The irreducible epistemic atoms underlying the curriculum. 4,828 atoms across 8 types and 2 molecules
Capture spontaneous insights within 5 seconds using whatever tool is immediately accessible, because signal fidelity degrades exponentially with delay and retrieval fluency drops 42% within 20 minutes.
Use voice capture for spontaneous insights during movement to achieve sub-3-second latency, because friction above this threshold creates selection bias toward only high-activation thoughts.
Match capture modality to information structure: use text for sequential verbal content, voice when hands are occupied, and photographs for spatial or visual information.
When a note contains multiple ideas connected by 'and' or 'also,' create separate notes—one per idea—with explicit links between them, rather than allowing compound ideas to remain fused in a single container.
Assign a unique identifier to every note before writing any content, treating the addressing decision as the first step that enables all subsequent linking and referencing.
Apply the 'link test' by checking whether all links from a note feel relevant to the entire note—if some links connect only to parts, the note contains multiple units requiring separation.
When AI retrieval quality degrades despite good source material, diagnose whether notes are self-contained units or fragments requiring external context, because fragmentation produces context confusion that corrupts AI reasoning.
Match note granularity to retrieval frequency and question complexity: create fine-grained atomic notes (single claims) for domains where you need precise retrieval, and coarser aggregated notes for domains where you need high-level orientation.
When encountering the same insight expressed in three or more separate notes across different contexts, extract the shared structural pattern into a single canonical note with a precise name, then replace the duplicate instances with links to the canonical abstraction.
When considering whether to merge two similar notes, test whether the underlying structure is identical (same entities, same relationships, same claims) rather than whether the vocabulary overlaps, because structural identity warrants abstraction while surface similarity does not.
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.
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.
Favor verb-based and pattern-based tags (#deciding, #recurring-blocker) over abstract category tags (#productivity, #management) to capture actionable relationships.
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.
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.
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.
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.
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.
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.
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.
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.
Design capture tools to minimize decisions between intent and recording, targeting one gesture or keystroke from any application context.
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.
For analog captures intended for long-term use, implement a pipeline that photographs or transcribes key entries into digital storage during weekly review—preserving handwriting's cognitive benefits during capture while enabling digital searchability and AI-readability for retrieval.