-
@ 1ae011cb:1257a556
2025-02-26 23:00:51
Block 885472
6 - high priority
5 - medium priority
5 - low priority
2 - no priority
1 - purging
#bitcoinfees #mempool
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@ cbab7074:f9f0bd61
2025-02-26 23:00:51
Block 885472
6 - high priority
5 - medium priority
5 - low priority
2 - no priority
1 - purging
#bitcoinfees #mempool
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@ e7bf8dad:839ef3db
2025-02-26 23:00:51
Block 885472
6 - high priority
5 - medium priority
5 - low priority
2 - no priority
1 - purging
#bitcoinfees #mempool
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@ f03df3d4:a4d4f676
2025-02-26 23:00:51
Block 885472
6 - high priority
5 - medium priority
5 - low priority
2 - no priority
1 - purging
#bitcoinfees #mempool
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@ f03df3d4:a4d4f676
2025-02-26 22:55:51
Block 885472
5 - high priority
5 - medium priority
5 - low priority
2 - no priority
1 - purging
#bitcoinfees #mempool
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@ 518b2e29:cb6e6b78
2025-02-26 22:55:09
Everything is coming soon. Wen geer?
-
@ a41759d8:d3cbb6c2
2025-02-26 22:53:25
lets simplify the task to see that the code works and model learns.
this version generates sample data where output = input + 1.
//
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import argparse
import hashlib
import random
import os
class NeuralNetwork(nn.Module):
def __init__(self):
super(NeuralNetwork, self).__init__()
self.layer1 = nn.Linear(256, 256)
self.layer2 = nn.Linear(256, 256)
self.layer3 = nn.Linear(256, 256)
self.output_layer = nn.Linear(256, 256)
def forward(self, x):
x = torch.relu(self.layer1(x))
x = torch.relu(self.layer2(x))
x = torch.relu(self.layer3(x))
x = torch.sigmoid(self.output_layer(x))
return x
def train_model(model, X_train, y_train, epochs=10, batch_size=32, save_path='model_weights.pth'):
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters())
X_train_tensor = torch.tensor(X_train, dtype=torch.float32)
y_train_tensor = torch.tensor(y_train, dtype=torch.float32)
for epoch in range(epochs):
permutation = torch.randperm(X_train_tensor.size()[0])
total_loss = 0
for i in range(0, X_train_tensor.size()[0], batch_size):
indices = permutation[i:i + batch_size]
batch_x, batch_y = X_train_tensor[indices], y_train_tensor[indices]
optimizer.zero_grad()
outputs = model(batch_x)
loss = criterion(outputs, batch_y)
loss.backward()
optimizer.step()
total_loss += loss.item() * len(indices)
average_loss = total_loss / len(X_train_tensor)
print(f'Epoch [{epoch+1}/{epochs}], Loss: {average_loss:.4f}')
torch.save(model.state_dict(), save_path)
print(f"Model weights saved to {save_path}")
def train(args):
random_numbers = [random.randint(0, 2**256) for _ in range(args.num_samples)]
#random_numbers = range(args.num_samples)
random_numbers_bitlists = []
hashes = []
inputs = []
try:
os.remove("trainingdata")
except OSError:
pass
print("generating samples")
with open("trainingdata", "a") as samplesfile:
for idx, number in enumerate(random_numbers):
print(idx)
#number_bit_list = [int((number >> i) & 1) for i in range(255, -1, -1)]
number_bit_list = [int((number >> i) & 1) for i in range(256)]
#print("number_bit_list ", number_bit_list)
random_numbers_bitlists.append(number_bit_list)
#number_bytes = number.to_bytes(32, byteorder='big')
#digest = hashlib.sha256(number_bytes).digest()
#hashint = int.from_bytes(digest, byteorder='big')
hashint = number + 1
#print("number", number, hex(number)[2:])
#print("hashint", hashint, hex(hashint)[2:])
bit_list = [int((hashint >> i) & 1) for i in range(256)]
samplesfile.write(f"{hex(hashint)[2:]} {hex(number)[2:]}\n")
inputs.append(bit_list)
X_train = np.array(inputs, dtype=np.uint8)
y_train = np.array(random_numbers_bitlists, dtype=np.uint8)
model = NeuralNetwork()
#train_model(model, X_train, y_train, epochs=1)
print("training model")
train_model(model, X_train, y_train, epochs=args.num_epochs)
def process_infer_input(input_string):
if len(input_string) == 64:
binary_values = []
for i in range(0, 64, 2):
hex_pair = input_string[i:i+2]
decimal_value = int(hex_pair, 16)
binary_representation = format(decimal_value, '08b')
binary_values.extend([int(char) for char in binary_representation])
return np.array(binary_values).reshape(1, -1)
