Cartography V1
An explainable behavioral-pattern engine that turns subjective reflections into structured, evidence-backed patterns without diagnosing the user or treating context-dependent behaviors as inherently good or bad.
What this proves: Demonstrates explainable inference, state tracking, and local-first architecture—turning ambiguous information into a structured, usable product system.
Coordinate Input
Enter an observation or click a preset pathway to map subjective reflections into continuous 2D coordinates.
Vector Sequences
Watch vector sequences reveal behavioral transitions across quadrants rather than static clinical labels.
Deterministic Ledger
Inspect the deterministic state record built entirely client-side without external tracking or diagnostic bias.
Why Explainable Topologies Outperform Clinical Labels
Conventional productivity and psychological tracking tools force complex, context-dependent human moments into crude binary judgments: "good habits" vs "bad habits", or diagnostic classifications. Cartography approaches personal information architecture with mathematical neutrality.
Continuous Coordinate Geometry
Instead of rigid discrete tags, moments exist on a continuous 2D manifold. Behaviors are defined relative to active situational demands and internal cognitive reserves.
Non-Diagnostic Taxonomy
The Perimeter (high vigilance) is not pathologized as "paranoia" or "maladaptive"—it is recognized as an active protective posture responding to environmental conditions.
State Transitions & Loops
The engine tracks state vectors over time, revealing recurring behavioral loops (e.g., Threshold → Perimeter → Wilds → Sanctuary) and identifying root inflection points.
Local-First Privacy
All spatial calculations, node indices, and state ledgers execute strictly in client-side memory with zero external telemetry, keeping reflections entirely private.
Interested in custom behavioral or evidence engines?
I build custom operational machinery, algorithmic tools, and diagnostic web products.