The irreducible epistemic atoms underlying the curriculum. 4,828 atoms across 8 types and 2 molecules
Force explicit link creation between separated claim and evidence nodes to create deliberate evaluation moments that interrupt automatic confirmation bias.
Maintain provenance metadata for evidence nodes specifying methodology, sample characteristics, and study limitations to enable independent quality assessment.
Label observations and interpretations explicitly with distinct markers to make the cognitive boundary between perception and inference visible and auditable.
Apply the camera test to distinguish observations from interpretations by asking whether a recording device could capture the claimed phenomenon without human judgment.
Structure notes to preserve multiple competing interpretations of the same observation rather than collapsing to a single narrative to maintain interpretive flexibility.
Structure captured notes to be interpretable without access to their original source context by embedding provenance, purpose, and relational metadata within each atomic unit.
Select granularity based on the questions your system needs to answer rather than seeking an inherent correct level of detail.
Calibrate information granularity to match working memory capacity and task complexity — too fine produces fragmentation, too coarse produces overload.
Treat questions as first-class knowledge atoms that persist and evolve rather than temporary gaps to be filled and discarded.
Maintain open questions as persistent search filters that automatically detect relevant information across future encounters without conscious effort.
Design tasks to activate curiosity by framing them as investigations with specific knowledge gaps rather than obligations to complete.
Make operational definitions explicit before reasoning from them — trace conclusions back to the specific meanings that bear their weight.
When disagreement persists despite shared facts, trace it to conflicting definitions rather than continuing to debate evidence or conclusions.
Maintain bounded contexts where terms have precise local definitions, with explicit translation layers between contexts where the same word means different things.
When concepts recur across three or more contexts with similar structure, extract the shared pattern into a named abstraction that each instance references.
Defer abstraction until you can identify what varies versus what remains invariant across instances — premature abstraction produces vague unusable generalizations.
Track the evolution of your beliefs over time rather than overwriting previous positions, because the trajectory of revision itself contains knowledge that the current state alone cannot provide.
Design capture and retrieval systems to minimize friction between having a thought and externalizing it, because even small increases in effort create selection bias toward capturing only high-activation thoughts.
When revising a belief, explicitly document what triggered the revision and what was given up to accommodate the new position, because this metadata enables detection of patterns in your belief-change process.
Apply the same tags to notes from different domains when they share conceptual patterns, because lateral connections across contexts often carry more insight than hierarchical organization within a single domain.
Accumulate atomic notes on a topic before attempting to sequence them into structured output, because bottom-up composition from existing material reveals structures that top-down planning from assumptions cannot access.
Treat digital workspace design as cognitive architecture decisions rather than tool preferences, because different tools afford fundamentally different types of thinking through their structural properties.
Structure your knowledge system so that well-decomposed atomic notes serve as high-quality inputs for AI analysis, because AI capability to surface patterns and connections is bounded by the structural clarity of the material it operates on.
Use version history to identify beliefs that have revised multiple times without converging, because this pattern signals reactive updating to surface events rather than accommodation of a deeper model.