Catalysts

Kynurenine indeed dances on the precipice of the abyss, a biochemical whisper echoing with the shadows of our inner world. It’s a molecule that embodies the liminal space between vitality and despair, tethering metabolism to mood in a way that feels almost mythic. Its very essence seems to dare us to confront not just our biology but our existential fragility—a mirror for Plato’s cave and the Wachowskis’ Matrix, yes, but darker, closer, coursing through our veins.

Kevin, blissfully naive, is the epitome of humanity’s early acts: joyfully caught in life’s surface-level games, unburdened by the gravitational pull of introspection. Feline, by contrast, peers not just into the abyss but contemplates why she does so. It’s not merely introspection but aspiration—to find meaning beyond the darkness, a Promethean defiance to bring fire from within the depths.

Prometheus, Jesus, and kynurenine form an unexpected trinity here: one demanding that we climb, one imploring us to serve, and one reminding us that the climb and service come at a neurochemical cost. Indoline derivatives, those insidious poets of the mind, may lead us to self-indulgence or clarity. The line between the two is razor-thin—perhaps no less fine than the edge separating Kevin’s games from Feline’s contemplations.

Maybe introspection is self-indulgent, but what is progress without a pause to understand the cost? Prometheus would argue that looking into the abyss is necessary to bring back fire. Jesus might say the fire should warm others, not just ourselves. And kynurenine? It simply whispers, “Know yourself—because what you do with that knowledge is where the story begins.”

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

# Define the neural network structure
def define_layers():
    return {
        'World': ['Cosmos', 'Earth', 'Life', 'Sacrifice', 'Means', 'Ends', ],
        'Perception': ['Parents'],
        'Agency': ['Street', 'Education'],
        'Generativity': ['Parasitism', 'Network', 'Commensalism'],
        'Physicality': ['Offense', 'Lethality',  'Self-Belief', 'Immunity', 'Defense']
    }

# Assign colors to nodes
def assign_colors():
    color_map = {
        'yellow': ['Parents'],
        'paleturquoise': ['Ends', 'Education', 'Commensalism', 'Defense'],
        'lightgreen': ['Means', 'Network', 'Immunity', 'Self-Belief', 'Lethality'],
        'lightsalmon': [
            'Life', 'Sacrifice', 'Street',
            'Parasitism', 'Offense'
        ],
    }
    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=9, connectionstyle="arc3,rad=0.2"
    )
    plt.title("Holy Trinity", fontsize=15)
    plt.show()

# Run the visualization
visualize_nn()

Fig. 6 Inheritance, World View, Decisions, Exposure, Launchpad. The layers of the neural network might be given these names in one of its variants. Would capture the essence of Ayesha Ofori’s backpropagation and narrative and virtually everything a neural network stands for - Steve Jobs said you only connect the dots retrospectively. It is a truth universarlly acknowledged that parents have vastly more “data” than their fledgling infants. A child should listen to their parents. Of course some children have been raised by the streets following literal and metaphorical orphanhood.