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Essays, experiment logs, and technical notes across AI governance, reasoning systems, BioAI, and engineering practice.
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95% of AI Businesses Will Die. Here’s How to Not Be One of Them.
What the data, a founder’s confession, and 70 years of tech history tell us about who actually survives.
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.
What AI Changed About Research Code — and What It Didn’t
The old bottleneck was writing the code. The new bottleneck is proving that the code still means what the theory meant.
What an AI Reasoning Engine Built for Alzheimer's Metabolic Research: A Code Walkthrough
A code walkthrough of an AI reasoning engine for Alzheimer’s metabolic research, showing how literature ingestion, causal inference, and executable biomarker scaffolds generate falsifiable pre-validation hypotheses.
Is MCP Really Dead? A History of AI Hype — Told Through the Rise and Fall of a Protocol
When a protocol doesn’t die — it just stops being interesting. A forensic look at MCP, OpenClaw, and the psychology of AI hype cycles.
Prompt, Pray & Push: Why Your AI Agent Keeps Failing You
The one concept that turns expensive spaghetti into great agentic engineering.
Chaos Engineering for AI: Validating a Fail-Closed Pipeline with Fake Data and Math
A case study in AI governance showing how synthetic invalid inputs, structural disagreement, SIDRCE ethics checks, and end-to-end reliability scoring triggered a safe BLOCK verdict in a biomedical pipeline.
The Pull Request Illusion: How AI Is Hollowing Out Software’s Last Line of Defense
GitHub Just Added a Switch to Turn Off Pull Requests. That’s Not a Feature. It’s a Warning.
Beyond AI FOMO — From Tulip Mania to OpenClaw 2026: The Governor That Saves You
The real breach wasn’t in the code. It was in you.
From 97% Model Accuracy to 74% Clinical Reliability: Building RSN-NNSL-GATE-001
Learn how RSN-NNSL-GATE-001 turns high model accuracy into system-level clinical reliability by blocking unsafe AI pipeline decisions, measuring end-to-end risk, and enforcing fail-closed governance.
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.
I Integrated AlphaFolder3 & AlphaGenome. It Looked Perfect. Then It Failed the "Honesty Test."
A real-world experiment integrating AlphaFold3 and AlphaGenome revealed a critical lesson: AI predictions that look perfect can still fail the ‘honesty test.’ A deep dive into bioinformatics, model validation, and AI reliability in drug discovery.
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