Writing Hub
AI governance essays, reasoning systems notes, experiment logs, and technical writing across BioAI and engineering practice.
Project Topics
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.
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.
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.
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.
My AI Maintainer Kept Making Wrong Calls. So I Made It Report Its State Before Touching Anything.
Part 6 moves from landscape to operation. This is what MICA looks like when it is actually running inside a real maintenance workflow — session report, self-test, drift, invariants, and operator judgment.
Prompt → RAG → MCP → Agent → Harness, and What?
Why the next layer in AI may be governance infrastructure, not just better agents.
The Harness Is the Product: What the Claude Code Leak Actually Revealed About AI Agent Architecture
The Claude Code leak exposed more than source. It revealed that modern AI agent performance depends heavily on the harness around the model.
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.
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.
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.
Prompt, Pray & Push: Why Your AI Agent Keeps Failing You
The one concept that turns expensive spaghetti into great agentic engineering.
Showing page 1 of 3 · 35 matching posts