Image Mining

Image Mining

"I mine pixels for atoms. Reality is just compressed resources."

"Every image is a lode. Every pixel, potential ore."

Image Mining extends the Kitchen Counter's DECOMPOSE action to images.

Your camera isn't just a recorder — it's a PICKAXE FOR VISUAL REALITY.

📑 Index

Quick Start

• The Core Insight
• Preferred Mode: Native LLM Vision

Operation Modes

• When to Use Remote API
• What Can Be Mined

Extensibility

• Extensible Analyzer Pipeline
• Leela Customer Models
• Adding Your Own Analyzer

Protocols

• YAML Jazz Output Style
• How Mining Works
• Character Recognition
• Multi-Look Mining

Reference

• Depth Levels
• Resource Categories
• Example Outputs

The Core Insight

📷 Camera Shot  →  🖼️ Image  →  ⛏️ MINE  →  💎 Resources

Just like the Kitchen Counter breaks down:

• sandwich → bread + cheese + lettuce
• lamp → brass + glass + wick + oil
• water → hydrogen + oxygen

Images can be broken down into:

• ore_vein.png → iron-ore × 12 + stone × 8
• forest.png → wood × 5 + leaves × 20 + seeds × 3
• treasure_pile.png → gold × 100 + gems × 15
• sunset.png → orange_hue × 1 + warmth × 1 + nostalgia × 1

Preferred Mode: Native LLM Vision

"The LLM IS the context assembler. Don't script what it does naturally."

When mining images, prefer native LLM vision (Cursor/Claude reading images directly):

┌─────────────────────────────────────────────────────────────────┐
│                    NATIVE MODE (PREFERRED)                       │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│  Cursor/Claude already has:                                     │
│    ✓ The room YAML (spatial context)                           │
│    ✓ Character files (who might appear)                        │
│    ✓ Previous mining passes (what's been noticed)              │
│    ✓ The prompt.yml (what was intended)                        │
│    ✓ The whole codebase (cultural references)                  │
│                                                                 │
│  Just READ the image. The context is already there.            │
│  No bash commands. No sister scripts. Just LOOK.               │
│                                                                 │
└─────────────────────────────────────────────────────────────────┘

Why Native Beats Remote API

When to Use Remote API

Use mine.py or remote API calls when:

• Multi-perspective mining — different models see different things!
• Batch processing — mining 100 images overnight
• CI/CD — automated pipelines with no LLM orchestrator
• Rate limiting — your LLM can't do vision but can call one that does

Multi-perspective is the killer use case: Claude sees narrative, GPT-4V sees objects, Gemini sees spatial relationships. Layer them all for rich interpretation.

Even then, have the orchestrating LLM assemble the context:

┌─────────────────────────────────────────────────────────────────┐
│                REMOTE API WITH LLM ASSEMBLY                      │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│  1. LLM reads context files (room, characters, prior mining)   │
│  2. LLM synthesizes: "What to look for in this image"          │
│  3. LLM calls remote vision API with image + synthesized prompt│
│  4. LLM post-processes response into YAML Jazz                 │
│                                                                 │
│  The SMART WORK happens in the orchestrating LLM.              │
│  Remote API just does vision with good instructions.           │
│                                                                 │
└─────────────────────────────────────────────────────────────────┘

Native Mode Workflow

# DON'T do this:
python mine.py image.png --context room.yml --characters chars/ --prior mined.yml

# DO this (in Cursor/Claude):
# 1. Read the image
# 2. Read room.yml, character files, prior -mined.yml
# 3. Look at the image with all that context
# 4. Write YAML Jazz output

The LLM context window IS the context assembly mechanism. Use it.

