Practical notes on making AI useful after the demo.
By field notes, I mean short, practical essays from systems I actually run: what broke, what held up, and what changed my mind about how AI should fit into real work.
Not a link dump. A point of view from the workbench.
The public version stays small on purpose: what happened, why it matters, and what I am changing in my own systems because of it. If there is no decision or tradeoff, it stays private.
Read approved issues →Get the next issue
One useful note when the signal is strong enough. The goal is judgment, not another automated digest.
Every issue has to make a decision clearer.
Pattern
A practical shift I am seeing while building systems that use models, memory, schedules, and review gates.
Implication
What that shift changes for builders, operators, and people deciding where AI belongs in the workflow.
Experiment
The concrete adjustment I am testing in Prism, HQ, The Take, ADW, or my own working setup because of it.
Summarize privately. Publish only with a point of view.
I use private notes and operating logs to find signal, but the published issue has to stand on its own. If it does not make a decision clearer, change how I build, or name a useful tradeoff, it stays in the notebook.
What actually happened
Why it matters for operators
What I am testing because of it
What is noise