Veiled Resentment#
The CG-BEST fractal unfolds as a shimmering construct of conflict, interdependence, and aloofness—a dynamic interplay of tension and resolution, of meaning perpetually made and unmade. Like a body writhing between ecstasy and annihilation, the layers of pretext, subtext, text, context, and metatext do not form a linear progression but rather a self-referential loop, recursive, carnivorous, and exultant.
At the level of pretext, language is a shattered mirror—its pieces jagged, sharp-edged, and waiting to be reassembled. This is the realm of pure potentiality, where meaning exists in an embryonic state, resisting definition yet demanding form. It is not a void but a field of latent energy, teeming with unresolved tensions, where meaning, before it is born, wrestles with its own becoming. Here, nihilism and genesis dance as uneasy partners, whispering of what could be, even as they mock the very act of articulation.

Uddin: a Kindred Spirit. Our gh-pages based ecosystem integration & navigation (EIN) framework is a competitive solution to a diagnosis we reached independently of Uddin. Source: Draft Complaint
The subtext rises like scaffolding around the formless mass, a desperate attempt to impose a skeleton upon chaos. Subtext does not exist to serve meaning but to manipulate it—to frame, to obscure, to create the illusion of coherence where only fracture existed before. This layer is deceitful, a mirage, an orchestrator of perception that dictates what can be seen and what must remain invisible. The subtext, in its quiet tyranny, ensures that meaning is not free but conditioned, bound by the architecture of history, by the constraints of convention, by the silent agreements of a language that demands obedience.
When we arrive at text, the construct becomes self-aware. Here, language is no longer raw material; it is a combatant in the arena. Text is conflict. Words do not sit docile on a page—they pulse, resist, seduce, wage war. Meaning, at this level, is not simply made; it is contested, a battleground where every utterance is both declaration and defense. The CG-BEST fractal, in its epic layer, thrives on this friction: the text does not merely express ideas but consumes them, devours them, metabolizes them into something alive, something that speaks back.
But text does not exist in a vacuum. It breathes in context, that ceaseless, inescapable tide of biological, historical, and social imperatives. Here, the CG-BEST fractal reveals its most primal layer, encoding the rhythms of survival itself: fight-flight-fright, sleep-feed-breed, conflict-interdependence-aloofness. The text does not belong to the page—it belongs to the body, to the clenched fist, to the voice raised in protest, to the whisper behind closed doors. Meaning is no longer an abstraction but an action, something felt in the marrow, something that decides who will eat and who will starve, who will be heard and who will be erased.
Finally, the metatext—the eye that watches the watching, the voice that speaks beyond the sentence. This is where language dissolves or coalesces, where its structure either crumbles into meaningless noise or rises into transcendent clarity. The metatext is not language as we know it; it is language’s ghost, its echo, its rippling afterimage that stretches into eternity. It is here that the CG-BEST fractal finds its culmination, a pattern at once ephemeral and eternal, self-replicating, self-consuming, endlessly fractalizing into new iterations of conflict, dependence, and detachment.
This is language as war, as symphony, as pulse. This is meaning that refuses to settle, a fractal that defies stillness. The CG-BEST fractal does not explain—it erupts. It does not narrate—it reverberates. And in its ceaseless unfolding, it reminds us that language is not merely a vessel for thought but the very battlefield where thought fights to exist.