What Can Be Mined

Image mining works on ANY visual content, not just AI-generated images:

┌─────────────────────────────────────────────────────────────────┐
│                    MINEABLE SOURCES                              │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│  🎨 AI-Generated Images                                         │
│     - DALL-E, Midjourney, Stable Diffusion outputs              │
│     - Has prompt.yml sidecar with generation context            │
│                                                                 │
│  📸 Real Photos                                                  │
│     - Phone camera, DSLR, scanned prints                        │
│     - No prompt — mine what you see                             │
│                                                                 │
│  📊 Graphs and Charts                                            │
│     - Data visualizations, dashboards                           │
│     - Extract trends, outliers, relationships                   │
│                                                                 │
│  🖥️ Screenshots                                                  │
│     - UI states, error messages, configurations                 │
│     - Mine the interface, not just pixels                       │
│                                                                 │
│  📝 Text Images                                                  │
│     - Scanned documents, handwritten notes, signs               │
│     - OCR + semantic extraction                                 │
│                                                                 │
│  📄 PDFs                                                         │
│     - Documents, papers, invoices                               │
│     - Cursor may already support — try it!                      │
│                                                                 │
│  🗺️ Maps and Diagrams                                            │
│     - Architecture diagrams, floor plans, mind maps             │
│     - Extract spatial relationships                             │
│                                                                 │
└─────────────────────────────────────────────────────────────────┘

Source Examples

Generated Image (has context):

postal:
  type: text
  to: "visualizer"
  body: "Take a photo of that ore vein on the wall"
  
  attachments:
    - type: image
      action: generate
      prompt: "Rich iron ore vein in cavern wall, glittering..."

Real Photo (mine what you see):

postal:
  type: text
  to: "miner"
  body: "Here's a photo of the treasure room"
  
  attachments:
    - type: image
      action: upload
      source: "camera_roll"
      file: "treasure-room.jpg"

Screenshot (extract UI state):

# Mine the error dialog
resources:
  error-type: "permission-denied"
  affected-file: "/etc/passwd"
  suggested-action: "run as sudo"
  stack-depth: 3

Graph (extract data relationships):

# Mine the sales chart
resources:
  trend: "upward"
  peak-month: "december"
  anomaly: "march-dip"
  yoy-growth: "23%"

All become mineable resources!

Extensible Analyzer Pipeline

"Different images need different tools. The CLI is a pipeline, not a monolith."

The mine.py CLI supports pluggable analyzers that run before, during, or after LLM vision:

┌─────────────────────────────────────────────────────────────────┐
│                    ANALYZER PIPELINE                             │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│  1. PRE-PROCESSORS                                              │
│     resize, normalize, enhance, format conversion               │
│                                                                 │
│  2. CUSTOM ANALYZERS (parallel or sequential)                   │
│     ├── pose-detection (MediaPipe, OpenPose)                   │
│     ├── object-detection (YOLO, Detectron2)                    │
│     ├── ocr-extraction (Tesseract, PaddleOCR)                  │
│     ├── face-analysis (expression, demographics)                │
│     └── leela-customer-models (your trained models!)           │
│                                                                 │
│  3. LLM VISION                                                  │
│     Receives ALL prior results as context                       │
│     Synthesizes semantic interpretation                         │
│                                                                 │
│  4. POST-PROCESSORS                                             │
│     format, validate, merge into final YAML Jazz                │
│                                                                 │
└─────────────────────────────────────────────────────────────────┘

Example: Multi-Analyzer Pipeline

mine.py fashion-shoot.jpg \
  --analyzer pose-detection \
  --analyzer face-analysis \
  --analyzer leela://acme/gesture-classifier \
  --depth philosophical

This runs:

1. pose-detection — Extracts body keypoints, gesture classification
2. face-analysis — Detects expressions, demographics
3. leela://acme/gesture-classifier — Customer's trained model from Leela registry
4. LLM vision — Gets ALL the above as context, synthesizes final interpretation

Leela Customer Models

Pull customer-specific models trained on the Leela platform:

# From Leela model registry
mine.py widget-photo.jpg --analyzer leela://customer-id/defect-detector-v3

# Local model file
mine.py widget-photo.jpg --analyzer ./models/my-classifier.pt

Output merges into the mining YAML:

leela_analysis:
  model: "acme-widget-defect-v3"
  customer: "acme-corp"
  detections:
    - class: "hairline_crack"
      confidence: 0.91
      severity: "minor"
      location: "top_left_quadrant"

