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
Specifications Are the New API Between Product and Engineering
Picture this. Your team just adopted an AI coding agent. Cursor, Claude Code, Copilot, it doesn’t matter which. The first week is electric. A developer promp...
From PRFAQ to Backlog: How Amazon’s Working Backwards Process Becomes an AI Pipeline
In my previous post on Amazon’s Working Backwards SDLC for SMBs, I walked through the structured artifact chain (personas, use cases, PRFAQ, capability maps,...
The Missing Half of AI-Assisted Development
There’s a growing conversation in the industry about building a “Cursor for product management,” an AI-native system focused on helping teams figure out what...
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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.