Writing Hub
AI governance essays, reasoning systems notes, experiment logs, and technical writing across BioAI and engineering practice.
Project Topics
We Built AI Verification Infrastructure. Then It Found Our Blind Spots.
A technical account of the Flamehaven Verification Ledger — what it found, where it failed, and what we need the field to tell us
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.
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.
Each /slop Is a Calibration Signal — AI-SLOP Detector v3.6.0 and the Claude Code Skill
Every /slop invocation records to a project-scoped history. After 10 re-scanned files, bounded self-calibration adjusts detection weights for your codebase. Here is the mechanism, the data, and what actually shipped in v3.6.0.
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.
The Sheepwave Has a New Shape: OpenMythos and the Rise of Architecture Hype
A technical-opinion essay on OpenMythos, Claude Mythos, README-driven AI hype, and why architecture claims need source-level verification before becoming public belief.
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.
It Gets Smarter Every Scan: AI-SLOP Detector v3.5.0 and the Self-Calibration Loop
AI-built apps are starting to fail in public. Not every failure is static-analysis territory, but many share the same upstream condition: plausible-looking code passing review without carrying enough real logic. AI-SLOP Detector v3.5.0 adds a self-calibration loop to reduce that gap.
Can AI Review Physics? Yes — That Is Why We Built SPAR
SPAR is a deterministic framework for claim-aware review: checking whether an output deserves the claim attached to it.
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