else:
binary_values = [int(char) for char in input_string]
return np.array(binary_values).reshape(1, -1)
def count_same_bits(a, b):
xor_result = a ^ b
same_bits_count = bin(xor_result).count('0')
return same_bits_count
def infer(args):
inputbinstr = ""
if len(args.input) == 64:
binary_values = []
for i in range(0, 64, 2):
hex_pair = args.input[i:i+2]
decimal_value = int(hex_pair, 16)
inputbinstr += format(decimal_value, '08b')
#binary_values.extend([int(char) for char in binary_representation])
#return np.array(binary_values).reshape(1, -1)
elif len(args.input) == 256:
inputbinstr = args.input
else:
print("input must be 256 bit value as hex or binary string")
return
X_test = process_infer_input(inputbinstr)
X_test_tensor = torch.tensor(X_test, dtype=torch.float32)
model = NeuralNetwork()
model.load_state_dict(torch.load('model_weights.pth'))
model.eval()
with torch.no_grad():
predictions = model(X_test_tensor)
#print("predictions.numpy()", predictions.numpy())
roundedpredictions = np.round(predictions.numpy())
#print("roundedpredictions", roundedpredictions)
binstr = ''.join(map(str, roundedpredictions.flatten().astype(int)))
bitdiff = (int(inputbinstr, 2) ^ int(binstr, 2)).bit_count()
print(f"ibin:", inputbinstr)
print(f"obin:", binstr)
print(f"bitdiff: {bitdiff}")
print(f"ohex: {hex(int(binstr, 2))[2:]}")
def main():
parser = argparse.ArgumentParser(description='Train or infer with a neural network.')
subparsers = parser.add_subparsers(dest='command')
train_parser = subparsers.add_parser('train', help='Train the model')
train_parser.add_argument('--num_samples', type=int, nargs='?', default=500000, help='Number of samples to generate')
train_parser.add_argument('--num_epochs', type=int, nargs='?', default=2, help='Number of epochs to train')
train_parser.set_defaults(func=train)
infer_parser = subparsers.add_parser('infer', help='Infer with the model')
infer_parser.add_argument('input', type=str, help='Path to input data for inference')
infer_parser.set_defaults(func=infer)
args = parser.parse_args()
if 'func' in args:
args.func(args)
else:
parser.print_help()
if __name__ == "__main__":
main()
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@ f03df3d4:a4d4f676
2025-02-26 22:50:51
Block 885471
5 - high priority
5 - medium priority
5 - low priority
2 - no priority
1 - purging
#bitcoinfees #mempool
-
@ e7bf8dad:839ef3db
2025-02-26 22:50:51
Block 885471
5 - high priority
5 - medium priority
5 - low priority
2 - no priority
1 - purging
#bitcoinfees #mempool
-
@ f03df3d4:a4d4f676
2025-02-26 22:45:51
Block 885470
5 - high priority
5 - medium priority
5 - low priority
2 - no priority
1 - purging
#bitcoinfees #mempool
-
@ e7bf8dad:839ef3db
2025-02-26 22:40:51
Block 885470
5 - high priority
5 - medium priority
5 - low priority
2 - no priority
1 - purging
#bitcoinfees #mempool
-
@ f03df3d4:a4d4f676
2025-02-26 22:40:51
Block 885470
5 - high priority
5 - medium priority
5 - low priority
2 - no priority
1 - purging
#bitcoinfees #mempool
-
@ f03df3d4:a4d4f676
2025-02-26 22:35:51
Block 885468
6 - high priority
6 - medium priority
5 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool
-
@ df3b8a38:40ca8150
2025-02-26 22:32:54
Wednesday #SelfieDay 🤳 Driving towards my dreams 🌟💨 #LoveSelfieCar #LivingOnTheEdge #SheDrives https://images2.imgbox.com/af/76/dRqPOOPt_o.jpg
-
@ b133bfc5:49d5789d
2025-02-26 22:32:30
#gunstr presale at https://matadorarms.com/product/the-mat-9-pistol-with-stizon-lower-receiver-w-9mm-stizon-helical-feed-magazine/ https://image.nostr.build/770269a09f582453ddef6b58aecafedf6ac643ab839ede1da34d711bfe79b4d7.jpg
-
@ e7bf8dad:839ef3db
2025-02-26 22:30:51
Block 885466
7 - high priority
7 - medium priority
7 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool
-
@ cbab7074:f9f0bd61
2025-02-26 22:30:51
Block 885466
7 - high priority
7 - medium priority
7 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool
-
@ f03df3d4:a4d4f676
2025-02-26 22:30:51
Block 885466
7 - high priority
7 - medium priority
7 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool
-
@ f03df3d4:a4d4f676
2025-02-26 22:25:51
Block 885466
7 - high priority
7 - medium priority
6 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool
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@ b133bfc5:49d5789d
2025-02-26 22:23:00
