Facts, concepts & general knowledgeTimeless informationExamples: API docs, definitions, configurations
Decay
Half-life: 139 days (slow)Month 1: Fresh
Month 3: Still strong (0.85)
Month 6: Moderate (0.65)
Year 1: Fading (0.40)
Use for:
API documentation and references
Technical definitions
Company policies
Product feature descriptions
Configuration settings
Integration docs
Examples:
API Docs
Product Knowledge
Technical Concepts
Copy
"Our REST API uses JWT bearer tokens.Tokens expire after 30 days inactivity.Header: Authorization: Bearer {token}Generate: POST /api/auth/tokenRefresh: POST /api/auth/refresh"Sector: semantic
Why: Facts about product, useful long-term, rarely changes
Copy
"Semantic waypoints:- Graph edges connecting similar memories- Created when similarity ≥0.75- BFS search up to 3 hops- 20% salience decay per hopPurpose: Automatic context expansion"Sector: semantic
Why: Conceptual knowledge, relevant as long as feature exists
Why fast decay: Sentiment changes quickly, old emotions less relevantExamples:
Customer Feedback
Team Morale
User Reaction
Copy
"Acme Corp frustrated with API rate limits.Expressed urgency - blocking their launch.Tone: Frustrated but professional.Resolution: Upgraded plan, now satisfied.Sentiment: Frustrated → Satisfied"Sector: emotional
Why: Sentiment snapshot, less relevant after resolution
Copy
"Team morale high after successful launch.Everyone excited about positive customer feedback.Celebrating at team dinner tonight.Context: v2.0 launch week"Sector: emotional
Why: Emotional state, changes as project progresses
Copy
"Users love the new search feature!Twitter feedback overwhelmingly positive.Multiple users saying 'game changer.'Feature: Semantic waypoints"Sector: emotional
Why: Reaction to feature, fades as feature becomes expected
Half-life: 693 days (very slow)Month 6: Still 0.95
Year 1: Still 0.85
Year 2: Still 0.70
Year 3: Still 0.60
Use for:
Architecture decisions
Technology choices
Lessons learned
Strategic pivots
Post-mortems
“Why we did this”
Why slow decay: Strategic insights remain valuable for yearsExamples:
Architecture
Lesson Learned
Tech Choice
Copy
"Why we chose microservices over monolith:Context: Scaling to 100+ developers across teamsDecision: Microservices architectureReasoning:- Team autonomy (deploy independently)- Technology flexibility (right tool per service)- Fault isolation (one service down ≠ all down)Trade-offs accepted:- Increased operational complexity- Distributed system challenges- Network latency between servicesDate: Jan 2024"Sector: reflective
Why: Strategic decision that informs future architecture choices
Copy
"Production outage retrospective - Dec 2024What happened: Database migration ran during peak trafficImpact: 45min downtime, 500 affected usersRoot cause: No maintenance window policyLessons:- Always schedule maintenance windows- Run migrations in off-peak hours- Test migrations on staging first- Have rollback plan readyActions: Created maintenance window policy"Sector: reflective
Why: Hard-won wisdom that prevents future incidents
Copy
"Why we chose React over Vue for frontend:Context: Rebuilding admin dashboardDecision: React + Next.jsReasoning:- Larger talent pool (hiring)- Better TypeScript support- Rich ecosystem (libraries)- Server components (performance)Alternatives considered:- Vue 3: Smaller ecosystem- Svelte: Less matureDate: Feb 2024"Sector: reflective
Why: Technology rationale that guides future tech decisions
Is it about feelings/sentiment? → Emotional (fast decay)Is it a time-bound event? → Episodic (medium-fast decay)Is it a step-by-step process? → Procedural (medium decay)Is it a fact or definition? → Semantic (slow decay)Is it strategic insight or "why"? → Reflective (very slow decay)