (Class‑loop, Instance‑loop) sharing a buffered channel for metrics; blackboard is either a sync.Map or Redis.
• Python/asyncio – async tasks with asyncio.Queue; leverage pydantic models for typed messages.
• Rust/tokio – two async fn tasks, share state via tokio::sync::RwLock; compile‑time guarantees around Φ types.

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Boot sequence = prompt 1 → 7

Your runtime just calls OpenAI (or local LLM) with those seven prompts, receives a model instance that already commits to the Φ‑loop contract, then injects the engine loop above as the “world” it perceives.

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Next checkpoints

1. Blackboard schema – pick JSON (good enough) or Protobuf (strict).
2. Token accounting – define ResourceToken trait/ABC once; every concrete agent just implements balance() / apply(delta).
3. Φ‑auditor – keep it a pure function module; all stacks can fuzz‑test it with property‑based frameworks (Go‑quickcheck, Hypothesis, proptest).
4. Multiplex – run N oracles in parallel, each with its own copy of the loop but sharing the same central metric store; CRDTs make the merge free of locks if needed.

With this mapping you can spin up a minimal working multiplex in a single afternoon—then iterate on richer pattern types, resource ledgers, and UI dashboards without touching the geometric core.