Rules
1,053 rules · page 1 of 22
Externalize competing thoughts as separate labeled…
Write down competing thoughts as separate, explicitly labeled statements rather than attempting to reconcile them internally, because working memory cannot hold two positions while simultaneously evaluating them.
Capture insights within 5 seconds — speed beats format…
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.
When disagreement persists despite shared facts, draw both…
When confusion or disagreement persists despite shared facts, externalize all mental models spatially (whiteboard, diagram, parallel columns) before continuing verbal discussion, because visual comparison reveals structural misalignment that sequential verbal exchange cannot surface.
Use causal and insight language when processing difficult…
Write with causal language (because, therefore, leads to) and insight language (realize, understand, recognize) when processing difficult experiences, because this linguistic structure forces transformation from raw venting to structured sense-making that produces measurable health benefits.
Do mental inventories in different contexts — what…
Conduct separate mental inventory sessions in different physical and emotional contexts (office vs. home, morning vs. evening, calm vs. stressed), then compare outputs to reveal context-dependent retrieval gaps, because state-dependent memory causes approximately 50% retrieval variance based on context matching.
Feeling of 'thorough consideration' is a warning signal…
When you feel you have 'thoroughly considered' a decision, treat that feeling as a warning signal requiring additional externalized inventory, because WYSIATI (What You See Is All There Is) creates confidence from narrative coherence rather than completeness.
Use voice memos while walking or moving — typing friction…
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.
Writing gaps are where the thinking lives — lean…
When you encounter a gap mid-writing where you cannot articulate the next step, treat that gap as the actual location of your thinking work rather than evidence of poor preparation.
Verify you can reconstruct AI-generated reasoning…
When reviewing AI-generated text, verify whether you could reconstruct the reasoning independently - if not, you have received polish without cognitive gain and should write your own version first.
When stuck, write about the stuckness — resolution often…
When stuck on a problem, write about being stuck by describing the problem, what you've tried, what you expected versus what happened, as the narrative structure itself often produces resolution by the third paragraph.
When your mind says 'risky' or 'bad,' expand it — what…
When your inner monologue compresses a concern into a single-word assessment like '...risky,' immediately expand it in writing by specifying subject, object, and specific mechanism to decompress the elided context.
Write looping thoughts down verbatim to break the loop…
When a thought loops repeatedly, write it down verbatim as it appears in your mind rather than analyzing it, because the shift from automatic to deliberate processing breaks the loop by changing the neural circuits handling it.
'I'll remember this later' is the signal to capture NOW…
When you notice 'I'll remember this' or 'I'll write it up properly later' during an insight, treat that thought itself as an immediate trigger to capture the insight in any available medium, because the delay thought is a predictor of total loss.
Dump thoughts for 3 minutes then tag each as Signal…
Conduct a 3-minute thought dump without filtering, then immediately tag each thought as S (signal: novel, surprising, actionable, connective) or N (narration: repetitive, self-referential, habitual, defensive) to establish your baseline signal-to-noise ratio.
Filter thoughts by 'which is newest?' not 'which…
Replace emotional intensity as your thought-filtering criterion with informational value by asking 'which thought is newest?' and 'which thought changes what I should do?' rather than 'which thought is loudest?'
Filter thoughts before feeding them to AI — signal only
Feed AI only your signal-tagged thoughts rather than your unfiltered mental stream, because AI amplification of noise-plus-signal produces noise-amplified-by-compute rather than useful pattern detection.
Mark every hesitation and vague word while writing…
When attempting to write an explanation of something you believe you understand, mark every sentence where you hesitate, use vague language, or skip a step as diagnostic evidence of incomplete understanding.
Writing stalls on understood topics are knowledge gaps…
When writing stalls on a supposedly understood topic, treat the stall point as a specific learning target rather than a writing problem.
Match capture tool to content type — text for words, voice…
Match capture modality to information structure: use text for sequential verbal content, voice when hands are occupied, and photographs for spatial or visual information.
