Veiled Resentment

Fig. 23 The Next Time Your Horse is Behaving Well, Sell it. The numbers in private equity don’t add up because its very much like a betting in a horse race. Too many entrants and exits for anyone to have a reliable dataset with which to estimate odds for any horse-jokey vs. the others for quinella, trifecta, superfecta. The eternal recurrence of the same, as Nietzsche framed it, unfolds a cosmos devoid of beginning or end, an architecture where every output node circles back as input to an identical iteration, endlessly. In such a framework, the demand for moral purpose dissolves into absurdity. The cosmos, infinite in time and space, operates beyond binaries of good and evil, refusing to submit its vastness to human conceits of narrative closure. Yet Goethe’s defiance of moral demands, while celebrating the autonomy of the artist, rings hollow when confronted with the persistent itch for a conclusion, as Anthony Capella’s narrator discovers. To demand purpose from art, perhaps, is not to ruin it, but to humanize it, to tether infinite loops of meaning to a finite self yearning for understanding. In rejecting moral purpose, we may resist simplifying complexity, but we also risk severing art from its audience—an audience that, like Capella’s Victorian memoirist, craves resolution, even if only to satisfy itself. And so, while Nietzsche’s cosmos tumbles through its eternal recurrence, it must tolerate those who insist on finding lessons, a phenomenon less a contradiction than an inevitable consequence of human architecture mirroring the infinite. What have we learned? Perhaps only that the output of one iteration can be both conclusion and renewal, endlessly oscillating between the finite and the infinite.

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import numpy as np
import matplotlib.pyplot as plt
import networkx as nx

# Define the neural network fractal
def define_layers():
    return {
        'World': ['Particles-Compression', 'Vibration-Particulate.Matter', 'Ear, Cerebellum-Georientation', 'Harmonic Series-Agency.Phonology', 'Space-Verb.Syntax', 'Time-Object.Meaning', ], # Resources
        'Perception': ['Rhythm, Pockets'], # Needs
        'Agency': ['Open-Nomiddleman', 'Closed-Trusted'], # Costs
        'Generative': ['Ratio-Weaponized', 'Competition-Tokenized', 'Odds-Monopolized'], # Means
        'Physical': ['Volatile-Revolutionary', 'Unveiled-Resentment',  'Freedom-Dance in Chains', 'Exuberant-Jubilee', 'Stable-Conservative'] # Ends
    }

# Assign colors to nodes
def assign_colors():
    color_map = {
        'yellow': ['Rhythm, Pockets'],
        'paleturquoise': ['Time-Object.Meaning', 'Closed-Trusted', 'Odds-Monopolized', 'Stable-Conservative'],
        'lightgreen': ['Space-Verb.Syntax', 'Competition-Tokenized', 'Exuberant-Jubilee', 'Freedom-Dance in Chains', 'Unveiled-Resentment'],
        'lightsalmon': [
            'Ear, Cerebellum-Georientation', 'Harmonic Series-Agency.Phonology', 'Open-Nomiddleman', 
            'Ratio-Weaponized', 'Volatile-Revolutionary'
        ],
    }
    return {node: color for color, nodes in color_map.items() for node in nodes}

# Calculate positions for nodes
def calculate_positions(layer, x_offset):
    y_positions = np.linspace(-len(layer) / 2, len(layer) / 2, len(layer))
    return [(x_offset, y) for y in y_positions]

# Create and visualize the neural network graph
def visualize_nn():
    layers = define_layers()
    colors = assign_colors()
    G = nx.DiGraph()
    pos = {}
    node_colors = []

    # Add nodes and assign positions
    for i, (layer_name, nodes) in enumerate(layers.items()):
        positions = calculate_positions(nodes, x_offset=i * 2)
        for node, position in zip(nodes, positions):
            G.add_node(node, layer=layer_name)
            pos[node] = position
            node_colors.append(colors.get(node, 'lightgray'))  # Default color fallback

    # Add edges (automated for consecutive layers)
    layer_names = list(layers.keys())
    for i in range(len(layer_names) - 1):
        source_layer, target_layer = layer_names[i], layer_names[i + 1]
        for source in layers[source_layer]:
            for target in layers[target_layer]:
                G.add_edge(source, target)

    # Draw the graph
    plt.figure(figsize=(12, 8))
    nx.draw(
        G, pos, with_labels=True, node_color=node_colors, edge_color='gray',
        node_size=3000, font_size=8, connectionstyle="arc3,rad=0.2"
    )
    plt.title("Music", fontsize=13)
    plt.show()

# Run the visualization
visualize_nn()

Fig. 24 Vet Advised When to Sell Horse. Right after an outstanding performance. Because you want to control how its perceived, the narrative backstory, and how its future performance might be imputed. Otherwise, reality manifests with point and interval estimates. This is the subject matter of the opening dialogue in Miller’s Crossing.