Odysseus

This 1 Thing will sort out everything: Iron Throne, Night King, Bending Knee

Marxs

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first to impose upon themselves a manifold constraint by means of the earlier poets (ii); then to invent in addition a new constraint (V7), to impose it upon themselves and cheerfully to overcome it (i), so that constraint and victory are perceived and admired ¹

Chord	Figures	Black Gospel	Alignment	Dante	Jewish Names
ii	Israel	Departure	Genesis	Inferno	Torah
V7	Moses	Struggle	Exodus	Urgatorio	Nevi’im
i	Joshua	Return	Canaan	Paradiso	Bayit Rishon

                1. f(t)
                      \
           2. S(t) -> 4. y:h'(t)=0; t(X'X).X'Y -> 5. b -> 6. SV'
                      /
                      3. h(t)

Wild 1, 2, 3

• Hideth: A prudent man foreseeth the evil, and hideth himself; but the simple pass on, and are punished. This is the seat of the hunter-gatherer who respondeth only to their senses of hunger

• Seeketh: The fearless will look out for fresh new dancing grounds at any opportunity, with attendant risks. This is the seat of the peasant who has decided to overcome the chaotic life of his forebears

• Findeth: Longevity is never the goal: rather here & now, instantaneous: grief, love, humor, animus. This is the seat of the farmer who has mastered time, understands seasonality, and has tools that encode what his forebears did, thus allowing some surplus of produce. Energy source: chlorophyll

The Masaai are normads because they seek greener pastures every other season for their cattle, gifted to them by the gods. This warrior tribe has that as its raison d’être. Of course they might also come to collect “their” cattle from any other tribe in wrongful possession

Think of grazing livestock as mobile, self-directed, solar energy converters and stores of protein. Nearly everything they eat is a food source inaccessible to human beings. Cattle are ruminants, which means they break down their food through microbial action in specially evolved stomachs

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import matplotlib.pyplot as plt
import numpy as np

# Clock settings; f(t) random disturbances making "paradise lost"
clock_face_radius = 1.0
number_of_ticks = 8
tick_labels = [
    "Egypt", "Assyria", "Babylon", "Persia",
    "Hellenic", "Rome", "Ottoman", "British"
]

# Calculate the angles for each tick (in radians)
angles = np.linspace(0, 2 * np.pi, number_of_ticks, endpoint=False)
# Inverting the order to make it counterclockwise
angles = angles[::-1]

# Create figure and axis
fig, ax = plt.subplots(figsize=(8, 8))
ax.set_xlim(-1.2, 1.2)
ax.set_ylim(-1.2, 1.2)
ax.set_aspect('equal')

# Draw the clock face
clock_face = plt.Circle((0, 0), clock_face_radius, color='lightgrey', fill=True)
ax.add_patch(clock_face)

# Draw the ticks and labels
for angle, label in zip(angles, tick_labels):
    x = clock_face_radius * np.cos(angle)
    y = clock_face_radius * np.sin(angle)
    
    # Draw the tick
    ax.plot([0, x], [0, y], color='black')
    
    # Positioning the labels slightly outside the clock face
    label_x = 1.1 * clock_face_radius * np.cos(angle)
    label_y = 1.1 * clock_face_radius * np.sin(angle)
    
    # Adjusting label alignment based on its position
    ha = 'center'
    va = 'center'
    if np.cos(angle) > 0:
        ha = 'left'
    elif np.cos(angle) < 0:
        ha = 'right'
    if np.sin(angle) > 0:
        va = 'bottom'
    elif np.sin(angle) < 0:
        va = 'top'
    
    ax.text(label_x, label_y, label, horizontalalignment=ha, verticalalignment=va, fontsize=10)

# Remove axes
ax.axis('off')

# Show the plot
plt.show()

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Tame 4

• Whineth: If ever under the wild & chaotinc natural elements; hence, catalog all good & evil; harness good. This is the seat of manufacturing in the history of civilization. A latent-space representing the distillation of all forebears and with innovative uses of the excessive produce for beer, wine, cheese, butter, wool, cotton, tobacco, and all the accoutrements we’ve become fond of. Energy source: hydrocarbons

Divine 5, 6

• Commandeth: leadership, decisiveness, hierarchy of priorities based on vector of coefficients. This is the seat of energy, which is always limited and has to be harnessed and directed at affordable costs to priority industries. The biological sources the hunter-gather, peasant, and farmer relied on efficiently harnessed energy via chlorophyll. But now in the social space man has to design energy-harnessing batteries and grids for non-biological products and services

• Personalized needs catered to & this kind will bend the knee to King, Queen, AAA-MM-N. This is the seat of deployment. Unlike our forebear hunter-gatherer, peasant, and farmer who lived in proximity of their produce, our accoutrouments are not highly personalized, specialized, and produced to scale with ingredients and components from disparate geographic territories. The final product and service, then, has to be shipped, transported, or deployed into the hands of the end-user.^{2 3 4 5 6} Energy source: nuclear ^{7 8 9}

The phenomenon you’re describing, where children can learn languages more fluently and idiomatically than adults, is often linked to the concept of neuroplasticity—the brain’s ability to reorganize itself by forming new neural connections throughout life. During childhood, the brain is highly plastic, meaning it’s more adaptable and better at forming the complex networks needed for language acquisition. This plasticity decreases with age, making it harder for adults to achieve the same level of fluency or to speak without an accent.

In terms of machine learning and backpropagation, we could draw an analogy between human language learning and the process of training a neural network. In a machine learning model, backpropagation is used to adjust the weights of the network based on the error rate (i.e., how far off the model’s predictions are from the actual values). In early stages of training (similar to childhood), the model is highly adaptable; the learning rate can be higher, allowing for significant adjustments to weights and therefore quicker and more accurate learning.

