Writing Hub
AI governance essays, reasoning systems notes, experiment logs, and technical writing across BioAI and engineering practice.
Project Topics
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.
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.
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.
Prompt → RAG → MCP → Agent → Harness, and What?
Why the next layer in AI may be governance infrastructure, not just better agents.
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.
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.
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.
Prompt, Pray & Push: Why Your AI Agent Keeps Failing You
The one concept that turns expensive spaghetti into great agentic engineering.
When AI Models Fight, Truth Wins: The “Eureka” Moment for Tired Researchers
To the grad student staring at a pLDDT of 90 and wondering why the ligand won’t bind.
Orchestrating AlphaFold 3 & 2 with Python: Handling AI Hallucinations using Adapter Patter (Trinity Protocol Part 1)
Learn how to orchestrate AlphaFold 3 and AlphaFold 2 with Python using the Adapter Pattern to detect AI hallucinations, measure structural drift, and improve protein prediction reliability.
Your Agentic Stack Has Two Layers. It Needs Three.
Most agentic stacks cover tools and skills, but miss intent governance. Learn why a third layer is needed to stop AI drift, scope creep, and technically correct systems heading in the wrong direction.
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