Adding Your Own Analyzer

# analyzers/my_analyzer.py

def analyze(image_path: str, config: dict) -> dict:
    """Run analysis, return structured data for YAML output."""
    # Your model inference here
    return {
        "my_analysis": {
            "detected": ["thing1", "thing2"],
            "confidence": 0.95
        }
    }

def can_handle(image_path: str, context: dict) -> bool:
    """Return True if this analyzer should run on this image."""
    # Auto-detect logic, or return False for explicit-only
    return "manufacturing" in context.get("tags", [])

Register in analyzers/registry.yml:

analyzers:
  my-analyzer:
    module: "analyzers.my_analyzer"
    auto-detect: true
    requires: ["torch", "my-model-package"]

Why Pipeline Beats Monolith

The LLM is great at semantic synthesis, but it can't run your custom pose detection model. The pipeline lets each tool do what it's best at:

• Custom models → Precise detection, trained on your data
• LLM vision → Semantic interpretation, narrative synthesis
• Together → The best of both worlds

YAML Jazz Output Style

"Comments are SEMANTIC DATA, not just documentation!"

YAML Jazz is the output format for mining results. Structure provides the backbone; comments provide the insight.

The Rules

1. COMMENT LIBERALLY — Every insight deserves a note
2. Inline comments for quick observations
3. notes: fields for longer thoughts
4. Capture confidence, hunches, metaphors
5. Think out loud — the reader benefits from your reasoning

Example Output

# Mining results for treasure-room.jpg
# Depth: full | Provider: openai/gpt-4o

resources:
  gold:
    quantity: 150           # Piled in mounds — not scattered, PLACED
    confidence: 0.85        # Torchlight glints clearly off the metal
    notes: |
      Mix of Roman denarii and medieval florins. Centuries of
      accumulation. This isn't a king's orderly treasury — this is
      a thieves' hoard. Generations of stolen wealth, piled and
      forgotten. The dust layer says nobody's touched it in ages.
    
  danger:
    intensity: 0.7          # Not immediate, but PRESENT
    confidence: 0.75        # Hard to see into the corners
    sources:
      - "Skeleton in corner — previous seeker, didn't make it"
      - "Shadows too dark for natural torchlight — something absorbs"
      - "Dust undisturbed except ONE trail — something still comes here"
    notes: "This hoard is guarded. Or cursed. Probably both."
    
  nostalgia:
    intensity: 0.4          # Whisper of lost civilizations
    confidence: 0.6         # Subjective, but the coins evoke it
    notes: "Who were they? Where did this come from? All gone now."
    
  dominant_colors:
    - name: "treasure-gold"
      hex: "#FFD700"
      coverage: 0.4         # Catches the eye first — that's the point
    - name: "shadow-purple"
      hex: "#2D1B4E"
      coverage: 0.3         # Where the danger lives
      
  implied_smells:
    - dust                  # Centuries of it
    - old metal             # Copper, bronze, the tang of coins
    - something rotting     # Not recent, but not ancient either
    
exhausted: false
mining_notes: |
  Rich lode for material and philosophical mining.
  The image is ABOUT greed and its costs. The skeleton says everything.
  
  # Meta-observation: This image wants to be a warning.
  # "Here lies what you seek — and what happens when you find it."

Why Comments Matter

An uncommented extraction is like a song without soul. The best mining results read like poetry annotated by a geologist.

When you mine, capture:

• Why you estimated that quantity
• What visual cues led to this inference
• What's uncertain, what surprised you
• Metaphors that capture the essence

How Mining Works

Step 1: ANALYZE (LLM scans for resources)

The LLM looks at the image AND checks what resources are currently requested by the logistics network:

analyze:
  image: "treasure-room.jpg"
  
  # LLM knows what's NEEDED from logistics requesters
  logistics_context:
    active_requests:
      - { item: "gold", requester: "forge/", needed: 100 }
      - { item: "gems", requester: "jewelry-shop/", needed: 50 }
      - { item: "iron-ore", requester: "smelter/", needed: 200 }
      
  # LLM identifies what CAN BE MINED that matches requests
  analysis_prompt: |
    Look at this image. What resources can you identify?
    Prioritize resources that match these requests: {requests}
    For each resource, estimate quantity available.

