Rules
1,053 rules · page 5 of 22
Keep observations and conclusions in separate visible…
When conducting week-long observation audits of high-stakes domains, structure daily entries with physically separated sections for 'what I observed' and 'what my mind wanted to conclude,' keeping both sections visible simultaneously to train recognition of the observation-evaluation gap.
Apply the camera test to your data before letting AI…
Before using AI for pattern analysis on observational data, ensure your input consists of descriptive observations rather than evaluative conclusions by applying the camera test to each input statement, because AI analyzing your conclusions produces confirmation of your biases rather than structural insights.
Three occurrences in 30 days = pattern candidate…
When a behavior, reaction, or outcome recurs three or more times across a 30-day period, classify it as a pattern candidate requiring structural analysis rather than treating it as coincidence.
Log suspected patterns with dates before concluding…
Record each occurrence of a suspected pattern with date and context in a dedicated log before drawing conclusions, because memory-based frequency assessment systematically overestimates recurrence through the frequency illusion.
Same three symptoms before three failures = name…
When the same symptom triad precedes system failures across three independent incidents, document it as a named detection pattern and build an automated alert triggered by that specific combination.
Name patterns in 2-4 words that compress…
Name behavioral patterns using 2-4 word descriptors that compress the full trigger-response sequence into a recognizable label, enabling real-time pattern recognition under cognitive load.
Document every pattern with three fields
For each named pattern in your Pattern Dictionary, document three required fields: the pattern name, its observable trigger conditions, and the default behavioral response it produces.
Recognition does not eliminate patterns — track…
Do not expect pattern recognition alone to eliminate the pattern—track the ratio of pattern-following to pattern-breaking instances over weeks rather than demanding immediate control, because automaticity requires repeated override practice to weaken.
Cross-domain patterns must share causal structure…
Validate cross-domain pattern candidates by verifying that the relational structure (not surface similarity) matches across domains—two patterns share structure when the causal relationships between elements are preserved even when the elements themselves differ completely.
Before optimizing a positive pattern, test causation…
Before optimizing around a perceived positive pattern, verify through deliberate removal tests whether the pattern persists when suspected causal factors are absent.
Protect a keystone habit by guarding its triggers…
When a keystone habit produces cascading benefits, protect its trigger conditions and structural enablers rather than optimizing the habit's internal execution.
Test personal correlations for confounding variables…
Before building optimization systems around a personal correlation, test whether the correlation survives when you control for potential confounding variables through deliberate experimental variation.
When a pattern reverses across subgroups, disaggregate…
When a pattern appears to reverse across subgroups in your data, disaggregate by relevant context variables (sleep, stress, social setting) before drawing conclusions from the aggregate pattern.
Every meta-pattern claim must ground in at least three…
When identifying meta-patterns, require each second-order claim to ground in at least three documented first-order pattern instances to distinguish genuine meta-patterns from intellectual speculation.
When 3+ patterns share formation dynamics, change…
Allocate pattern-change effort to second-order interventions (changing how patterns form) over first-order fixes (changing individual patterns) when three or more first-order patterns share formation or dissolution characteristics.
Build support structures before the trough hits…
Pre-load support structures during the phase before a known cyclical trough rather than attempting to maintain behavior through willpower during the trough itself.
Same tension, different people = your relational template…
When multiple relationships produce the same tension pattern despite different people, map your own contribution to the dynamic before attributing the pattern to others' behavior.
Log the emotion, substitute activity, and rationalization…
For each avoided task that persists beyond 48 hours, log the emotion triggered, the substitute activity performed, and the rationalization constructed, as these three elements constitute the replicable structure of personal avoidance patterns.
Extract your replicable success pattern from 5 fields…
For each genuine success in the past two years, document conditions present, behaviors that differed from defaults, people involved, internal state, and 48-hour setup—then surface elements appearing in three or more instances as your replicable success pattern.
For persistent problems, study the exceptions — when did…
When a problem persists across multiple attempts, identify exceptions where the problem was absent or reduced rather than analyzing why the problem occurs, as exception conditions are more directly actionable than problem mechanisms.
Before starting a project, cross-reference your success…
Cross-reference your success pattern elements against your upcoming project plan before starting work, building in the overlapping conditions deliberately rather than hoping they emerge, as replication requires engineering.
Life transitions are habit-redesign windows — old cues…
During major life transitions (moves, job changes, context disruptions), deliberately redesign behavioral patterns rather than waiting for them to reform automatically, because environmental cue disruption creates a window where habits are more amenable to conscious redesign.
Read all notes without editing first, then extract themes…
When reviewing notes for patterns, read through all entries without editing or organizing first, then extract recurring themes on a separate page, to prevent premature categorization from filtering out emergent structures.
