The archive, mapped

A short, adoptable AI usage policy for engineering teams: what to put on the page, what to leave off, and why the policy matters less than the habits it makes explicit.

How I would roll out AI-assisted development on a team that has not standardized: what to do in week one, what to earn the right to argue about later, and what almost always goes wrong.

What it took to get Gemma 4 E4B serving cleanly on Radeon through FlexInfer: a stable TRITON lane on a 7900 XTX, an experimental TurboQuant long-context lane on a second node, and the GPTQ pipeline work still underway.

AI-assisted development is useful leverage, but only if you convert borrowed competence into real judgment before the support becomes a dependency.

Treat your repo like a control system: instruction hierarchy, workflows, hooks, and shared memory that make AI-assisted dev fast, reproducible, and hard to derail.

How Loom keeps MCP servers and skills in sync across Codex, Claude, Gemini, VS Code, Antigravity, Kilocode, OpenCode, and Zed.

How to keep AI-assisted development fast and token-efficient: one proxy entry, a Go daemon that routes calls, and a small set of Go-native MCP servers.

How I redistributed 6 models across 3 GPU nodes to eliminate contention, using priority-based shared groups and label-based aliases for routing and failover.

How I run multiple OpenAI-compatible LLM endpoints on a small K3s cluster with AMD Radeon GPUs, and what I had to do to make it stable.

A case study in healthcare integration: how Source Profiles, a three-phase parsing pipeline, and a workflow DSL turn messy legacy formats into semantic events.

What actually broke when I deployed MLC-LLM across two RX 7900 XTX nodes, and the fixes that made it stable: quantization, KV cache sizing, and Kubernetes GPU hygiene.

How to define meaningful SLOs for production inference workloads, and what to do when they break.

Field notes from building and operating a small private GPU platform with Harvester, K3s, and a GitLab -> Flux delivery loop.

A practical guide to running GPU workloads on-prem or hybrid, using Kubernetes and GitOps patterns that make operations boring.

Common GPU infrastructure failures in production and how to diagnose them before they become incidents.

Before you can cut GPU costs, you need to measure them correctly. Here is what to track and what the cloud console will not tell you.

A practical checklist for auditing production AI infrastructure: GPU cost baselines, reliability risks, and an executable roadmap.

A deep dive into optimizing Canvas 2D and Three.js visualizations for Kubernetes dashboards, covering algorithmic complexity, memory management, and GPU-efficient rendering patterns.

A real integration failure mode: when an API contract is implicit, patient matching becomes a risk management problem. Here is the defensive pattern (validation + contract tests + instrumentation) that makes it operable.

The infrastructure, product surfaces, and live demos behind the FlexInfer, Loom, and fi-fhir work I publish here.

What still works, what changed, and which guardrails matter when you run AMD Radeon GPUs for always-on inference.

What makes an AI agent reliable in production: explicit loops, bounded tools, and visible operator state.

How I built FlexDeck, a full-stack operations dashboard with real-time K8s monitoring, GitLab CI/CD visualization, and AI model management using Go and SolidJS.