September 6, 2026
When AI ships the app, IT’s job is to shepherd
Users can stand up tools overnight with AI. IT’s job is to shepherd what gets built so the company still has one stack, one set of numbers, and a reverse path.
Read the postInsights
Blog
How we think about AI and ML development and implementation, data, and the operating cadence around them. Written so operators and owners can use it — and so search engines can find it.
September 5, 2026
Managing code repositories when AI writes the code
AI can ship branches faster than a junior. Your git hygiene has to keep up — or Monday becomes archaeology.
Read the postAugust 24, 2026
AI and ML implementation for growing companies
How small and mid-size companies get value from AI and machine learning without a data-science department — by implementing models on the data they already have.
Read the postAugust 18, 2026
Generative AI that answers from your data, not the open internet
Why growing companies should ground generative AI in their own documents and systems — and how implementation keeps assistants useful after the demo.
Read the postAugust 11, 2026
Why machine learning projects die in notebooks
Most ML work never reaches production. Here is how implementation, MLOps, and a smaller first job keep models alive after the pilot.
Read the post