Validate patterns with three filters
Before concluding a pattern is meaningful, verify it survives three independent filters: sample size check (occurrences vs. opportunities), base rate comparison (frequency vs. background rate), and alternative explanation generation (minimum two alternatives).
Keep separate Candidate and Confirmed pattern lists…
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.
Project daily patterns forward 365 times before deciding…
Project each daily pattern forward using compound math (what does 365 repetitions produce?) before deciding whether to maintain or change it, because linear intuition systematically underestimates exponential outcomes.
Monthly
Audit your daily pattern portfolio by labeling each recurring behavior as appreciating (+), depreciating (-), or neutral (=), then make exactly one trade per month (reduce one depreciating pattern by 50%, install one appreciating pattern at minimal scale).
Apply the two-hour test to urgency
When something arrives marked urgent, apply the two-hour test by asking what happens if you address it in two hours instead of immediately—if the answer is 'nothing changes,' defer it.
Tier your information sources
Create a three-tier source hierarchy—5-10 daily sources (Tier 1), 10-20 weekly sources (Tier 2), everything else checked only on specific need (Tier 3)—and review monthly, demoting sources that haven't changed your thinking.
Track every urgent-feeling demand for a week — your false…
Run a daily urgency log for one week, recording every urgent-feeling demand with timestamp, then scoring each on actual time-sensitivity and impact-if-delayed-two-hours to build calibration data on false urgency rates.
Filter messages by 'does this serve my goal?' — skip…
When scanning incoming messages with a defined goal, ask whether each item directly serves that goal—if yes process it, if no skip/archive/batch it—and do not evaluate whether it's 'interesting' or 'might be useful someday.'
One source at full depth per week beats ten at surface…
Process one information source at full depth (notes, connections, could-explain-to-others) per week rather than ten sources at surface level to build signal detection capacity in your critical domains.
Delegate peripheral scanning to AI, reserve deep attention…
Use AI to scan peripheral domains weekly and deliver filtered summaries, while reserving human attention for 2-3 deep engagements per week in your core signal domains.
Write your target and set a 5-minute timer before opening…
Before opening any social media platform, write down your specific intended information target and set a 5-minute timer; when the timer fires, close the app regardless of whether you found the target.
Strip emotional language from triggering information…
When information triggers strong emotion, restate it with all emotionally loaded language stripped before evaluating whether the neutral version warrants the reaction the framed version produced.
Pair every important outcome with 1-2 leading indicators…
For each important outcome you care about, identify one lagging indicator (the outcome) and pair it with 1-2 leading indicators (upstream behaviors that predict it), tracking both to validate the predictive relationship.
When the leading indicator improves but the outcome…
When a leading indicator improves but its paired lagging outcome does not follow within the expected timeframe, treat the leading indicator as broken (gamed, confounded, or non-predictive) and replace it.
Test understanding by generating three examples…
Before claiming to understand a complex topic, generate at least three concrete examples from different domains that instantiate the concept, and if you cannot produce varied examples, treat this as evidence that you have acquired vocabulary without understanding.
After an information fast, sort inputs
After each information fast, document three specific categories: inputs you genuinely missed, inputs you craved but didn't need, and inputs you forgot existed, then eliminate or downgrade items in the latter two categories.
Quarterly
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.
Write 'This connects to [X] because [Y]' after consuming…
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.
Document what you have learned to ignore — not just what…
During expertise development, explicitly document both what you now prioritize and what you have learned to ignore, as conscious articulation of ignored features is the operational test of signal efficiency.
When expert intuition locks on, ask
When expert pattern recognition locks onto a familiar solution, force consideration of alternatives by asking 'I see X—but what if it is not X? What would I expect if it were Y?' before committing.
Wait 48 hours before acting on ambiguous decisions…
For decisions under genuine ambiguity, impose a mandatory 48-hour waiting period before acting unless you can specify concrete significant costs of delay.
Before waiting on a decision, write tripwires — what…
Write explicit tripwire conditions specifying what observations would resolve ambiguity in each direction before entering any strategic waiting period.
Execute cuts during the audit session itself — deferral…
Execute all identified cuts during the audit session itself rather than creating a deferral list, as audit outputs are decisions implemented immediately not intentions documented for later.
Define goals and criteria before using AI for signal…
When using AI for signal detection, provide explicit goals and evaluation criteria first, then use AI to scale pattern recognition, because AI without human-defined goals produces generic output from noisy channels.
When angry, seek disconfirming evidence — anger inflates…
When angry, deliberately seek disconfirming evidence and independent risk assessments, as anger systematically inflates certainty, deflates risk perception, and increases risk-seeking behavior.