Cunning Strategy

Life is Trial & Error

Biology is stubbornly time-bound, and no shortcut, pill, or panacea can replace the work required to cultivate resilience, strength, and mastery. Jiu-Jitsu, like any embodied practice, reveals the truth that real transformation comes from iteration, struggle, and adaptation over time. It’s a slow symphony of failure and learning, where the payoff isn’t instantaneous but grows exponentially as skill and understanding deepen.

The allure of the “one thing”—whether a pill, a product, or a divine promise—taps into our desire to escape effort, to compress time into a neat solution. Capitalism thrives on this promise of shortcuts, packaging convenience as transcendence. Religion, too, often operates on the premise of singular salvation, though at its best, it acknowledges the value of enduring trials as a path to spiritual growth.

But Jiu-Jitsu—or any practice that demands embodied commitment—teaches a different wisdom: there’s no bypassing the grind. You roll on the mat, you adapt, you get choked, you learn to breathe under pressure. It’s iterative and adversarial, and that’s where its power lies. You can’t fake time under tension, just as machine learning can’t cheat its training data. The layers of skill, instinct, and composure are built over thousands of interactions, just as neural networks demand countless epochs to tune weights into something meaningful.

Biology, unlike machine learning, doesn’t forgive impatience. The body doesn’t upload an update overnight; it reshapes itself incrementally, metabolizing effort over weeks, months, years. Whether it’s synaptic plasticity, tendon strength, or cardiovascular endurance, the transformation happens through time, through embodied struggle, through sweat that capitalism can’t commodify.

In the end, maybe the beauty of practices like Jiu-Jitsu is precisely that they resist the capitalist drive to commodify time. They demand that you show up, engage, and evolve—not for some instantaneous result but for the ongoing process. It’s a quiet rebellion against the shortcut culture, reminding us that what’s earned, not bought, shapes who we are.

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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/World': ['Cosmos', 'Earth', 'Life', 'Nvidia', 'Parallel', 'Time'],
        'Yellowstone/PerceptionAI': ['Interface'],
        'Input/AgenticAI': ['Digital-Twin', 'Enterprise'],
        'Hidden/GenerativeAI': ['Error', 'Space', 'Trial'],
        'Output/PhysicalAI': ['Loss-Function', 'Sensors', 'Feedback', 'Limbs', 'Optimization']
    }

# Assign colors to nodes
def assign_colors(node, layer):
    if node == 'Interface':
        return 'yellow'
    if layer == 'Pre-Input/World' and node in [ 'Time']:
        return 'paleturquoise'
    if layer == 'Pre-Input/World' and node in [ 'Parallel']:
        return 'lightgreen'
    elif layer == 'Input/AgenticAI' and node == 'Enterprise':
        return 'paleturquoise'
    elif layer == 'Hidden/GenerativeAI':
        if node == 'Trial':
            return 'paleturquoise'
        elif node == 'Space':
            return 'lightgreen'
        elif node == 'Error':
            return 'lightsalmon'
    elif layer == 'Output/PhysicalAI':
        if node == 'Optimization':
            return 'paleturquoise'
        elif node in ['Limbs', 'Feedback', 'Sensors']:
            return 'lightgreen'
        elif node == 'Loss-Function':
            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/World', 'Yellowstone/PerceptionAI'), ('Yellowstone/PerceptionAI', '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("Archimedes", fontsize=15)
    plt.show()

# Run the visualization
visualize_nn()

Fig. 7 Nostalgia & Romanticism. When monumental ends (victory), antiquarian means (war), and critical justification (bloodshed) were all compressed into one figure-head: hero