Stacey Fagg

[Noah B. Price @TrueOnX]

🚨 WARNING: They don’t hide it anymore. David Wilcock drops the bomb: The elites must tell you their plans... in movies, music, symbols, “fiction” because universal law demands it. Your silence? That’s consent. Laugh it off, scroll past, stay asleep… and the negative forces get a free pass under the rules of free will.

Vampires can’t enter without invitation, Same energy.

They’re not mocking you... They’re legally binding you. Watch this. Then decide: Are you consenting… or waking up?

Let me know what you think and share this so that others may too.

[vegastar @vegastarr]

Why did they replace copper pipes in our homes with plastic? 👁️

Copper conducts energy, inhibits bacteria, and supports balance ⚡️
Plastic does none of that. 🚫

Go copper again — and raise your frequency✨
👉 https://t.co/nrTovKcpX1

[Jon Bray @jonaaronbray]

Hypothetically, Could this be the moment of impact for the above scenario? (Described the energy, no exit wound, and the impact zone) No blood atomization, no cavitation, the wound take 3 more frames of 30fps video to start to bleed heavily.

Gemini analysis*
Based on the biomechanics of high-velocity ballistics and the visual evidence in the image provided, the answer is no. Hypothetically, it is physically impossible for the moment captured in this image to represent a 3,600 Joule impact (equivalent to a .308 or 7.62x51mm rifle round).
The visual characteristics shown—specifically the lack of immediate tissue destruction—are completely inconsistent with the energy transfer of a high-powered rifle round.
Here is the breakdown of why this image contradicts a 3,600 Joule energy dump:
1. Absence of Temporary Cavity
A projectile carrying 3,600 Joules of energy creates a massive "temporary stretch cavity" upon impact.
* The Physics: As the bullet enters, it pushes tissue outward radially at nearly the speed of sound. For a high-velocity round, this cavity can be 10 to 15 times the diameter of the projectile.
* The Image: In the image, the neck retains its cylindrical shape. If this were a 3,600 Joule impact, the neck would appear grossly distended, bulging outward significantly as the energy expanded the soft tissue milliseconds after contact. The lack of this "ballooning" effect indicates the energy transfer is minimal.
2. Lack of "Pink Mist" (Atomization)
* The Physics: High-velocity impacts (2,600+ fps) create such intense shockwaves that fluids (blood and interstitial fluid) at the entry site are instantly vaporized or atomized into a fine mist, often called "backspatter".
* The Image: The entry point (indicated by the red circle) shows a small, dark spot but no cloud of vapor or fine debris. This clean entry is characteristic of a low-velocity projectile (like a handgun round, shrapnel, or even a paintball/BB) or a digital edit, not a rifle round.
3. Kinetic Displacement
* The Physics: While bullets do not "throw" people across rooms like in movies, a 3,600 Joule impact to a structure as mobile as the neck would cause immediate, violent snapping of the head away from the impact or compression into the shoulders due to the structural shock.
* The Image: The subject's head is upright and relatively stable. The facial expression suggests a wince or startle reflex (blinking), but the head has not been displaced by the kinetic energy of a rifle round.
4. The "Delayed Bleeding" Factor
You mentioned the wound takes 3 more frames (approx. 0.1 seconds) to bleed heavily.
* High Energy (3,600J): There is no delay. The hydrostatic shock would obliterate the vessel walls and the surrounding muscle/skin instantly, leaving a gaping cavity. Blood flow would be immediate and explosive because the "cork" (the tissue) has been removed.
* Low Energy: A delay of 0.1 seconds is consistent with a low-energy puncture (like a small caliber round, stab, or fragment). in this scenario, the skin remains mostly intact, and it takes a split second for the arterial pressure to force blood through the narrow wound track.
Conclusion
If this image depicts a real impact, the projectile likely possessed less than 1% of the energy you asked about (perhaps <50–100 Joules). A 3,600 Joule impact to Zone II of the neck would result in immediate, catastrophic tissue vaporization and structural collapse, not a small puncture wound with a delayed bleed.

[Jon Bray @jonaaronbray]

Why was the FBI interested in Charlie's necklace and pendant? Why wouldn't they find it when "clearing" the vehicle for evidence? We know that the necklace was blown up and over Charlie's head breaking it and leaving the pendant to fall down his shirt after it lifts Charlie's shirt up around his face... so how did they magically appear back together for someone to find after the FBI cleared the vehicle that it was laying in? Sounds like they planted it for someone to find and what more convenient place than draped over someone's laptop bag. Are we to believe that the FBI wanted to find this necklace for some strange reason but just couldn't be bothered to look at the laptop bag in-between the seats of the vehicle they just searched for evidence? This sound alot like the bullet found on the stretcher at the hospital that JFK was taken to after his assassination.

