Notes from
the field.
Engineering, eval, and the boring infrastructure of production AI. Six launch posts below. Sixteen by the end of Phase 7.
What an LLM is really doing: a field guide to the transformer
Strip away the mystique and a large language model does one thing: it guesses the next token. We trace a single sentence through the whole machine, stage by stage, with diagrams for each part. Tokens, embeddings, RoPE, attention, multi-head, the feed-forward network, the residual stream, and the prediction loop, plus where the architecture is heading.
How to calculate ROI for LLM implementation
A CFO-friendly framework for sizing the payback on direct-LLM agent work, before you commit a budget.
RAG vs fine-tuning: which is right for your use case?
A short, opinionated decision tree. Most teams need RAG. A small minority benefit from fine-tuning.
The Frontier Firm playbook: a twelve-week field guide
How a 200-person company restructures its org chart around AI agents without firing anyone or burning a year of credibility.
Why most AI projects fail (and what we do differently)
Six failure patterns we see in every audit, ranked by how much money they cost, plus the cheap fixes that prevent them.
Dispatch, don't paste: how CEOs brief engineers in the age of AI
AI gives you the tip of the iceberg in thirty seconds. The other ninety percent is what the K-Framework maps: users, outcomes, eval bars, data contracts, rollback paths, open decisions. A friendly tour of what is under the waterline.