Writing Hub
AI governance essays, reasoning systems notes, experiment logs, and technical writing across BioAI and engineering practice.
Project Topics
STEM-BIO-AI Audit Report: yorkeccak/bio
When a README Claim Meets a Deterministic Scanner
Building a Deterministic Governance Kernel: Separating Custody from Truth
CGF separates domain truth from custody mechanics, turning AI governance from Markdown/YAML policy language into deterministic, inspectable artifacts.
Crimson Desert and the Innovation Tax
Crimson Desert and the Innovation Tax: an essay on why ambitious systems can look like a 6/10 before their grammar becomes legible — and why AI teams must know what to patch, what to preserve, and how to turn criticism into a map.
From Score to Workflow: Turning STEM BIO-AI Into a Local Audit System
Bio/medical AI trust should not collapse into one score. STEM BIO-AI v1.6.2 shows how deterministic auditing, evidence-led diagnostics, regulatory traceability, and bounded AI advisory can become an inspectable local workflow.
The Alchemy of Ego - How AI Turns Unfinished Thought Into Fluent Certainty
A personal essay on how AI can turn unfinished thoughts into fluent certainty, why internal coherence is not external proof, and why falsifiability, failure conditions, and visible execution matter in AI-assisted thinking.
How Do You Trust the AI Auditor? STEM-AI v1.1.2 and Memory-Contracted Bio-AI Audits
STEM-AI v1.1.2 binds a bio/medical AI repository audit to a machine-checkable memory contract, then demonstrates it on a real open-source bioinformatics repository.
When an AI Pipeline Passes — But One Path Still Must Be Held: EXP-034
EXP-034 tested whether a method-locked Bio-AI governance pipeline could survive modal expansion, AlphaFold EBI observer wiring, and AG-live measurement without breaking its PASS/BLOCK judgment baseline.
Bridging the Gap: From AI Slop to Mathematical Governance
A mathematical framework for detecting AI-generated code slop using AST distributions, Jensen-Shannon divergence, and geometric governance gates.
Prompt → RAG → MCP → Agent → Harness, and What?
Why the next layer in AI may be governance infrastructure, not just better agents.
What Anthropic’s 81k Survey Reveals About What the AI Market Still Gets Wrong
Users Don’t Want Faster AI — They Want AI That Helps Them Live Better Without Losing Their Humanity.
After Auditing 10 Bio-AI Repositories, I Think We're Scaling the Wrong Layer
After auditing 10 open-source Bio-AI repositories, one pattern stood out: the field is scaling packaging faster than verification. Here is what that gap actually costs.
I Audited 10 Open-Source Bio-AI Repos. Most Could Produce Outputs. Few Could Establish Trust.
I audited 10 visible repositories. Most could produce outputs. Very few could establish what those outputs meant.
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