Engineering

If I’d managed sons for him instead of all those little girls,
I’d still be Queen of France and we should not have known each other.
Such, my angels, is the role of sex in history
– Eleanor

Sympathetic Engineering

This exchange from The Lion in Winter crackles with the acidic wit and raw power struggles that define the relationship between Eleanor of Aquitaine and Richard. It’s a masterpiece of layered dialogue, where affection and manipulation intertwine so seamlessly that even declarations of love seem like moves in a high-stakes chess game.

Eleanor, ever the strategist, begins with a disarming line—“Just to talk”—but quickly pivots to set the tone of her power and intellect. Her flippant yet poignant question, “How long is a lamb?” simultaneously mocks and mourns the passage of time and lost intimacy. She flatters Richard’s war prowess while hinting at her enduring influence—“I follow all your slaughters from a distance.” Her invitation for Richard to sit is less a gesture of warmth and more a command cloaked in civility.

Is this an audience, a good night hug with kisses, or an ambush?
– Richard I

Richard, equally guarded, responds with cutting skepticism: “An audience, a good night hug with kisses, or an ambush?” It’s a brilliant encapsulation of his mistrust, his readiness for confrontation, and his lingering resentment. His cool dismissal of Eleanor’s plea for love—“My love, of all things. What would you want it for?”—reveals a man hardened by power and politics, unwilling to entertain vulnerability.

Eleanor’s retort, comparing Richard to plainsong, underscores her scorn for his rigidity. Her mention of giving up the Church “out of boredom” is quintessential Eleanor—irreverent, defiant, and deeply aware of her agency. It’s a jab that cuts to Richard’s insecurity, implying that he, like the Church, is predictable and uninspiring to her.

The final line, with Richard asserting his hold over the Aquitaine, strips away the pretense of familial reconciliation, laying bare the transactional nature of their bond. Love, in their dynamic, is secondary to power—a bitter truth that defines their relationship and the political machinations of their world.

This scene is a dance of sharp-edged words, where affection is wielded as a weapon, and power dynamics are in constant flux. It’s a stark reminder that in their world, love and loyalty are commodities to be negotiated rather than freely given.

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

# Define the neural network structure; modified to align with "Aprés Moi, Le Déluge" (i.e. Je suis AlexNet)
def define_layers():
    return {
        'Pre-Input/CudAlexnet': ['Life', 'Earth', 'Cosmos', 'Sacrifice', 'Means', 'Ends'],
        'Yellowstone/SensoryAI': ['Coin Toss'],
        'Input/AgenticAI': ['Girls', 'Boys'],
        'Hidden/GenerativeAI': ['Mistress', 'Deceite', 'Queen'],
        'Output/PhysicalAI': ['Levant', 'Wisdom', 'Warrior', 'Impostume', 'Temple']
    }

# Assign colors to nodes
def assign_colors(node, layer):
    if node == 'Coin Toss':
        return 'yellow'
    if layer == 'Pre-Input/CudAlexnet' and node in [ 'Ends']:
        return 'paleturquoise'
    if layer == 'Pre-Input/CudAlexnet' and node in [ 'Means']:
        return 'lightgreen'
    elif layer == 'Input/AgenticAI' and node == 'Boys':
        return 'paleturquoise'
    elif layer == 'Hidden/GenerativeAI':
        if node == 'Queen':
            return 'paleturquoise'
        elif node == 'Deceite':
            return 'lightgreen'
        elif node == 'Mistress':
            return 'lightsalmon'
    elif layer == 'Output/PhysicalAI':
        if node == 'Temple':
            return 'paleturquoise'
        elif node in ['Impostume', 'Warrior', 'Wisdom']:
            return 'lightgreen'
        elif node == 'Levant':
            return 'lightsalmon'
    return 'lightsalmon'  # Default color

# Calculate positions for nodes
def calculate_positions(layer, center_x, offset):
    layer_size = len(layer)
    start_y = -(layer_size - 1) / 2  # Center the layer vertically
    return [(center_x + offset, start_y + i) for i in range(layer_size)]

# Create and visualize the neural network graph
def visualize_nn():
    layers = define_layers()
    G = nx.DiGraph()
    pos = {}
    node_colors = []
    center_x = 0  # Align nodes horizontally

    # Add nodes and assign positions
    for i, (layer_name, nodes) in enumerate(layers.items()):
        y_positions = calculate_positions(nodes, center_x, offset=-len(layers) + i + 1)
        for node, position in zip(nodes, y_positions):
            G.add_node(node, layer=layer_name)
            pos[node] = position
            node_colors.append(assign_colors(node, layer_name))

    # Add edges (without weights)
    for layer_pair in [
        ('Pre-Input/CudAlexnet', 'Yellowstone/SensoryAI'), ('Yellowstone/SensoryAI', 'Input/AgenticAI'), ('Input/AgenticAI', 'Hidden/GenerativeAI'), ('Hidden/GenerativeAI', 'Output/PhysicalAI')
    ]:
        source_layer, target_layer = layer_pair
        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=10, connectionstyle="arc3,rad=0.1"
    )
    plt.title("Role of Sex", fontsize=15)
    plt.show()

# Run the visualization
visualize_nn()

Fig. 9 Agent AI Nvidia Vision & Test-Time Scaling. Here’s a scheme: user or machine (yellow node) -> prompt/response -> agent AI -> [plan/monumental, tool use/antiquarian, critique/critical] -> [Calculator, Web Search, Semantic Search, SQL Search, Generate Podcast]