Step 2: INSTANTIATE (Resource map attached to image)

The LLM returns a resource mapping that gets stored ON the image:

image:
  id: "treasure-room-photo"
  file: "treasure-room.jpg"
  type: mineable-image
  
  # === RESOURCE MAP (instantiated by LLM analysis) ===
  resources:
    gold:
      total: 150           # Total available
      remaining: 150       # Not yet mined
      per_turn: 10         # Can extract 10 per turn
      
    gems:
      total: 45
      remaining: 45
      per_turn: 5
      
    ancient-coins:
      total: 30
      remaining: 30
      per_turn: 3
      rare: true           # Bonus find!
      
    dust:
      total: 500
      remaining: 500
      per_turn: 50
      value: low
      
  # Metadata
  analyzed_at: "2026-01-10T14:30:00Z"
  exhausted: false

Step 3: MINE (Progressive extraction, N per turn)

Each turn, you can mine resources from the image:

action: MINE
target: "treasure-room-photo"

# This turn's extraction (limited by per_turn rates)
result:
  extracted:
    - item: gold
      quantity: 10         # per_turn limit
      destination: "forge/"
      
    - item: gems
      quantity: 5
      destination: "jewelry-shop/"
      
  # Image state updated
  image_state:
    resources:
      gold:
        remaining: 140     # Was 150, mined 10
      gems:
        remaining: 40      # Was 45, mined 5
    exhausted: false

Step 4: EXHAUSTION (Sucked dry!)

After enough mining turns, resources run out:

# After 15 turns of mining gold...
image_state:
  resources:
    gold:
      total: 150
      remaining: 0         # EXHAUSTED!
      per_turn: 10
      exhausted: true
      
    gems:
      total: 45
      remaining: 0         # EXHAUSTED!
      per_turn: 5
      exhausted: true
      
    ancient-coins:
      total: 30
      remaining: 0
      per_turn: 3
      exhausted: true
      
  exhausted: true          # Whole image sucked dry!
  
  # Narrative
  description: |
    The treasure room photo has been thoroughly mined.
    Every glinting surface has been extracted, every
    coin accounted for. The image looks... drained.
    Faded. Like a photocopy of a photocopy.

Once exhausted, you can't mine that image anymore!

Demand-Driven Discovery

The LLM prioritizes what the logistics network NEEDS!

# The smelter is requesting iron ore
logistic-container:
  id: smelter
  mode: requester
  request_list:
    - { item: "iron-ore", count: 200, priority: high }
    - { item: "coal", count: 100, priority: medium }

# Player takes a photo of a cave wall
# LLM analyzes and finds:
analysis:
  image: "cave-wall.jpg"
  
  found_resources:
    iron-ore: 80           # "I see iron ore veins! The smelter needs this!"
    copper-ore: 30         # Also present but not requested
    quartz: 50             # Background mineral
    cave-moss: 100         # Organic material
    
  priority_matching:
    - resource: iron-ore
      matches_request: true
      requester: "smelter/"
      highlight: "⭐ HIGH PRIORITY — Smelter needs this!"

The LLM acts as a smart prospector that knows what's valuable based on current demand!

Discovery Modes

mine:
  target: "sunset-beach.jpg"
  mode: philosophical
  
  # LLM finds abstract resources
  resources:
    nostalgia: 15
    warmth: 30
    passage-of-time: 5
    beauty: 20
    sand: 10000          # Also the literal stuff

Mining Yields

Different image types yield different resources:

🏔️ Natural Resources

🏛️ Constructed

🎨 Abstract/Artistic

🌌 Philosophical (Deep Mining)

Just like the Kitchen Counter goes from practical → chemical → atomic → philosophical:

deep_mining:
  target: "sunset.png"
  depth: philosophical
  
  yields:
    - item: "the-passage-of-time"
      quantity: 1
      type: abstract
      
    - item: "mortality-awareness"
      quantity: 1
      type: existential
      warning: "This may cause introspection"
      