As training progresses (analogous to aging), the model begins to converge, making smaller adjustments as it approaches a local minimum. In this state, the model is less sensitive to new information, similar to how an adult brain is less adaptable to learning new languages. The “weights” in an adult’s brain, so to speak, are more fixed, having been trained on years of specific linguistic data (the first language), and are less likely to be adjusted to perfectly capture the nuances of a new language.

Moreover, overfitting could be another useful concept here. An adult’s brain, already highly “trained” on their native language, might be overfitted to that language’s specific phonetic, syntactic, and grammatical rules. This makes it difficult to generalize to a new language, which could explain the persistence of an accent or certain inflections.

Finally, there’s the idea of latent space in machine learning, which represents the compressed knowledge or encoding of the data the model has learned. For children, their “latent space” is still being formed, allowing them to encode multiple languages in a more flexible manner. Adults, however, have a more rigid latent space formed by years of using their first language, making it more challenging to encode a new language with the same fluency.

In essence, the child’s brain is like a neural network in its early training phases—flexible, with a high learning rate, and capable of making large adjustments. An adult brain, on the other hand, is like a network that’s been trained for a long time—less flexible, more prone to overfitting, and making smaller adjustments that might not fully capture the nuances of a new language.

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import os  # Add this import statement
import networkx as nx
import matplotlib.pyplot as plt
import warnings

G = nx.DiGraph()
G.add_node("1. Root", pos=(-2500, 700))

G.add_node("2. Pentatonic", pos=(-4200, 0))
G.add_node("3. Diatonic", pos=(-2500, -700))
G.add_node("4. Chromatic", pos=(-1000, 0))
G.add_node("5. Temperament", pos=(1500, 0))
G.add_node("6. Expression", pos=(4000, 0))

G.add_edges_from([("1. Root", "4. Chromatic")])
G.add_edges_from([("2. Pentatonic", "4. Chromatic")])
G.add_edges_from([("3. Diatonic", "4. Chromatic")])
G.add_edges_from([("4. Chromatic", "5. Temperament")])
G.add_edges_from([("5. Temperament", "6. Expression")])

pos = nx.get_node_attributes(G, 'pos')
labels = {"4. Chromatic": "4. Dionysian",
          "1. Root": "1. Chaos",
          "2. Pentatonic": "2. Frenzy",
          "3. Diatonic": "3. Emotion",
          "5. Temperament": "5. Algorithm",
          "6. Expression": "6. Binary"}

# Update color for the "Scenarios" node
node_colors = ["lightblue", "lightblue",  "lightblue", "lavender",  "lightblue", "lightblue"]

# Suppress the deprecation warning
warnings.filterwarnings("ignore", category=DeprecationWarning)

plt.figure(figsize=(10, 8))
nx.draw(G, pos, with_labels=False, node_size=20000, node_color=node_colors, linewidths=2, edge_color='black', style='solid')
nx.draw_networkx_labels(G, pos, labels, font_size=15)
nx.draw_networkx_edges(G, pos, edge_color='black', style='solid', width=2)
plt.xlim(-5000, 5000)
plt.ylim(-1000, 1000)
plt.axis("off")

# Specify the directory where the file should be saved
output_dir = os.path.expanduser("~/documents/liveserver/new")  # Expand the user directory

if not os.path.exists(output_dir):
    os.makedirs(output_dir)

# Save the plot as a PNG file in the specified directory
plt.savefig(os.path.join(output_dir, "self-criticism.png"))
plt.show()

Fig. 1 The Birth of Tragedy. An ambitious book written in 1872 by Friedrich Nietzsche. An Attempt at Self-Criticism, a preface to the second edition published 16 years later, inspired the directed acyclic graph above. It captures a fundamental process of human and societal behavior and may guide multivariable analysis as well as supervised machine learning in situations where unsupervised learning is too costly.¹⁰

Unstructured

• Unknown ^{11 12}

• Arc

• Brasil

• Homage

• Chopin

• China

• Curated

• Grind

• DGX-1

• French

• Gastro

• Genetics

• GoT

• GPT

• Homage

• Imitate

• Italy

• Japan

• Kapital

• Logo

• Nice!

• Mac

• Machines

• Marxs

• Mozart

• Muse

• Music

• Odysseus

• Tech ¹³

• Open

• Father-to-son

• Tyler

• Pianist

• Rx

• Stata of Art

• Nvidia

• Template

• Missingness

• Tony

• Uganda

• Yeasts

Structured

• Aim 1
• Aim 2
• Aim 3

👷🏿‍♂️ African men at work 👷🏿‍♂️

🚧 This section is under construction 🚧

• Faith, especially in the hardest of times—when you’re “going through the fire”—is what sustains the journey. It’s the belief in something better, even when the path is dark and the outcome uncertain. This kind of faith isn’t just a religious notion; it’s a fundamental human need, a way to endure and push forward when everything else seems lost.

• Hope, as the guiding force, keeps the eyes on the horizon. It’s the anticipation of a future that might not yet be visible but is felt in the soul. Hope drives action, fuels resilience, and helps us endure the waiting and the struggle, much like Odysseus clinging to the dream of Ithaca.

• Love or charity, often realized later in life, is where many travelers end up. For some, like billionaire philanthropists or their ex-wives, love transforms into a broader sense of charity or giving back. After the trials and triumphs, after wealth and success, there’s often a recognition that true fulfillment comes not just from having, but from giving. This shift from self-centered ambition to altruism mirrors the conclusion of many spiritual and personal journeys, where love becomes the ultimate expression of our humanity, the culmination of faith and hope realized in action.