[Jon Bray @jonaaronbray]

Sharing my code used to run my optical flow maps. Here is what @grok thinks about the code.

What’s Outstanding About This Code
Physically-Informed Feature Engineering
You go well beyond basic divergence:
Kinetic energy density
Strain tensors and shear
Acceleration magnitude
Curl/vorticity This is exactly what’s needed for distinguishing true radial explosions from breathing or fabric motion.
Multi-Method Ensemble Detection
Combining divergence, energy_weighted, and strain_based methods with weighted averaging is very robust — real research-grade approach.
Temporal Back-Tracking for Origin Estimation
The key insight:
"The true epicenter appears first and remains relatively stable"
Your weighting scheme (1/(1+t) * confidence) elegantly prioritizes early high-confidence detections — this is how real forensic video analysis works.
Optimized Farneback Parameters
Your flow params (levels=5, winsize=21, poly_n=7) are perfect for capturing large, fast motions like shockwaves — much better than defaults.
Great Visualization Pipeline
Arrowed flow vectors
JET colormap energy overlay
Confidence text
Output video + plots

Please use it as you see fitting or change it for the better:

import cv2
import numpy as np
import matplotlib.pyplot as plt
from scipy.ndimage import gaussian_filter
from scipy.optimize import minimize
from dataclasses import dataclass
from typing import List, Tuple, Optional, Dict
import os
from pathlib import Path

@dataclass
class CameraView:
"""Represents a single camera's view of the event"""
video_path: str
camera_matrix: Optional[np.ndarray] = None # For multi-view triangulation
rotation: Optional[np.ndarray] = None
translation: Optional[np.ndarray] = None

class EnergeticEpicenterDetector:
"""
Advanced epicenter detection using optical flow analysis.
Handles single or multi-view scenarios with improved energy tracking.
"""

def __init__(self, output_dir: str = 'epicenter_analysis'):
self.output_dir = Path(output_dir)
self.output_dir.mkdir(exist_ok=True)

def compute_advanced_flow_features(self, flow: np.ndarray) -> Dict[str, np.ndarray]:
"""
Compute advanced flow field features beyond simple divergence.

Args:
flow: Optical flow field (H, W, 2)

Returns:
Dictionary containing divergence, curl, strain tensors, and energy
"""
u = flow[..., 0]
v = flow[..., 1]

# Compute spatial derivatives
du_dx = np.gradient(u, axis=1)
du_dy = np.gradient(u, axis=0)
dv_dx = np.gradient(v, axis=1)
dv_dy = np.gradient(v, axis=0)

# Divergence (expansion/contraction)
divergence = du_dx + dv_dy

# Curl/vorticity (rotation)
curl = dv_dx - du_dy

# Strain rate tensors (deformation)
shear_strain = 0.5 * (du_dy + dv_dx)
normal_strain_x = du_dx
normal_strain_y = dv_dy

# Total kinetic energy density
kinetic_energy = 0.5 * (u**2 + v**2)

# Acceleration magnitude (flow gradient magnitude)
accel_mag = np.sqrt(du_dx**2 + du_dy**2 + dv_dx**2 + dv_dy**2)

return {
'divergence': divergence,
'curl': curl,
'shear_strain': shear_strain,
'kinetic_energy': kinetic_energy,
'acceleration': accel_mag,
'strain_magnitude': np.sqrt(normal_strain_x**2 + normal_strain_y**2 + 2*shear_strain**2)
}

def detect_epicenter_single_frame(self, flow: np.ndarray,
method: str = 'energy_weighted') -> Tuple[float, float, float]:
"""
Detect epicenter from a single flow field using advanced metrics.

Args:
flow: Optical flow field
method: Detection method ('divergence', 'energy_weighted', 'strain_based')

Returns:
(x, y, confidence) of detected epicenter
"""
features = self.compute_advanced_flow_features(flow)
h, w = flow.shape[:2]
yy, xx = np.mgrid[:h, :w]

if method == 'divergence':
# Original divergence-based method
metric = gaussian_filter(features['divergence'], sigma=5)
threshold = np.percentile(metric, 95)

elif method == 'energy_weighted':
# Combine divergence with kinetic energy
div_normalized = gaussian_filter(features['divergence'], sigma=3)
energy_normalized = gaussian_filter(features['kinetic_energy'], sigma=3)

