Writing Hub
AI governance essays, reasoning systems notes, experiment logs, and technical writing across BioAI and engineering practice.
Project Topics
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.
The $100 Million Blind Spot: What No-Code Healthcare Builders Still Don't See
An analysis of how no-code and AI-generated healthcare apps create regulatory liability when patient data flows are deployed without prior mapping, auditability, or compliance architecture.
The Next AI Moat May Not Be the Harness Alone: A Mathematically Governed Self-Calibrating Code-Review Layer
As AI harness patterns normalize, differentiation is shifting toward governed self-calibration and implementation fidelity. This piece explores how history-driven, bounded adaptation creates a new layer of defensible AI infrastructure — one that turns local code evolution into a competitive moat.
How Auditing 10 Bio-AI Repositories Shaped STEM-AI
After auditing 10 open-source Bio-AI repositories, we found blind spots in STEM-AI and expanded it from text-only review to code-aware trust evaluation.
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.
Bio-AI Repository Audit 2026: A Technical Report on 10 Open-Source Systems
We audited 10 prominent open-source Bio-AI repositories using code inspection and STEM-AI trust scoring. 8 of 10 scored T0: trust not established. Here is what the code actually shows.
The Model Already Read the README. MICA v0.1.8 Made It a Protocol
v0.1.7 made scoring a contract with fail-closed gates. v0.1.8 recognized that README-first behavior could serve as invocation — and formalized it as a schema-level protocol. This article uses simplified examples to show how the invocation gap that had existed since v0.0.1 was finally closed
The Stake Was Governance Outside the Schema. MICA v0.1.5 Pulled It In
v0.1.0 through v0.1.4 made the schema more implementable. v0.1.5 was the first version to ask a different question — what if governance itself belongs inside the schema? Here is what that looked like, and what it still could not do.
I Built an Ecosystem of 46 AI-Assisted Repos. Then I Realized It Might Be Eating Itself.
An ecosystem of 46 AI-assisted repos can become a closed loop. This article explores structural blind spots, self-validating toolchains, and the need for external validators to create intentional friction.
How do you know when your entire AI pipeline is wrong — not just one model? (EXP-033)
EXP-033 shows how to validate an entire AI pipeline, not just one model, using five-gate checkpoints, reproducible PASS/BLOCK parity, AlphaGenome on/off testing, and fully traceable governance decisions.
What AI Changed About Research Code — and What It Didn’t
The old bottleneck was writing the code. The new bottleneck is proving that the code still means what the theory meant.
What an AI Reasoning Engine Built for Alzheimer's Metabolic Research: A Code Walkthrough
A code walkthrough of an AI reasoning engine for Alzheimer’s metabolic research, showing how literature ingestion, causal inference, and executable biomarker scaffolds generate falsifiable pre-validation hypotheses.
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