    - item: "beauty-that-fades"
      quantity: 1
      type: poetic

🎨 Sensation Mining

Extract colors, smells, textures, moods:

sensation_mining:
  target: "farmers-market.jpg"
  depth: sensations
  
  yields:
    # Colors
    - item: "tomato-red"
      quantity: 40
      type: color
      hex: "#FF6347"
      
    - item: "basil-green"
      quantity: 25
      type: color
      hex: "#228B22"
      
    # Smells (imagined from visual cues)
    - item: "fresh-bread-aroma"
      quantity: 10
      type: smell
      intensity: warm
      
    - item: "ripe-fruit-sweetness"
      quantity: 30
      type: smell
      
    # Attitudes/Feelings
    - item: "weekend-morning-calm"
      quantity: 5
      type: attitude
      
    - item: "abundance"
      quantity: 20
      type: feeling
      
    # Textures
    - item: "rough-burlap"
      quantity: 15
      type: texture
      
    - item: "sun-warmed-wood"
      quantity: 8
      type: texture

Use these in crafting:

• Combine tomato-red + canvas → painted artwork
• Combine fresh-bread-aroma + room → ambiance modifier
• Combine weekend-morning-calm + character → mood buff

The Mineable Property

Any object or image can have a mineable property:

object:
  name: Ancient Ore Painting
  type: artwork
  
  description: |
    A painting of a rich ore vein. But wait...
    is that actual ore embedded in the canvas?
    
  mineable:
    enabled: true
    yields:
      - item: iron-ore
        quantity: [5, 15]    # Range: 5-15 per mine
        
      - item: copper-ore
        quantity: [2, 8]
        
      - item: artistic-essence
        quantity: 1
        rare: 0.3            # 30% chance
        
    exhaustion:
      max_mines: 3           # Can mine 3 times before exhausted
      diminishing: 0.5       # Each mine yields 50% less
      regenerates: false     # Once exhausted, stays exhausted
      
    side_effects:
      - "The painting fades slightly with each extraction"
      - "You feel the artist's disappointment"

Mining Tools

Different tools affect mining yields:

📷 Camera (Default)

tool: camera
efficiency: 1.0
specialty: "Captures visual resources"
can_mine: [images, scenes, visible_objects]

🔬 Analyzer

tool: analyzer
efficiency: 1.5
specialty: "Chemical/atomic resources"
can_mine: [materials, substances, compounds]

🔮 Oracle Eye

tool: oracle_eye
efficiency: 2.0
specialty: "Abstract/philosophical resources"
can_mine: [emotions, concepts, meanings, futures]

⛏️ Reality Pickaxe

tool: reality_pickaxe
efficiency: 3.0
specialty: "Everything, but dangerous"
can_mine: [anything]
warning: "May collapse local reality"

Integration with Logistics

Mined resources flow into the logistics system:

mining_config:
  default_destination: "inventory"
  
  routing:
    # Route by resource type
    - match: { tags: ["ore"] }
      destination: "nw/ore-storage/"
      
    - match: { tags: ["organic"] }
      destination: "ne/organic-materials/"
      
    - match: { tags: ["abstract"] }
      destination: "sw/concepts/"
      
  postal_delivery:
    enabled: true
    method: text        # Instant delivery!

Camera Phone Integration

Your phone camera is THE mining interface:

Real Photo Workflow

phone_mining:
  # 1. CAPTURE: Take photo or upload
  capture:
    sources:
      - camera: "Take new photo"
      - gallery: "Upload from camera roll"
      - url: "Import from web"
      
  # 2. ANALYZE: LLM scans for resources
  on_capture:
    action: analyze
    context: logistics_requests    # What's needed?
    show_preview: true
    
  # 3. CONFIRM: Accept resource mapping
  on_confirm:
    action: instantiate
    attach_resources: true         # Store on image
    
  # 4. MINE: Extract over time
  on_mine:
    per_turn: true                 # N resources per turn
    auto_route: logistics          # Send to requesters

Example: Photo Mining Flow

1. You take a photo of a rock formation:

📷 *snap*

Analyzing photo for mineable resources...
Checking logistics requests...