Split notes at every 'and' or 'also' — one idea per note…
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.
Create the note ID first, before writing any content…
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.
Map the whole idea first, then decompose — knowledge gaps…
Before attempting decomposition of any complex idea, map it as a whole with your current understanding externalized, then decompose systematically until you encounter steps you cannot explain clearly—those uncertainty points are your actual knowledge gaps.
If some links only connect to part of a note, the note…
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.
Extract and classify every assumption before committing…
Before committing resources to a plan, extract every assumption it depends on and classify each by importance (would the plan fail if this is wrong?) and vulnerability (how likely is this to be wrong?), then test assumptions marked both high-importance and high-vulnerability first.
Defending the whole plan when one part is challenged…
When someone challenges one part of your compound plan and you defend the whole thing, treat this as a diagnostic signal that you're still operating on fused ideas rather than independent assumptions.
Ask AI to enumerate assumptions, not answer directly…
When presenting compound statements to AI systems, explicitly ask for assumption enumeration rather than direct answers, then critically verify the decomposition's completeness since the AI may introduce its own hidden assumptions.
Decompose tasks into steps before estimating — holistic…
When unpacking task estimates, decompose complex tasks into component steps before estimating duration—unpacking improves accuracy by forcing visibility of dependencies and transitions that holistic estimation skips.
Difficulty naming a concept means you don't understand…
When encountering difficulty naming a concept precisely, treat that difficulty as a diagnostic signal revealing incomplete understanding requiring further processing rather than a labeling problem.
Store evidence as independent nodes with methodology…
Store evidence with full methodological metadata (sample size, control conditions, limitations) as independent nodes rather than as decorative citations on claims, to enable proportionality assessment and multi-argument reuse.
Log contradictions instead of resolving them — patterns…
Before forcing resolution of contradictory observations or beliefs, accumulate multiple instances in a contradiction log to enable pattern detection impossible from individual contradictions.
When AI retrieval degrades, check if notes…
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.
Fine-grained notes for frequent precise lookups, coarse…
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.
Maintain multiple granularity levels for AI knowledge…
When using AI systems with your knowledge base, maintain multiple granularity levels of the same material (fine-grained for precise retrieval, coarse-grained for contextual reasoning) rather than forcing a single chunk size, because different query types require different resolutions.
Give questions the same structural treatment as answers…
Store well-formed questions as first-class atoms in your knowledge system with the same structural treatment (unique identifiers, bidirectional links, metadata) as claims and answers, because questions organize attention and generate persistent search filters.
Link partial answers to the question — never replace…
When a question receives a partial answer, preserve the original question as a persistent atom and link the answer to it rather than replacing the question, creating a visible record of how understanding evolves from open inquiry to accumulated evidence.
Feed AI your full evidence constellation, not a cold…
When using AI to analyze accumulated evidence around an open question, provide the constellation of linked notes (question + partial answers + contradictions + gaps) as context rather than asking the AI to answer from scratch, because the accumulated context enables pattern recognition your cold query cannot access.
Write operational definitions for every high-stakes term…
For every high-stakes term in your reasoning (quality, success, productive, fair), write an operational definition specifying observable conditions that must be true for the term to apply, then store that definition as a canonical reference atom in your knowledge system.
When persistent conflict uses the same words, audit…
When two people or two parts of your own thinking use the same term with persistent conflict, pause the debate and conduct a definition audit: have each party write their operational definition independently, then compare—if definitions diverge, the conflict is definitional not factual and should be resolved at the definition level.
Give AI your operational definitions before asking…
Feed your operational definitions to AI systems as explicit context before generating analysis or recommendations, treating your personal glossary as the translation layer between the model's probability-weighted semantics and your specific conceptual framework.
After three instances of the same insight, extract…
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.
Test for structural identity, not vocabulary overlap…
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.
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.