A practical method for finding the decision points, data boundaries, and failure paths that determine whether an AI automation will work in production.
Retrieval is only one layer of a reliable RAG system. Production designs also need permissions, evidence lineage, confli...
A production evaluation plan for agentic systems that measures completed work, tool behavior, evidence quality, cost, la...
How to place approvals around high-impact AI actions without turning every workflow into a manual queue....
A decision framework for hosted APIs, dedicated environments, and self-hosted models based on data boundaries, operation...
A practical look at the workflows where voice AI helps, the places where it does not, and the production details that de...
How a local language model, Gmail integration, and preference memory can reduce inbox checking without sending email con...
Use a language model to accelerate the first draft, then validate every instruction against the product, code, and inten...
A small machine-learning experiment that maps color names to RGB values using word embeddings and a feed-forward neural ...
A technical walkthrough of preparing data, training BERT, and evaluating a classifier that maps natural-language command...
