Writing Hub
AI governance essays, reasoning systems notes, experiment logs, and technical writing across BioAI and engineering practice.
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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.
We Built AI Verification Infrastructure. Then It Found Our Blind Spots.
A technical account of the Flamehaven Verification Ledger — what it found, where it failed, and what we need the field to tell us
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.
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.
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.
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.
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 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.
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