Essays
AI systems that actually ship — notes from production. Newest first.
Your LLM Evals Are Lying to You
Your eval suite passes. Your users still get garbage. The metrics that mean something, the framework landscape without the marketing, and the build order that closes the gap.
Read →RAG vs. CAG vs. KAG: Pick the Boring One
RAG, CAG, and KAG compared by someone who runs them in production: what each actually is, what it costs, and a decision framework that ignores the acronyms.
Read →The $400B Lie: Why Most Enterprise AI Projects Fail Before They Start
After leading AI transformations at multiple enterprises, I've found the same fatal mistakes every time. The problem isn't the technology — it's the question you're asking it to answer.
Read →Multi-Agent Systems Are the New Microservices — And Nobody's Ready
Multi-agent architecture patterns from eighteen months in production: orchestration vs. choreography, agent communication, failure modes, and deployment — with diagrams.
Read →I Replaced Our Entire Data Pipeline with an LLM. Here's What Broke.
A first-person post-mortem on replacing a traditional ETL pipeline with LLM-powered processing. What worked, what exploded in cost, what hallucinated, and the hybrid architecture we finally settled on.
Read →RAG Is Dead. Long Live Agentic Retrieval.
RAG vs. agentic retrieval, from production: why vanilla RAG hit its ceiling, what agents that plan their own retrieval look like, and the hybrid architecture that beat it by 40%.
Read →The AI-Native Frontend: How ML Models Are Eating the UI Layer
ML models are no longer just backends — they're reshaping the UI layer itself. Predictive prefetching, adaptive layouts, and AI-generated components are here.
Read →16 Years in Tech: The Only 5 Things That Actually Mattered
From junior dev in Detroit to CEO of Ryshe — 16 years of hard-won lessons distilled into the only 5 things that actually changed the trajectory of my career.
Read →Fine-Tuning is a Trap. Here's What to Do Instead.
RAG vs. fine-tuning vs. prompt engineering, settled with real numbers: 8 experiments, $40K in GPU costs, one honest success. A decision framework from production.
Read →One honest AI essay, every week. No hype, no vendor pitches.
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