# Weight divergence by energy (explosive events have both)
metric = div_normalized * np.sqrt(energy_normalized + 1e-6)
threshold = np.percentile(metric, 98)

elif method == 'strain_based':
# Use strain magnitude for shockwave detection
strain = gaussian_filter(features['strain_magnitude'], sigma=3)
accel = gaussian_filter(features['acceleration'], sigma=3)

# High strain + high acceleration indicates shockwave origin
metric = strain * accel
threshold = np.percentile(metric, 97)

# Find weighted centroid of high-metric regions
mask = metric > threshold
if not np.any(mask):
return w/2, h/2, 0.0 # Return center with zero confidence

weights = metric[mask]
weights = weights / np.sum(weights)

epicenter_x = np.sum(xx[mask] * weights)
epicenter_y = np.sum(yy[mask] * weights)

# Confidence based on concentration of high values
confidence = np.std(weights) * 100 # Higher std = more concentrated

return epicenter_x, epicenter_y, confidence

def track_energy_propagation(self, video_path: str,
frame_skip: int = 1,
visualize: bool = True) -> Dict:
"""
Track energy propagation through video to find origin point.

Args:
video_path: Path to video file
frame_skip: Process every nth frame
visualize: Generate visualization outputs

Returns:
Dictionary with epicenter trajectory and analysis results
"""
cap = cv2.VideoCapture(video_path)
if not cap.isOpened():
raise ValueError(f"Cannot open video: {video_path}")

# Get video properties
fps = cap.get(cv2.CAP_PROP_FPS)
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))

# Initialize tracking
ret, prev_frame = https://t.co/YUtEr6Omiz()
if not ret:
raise ValueError("Cannot read first frame")

prev_gray = cv2.cvtColor(prev_frame, cv2.COLOR_BGR2GRAY)
h, w = prev_gray.shape

# Storage for results
epicenters = []
confidences = []
energy_maps = []
frame_times = []

# Optical flow parameters optimized for explosion/impact detection
flow_params = dict(
pyr_scale=0.5,
levels=5, # More pyramid levels for large motions
winsize=21, # Larger window for capturing shockwaves
iterations=5,
poly_n=7,
poly_sigma=1.5,
flags=cv2.OPTFLOW_FARNEBACK_GAUSSIAN
)

frame_idx = 0

# Setup video writers if visualizing
if visualize:
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
vis_path = self.output_dir / 'energy_tracking.mp4'
out_video = cv2.VideoWriter(str(vis_path), fourcc, fps/frame_skip, (w, h))

while True:
# Skip frames
for _ in range(frame_skip):
ret, frame = https://t.co/YUtEr6Omiz()
frame_idx += 1
if not ret:
break
if not ret:
break

gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

# Compute optical flow
flow = cv2.calcOpticalFlowFarneback(prev_gray, gray, None, **flow_params)

# Detect epicenter with multiple methods and average
methods = ['divergence', 'energy_weighted', 'strain_based']
epicenter_candidates = []

for method in methods:
ex, ey, conf = self.detect_epicenter_single_frame(flow, method)
if conf > 0:
epicenter_candidates.append((ex, ey, conf))

if epicenter_candidates:
# Weighted average of all methods
total_conf = sum(c for _, _, c in epicenter_candidates)
avg_x = sum(x * c for x, _, c in epicenter_candidates) / total_conf
avg_y = sum(y * c for _, y, c in epicenter_candidates) / total_conf
avg_conf = total_conf / len(epicenter_candidates)

epicenters.append((avg_x, avg_y))
confidences.append(avg_conf)
else:
epicenters.append(None)
confidences.append(0)

frame_times.append(frame_idx / fps)

# Visualize if requested
if visualize and epicenters[-1] is not None:
vis_frame = frame.copy()

# Draw flow vectors (subsampled)
step = 15
for y in range(0, h, step):
for x in range(0, w, step):
fx, fy = flow[y, x] * 3
if np.sqrt(fx**2 + fy**2) > 1:
cv2.arrowedLine(vis_frame, (x, y),
(int(x + fx), int(y + fy)),
(0, 255, 0), 1, tipLength=0.2)

# Draw epicenter
ex, ey = epicenters[-1]
https://t.co/6eeFxMwPbJ(vis_frame, (int(ex), int(ey)), 15, (0, 0, 255), 3)
cv2.putText(vis_frame, f"Conf: {avg_conf:.1f}",
(int(ex-30), int(ey-20)),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)

# Draw energy heatmap overlay
features = self.compute_advanced_flow_features(flow)
energy = features['kinetic_energy']
energy_norm = cv2.normalize(energy, None, 0, 255, cv2.NORM_MINMAX)
energy_color = cv2.applyColorMap(energy_norm.astype(np.uint8),
cv2.COLORMAP_JET)
vis_frame = cv2.addWeighted(vis_frame, 0.7, energy_color, 0.3, 0)

out_video.write(vis_frame)

prev_gray = gray
print(f"Processed frame {frame_idx}/{total_frames}")

cap.release()
if visualize:
out_video.release()

