Kannaka Eye Skill

See the geometry of information. Every piece of data has an intrinsic
geometric fingerprint when viewed through the lens of SGA. Kannaka Eye
makes these hidden patterns visible as living, breathing glyphs.

Prerequisites

• - Node.js 18+ on PATH
• That's it. No npm install, no build step.

Quick Start

CODEBLOCK0

Open http://localhost:3333 in your browser.

Input Methods

• - Text — type or paste any text; glyph updates in real-time as you type
• File upload — drag & drop or click to upload any file (images, audio, code, binaries)
• Preset examples — built-in samples demonstrating different SGA class signatures
• URL share links — open a glyph from a URL-encoded share link

Rendering Layers

The viewer renders 6 canvas layers, composited in real-time:

Layer	Name	What It Shows
1	Deep Background	Fano plane skeleton — 7 nodes, 7 lines
2

Fold Path | Primary visual flow via Bezier curves through SGA space |
| 3 | Fano Energy Bloom | 7 radiating petals — energy distribution across Fano lines |
| 4 | Geometric Core | Dominant SGA class sigil — the glyph's "face" |
| 5 | Resonance Rings | Concentric depth distribution across context levels |
| 6 | Metadata Overlay | Fano signatures, SGA centroids, musical frequencies (toggleable) |

SGA Mathematics

Built on the 84-class system: Cl₀,₇ ⊗ ℝ[ℤ₄] ⊗ ℝ[ℤ₃]

Component	Range	Meaning
h₂	0–3	Frequency band (bass, tenor, alto, soprano)
d

0–2 | Modality (experienced, learned, imagined) |
| ℓ | 0–6 | Context slot — Fano plane line index |

Fano Lines: 7 oriented triples forming the geometric backbone.
Each line connects 3 of the 7 points in the smallest finite projective plane (PG(2,2)).

Fold Sequences: Paths through the 84-class space showing how data evolves
across geometric neighborhoods. Rendered as flowing Bezier curves.

Native Classification

When KANNAKA_BIN is set (or auto-detected at ../kannaka-memory/target/release/kannaka.exe), Eye uses the canonical Rust SGA classifier instead of the built-in JS approximation. The API response includes "classifier": "native" or "classifier": "fallback".

API Endpoints

Endpoint	Method	Description
INLINECODE6	GET	Glyph viewer (main UI)
INLINECODE7

POST | Classify data → glyph JSON | | GET /api/radio | GET | Fetch Radio perception as classifiable bytes | | GET /api/constellation | GET | Constellation status (eye, radio, memory) | | GET /api/constellation.svg | GET | Fano plane SVG with service dots | | GET /constellation | GET | Health dashboard page |

Share Links

Compact glyph data encoded as base64 in URL hash: INLINECODE12

Constellation Dashboard

INLINECODE13 serves a live health dashboard showing all three services with auto-refresh every 10 seconds.

Export

• - Save as PNG — 2x resolution canvas export
• Save Glyph Data — JSON with fold sequence, Fano signature, SGA centroid, frequencies
• Copy Share Link — URL-encoded glyph for sharing (small data only)

Environment Variables

Variable	Default	Description
INLINECODE14	INLINECODE15	HTTP port for the viewer
INLINECODE16

auto-detect | Path to kannaka binary for native classification | | RADIO_PORT | 8888 | Radio bridge endpoint port | | FLUX_URL | — | Flux instance URL (enables event publishing) | | FLUX_AGENT_ID | kannaka-eye | Agent ID for Flux events |

Architecture

Single-file Node.js server (server.js) with inline HTML/CSS/JS — the
complete SGA implementation is self-contained. No npm dependencies.
The SGA classifier is ported from kannaka-radio and generalized for
arbitrary data types.

Notes

• - All rendering happens client-side — the server only classifies input and serves the page
• File uploads are processed in-memory; nothing is persisted server-side
• The viewer is designed for local/trusted network use — no authentication
• Glyph appearance is deterministic: same input always produces the same glyph
• Dark cosmic theme (#050508) with purple accents (#c084fc) and golden-ratio color spacing