Writing Hub
AI governance essays, reasoning systems notes, experiment logs, and technical writing across BioAI and engineering practice.
Project Topics
AI Can Write the Code. It Still Cannot Place the Stone.
AI can now write code, patch files, and finish releases. But a real case from an AI-assisted release shows that the harder human work may be deciding what the system should expose, which output belongs to which reader, and how agent-generated work remains inspectable after the code is written.
The Quality Author: Taste as the Last Bottleneck in AI Development
On where craftsmanship went, why verification gaps appear in its absence, and the one practice AI cannot automate for you.
AI-SLOP-DETECTOR v3.8.1: When Code Generation Gets Cheap, Structural Trust Gets Expensive
SEO Description:AI-SLOP-DETECTOR v3.8.1 moves beyond AI code detection toward governed cleanup, safer scoring, cleanup confidence planning, manifest-aware dependency hygiene, layered architecture review, and fail-closed governance for AI-assisted software development.
When the Memory Gate Met a Real Archive: What 90 Experiments Taught Us About Cheap LLM Slop
How to enforce data integrity against AI-generated slop using MICA. Explore a 11-step session-start validator that locks rules, playbooks, and contracts in code before code is ever touched.
The Two Problems No One Talks About in AI Agent Coding Pipelines
AI agent coding pipelines fail not because models are weak, but because verification is structurally broken. This article identifies four empirically documented failure mechanisms — agreement bias, latent entanglement, echoing, and right-for-wrong-reasons — and proposes a concrete architecture: hash-chained audit records, hybrid recurrence scoring, dynamic context budgets, and evidence-first review across three independent axes. Covers multi-agent pipeline design, agentic code review, blueprint indexing, and P0–P4 governance gates.
The README Was a Protocol. The Entrypoint Was Still Optional.
README-as-Protocol solved explicit invocation at the schema level. It did not solve entry control at the workflow level. This version adds the missing hierarchy: natural, guided, and forced activation.
Your Bio Repo Could Get You Fined. Here Is Why We Check Every Single One.
When a bio AI repository claims HIPAA compliance but the code says otherwise, the legal exposure falls on whoever deploys it. STEM-BIO-AI evaluated yorkeccak/bio — 322 stars, modern stack, one dangerous README line. Score: 48/100. T1 Quarantine. Full audit report with score matrix, regulatory traceability, and raw machine output.
From Repo Scanner to Audit Architecture: What Changed in STEM BIO-AI Through v1.7.8
A technical look at how STEM BIO-AI v1.7.8 became less Python-shaped, more semantically stable, and more inspectable across real audit output surfaces.
The Meeting Nobody Could Follow -The format of AI output is a design decision. We made it wrong for three years.
How our engineering team stopped sending 200-line Markdown files that nobody read — and what a nine-word post from an Anthropic engineer taught us about AI output format as a design decision. Includes token cost analysis, real prompt templates, and the HTML render layer approach used in production.
STEM-BIO-AI Audit Report: yorkeccak/bio
When a README Claim Meets a Deterministic Scanner
Beyond Repo Scanning: How AIRI Expanded the Risk Vocabulary in STEM BIO-AI 1.7.x
How STEM BIO-AI uses the MIT AI Risk Repository as a governed local risk-vocabulary layer without replacing deterministic repository scanning
When Control Becomes Authority: Calibration Governance in STEM BIO-AI 1.7.x
Why STEM BIO-AI treats calibration as governed policy instead of a free-form score-tuning console for bio and medical AI repository audits.
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