Watts on today? · human-reviewed
The reflexive question: Can AI do this?
A daily micro-lesson on using AI for real work.
// the weekly edition · latest
Measurement debt: when belief in AI outruns the proof
In March 2026, researchers surveyed 528 inhouse legal leaders across six countries, most of them at companies with more than US$1 billion in revenue. Two findings came back from the same respondents. Every single team…
// the curriculum · six pillars
browse the library → 01
Prompting & context
Directing the model; what you feed it
02
Memory & recall
What persists across turns and sessions; retrieval
03
Tools & connectors
Extending the model: integrations, connectors, setup
04
Workflows & iteration
Multi-step work, orchestration, refining to a result
05
Judgment & limits
Knowing when not to; failure modes, over-reliance
06
The landscape
Reading model developments; signal from hype
// the trail · published & reviewed
2026·07·23 Inherited capture: on someone else's call, the note-taker runs on their defaults Judgment & limits
2026·07·22 The Silent Engine Swap: What to Do When Your AI Tool Changes Underneath You Tools & connectors
2026·07·21 The density tax: why a short, fact-packed paste can be harder than a long one Prompting & context
2026·07·17 The ambition technology: why maximum AI value starts with a bigger ask, not a smaller workload weekly The landscape
↓ 43 modules in the archive · browse by pillar or by date