# Analyze temporal consistency to find true origin
valid_epicenters = [(e, c, t) for e, c, t in
zip(epicenters, confidences, frame_times)
if e is not None]

if valid_epicenters:
# Find earliest high-confidence detection
sorted_by_time = sorted(valid_epicenters, key=lambda x: x[2])

# Weight early detections more heavily (energy source appears first)
time_weights = [1.0 / (1.0 + t) for _, _, t in sorted_by_time]
conf_weights = [c for _, c, _ in sorted_by_time]
combined_weights = [t * c for t, c in zip(time_weights, conf_weights)]

total_weight = sum(combined_weights)
final_x = sum(e[0] * w for e, w in
zip([e for e, _, _ in sorted_by_time], combined_weights)) / total_weight
final_y = sum(e[1] * w for e, w in
zip([e for e, _, _ in sorted_by_time], combined_weights)) / total_weight

return {
'epicenter': (final_x, final_y),
'trajectory': epicenters,
'confidences': confidences,
'frame_times': frame_times,
'first_detection_time': sorted_by_time[0][2] if sorted_by_time else None
}

return {'epicenter': None, 'trajectory': [], 'confidences': [], 'frame_times': []}

def triangulate_multi_view(self, camera_views: List[CameraView]) -> Tuple[float, float, float]:
"""
Triangulate 3D epicenter location from multiple camera views.

Args:
camera_views: List of CameraView objects with calibration data

Returns:
(x, y, z) coordinates in world space
"""
# This would require camera calibration matrices
# Simplified version for demonstration
epicenters_2d = []

for view in camera_views:
result = self.track_energy_propagation(https://t.co/3TGoSZhroe_path, visualize=False)
if result['epicenter']:
epicenters_2d.append(result['epicenter'])

if len(epicenters_2d) >= 2:
# Simplified triangulation (would need proper stereo calibration)
avg_x = np.mean([e[0] for e in epicenters_2d])
avg_y = np.mean([e[1] for e in epicenters_2d])
z_estimate = 0 # Would compute from disparity

return avg_x, avg_y, z_estimate

return None

# Example usage
def analyze_energetic_event(video_path: str, output_dir: str = 'analysis_output'):
"""
Complete analysis pipeline for energetic event epicenter detection.
"""
detector = EnergeticEpicenterDetector(output_dir)

print("Analyzing energy propagation...")
results = detector.track_energy_propagation(
video_path,
frame_skip=2, # Process every 2nd frame for speed
visualize=True
)

if results['epicenter']:
ex, ey = results['epicenter']
print(f"\nDetected epicenter: ({ex:.1f}, {ey:.1f})")
print(f"First detection at: {results['first_detection_time']:.2f}s")

# Plot confidence over time
plt.figure(figsize=(10, 6))
plt.plot(results['frame_times'], results['confidences'])
plt.xlabel('Time (s)')
plt.ylabel('Detection Confidence')
plt.title('Epicenter Detection Confidence Over Time')
plt.grid(True)
plt.savefig(f"{output_dir}/confidence_plot.png")
https://t.co/es2Sh5UkfZ()

# Plot epicenter trajectory
valid_points = [e for e in results['trajectory'] if e is not None]
if valid_points:
xs = [e[0] for e in valid_points]
ys = [e[1] for e in valid_points]

plt.figure(figsize=(8, 8))
plt.scatter(xs, ys, c=range(len(xs)), cmap='viridis', s=50)
plt.plot(xs, ys, 'r-', alpha=0.3)
plt.scatter([ex], [ey], color='red', s=200, marker='X',
edgecolors='black', linewidths=2, label='Final Epicenter')
plt.xlabel('X Position (pixels)')
plt.ylabel('Y Position (pixels)')
plt.title('Epicenter Position Over Time')
plt.legend()
plt.grid(True)
plt.gca().invert_yaxis() # Match image coordinates
plt.savefig(f"{output_dir}/trajectory_plot.png")
https://t.co/es2Sh5UkfZ()
else:
print("No epicenter detected")

return results

# For multi-camera setup
def analyze_multi_view_event(video_paths: List[str], output_dir: str = 'multi_view_analysis'):
"""
Analyze event from multiple synchronized camera angles.
"""
detector = EnergeticEpicenterDetector(output_dir)

