Whitepapers
The tools I use came from a methodology I formalized over three years of building with AI. These papers document the thinking behind the tools. They cover how raw input becomes structured output, how voice survives the process, and how evaluation works when the system doing the work is also the system being evaluated.
If you’re here from a search engine or a citation, this is the right place. If you’re here from the site, you probably came through How I Work, which introduces the same ideas without the academic register.
AI Governance as Accommodation Design
A Pedagogical Framework for Human-AI System Architecture
A whitepaper applying special education pedagogy to AI system architecture. Rather than constraining behavior, accommodation design asks what both systems in the room actually need: the model's processing constraints and the human's.
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Input Inversion
Why Unstructured Human Thinking Produces Better AI Output
A whitepaper challenging the foundational assumption of prompt engineering: that quality AI output requires structured human input. Three years of applied evidence demonstrates that raw, unstructured thinking produces better results when purpose-built tools handle the translation.
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Lens Extraction
Decomposed Evaluation Through Practitioner-Derived Criteria
A whitepaper proposing lens extraction: a protocol for extracting named practitioners' evaluative frameworks, codifying them as testable criteria, and running multiple lenses independently against the same work. Where they disagree is where the maker's judgment is needed.
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A Different Kind of Harness
AI as Cognitive Prosthetic Through Mutual Accommodation
A whitepaper proposing that AI is most productive as a cognitive prosthetic: an extension of the practitioner's thinking through mutual accommodation. The model extends cognitive reach. The practitioner directs cognitive intent. Purpose-built interfaces handle the coupling.
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Semantic Flattening and the Case for Human-Marked Importance in AI Memory
Why Machine-Scored Memory Systems Erase What Matters Most
A whitepaper arguing that semantic importance in AI memory is a relationship between content and intent, not a property of content alone. Presents Savepoint Syntax as human-marked semantic hierarchy and contrasts it with machine-determined approaches.
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Voice Governance
Generation Constraints vs. Post-Hoc Filtering in AI-Mediated Writing
A whitepaper arguing that voice constraints applied during AI text generation produce structurally different output than the same constraints applied as post-hoc filters. Presents a forty-rule voice protocol developed over three years of applied practice.
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