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AI governance essays, reasoning systems notes, experiment logs, and technical writing across BioAI and engineering practice.
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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.
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.
I Audited 10 Open-Source Bio-AI Repos. Most Could Produce Outputs. Few Could Establish Trust.
I audited 10 visible repositories. Most could produce outputs. Very few could establish what those outputs meant.
Everyone Was Talking About Context Engineering. Nobody Had Solved Governance.
Everyone Was Talking About Context Engineering. Nobody Had Solved Governance.
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.
Medical AI Repositories Need More Than Benchmarks. We Built STEM-AI to Audit Trust
STEM-AI is a governance audit framework for public medical AI repositories. It scores README integrity, cross-platform consistency, and code infrastructure — because benchmarks alone don't tell you if a bio-AI tool is safe to trust.
The Schema Existed. The Model Had No Way to Know.
v0.0.1 proved that context could be structured. It did not prove that the structure could govern what shaped the session. Three failures — and why only one made the others meaningless.
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.
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