Practical writing on responsible AI adoption — what we've seen work in real deployments, where pilots fall apart, and how to evaluate AI investments honestly.
Five things to fix before your demo becomes a deployment — and why "it works in my notebook" isn't a milestone.
Building a culture of trust and transparency around AI tools — without the 60-page policy document no one reads.
A field guide to LLM evaluation that catches regressions before users do — without becoming its own engineering project.
A buyer's guide to the unstructured-data side of your business — and the four use-case patterns that pay off fastest.
When to use a single Claude call, when to reach for an agent, and how to keep agents from going off the rails.
Caching, routing, and budget guardrails that turn unpredictable model bills into a line item you can actually plan around.
Patterns that work, mistakes to avoid, and field notes from real deployments. No fluff.
No spam. Unsubscribe anytime.