Concepts
The irreducible epistemic atoms underlying the curriculum. 4,828 atoms across 8 types
Search for semantic duplicates every time you create a new…
Run semantic similarity searches against your existing notes when creating new notes to detect conceptual duplication hidden behind different vocabulary, treating AI-surfaced matches as candidates for potential abstraction or cross-linking.
Prioritize cross-domain links over within-cluster links…
Create cross-domain links between notes from different topic clusters rather than only within-cluster links, because weak ties that bridge disparate domains generate more surprising insights than strong ties that reinforce existing knowledge clusters.
Review backlinks as a serendipity engine — they reveal…
When a note has accumulated multiple backlinks from different contexts, review those backlinks as a discovery mechanism to identify emergent patterns and connections your original authorship did not anticipate, treating the backlink panel as a serendipity engine.
Treat every note link as infrastructure for AI graph…
When building knowledge systems that will interface with AI, treat every link you create as infrastructure that future graph traversal algorithms will follow, prioritizing explicit relationship encoding over implicit semantic similarity because GraphRAG systems require edges to perform multi-hop reasoning.
If a belief flips three times without converging…
When a belief revises three or more times in a short period without converging, treat this as a diagnostic signal that you are reacting to surface events rather than updating a deeper model.
When two people map the same situation differently…
When two schemas of the same situation diverge between people, treat the divergence itself as information about complexity the territory contains that neither schema fully captured.
Tag with 1-3 retrieval words
Tag notes with 1-3 keywords answering 'If I had this insight again in a different context, what word would I search for?' rather than building taxonomies before you have enough atoms.
Use verb and pattern tags (#deciding, #recurring-blocker)…
Favor verb-based and pattern-based tags (#deciding, #recurring-blocker) over abstract category tags (#productivity, #management) to capture actionable relationships.
Delete singleton tags, keep tags connecting 5+ notes…
When a tag appears on only one note, delete it during review; when a tag connects five notes from three different months, preserve it as earning its maintenance cost.
Gather atomic notes first, then arrange — do not start…
When attempting to structure an argument or presentation, gather existing atomic notes on the topic first, then arrange them into a sequence that produces a natural train of thought, rather than starting with an outline.
Ask AI for multiple sequences of your notes, not one best…
Present AI systems with your atomic notes and ask for multiple possible sequences (chronological, causal, problem-solution) rather than asking for a single best structure, using AI to discover sequences rather than impose them.
Split notes at 800 words or 3 topics — decomposition…
When a note exceeds 800 words or covers three distinct topics, decompose it into 2-4 separate atomic notes and rewrite the connections between them to reveal causal chains invisible in the original structure.
Before splitting a note, decide
When splitting a compound note during refactoring, make explicit decisions about which idea is the core claim, what was supporting evidence versus separate argument, and how the pieces causally relate before completing the split.
Let AI detect structural debt — make refactoring decisions…
Use AI to audit your knowledge base for structural debt (compound notes, duplicates, orphans, broken connections) but perform the actual refactoring decisions yourself to gain the cognitive benefit.
Sequence gaps during refactoring are specs for new notes…
When refactoring reveals that notes in a sequence jump or break, treat those gaps as specifications for new atoms to write rather than as sequence failures.
Uncertain about atomicity during capture? Write it now…
When unable to determine if a note contains one idea or two, write it as-is during capture, then return during a dedicated review session to attempt decomposition without the pressure of real-time capture.
Improve one thing every time you touch a note — kaizen…
Each time you review or link a note, make one small improvement (sharpen title, add missing context, split tangled claim) rather than scheduling separate cleanup sessions.
Speak to capture during movement — voice is 3-4x faster…
Use voice capture for thoughts occurring during movement, driving, or exercise, as speaking is 3-4x faster than mobile typing and preserves complete thought structure before decay.
One gesture from thought to capture — every extra step…
Design capture tools to minimize decisions between intent and recording, targeting one gesture or keystroke from any application context.
Anchor one capture trigger to an existing habit — six…
Start with one capture trigger anchored to your most natural existing habit, run it for six weeks minimum before adding a second trigger.
Track which inbox items truly needed real-time response…
Track which inbox items actually required real-time response rather than batch-window response to gather evidence about whether continuous processing is truly necessary for your role.
The flinch away from writing a thought down IS the signal…
When a thought triggers resistance to capture (a 'flinch' away from writing it down), use that resistance feeling as the capture trigger rather than a reason to skip—thoughts that produce hesitation are the highest-value capture targets.
In power-dynamic conversations, defer notes — capture…
During conversations where power dynamics make visible note-taking signal service role rather than equal participation, defer capture until immediately after the conversation ends—step outside within 2 minutes and externalize the three most important points while still in short-term memory.
Keep raw inbox captures out of AI retrieval scope
Index only processed permanent notes in AI-searchable systems while keeping unprocessed inbox captures outside retrieval scope, because AI systems cannot distinguish epistemic status and will retrieve raw captures with equal confidence to verified knowledge.