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.
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 Difference Between a Harness and a Leash
A practical essay on why most AI 'harnesses' are still leashes: guides shape behavior, but only justified external measurement creates a real governance boundary.
OpenMythos v0.5.0 Code Review - Audit Report
OpenMythos collected thousands of GitHub stars and dominated AI discourse for a week. This is what happens when you actually read the code — and why the people who do always arrive too late to matter.
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.
FLAMEHAVEN FileSearch: Why This RAG Engine Feels Different from the Usual Stack
A technical look at FLAMEHAVEN FileSearch: BM25+RRF hybrid retrieval, chunk-addressable indexing, deterministic DSP vectors, and the trade-offs behind a lower-overhead self-hosted RAG engine.
AI-SLOP Detector v3.5.0 — Every Claim, Verified Against Source Code
AI-SLOP Detector v3.5.0 made 7 claims on LinkedIn —self-calibration logic, download numbers, defect detection. Here's every claim verified against actual file paths and line numbers. The code speaks for itself.
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.
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.
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