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Case studies and blog posts on architecture, operations, and delivery: what worked, what failed, and what is reusable.

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Case Studies

Real implementations with metrics, stack details, and lessons learned.

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Building a Multi-Model LLM Platform on Consumer GPUs
AI InfrastructureFeatured

Building a Multi-Model LLM Platform on Consumer GPUs

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.

24GB
GPU VRAM
4
Models Served
OpenAI API
Interface
KubernetesvLLMAMD Radeon 7900 XTXPrometheus+2 more
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HealthcareFeatured

fi-fhir: Format-Agnostic Healthcare Integration

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.

5+
Formats Supported
70-90%+
Parser Coverage
Config-first
Integration Time
GoYAMLCELSvelteKit+8 more
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Healthcare

Healthcare API Integration: Patient Matching & Documentation Gaps

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.

99%+
Match Accuracy
~2M/mo
API Calls
-75%
Debug Time
FHIR R4HL7v2GoPostgreSQL+2 more
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DevOps

Building a Real-Time Kubernetes Operations Dashboard

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.

<100ms
Update Latency
3
Clusters
99.9%
Uptime
GoSolidJSKubernetesD3.js+2 more
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Blog

Technical notes, implementation updates, and deeper dives.

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February 9, 2026·7 min read
Lab

Two-Lane Text GPU Allocation: Quality + Vision/Fast (Plus a Media Lane)

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.

gpukubernetesmlc-llmrocm+4 more
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February 9, 2026·6 min read
Lab

Loom-Mode MCP for Advanced, Fast AI-Assisted Dev (Go-Native, Proxy+Daemon)

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.

loomloom-coremcpgo+4 more
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