Notes from the work, not the hype cycle.
Operator-grade thinking on putting AI to work: where it earns its place, how to build agents and Skills, and what it takes to run AI in production. Written by the people who ship it.
AI-driven, not AI-decorated: where AI actually earns its place
Most AI programs stall because they add tools instead of changing the work. Becoming AI-driven means embedding AI where it creates advantage — and refusing it where it does not.
Stop Treating LLM Output as a Promise
Structured LLM output is not reliable because the prompt is clever. It becomes reliable when the model is surrounded by validation, retries, fallbacks, auditability, and business-aware control logic.
Skills: turning your methodology into an asset agents can run
The highest-leverage AI skill for a business is not prompting. It is encoding how your best people work into reusable Skills that agents can execute consistently across systems.
The token economy: maximum output at minimum cost
Running AI in production is an OpEx problem as much as a modeling one. The token economy is about choosing the right model for each task and controlling consumption as you scale.
Less reading about AI. More results from it.
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