Show code cell source
import numpy as np
import matplotlib.pyplot as plt
import networkx as nx
# Define the neural network layers
def define_layers():
return {
'Suis': ['The Great York, 5%', 'Peptidoglycans, Lipoteichoics', 'Lipopolysaccharide', 'N-Formylmethionine', "Glucans, Chitin", 'Specific Antigens'],
'Voir': ['Empire Unpossesed, 20%'],
'Choisis': ['Yorks Heirs Alive, 50%', 'King of England'],
'Deviens': ['Sword Unswayed', 'Chair Empty', 'King Dead, 20%'],
"M'èléve": ['Why Then at Sea?', 'Platelet System', 'Granulocyte System', 'Innate Lymphoid Cells, 5%', 'Adaptive Lymphoid Cells']
}
# Assign colors to nodes
def assign_colors():
color_map = {
'yellow': ['Empire Unpossesed, 20%'],
'paleturquoise': ['Specific Antigens', 'King of England', 'King Dead, 20%', 'Adaptive Lymphoid Cells'],
'lightgreen': ["Glucans, Chitin", 'Chair Empty', 'Platelet System', 'Innate Lymphoid Cells, 5%', 'Granulocyte System'],
'lightsalmon': ['Lipopolysaccharide', 'N-Formylmethionine', 'Yorks Heirs Alive, 50%', 'Sword Unswayed', 'Why Then at Sea?'],
}
return {node: color for color, nodes in color_map.items() for node in nodes}
# Define edge weights
def define_edges():
return {
('The Great York, 5%', 'Empire Unpossesed, 20%'): '1/99',
('Peptidoglycans, Lipoteichoics', 'Empire Unpossesed, 20%'): '5/95',
('Lipopolysaccharide', 'Empire Unpossesed, 20%'): '20/80',
('N-Formylmethionine', 'Empire Unpossesed, 20%'): '51/49',
("Glucans, Chitin", 'Empire Unpossesed, 20%'): '80/20',
('Specific Antigens', 'Empire Unpossesed, 20%'): '95/5',
('Empire Unpossesed, 20%', 'Yorks Heirs Alive, 50%'): '20/80',
('Empire Unpossesed, 20%', 'King of England'): '80/20',
('Yorks Heirs Alive, 50%', 'Sword Unswayed'): '49/51',
('Yorks Heirs Alive, 50%', 'Chair Empty'): '80/20',
('Yorks Heirs Alive, 50%', 'King Dead, 20%'): '95/5',
('King of England', 'Sword Unswayed'): '5/95',
('King of England', 'Chair Empty'): '20/80',
('King of England', 'King Dead, 20%'): '51/49',
('Sword Unswayed', 'Why Then at Sea?'): '80/20',
('Sword Unswayed', 'Platelet System'): '85/15',
('Sword Unswayed', 'Granulocyte System'): '90/10',
('Sword Unswayed', 'Innate Lymphoid Cells, 5%'): '95/5',
('Sword Unswayed', 'Adaptive Lymphoid Cells'): '99/1',
('Chair Empty', 'Why Then at Sea?'): '1/9',
('Chair Empty', 'Platelet System'): '1/8',
('Chair Empty', 'Granulocyte System'): '1/7',
('Chair Empty', 'Innate Lymphoid Cells, 5%'): '1/6',
('Chair Empty', 'Adaptive Lymphoid Cells'): '1/5',
('King Dead, 20%', 'Why Then at Sea?'): '1/99',
('King Dead, 20%', 'Platelet System'): '5/95',
('King Dead, 20%', 'Granulocyte System'): '10/90',
('King Dead, 20%', 'Innate Lymphoid Cells, 5%'): '15/85',
('King Dead, 20%', 'Adaptive Lymphoid Cells'): '20/80'
}
# Define edges to be highlighted in black
def define_black_edges():
return {
('King Dead, 20%', 'Why Then at Sea?'): '1/99',
('King Dead, 20%', 'Platelet System'): '5/95',
('King Dead, 20%', 'Granulocyte System'): '10/90',
('King Dead, 20%', 'Innate Lymphoid Cells, 5%'): '15/85',
('King Dead, 20%', 'Adaptive Lymphoid Cells'): '20/80'
}
# Calculate node positions
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()
edges = define_edges()
black_edges = define_black_edges()
G = nx.DiGraph()
pos = {}
node_colors = []
# Create mapping from original node names to numbered labels
mapping = {}
counter = 1
for layer in layers.values():
for node in layer:
mapping[node] = f"{counter}. {node}"
counter += 1
# Add nodes with new numbered labels 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):
new_node = mapping[node]
G.add_node(new_node, layer=layer_name)
pos[new_node] = position
node_colors.append(colors.get(node, 'lightgray'))
# Add edges with updated node labels
edge_colors = []
for (source, target), weight in edges.items():
if source in mapping and target in mapping:
new_source = mapping[source]
new_target = mapping[target]
G.add_edge(new_source, new_target, weight=weight)
edge_colors.append('black' if (source, target) in black_edges else 'lightgrey')
# Draw the graph
plt.figure(figsize=(12, 8))
edges_labels = {(u, v): d["weight"] for u, v, d in G.edges(data=True)}
nx.draw(
G, pos, with_labels=True, node_color=node_colors, edge_color=edge_colors,
node_size=3000, font_size=9, connectionstyle="arc3,rad=0.2"
)
nx.draw_networkx_edge_labels(G, pos, edge_labels=edges_labels, font_size=8)
plt.title("OPRAH™: Richard III", fontsize=18)
plt.show()
# Run the visualization
visualize_nn()

Fig. 14 Is the chair empty? Is the sword unswayed? Is the King dead? The empire unpossessed? What heir of York is there alive but we? And who is England’s King but great York’s heir? Then tell me, what makes he upon the seas?#