AI Systems & Case Studies
This archive collects process-driven examinations of how AI is used as a creative and analytical tool within this body of work. Each entry documents decisions, constraints, iterations, and refinements across visual, narrative, and systems-based projects. These posts are best read as working records—showing not just outcomes, but how intention and human judgment shape the final result.
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Human-Led Constraint Authorship in Machine-Assisted Creation
This archive documents a human-led approach to machine-assisted creation in which authorship, constraint, and refusal are treated as structural elements rather than corrective steps. Each case study examines how limits are defined in advance, how generative systems are bounded in practice, and how human judgment remains responsible for meaning, ethics, and the decision to stop.
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Finishing the Frame: AI Image Finishing & Color Discipline
AI generates the negative. Human judgment finishes the image. This process study documents a controlled image-finishing workflow that combines AI image generation with traditional photographic color grading and exposure discipline—focused on restraint, realism, and the final 10% where authorship still matters.
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The Lumivore Prompt: A System for Cinematic Canon Preservation in AI Image Generation
Lumivore is a prompt architecture designed to produce cinematic still imagery grounded in film language, observational realism, and visual continuity. This post details the structural rules, camera discipline, and human finishing practices that allow AI images to behave like cinema rather than illustration.
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Lumivore: The Origin & Constraint Architecture
Lumivore did not emerge from theory. It emerged from failure. Built during the collapse of a multi-character cinematic project, Lumivore is a constraint-driven system designed to enforce visual canon, identity stability, and cinematic logic across AI-generated imagery. This post documents the failures that forced its creation—and the architecture that finally made continuity possible.
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“Designing Balance”: Rebuilding a Global Investment Portfolio Through Systems Thinking
A design-led case study exploring how AI functions best as a cognitive partner rather than an authority. Through systems thinking, ethical constraint, and human judgment, this project reframes stock portfolio investing as an act of balance, restraint, and long-term architectural design.
Scott Bryant 