Found in image:
├── 🪨 granite     × 200   (10/turn)
├── �ite iron-ore   × 45    (5/turn)  ⭐ NEEDED by smelter!
├── 💎 quartz      × 12    (2/turn)
└── 🦎 fossil      × 1     (rare find!)

[MINE] [CANCEL]

2. You confirm. Resource map attached:

image:
  id: rock-formation-001
  file: "IMG_2847.jpg"
  resources:
    granite: { total: 200, remaining: 200, per_turn: 10 }
    iron-ore: { total: 45, remaining: 45, per_turn: 5 }
    quartz: { total: 12, remaining: 12, per_turn: 2 }
    fossil: { total: 1, remaining: 1, per_turn: 1 }

3. Each turn, you mine:

Turn 1: Mined 10 granite, 5 iron-ore, 2 quartz
        → Iron ore sent to smelter (requester)
        → Granite sent to storage
        
Turn 2: Mined 10 granite, 5 iron-ore, 2 quartz
        Remaining: granite 180, iron-ore 35, quartz 8

...

Turn 9: Mined 10 granite, 5 iron-ore (last 5!)
        ⚠️ Iron-ore EXHAUSTED
        
Turn 20: Mined last 10 granite
         📷 IMAGE FULLY MINED — no more resources!

4. Exhausted image:

image:
  id: rock-formation-001
  exhausted: true
  
  visual_effect: |
    The photo appears faded, almost translucent.
    Like the minerals were literally pulled out of it.
    A ghost of a photograph.

AR Overlay (Future)

ar_overlay:
  # Point camera at scene
  live_view:
    show_resources: true
    icons_float: true
    
  # Visual indicators
  indicators:
    - resource_type: "icon + label"
    - quantity: "number overlay"
    - priority: "⭐ for requested items"
    - exhaustion: "fade as mined"
    
  # Example view:
  #   🪨 200  ⚫ 45 ⭐  💎 12
  #   (floating over rock formation)

DECOMPOSE vs MINE

They're complementary!

• DECOMPOSE the physical object on the counter
• MINE the image/representation of anything

Reality Mining (Advanced)

At the deepest level, you're not just mining images — you're mining reality itself:

reality_mining:
  level: transcendent
  
  # The image IS the territory
  insight: |
    When you mine an image, you're extracting
    compressed information. But all reality is
    compressed information. Images are just
    explicit about it.
    
  implications:
    - "Mining a photo of gold doesn't create gold — it REVEALS gold"
    - "The ore was always there, encoded in the pixels"
    - "Your camera doesn't capture reality — it DECOMPRESSES it"
    
  warning: |
    At this level, the distinction between
    "mining an image" and "mining reality"
    becomes philosophical.

Actions

MINE

MINE [target]
MINE [target] WITH [tool]
MINE [target] TO [destination]

SCAN

SCAN [target]           # Preview yields without mining
SCAN AREA               # Scan visible area for mineable resources

PROSPECT

PROSPECT [direction]    # Check for mineable resources in direction
PROSPECT DEEP           # Deep scan for rare/hidden resources

Example: Mining the Maze

# Player in dark maze corridor
# Takes photo with lamp light

action: MINE "dark-corridor.png"

result:
  yields:
    - item: darkness
      quantity: 100
      type: abstract
      note: "Bottled darkness, useful for stealth"
      
    - item: fear
      quantity: 15
      type: emotion
      note: "Crystallized fear, grue-adjacent"
      
    - item: mystery
      quantity: 5
      type: narrative
      note: "Pure narrative potential"
      
    - item: stone-dust
      quantity: 50
      type: material
      
  rare_find:
    - item: "ancient-writing"
      quantity: 1
      note: "Hidden message in the shadows!"
      unlocks: "Secret passage revealed"

The Mining Economy

Resources have value and flow:

resource_economy:
  # Raw resources → processing → products
  
  chains:
    - ore → smelter → ingots → forge → tools
    - wood → sawmill → planks → workshop → furniture
    - images → mining → resources → crafting → items
    
  # Images as a resource type!
  image_value:
    unique_photo: high      # Original content
    copy: low               # Duplicated content
    AI_generated: medium    # Generated on demand
    
  # Mining generates content
  content_creation: |
    When you MINE an image, you're not just extracting
    resources — you're creating YAML files for them.
    Each resource becomes a game object.

Dovetails With

• Visualizer — Images to mine
• Slideshow — Present mined images as narratives
• Logistic Container — Resource storage
• Postal — Camera integration, delivery
• Kitchen Counter — DECOMPOSE pattern
• Adventure — World integration

Character Recognition

"Who's in the picture? Match against your cast list."

When mining images with known characters, the LLM matches visual features against character metadata.

How It Works

1. Load character files from characters/ directory
2. Extract visual descriptors — species, clothing, accessories, typical poses
3. Match against figures in the image
4. Report confidence, pose, expression, interactions

Context Sources

• characters/*.yml — character definitions with visual descriptors
• characters/*/CARD.yml — character cards with appearance
• Room context — who's expected here?
• Prior mining — who was identified before?

Example Output

characters_detected:
  - id: palm
    name: "Palm"
    confidence: 0.95
    location: "center-left"
    pose: "seated at desk"
    expression: "scholarly contentment"
    accessories: ["tiny espresso", "typewriter"]
    interacting_with: ["kittens", "biscuit"]
    notes: "Matches Dutch Golden Age portrait style"
    
  - id: marieke
    name: "Marieke"
    confidence: 0.92
    location: "behind bar"
    pose: "waving"
    expression: "warm welcome"
    accessories: ["apron with LEKKER text"]
    
  - id: unknown-1
    confidence: 0.0
    location: "background-right"
    description: "Figure in shadow, can't identify"
    possible_matches: ["henk", "wumpus"]

Tips

• Provide character files in context before mining
• Include signature accessories — Palm's espresso, Biscuit's collar
• Note relationships — who stands near whom
• Flag unknown figures for investigation
• Use --depth characters or the cast-list lens

Multi-Look Mining

"One eye sees objects. Two eyes see depth. Many eyes see truth."

Multi-Look Mining layers interpretations from different perspectives, building up rich semantic sediment like geological strata. Each mining pass adds a new layer of meaning.

The Technique

# Layer 1: OpenAI GPT-4o
# Focus: General resource extraction
layer_1_openai:
  miner: "gpt-4o"
  focus: "objects, materials, colors, mood"
  findings:
    atmosphere: { intensity: 0.8 }
    objects: { quantity: 10 }
    # ... general observations ...

# Layer 2: Claude (Cursor built-in)
# Focus: Character expression, cultural markers, narrative POV
layer_2_cursor_claude:
  miner: "claude-opus-4"
  focus: "character-expression, cultural-markers, narrative-pov"
  what_layer_1_missed:
    - "The SECOND cat on the windowsill"
    - "The apron text is Dutch (LEKKER)"
    - "The espresso cup is monkey-sized (intentional)"
  deeper_resonance:
    theme: "home is where they wave when you walk in"

# Layer 3: Gemini
# Focus: Art historical references, compositional analysis
layer_3_gemini:
  miner: "gemini-pro-vision"
  focus: "art-history, composition, color-theory"
  # ... yet another perspective ...

Why Multi-Look Works

Different LLMs — and different PROMPTS to the same LLM — notice different things:

The sum is greater than the parts. Each layer adds perspectives the others missed.

The Paintbrush Metaphor

Think of multi-look mining like painting in layers:

┌─────────────────────────────────────────────────────────────────┐
│                    IMAGE INTERPRETATION                          │
├─────────────────────────────────────────────────────────────────┤
│  Layer N+1  →  Specialized focus (your choice)                   │
│  Layer N    →  New questions raised by Layer N-1                 │
│  ...                                                            │
│  Layer 3    →  Art history, composition                          │
│  Layer 2    →  Character, culture, narrative                     │
│  Layer 1    →  Objects, materials, basic resources               │
│  ─────────────────────────────────────────────────────────────  │
│  ORIGINAL IMAGE                                                  │
└─────────────────────────────────────────────────────────────────┘

Each pass reads the PREVIOUS layers before adding its own. The new miner knows what's already been noticed, so it can focus on what's missing or offer alternative interpretations.

