Collection
Use AI where it helps, and keep the thinking that still matters
Turn recent human-AI research into practical rules for task fit, verification effort, decision ownership, learning retention and measurement of real work gains.
15 elements · Explore in any order.
2 checklists · 5 heuristics · 1 pattern · 1 principle · 6 protocols
15 elements
Map the task to the current AI frontier
AI capability is jagged: two tasks that feel equally hard to you may be very different for the model.
Read the idea ProtocolRe-benchmark AI when the model or task changes
An AI capability map expires faster than most process documentation.
Read the idea ProtocolKeep a human baseline for important AI tasks
Without a baseline, 'better with AI' can mean 'faster than I remember.'
Read the idea HeuristicIncrease scrutiny when AI feels effortlessly right
Ease of agreement is not evidence of correctness.
Read the idea ChecklistKeep task stewardship when AI does the middle
Delegating the middle does not delegate the job.
Read the idea ProtocolWrite acceptance criteria before asking AI to generate
If the rubric arrives after the answer, the answer gets to write part of the rubric.
Read the idea ProtocolWrite your rationale before asking AI for a recommendation
Give the model something to extend before you give it permission to steer.
Read the idea PatternAsk AI to extend the reasoning, not only answer it
A useful second brain can add branches without becoming the judge.
Read the idea HeuristicUse recommendation mode as an option generator, not a verdict
Novel is useful raw material. It is not an authority level.
Read the idea ProtocolAttempt the learning task before opening AI
If the goal is memory, friction can be part of the work rather than a bug.
Read the idea ProtocolTest retention later without AI
Recognition beside an AI window can impersonate memory.
Read the idea HeuristicAutomate the part you can change without waiting for everyone else
A local tool can change your drafting today. It cannot unilaterally cancel everyone else's meetings.
Read the idea ChecklistMeasure AI speed and quality as separate outcomes
Faster is one axis. Better is another.
Read the idea PrincipleDo not standardize an AI ritual before testing it
Structure can add friction without adding judgment.
Read the idea HeuristicTest 'AI as thought partner' on the work that needs judgment
A good metaphor for AI is still a hypothesis about behavior.
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