Practical perspectives on data engineering, analytics, and AI from senior practitioners.
Out-of-the-box agentic analytics systems lock your semantic layer, context, evaluation, and feedback loop inside one product. Those four belong to you.
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AI agents querying raw source systems inherit every data quality problem the transformation layer solves — then present wrong answers with confidence.
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Token counts and lines of code are easy to count and easy to game. Goodhart's law explains why AI productivity metrics fail and what to track instead.
MotherDuck Guides are a routing layer for analytics agents, not documentation. What to put in them, what to push down, and how to keep them correct.
A staged framework for deciding which data architecture components AI agents need, when to build them, and why, based on the value you are creating.
Use case lists are not strategy. A mechanism-based framework for identifying where AI creates value, organized by what the AI is doing.
AI accelerates the 21% of data work that is code generation. The other 79% is maintenance, and every new asset makes it worse.
MCP is now core infrastructure. Its real cost at enterprise scale, where the security model breaks, and how to route agent workloads deliberately.
An 8-question diagnostic framework for assessing which SaaS businesses AI threatens, validated against 2026 YTD stock performance.
How to decompose LLM workflows into task components, break down complex RAG systems, and select tools using 10 evaluation principles.
A practical framework for evaluating LLM use cases in enterprise settings, separating hype from high-ROI opportunities.
A framework of 11 critical questions to evaluate whether your use case needs an open-source LLM or a third-party API like OpenAI or Claude.
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