Concepts
The irreducible epistemic atoms underlying the curriculum. 4,828 atoms across 8 types
Record decisions with five fields
Document decisions using five fields: what you decided, alternatives considered, information available and missing, optimization criteria, and conditions for revisiting—rather than recording only conclusions.
Have AI argue against your decision before finalizing…
Before finalizing significant decision records, have an AI argue against your reasoning and append the strongest objection to your record, preserving the full deliberation rather than only your preferred conclusion.
Write reasoning as numbered steps with explicit warrants…
Externalize reasoning chains by writing numbered steps where each step connects to the next through an explicit warrant stating why step N leads to step N+1, marking any transition that relies on unstated assumptions.
If writing your reasoning produced zero surprises…
When a reasoning chain contains no surprises or pauses during construction—no moments where the next link was weaker than expected—you have transcribed conclusions rather than constructed reasoning and should restart with genuine step-by-step building.
After labeling an emotion, trace the cause then ask what…
After labeling each emotion, write one sentence identifying what is generating it using causal language ('because'), then check for emotional layers by asking 'What is underneath this?' to surface masking dynamics.
Use structured labeling instead of narrative venting…
When externalizing emotions, avoid narrative venting ('he did this and then that happened') and instead use structured labeling ('I feel X because Y') to convert fusion into defusion.
Let AI find patterns across journal entries — label…
Use AI to analyze patterns across multiple emotional externalization entries (recurring emotions, triggers, trends) rather than to label emotions for you, because the regulatory benefit comes from the act of labeling, not from being labeled.
Attach 'When [situation], I will [action]' to every…
Write implementation intentions beneath each written goal using 'When [specific recurring situation], I will [specific first action]' format, specifying concrete triggers rather than time-based or motivation-based conditions.
Track assumptions in a five-field register
Maintain an assumption register with five components for each assumption: the specific testable claim, what would change if false, current evidence for/against it, validation status, and next action to test it—reviewing weekly for active projects.
When a commitment will be missed, renegotiate proactively…
When a commitment cannot be met, communicate the fact proactively before the deadline and renegotiate terms explicitly, as silent dropping versus explicit renegotiation distinguishes reliable commitment systems from internal intention failures.
Draw mental models as boxes and labeled arrows within 10…
Draw mental models as diagrams with boxes for entities and labeled arrows for relationships within ten minutes, because spatial layout forces explicit specification of what connects to what and reveals gaps that prose automatically conceals.
Label diagram arrows with specific verbs — 'causes'…
When externalizing mental models, label every arrow with a specific verb describing the relationship mechanism (causes, enables, blocks, amplifies) rather than vague connectors like 'affects' or 'relates to', because unlabeled relationships reveal unexamined assumptions.
Audit every model diagram for missing feedback loops…
After drawing a mental model, audit it for missing feedback loops by tracing whether any effects circle back to influence their own causes, because circular causation governs most complex systems but is invisible to linear thinking.
Write blockers as 'I cannot [action] because [obstacle]'…
Write blockers in the form 'I cannot [specific action] because [specific obstacle]' immediately upon noticing friction to convert ill-structured problems into solvable ones.
Split compound blockers into separate obstacles…
Decompose compound blockers into separate obstacles with independent owners and solutions before attempting resolution, because monolithic blockers resist action through perceived complexity.
Structure learning notes as: claim, evidence, connection…
Write learning in the structure: claim (one sentence, your words), evidence (why believe it), connection (how it relates), question (what's unresolved) to force generation rather than transcription.
Write your explanation before asking AI — generation…
When using AI for learning, write your own explanation first, then use AI interrogation to find gaps, then revise—never let AI write the initial explanation because reading AI output does not produce the generation effect.
Capture feedback within 60 minutes in five structured…
Capture feedback within 60 minutes of receiving it using structured fields (date, source, verbatim content, emotional reaction, specific behavior) before memory reconstruction distorts the signal.
Classify failures into three types before analysis…
Categorize each failure as preventable (process deviation), complex (novel factor interaction), or intelligent (frontier experiment) before analysis, because different failure types require different questions.
Weekly
Audit thinking environments weekly by comparing actual conditions against documented specifications to detect entropy, because environmental decay through accumulated objects, browser tabs, and permission drift is constant and unnoticed without structured review.
Document your knowledge system in 5 parts
Document system operations in five components—capture rules, processing workflow, retrieval method, review protocol, and evolution history—because each component addresses a distinct failure mode in knowledge system sustainability.
Cross-reference domains in weekly review
During weekly reviews, cross-reference externalized domains to detect contradictions—compare stated priorities against time allocation, goals against commitments, assumptions against failure analyses—because isolated review of each domain misses the conflicts that degrade decision quality.
Test: can you reconstruct the reasoning without the AI?
Test AI integration by verifying whether interactions increase your independent understanding—if you cannot reconstruct the reasoning without the AI, the tool is replacing cognition rather than extending it.
Give AI your full knowledge system, not isolated questions
Feed complete externalized system context to AI assistants rather than isolated queries, because AI reasoning quality scales with the completeness and structure of the personal knowledge base it can traverse.