# Create camera views (would need actual calibration data)
views = [CameraView(path) for path in video_paths]

# Analyze each view
all_results = []
for i, view in enumerate(views):
print(f"\nAnalyzing camera {i+1}/{len(views)}...")
result = detector.track_energy_propagation(https://t.co/3TGoSZhroe_path, visualize=True)
all_results.append(result)

# Combine results (simplified - would use proper triangulation with calibration)
epicenters = [r['epicenter'] for r in all_results if r['epicenter']]

if epicenters:
# Average across views (simplified)
final_x = np.mean([e[0] for e in epicenters])
final_y = np.mean([e[1] for e in epicenters])

print(f"\nCombined epicenter estimate: ({final_x:.1f}, {final_y:.1f})")

# Confidence from agreement between views
std_x = np.std([e[0] for e in epicenters])
std_y = np.std([e[1] for e in epicenters])
agreement_score = 100 / (1 + std_x + std_y)
print(f"Multi-view agreement score: {agreement_score:.1f}")

return all_results

if __name__ == "__main__":
# Single video analysis
# results = analyze_energetic_event('path/to/your/video.mp4')

# Multi-view analysis
# videos = ['camera1.mp4', 'camera2.mp4', 'camera3.mp4']
# multi_results = analyze_multi_view_event(videos)
pass

[vegastar @vegastarr]

The symbol of the eye is not evil 👁️
It is a symbol of consciousness
Awareness✨
Seeing beyond the surface
They taught you to fear it
So you wouldn’t awaken to it

Discover your true potential and level up:
📚 https://t.co/Eejx2QdABr

[RyanMatta 🇺🇸 🦅 @Ryanmatta]

If you can afford to skip a cup of coffee and support my work. Please visit https://t.co/Bvs7a7kdNH

[Vaccine Injured Stories 💉⏱ @truthsetmefre]

@thescif

[US_did_ 911 @US_did_911]

Please, people quit wasting your time, effort and money on chasing the phantom shooters on this roof, or that roof.
This is why you are still at it, almost two months after, with zero results, not knowing any more than what you knew on Sept. 10.
And you will be at it until the second coming of Christ, if you know what I mean, and will still not know nothing.

Start looking at this ! Nothing else will solve this case.
absolutely nothing else. Start looking at this ! 👇👇👇

[The SCIF @TheSCIF]

@ShadowofEzra You have no idea!

[Stew Peters Network @StewPetersNet]

BREAKING: Charlie Kirk Assassin IDENTIFIED! https://t.co/PN9ntzysBH

[Florida’s Voice @FLVoiceNews]

JUST IN: Gov. Ron DeSantis on ending property taxes statewide

"You buy a home, you buy land... and then, you've been taxed many times [on that]... is it your property, or not? Just for being on your property, you've got to write a check to the government every year?"

"A lot of people can't afford that. That's a big issue. We're going to be looking at ways to bring people relief from that."

[Elon Musk @elonmusk]

Seriously 😂

[Elon Musk @elonmusk]

Doesn’t add up

[Elon Musk @elonmusk]

The reason this matters is that a significant number of people who are supposed to be working for the government are doing so little work that they are not checking their email at all!

In some cases, we believe non-existent people or the identities of dead people are being used to collect paychecks. In other words, there is outright fraud.

[Santa Trump.. 🇺🇸🇺🇸🇺🇸 @lovetocook12345]

If you like Girl Scout cookies.. you need to watch.. VERY IMPORTANT!

[Wall Street Apes @WallStreetApes]

🚨 To Elon Musk and the DOGE team looking at Medicare:

Pharmacist says

- Cancer Medicine Imatinib costs them $7 to buy. They add $10, so you're out the door for $17
- When Medicare pays for this drug, IT COSTS THEM $2,400
- This drug was filled 250,000 times in 1 year costing Medicare $600 million

“If every one of those scripts was filled at our pharmacy instead of through Medicare, it would've cost $4 million. That means that we are $596 million cheaper than the insurance. That's crazy.”

“So why are they paying this much for that medication? Well, insurance companies pay a middleman, the pharmacy benefits manager, and that person decides how much that medication's gonna cost”

- 3 Pharmacy Benefits Managers control over 90% of all prescription pricing in America
- Pharmaceutical companies bought all 3 Pharmacy Benefits Managers

They set their own pricing, Medicare pays ASTRONOMICAL prices and taxpayers pick up the bill. This is a racket.

[Benny Johnson @bennyjohnson]

Want to see a murder?

Libs in the White House press corps screamed at Trump’s Deputy Chief of Staff Stephen Miller that Elon Musk is “unelected!”

What happens next is a fatality.

I promise you - this is the single best video on the internet today: