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Field Node v2

The Explorer as System of Relation

Knowledge Infrastructure

Reimagined knowledge infrastructure as an ecology of blocks and relations, with the Explorer Page as a living interface for performing care through interaction

01. Figures

Landing page - Field Node v2 main entry point. Establishes environment.

Fig. 1

Landing page - Field Node v2 main entry point. Establishes environment.

Login interface - user authentication. Shows decision mechanism.

Fig. 2

Login interface - user authentication. Shows decision mechanism.

Home page - user dashboard and main workspace. Shows how logic operates.

Fig. 3

Home page - user dashboard and main workspace. Shows how logic operates.

Orientation - system introduction and user onboarding. Shows user control.

Fig. 4

Orientation - system introduction and user onboarding. Shows user control.

Nodes view - knowledge graph visualization. Supplemental explanation.

Fig. 5

Nodes view - knowledge graph visualization. Supplemental explanation.

Nodes interface - alternative nodes display. Supplemental explanation.

Fig. 6

Nodes interface - alternative nodes display. Supplemental explanation.

Explorer - knowledge navigation and discovery. Outcome view.

Fig. 7

Explorer - knowledge navigation and discovery. Outcome view.

Dropdown menu - navigation and feature access. Supplemental explanation.

Fig. 8

Dropdown menu - navigation and feature access. Supplemental explanation.

Tend to nodes - interaction and care interface for knowledge nodes. Closing visual.

Fig. 9

Tend to nodes - interaction and care interface for knowledge nodes. Closing visual.

02. Abstract

Field Node v2 is a knowledge infrastructure system that organizes information as blocks (imported ideas) and nodes (personal reflections) connected through relations. The Explorer Page enables visual, relational exploration of theory and ideas without traditional search or hierarchy.

03. Research Question

Can algorithmic surfacing enhance relational discovery without replacing human curatorial judgment? Specifically, can algorithms be designed to listen for resonance—shared emotional tone, conceptual proximity, overlapping contexts—rather than predict behavior or prioritize computational efficiency?

04. Hypothesis

When algorithms surface connections based on relational proximity (shared contexts, emotional resonance, conceptual overlap) rather than keyword matching or behavioral prediction, users will discover more meaningful connections and exploration will feel more like intimacy than extraction. However, if algorithms prioritize efficiency over relational care, they will group ideas by surface similarity rather than deeper resonance, reducing the system's capacity for meaningful discovery.
ElementPurpose / Specification
Color PaletteEspresso Brown, Neutral Gray, gray, Soft Beige, Taupe Gray, Clay Tan, black, pink (#1E1C1A, #2A2826, #6b7280, #DAD3CA, #8A837B, #CBBBA0, #000000, #ec4899)
TypographyInter
Design PrinciplesCare • Relation

Fig. 0 Visual system schematic with design rationale.