The irreducible epistemic atoms underlying the curriculum. 4,828 atoms across 8 types and 2 molecules
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 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.
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.
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.
During weekly reviews, ask four metacognitive questions—what did I capture well, what did I almost lose, where did I over-capture noise, and what am I avoiding—to monitor system health rather than just processing lists.
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.
Maintain two separate lists—'Pattern Candidates' and 'Confirmed Patterns'—promoting candidates only after they survive three independent observations, one alternative-explanation check, and one successful prediction.
Schedule quarterly depreciation reviews where you scan captured notes and bookmarks for information that has exceeded its useful life, then either update with current data, archive with context, or delete entirely to prevent outdated information from corrupting current decisions.
After consuming any piece of information, write one connecting sentence that relates it to existing knowledge using the structure 'This connects to [X] because [Y]'; if you cannot write this sentence within two minutes, classify the content as non-compounding noise regardless of its intrinsic quality.
Distinguish domain-specific facts (treatment protocols, software frameworks, market conditions) requiring aggressive temporal updating from structural principles (logic, mathematics, core psychological mechanisms) where age indicates Lindy-tested robustness, applying opposite update strategies to each type.
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.
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.
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.
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.
Store each schema with explicit scope documentation specifying the domain where it was built and the structural conditions it assumes, treating scope as mandatory metadata rather than optional annotation.
Document the purpose each category serves by completing the sentence 'this category exists to [do what] for [whom]' to distinguish functional infrastructure from inherited furniture.
Design multi-class classification systems with mutually exclusive categories when items can only be one type, and multi-label systems when items can legitimately belong to multiple categories simultaneously.
For each top-level category in your knowledge system, write one sentence explaining what value that category protects or promotes, then identify missing categories that would operationalize values you hold but aren't currently encoding.
When a 'Miscellaneous' or 'Other' category grows faster than named categories, it signals that your classification dimensions are missing a meaningful distinction that reality contains.
Connect each abstract concept in your knowledge system to at least three concrete examples from different domains, because single examples invite surface-feature overgeneralization while multiple examples force attention to shared structural patterns.