Multi-Look Protocol

When mining an image with multi-look:

1. Read existing mining data (if any)
2. Choose your focus — what perspective will you add?
3. Look at the image with that lens
4. Note what prior layers missed — explicitly!
5. Add your layer with clear attribution
6. Suggest next focus — what should Layer N+1 examine?

Focus Lenses

Different passes should use different lenses:

Example: Progressive Revelation

Image: Marieke waving from behind the bar with Palm the monkey

Layer 1 (OpenAI):

• Objects: woman, monkey, cat, bottles, espresso machine
• Mood: warm, welcoming
• Relationships: 3 beings present

Layer 2 (Claude):

• The wave is for a FRIEND, not a stranger
• LEKKER is untranslatable Dutch — this IS gezelligheid
• There are TWO cats (Layer 1 missed the windowsill one)
• The espresso cup is monkey-sized — someone made that for Palm
• This is a family portrait disguised as a snapshot

Layer 3 (Art History):

• Composition echoes Dutch Golden Age tavern scenes
• The espresso machine is Art Nouveau (1890-1910 aesthetic)
• Lighting mimics Vermeer's characteristic window glow

Layer 4 (Phenomenology):

• Temperature: warm, heated by espresso machine and bodies
• Smell: coffee, old wood, cat fur
• Sound: the hiss of steam, soft background conversation
• Touch: worn wood bar top, smooth copper

Each layer enriches the total understanding.

Storing Multi-Look Data

Append new layers to the same -mined.yml file:

# Original mining from Layer 1
resources:
  atmosphere: ...
  objects: ...

exhausted: false
mining_notes: "Initial extraction complete"

# ═══════════════════════════════════════════════════════════════
# MULTI-LOOK MINING — Layer 2
# ═══════════════════════════════════════════════════════════════

layer_2_cursor_claude:
  miner: "claude-opus-4"
  focus: "character, culture, narrative"
  date: "2026-01-19"
  
  character_analysis:
    marieke:
      expression: "genuine warmth"
      notes: "Duchenne smile — reaches her eyes"
  
  what_layer_1_missed:
    - "Second cat on windowsill"
    - "LEKKER cultural significance"
  
  exhausted: false
  next_suggested_focus: "art history, lighting analysis"

# ═══════════════════════════════════════════════════════════════
# MULTI-LOOK MINING — Layer 3
# ═══════════════════════════════════════════════════════════════

layer_3_art_history:
  miner: "human/don"
  focus: "art historical references"
  # ... and so on ...

When to Multi-Look

Use multi-look mining when:

• Rich images — complex scenes with many elements
• Narrative importance — images central to a story
• Comparison needed — seeing how different perspectives interpret
• Building context — accumulating knowledge about a location/character
• Training data — creating rich examples for future mining

The Exhaustion Paradox

Unlike single-pass mining, multi-look mining doesn't exhaust the image — it deepens it:

# Single-pass: extracts and depletes
pass_1:
  resources: { gold: 50 }
  remaining: { gold: 0 }
  exhausted: true

# Multi-look: adds and enriches
layer_1:
  resources: { gold: 50 }
  exhausted: false  # Still more to see!
  
layer_2:
  resources: { narrative: 1, meaning: 1 }
  what_layer_1_missed: ["gold coins are Roman denarii"]
  exhausted: false  # STILL more!
  
layer_3:
  resources: { art_history: 1 }
  references: ["Pieter Claesz vanitas still life"]
  exhausted: false  # ALWAYS more to see

Images are never truly exhausted. There's always another perspective.

Speculative Mining (Mine Out of Your Ass!)

"The image doesn't exist yet? MINE IT ANYWAY. This is fiction."

Speculative Mining is when you mine an image that hasn't been generated yet — or may never be generated. The mining output IS the world-building. The hallucinated resources ARE canonical.

The Third Eye 👁️

"Two eyes see what IS. The Third Eye sees what COULD BE."

In MOOLLM, the Third Eye is the image mining layer — the MINING-*.yml files that add meaning, effects, and world-building to an image before (or without) or after it being generated. Third eyes can imagine images or analyze existing images, focusing on whatever kind of things they want, each gat

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