Practical writing on applied AI, downloadable guides, and Oasium plugins designed around complete workflows.
Most AI training teaches tool features, and tool features now expire in weeks. What lasts is the layer underneath: context, checking, decomposition, judgment.
Half the AI arguments inside a company are two people using the same word for different things. One kind helps almost anyone tomorrow; the other is software.
The densest quarter on the model release calendar since GPT-5 shipped. One read of what shifted, what stayed, and what it changes for anyone making AI decisions right now.
The word lost most of its meaning in 2024 and has been slowly recovering it. A working definition, three failure modes, and a checklist.
A practical framework for the AI procurement decision every operator faces in 2026: build, buy, hybrid, or wait. Two scoring sheets, a worked example, an honest case for waiting, and a printable vendor-interview script.
An opinionated buyer's guide for teams considering on-prem AI deployments in 2026. Hardware tiers (Mac Studio, DGX Spark, Dell GB300), open-weight model picks per workload, the software stack, a workload decision matrix, a worked example, and the operational reality of month four.
How to build an evaluation harness before you build the AI system. Held-out test sets, scoring rubrics, tool selection (Inspect AI, Braintrust, Promptfoo, LangSmith), production observability, three worked examples at different scales, anti-patterns, and what month-four operational reality looks like.
