Demiurge © MIT 2025

repo-structure:

pyproject.toml         # Project-wide tool + build config
.env                   # Local secrets (ignored by Git)
.env.example           # Template for local environment vars
docker-compose.yml     # Base compose file (used in all environments)
dockerfile             # Dockerfile for external dependencies

/config/               # Canonical environment config for all lifecycles
  ├── dev.env
  ├── staging.env
  └── production.env

/.devcontainer/        # For Dev and Staging lifecycles only (VS Code dev environment)
  └── docker-compose-override.yml
  └── docker-compose-devcontainer.yml
  └── setup.sh
  └── devcontainer.json

/.github/              # GitHub-specific config (CI/CD + lifecycle management)
  └── install_hooks.sh
  ├── workflows/
  │   └── cicd.yml     # CI/CD pipeline
  └── hooks/           # Git hooks (local dev + CI hooks)
      ├── pre-commit
      ├── post-merge
      └── post-release

#lang racket
(require racket/string
         racket/port)

;; A simple parser for .env files: each line in the form KEY=VALUE
(define (parse-env-file filepath)
  (define env-hash (make-hash))
  (with-input-from-file filepath
    (lambda ()
      (for ([line (in-lines)])
        (when (and (not (string-blank? line))
                   (not (regexp-match? #rx"^\s*#" line))) ; skip blank lines and comments
          (define parts (string-split line "="))
          (when (= (length parts) 2)
            (hash-set! env-hash 
                       (string-trim (first parts))
                       (string-trim (second parts))))))))
  env-hash)

;; Load the top-level .env file (user-defined values)
(define top-env (parse-env-file ".env"))

;; Function to load a lifecycle-specific env file and merge with top-env
(define (get-lifecycle-env lifecycle)
  (define lifecycle-file (string-append "config/" lifecycle ".env"))
  (define lifecycle-env (parse-env-file lifecycle-file))
  ;; Merge: values from top-env override (or augment) the lifecycle file.
  (hash-union lifecycle-env top-env))

;; Example: Get the environment for the dev lifecycle
(define dev-env (get-lifecycle-env "dev"))
(displayln "Dev Environment:")
(for-each (lambda (key)
            (printf "~a = ~a\n" key (hash-ref dev-env key)))
          (hash-keys dev-env))

;; You could similarly load staging and production environments:
(define staging-env (get-lifecycle-env "staging"))
(define production-env (get-lifecycle-env "production"))

;; And now use these env-hashes to orchestrate your application behavior
;; For example, you might choose a branch from your repository hash:
(define repository
  (list (hash 'id 1 'branch "production" 'permissions '(x))
        (hash 'id 2 'branch "staging" 'permissions '(r x))
        (hash 'id 3 'branch "dev" 'permissions '(r w x))))

;; A function to pick the env based on branch name
(define (env-for-branch branch)
  (cond
    [(string=? branch "production") production-env]
    [(string=? branch "staging") staging-env]
    [(string=? branch "dev") dev-env]
    [else (error "Unknown branch" branch)]))

;; Example: Merge all branch envs (if needed)
(define merged-env
  (for/fold ([acc (make-hash)])
            ([branch repository])
    (hash-union acc (env-for-branch (hash-ref branch 'branch)))))
  
(displayln "\nMerged Environment for all branches:")
(for-each (lambda (key)
            (printf "~a = ~a\n" key (hash-ref merged-env key)))
          (hash-keys merged-env))

Quinic Statistical Dynamics, on Landau Theory, Landauer's Thoerem, Maxwell's Demon, General Relativity and differential geometry:

This document crystalizes the speculative computational architecture designed to model "quantum/'quinic' statistical dynamics" (QSD). By entangling information across temporal runtime abstractions, QSD enables the distributed resolution of probabilistic actions through a network of interrelated quanta—individual runtime instances that interact, cohere, and evolve.

Quinic Statistical Dynamics (QSD) centers around three fundamental pillars:

Probabilistic Runtimes:

Each runtime is a self-contained probabilistic entity capable of observing, acting, and quining itself into source code. This allows for recursive instantiation and coherent state resolution through statistical dynamics.

Temporal Entanglement:

Information is entangled across runtime abstractions, creating a "network" of states that evolve and resolve over time. This entanglement captures the essence of quantum-like behavior in a deterministic computational framework.

Distributed Statistical Coherence:

The resolution of states emerges through distributed interactions between runtimes. Statistical coherence is achieved as each runtime contributes to a shared, probabilistic resolution mechanism.

Runtimes as Quanta:

Runtimes operate as quantum-like entities within the system. They observe events probabilistically, record outcomes, and quine themselves into new instances. This recursive behavior forms the foundation of QSD.

Entangled Source Code:

Quined source code maintains entanglement metadata, ensuring that all instances share a common probabilistic lineage. This enables coherent interactions and state resolution across distributed runtimes.

Field of Dynamics:

The distributed system functions as a field of interacting runtimes, where statistical coherence arises naturally from the aggregation of individual outcomes. This mimics the behavior of quantum fields in physical systems.

Lazy/Eventual Consistency of 'Runtime Quanta':

Inter-runtime communication adheres to an availability + partition-tolerance (AP) distributed system internally and an eventual consistency model externally. This allows the system to balance synchronicity with scalability.

Theoretical Rationale: Runtime as Quanta

The idea of "runtime as quanta" transcends the diminutive associations one might instinctively draw when imagining quantum-scale simulations in software. Unlike subatomic particles, which are bound by strict physical laws and limited degrees of freedom, a runtime in the context of our speculative architecture is hierarchical and associative. This allows us to exploit the 'structure' of informatics and emergent-reality and the ontology of being --- that representing intensive and extensive thermodynamic character: |Φ| --- by hacking-into this ontology using quinic behavior and focusing on the computation as the core object, not the datastructure, the data, or the state/logic, instead focusing on the holistic state/logic duality of 'collapsed' runtimes creating 'entangled' (quinic) source code; for purposes of multi-instantiation in a distributed systematic probablistic architecture.

Each runtime is a self-contained ecosystem with access to:

Vast Hierarchical Structures: Encapsulation of state, data hierarchies, and complex object relationships, allowing immense richness in simulated interactions.

Expansive Associative Capacity: Immediate access to a network of function calls, Foreign Function Interfaces (FFIs), and external libraries that collectively act as extensions to the runtime's "quantum potential."

Dynamic Evolution: Ability to quine, fork, and entangle itself across distributed systems, creating a layered and probabilistic ontology that mimics emergent phenomena.

This hierarchical richness inherently provides a scaffold for representing intricate realities, from probabilistic field theories to distributed decision-making systems. However, this framework does not merely simulate quantum phenomena but reinterprets them within a meta-reality that operates above and beyond their foundational constraints. It is this capacity for layered abstraction and emergent behavior that makes "runtime as quanta" a viable and transformative concept for the simulation of any conceivable reality.

Quinic Statistical Dynamics subverts conventional notions of runtime behavior, state resolution, business-logic and distributed systems. By embracing recursion, entanglement, "Quinic-behavior" and probabilistic action, this architecture aims to quantize classical hardware for agentic 'AGI' on any/all plaforms/scales.

MOONLAPSED/demiurge

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