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From cards to code: how modeling a tarot spread as a decision flow works

Wow, seriously? I once sat with a deck, flipping cards and trying to make sense of it all, and thought—there has to be a clearer way, right?

I once tried interpreting spreads purely by intuition, until I learned that you can turn those symbolic choices into a structured decision flow in code. It’s like combining mystical insight with logic, a bit like pairing a ritual from Amarres De Amor Stone Park with an analytical twist.

Amarres De Amor Stone Park Il

5 Key Concepts (casual list)

  1. Encoding card meanings
  2. Branching logic based on spread positions
  3. Weighting combinations
  4. User feedback loops
  5. Outcome ranking

How to Build the Decision Flow

1. Define the Deck and Meanings 

deck = {
    'The Fool': {'up': 'new start', 'rev': 'hesitation'},
    'The Lovers': {'up': 'alignment', 'rev': 'conflict'}
}
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2. Map Spread Positions to Intent 

spread_positions = {
    'past': 0,
    'present': 1,
    'future': 2
}
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3. Create Branching Rules 

def evaluate_card(position, card, orientation):
    if position == 'present' and card == 'The Lovers' and orientation == 'up':
        return 'Focus on relationships'
    return 'General advice'
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4. Combine Multiple Cards 

def aggregate(spread):
    advice = []
    for pos, (card, ori) in spread.items():
        advice.append(evaluate_card(pos, card, ori))
    return advice
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5. Assign Confidence Scores 

def confidence(advice_list):
    base = 0.5
    for a in advice_list:
        if 'focus' in a.lower():
            base += 0.2
    return min(base, 1.0)
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6. Incorporate Feedback 

def update_flow(user_feedback, spread):
    # naive: adjust weight if feedback negative
    if user_feedback == 'off':
        # tweak internal rules
        pass
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7. Rank Outcomes 

def rank_outcomes(advice_list):
    return sorted(advice_list, key=lambda x: len(x), reverse=True)
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8. Serve via API 

from flask import Flask, request, jsonify
app = Flask(__name__)

@app.route('/read', methods=['POST'])
def read():
    spread = request.json['spread']
    advice = aggregate(spread)
    score = confidence(advice)
    return jsonify({'advice': advice, 'confidence': score})
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9. Simple Frontend Call 

fetch('/read', {
  method: 'POST',
  headers: {'Content-Type':'application/json'},
  body: JSON.stringify({spread: {'present': ['The Lovers','up']}})
}).then(r=>r.json()).then(console.log);
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10. Logging Decisions 

import json
def log(spread, result):
    with open('decision_log.json','a') as f:
        json.dump({'spread': spread, 'result': result}, f)
        f.write('\n')
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Mini-case / Metaphor

Think of the system as a blend of a ritual and a flowchart—like using Brujos en Stone Park to set intention, then letting the code guide choices, while the deeper cultural context of Limpieza espiritual en Stone Park anchors interpretation.

Resources

  • Python (core language)
  • Flask for endpoints
  • JSON for state
  • Simple UI for feedback
  • Historical card databases

Benefits

  • You get consistency in readings.
  • Easily tweak rules without re-learning spreads.
  • User feedback makes it smarter over time.
  • Bridges magic and logic—cool conversation starter.
  • Can integrate into apps or shareable widgets.

Conclusion + Call to Action

Give it a try this week—you’ll see how a tarot spread becomes an elegant decision flow. Share your version or branch logic tweaks below!

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