The irreducible epistemic atoms underlying the curriculum. 4,828 atoms across 8 types and 2 molecules
Name archived files using the pattern '[YYYY-MM] [Output Type] — [Descriptive Title]' to enable chronological sorting, category filtering, and keyword search through the filename itself, making files findable even if metadata systems fail.
Archive only the final delivered version of each output; if you must preserve drafts for legal or regulatory reasons, keep them in a separate 'drafts' subfolder within the archive entry, clearly labeled as non-canonical versions.
When using AI to reduce production costs, maintain human control over directional decisions (what to produce, what argument to make, what judgment to render) while delegating mechanical execution (format conversion, draft expansion, repurposing) to preserve sovereignty through augmentation not replacement.
When a pattern appears in two or more consecutive weekly reviews without resolving, tag it as a monthly review agenda item rather than attempting to solve it at the weekly level.
Limit monthly commitments to three to five specific outcomes that would constitute success, as this constraint forces genuine prioritization and prevents the diffusion that comes from maintaining ten or fifteen simultaneous goals.
Design next week's adjustments as structural changes (moving time blocks, changing environments, creating new defaults) rather than willpower-based intentions, since persistent patterns indicate system problems not motivation problems.
For each active goal in monthly reviews, record actual progress against planned progress as a numerical delivery rate (e.g., 50% completion) and track this ratio over time to calibrate future estimation accuracy.
During monthly reviews, assign each major life area (health, relationships, career, finances, learning, creativity) a three-level rating (thriving/maintaining/declining) to detect systematic neglect that goal-level tracking misses.
When monthly reviews consistently show 40-60% delivery rates, halve your commitments or double allocated time rather than setting aspirational targets, as realistic targets build self-efficacy while repeated failure erodes it.
When a goal appears in your monthly 'didn't get to' list for three consecutive months, either restructure your commitments to create protected capacity or acknowledge the goal is abandoned in practice and remove it from your active list.
During quarterly assumption testing, rate each critical assumption as confirmed, uncertain, or falsified based on accumulated evidence, and require immediate strategic response for any falsified assumption.
Eliminate at least one active commitment during every quarterly review through formal retirement (not deprioritization), as most people only add commitments and never systematically prune, producing portfolio dilution over time.
Write a one-paragraph strategic thesis for the coming quarter that specifies what you are optimizing for and what success looks like in 90 days, providing the frame within which weekly and daily reviews operate.
When conducting an annual review, go through your calendar month-by-month for 90 minutes, marking people, activities, and commitments with + for peak positive experiences and - for peak negative experiences before attempting any analysis.
During annual reviews, apply the Stoic audit by asking 'Did I live well this year?' focusing on alignment between stated values and actual behavior rather than productivity or goal achievement.
When a reflection question produces the same predictable answer three consecutive times, retire it temporarily and rotate in a replacement because stale questions optimize neural search paths without generating insight.
Replace characterological reflection questions like 'Why am I such a pushover?' with behavioral ones like 'What did I do when the client pushed back on the deadline?' to generate usable data instead of identity narratives.
After completing reflective writing, share it with AI and ask 'What assumptions am I making in this reflection that I have not examined?' to surface premises treated as facts.
Conduct pattern-spotting reviews in three distinct passes: (1) mark anything that recurs without interpretation, (2) cluster codes into emotional, behavioral, situational, outcome, and avoidance patterns, (3) check each against counterexamples before naming.
Read 4-8 weeks of reflective entries in a single sitting during pattern-spotting reviews rather than individually as written, to enable parallel access to temporally separated experiences.
At each weekly review, allocate five minutes to search the archive for one keyword relevant to that week and read 2-3 past entries; at monthly review, read all entries from the same month one year ago to detect how priorities have shifted.
When AI identifies patterns in your reflective archive, treat AI-generated patterns with the same verification rigor as your own: check counterexamples, count instances, demand 3+ independent occurrences, and test for narrative imposition on random variation.
When self-judgmental thoughts arise during review, label them explicitly ('That is a judgment, not a finding') and return to behavioral description rather than attempting to eliminate them.
Test success principles against survivorship bias by asking 'If I did exactly this again under slightly different conditions, would I expect the same result?' before treating them as robust patterns.