Welcome!
Hi, I’m David. I’ve spent my career building production systems at companies like AWS and Cisco — from distributed infrastructure to AI platforms to engineering teams that ship.
This blog covers what I’ve learned building systems that actually work: enterprise AI infrastructure, LLM-assisted development workflows, Kubernetes platforms, engineering culture, and the distributed systems patterns that separate demos from production.
Whether you’re deploying AI at scale, building with LLMs, or leading engineering teams, you’ll find battle-tested patterns and real-world lessons from someone who’s been there.
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Production AI Inference on Your Laptop in 8 Minutes
Deploy production-like AI inference infrastructure on your laptop with a single command using kFabric. Presented at CNCF Tampa Bay. Watch more videos →
Recent Posts
AI Agents Are Finding Vulnerabilities Faster Than You Can Patch Them
The Linux kernel security list used to receive 2-3 bug reports per week. That was two years ago. Last year it was 10 per week. This year it is 5-10 per day.
The Agentic SDLC Transition Is Happening Whether Your Team Is Ready or Not
A leaked internal memo from Red Hat’s CTO and SVP of Engineering, dated March 31, 2026, is making the rounds. The headline: all of Global Engineering is requ...
CRITICAL: Axios npm Package Backdoored — 100M Weekly Downloads, Cross-Platform RAT
CRITICAL: Axios npm Package Backdoored — 100M Weekly Downloads, Cross-Platform RAT
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Featured Topics
Enterprise AI Infrastructure
Production deployment patterns, Kubernetes for AI workloads, GPU cost optimization, hybrid cloud architectures, and infrastructure that scales from POC to production.
AI Governance & Compliance
HIPAA, SOX, GDPR compliance for AI systems. Data sovereignty, model governance, audit trails, and risk management frameworks for regulated industries.
Cost Optimization & ROI
GPU utilization strategies, spot instance management, resource allocation, chargeback models, and TCO analysis for cloud vs. on-premises AI infrastructure.
MLOps & Platform Engineering
KServe, KubeFlow, model serving, CI/CD for ML, monitoring, multi-tenancy patterns, and building production-grade AI platforms.