Watch Claude play Pokémon
https://www.twitch.tv/claudeplayspokemon
-
@ f03df3d4:a4d4f676
2025-02-26 22:20:51
Block 885466
6 - high priority
6 - medium priority
6 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool
-
@ e7bf8dad:839ef3db
2025-02-26 22:20:51
Block 885466
6 - high priority
6 - medium priority
6 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool
-
@ f03df3d4:a4d4f676
2025-02-26 22:15:51
Block 885464
6 - high priority
6 - medium priority
6 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool
-
@ f03df3d4:a4d4f676
2025-02-26 22:10:51
Block 885463
6 - high priority
6 - medium priority
6 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool
-
@ e7bf8dad:839ef3db
2025-02-26 22:10:51
Block 885463
6 - high priority
6 - medium priority
6 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool
-
@ f03df3d4:a4d4f676
2025-02-26 22:05:51
Block 885462
7 - high priority
7 - medium priority
7 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool
-
@ cbab7074:f9f0bd61
2025-02-26 22:00:52
Block 885462
6 - high priority
6 - medium priority
6 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool
-
@ e7bf8dad:839ef3db
2025-02-26 22:00:52
Block 885462
6 - high priority
6 - medium priority
6 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool
-
@ 1ae011cb:1257a556
2025-02-26 22:00:52
Block 885462
6 - high priority
6 - medium priority
6 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool
-
@ f03df3d4:a4d4f676
2025-02-26 22:00:52
Block 885462
6 - high priority
6 - medium priority
6 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool
-
@ f03df3d4:a4d4f676
2025-02-26 21:55:51
Block 885460
11 - high priority
10 - medium priority
8 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool
-
@ c7eda660:efd97c86
2025-02-26 21:55:34
https://media1.tenor.com/m/tK5fR0B-l5kAAAAC/its-happening.gif
https://m.primal.net/PMcc.jpg
-
@ f03df3d4:a4d4f676
2025-02-26 21:50:51
Block 885460
11 - high priority
10 - medium priority
8 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool
-
@ e7bf8dad:839ef3db
2025-02-26 21:50:51
Block 885460
11 - high priority
10 - medium priority
8 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool
-
@ 958b754a:5c39e88c
2025-02-26 21:49:51
does it work with .mov? https://image.nostr.build/56af89fbd6f00cf51ada9eb31dab1815ac468a33b7164aaa095c3d970d2bdf2b.jpg
-
@ b133bfc5:49d5789d
2025-02-26 21:47:45
I’d assume that supreme got permission
-
@ 18905d0a:0b229b08
2025-02-26 21:46:44
Pretty sure Aphex Twin is against collabs with brands. Especially ones like Supreme. This would be a great collector’s item though. Windowlicker is 🔥
-
@ d49a9023:990fa8df
2025-02-26 21:46:07
Can also depend on host... on Nostrmedia.com we do lossy compression but it's barely noticeable degradation on quality. That way the images/videos still look good and are fast to load. No one wants to wait on a giant image/video to render lol
-
@ f03df3d4:a4d4f676
2025-02-26 21:45:51
Block 885460
10 - high priority
9 - medium priority
8 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool
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@ 958b754a:5c39e88c
2025-02-26 21:44:05
over ssh?
-
@ 508cdbf6:8f717552
2025-02-26 21:41:17
as they should be!
-
@ f03df3d4:a4d4f676
2025-02-26 21:40:51
Block 885460
10 - high priority
8 - medium priority
7 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool
-
@ e7bf8dad:839ef3db
2025-02-26 21:40:51
Block 885460
10 - high priority
8 - medium priority
7 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool
-
@ 32e18276:5c68e245
2025-02-26 21:35:57
I guess nostr build might tho
-
@ f03df3d4:a4d4f676
2025-02-26 21:35:52
Block 885460
9 - high priority
8 - medium priority
7 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool
-
@ cbab7074:f9f0bd61
2025-02-26 21:30:48
Block 885460
8 - high priority
7 - medium priority
7 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool
-
@ f03df3d4:a4d4f676
2025-02-26 21:30:48
Block 885460
8 - high priority
7 - medium priority
7 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool
-
@ e7bf8dad:839ef3db
2025-02-26 21:30:48
Block 885460
8 - high priority
7 - medium priority
7 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool
-
@ f901616f:95c58e27
2025-02-26 21:30:34
nostr:note1e7u0czf9eqdzj3ngg9czthck5fxhayvgleypf5p2mlge33ejq43sv9hf0p
-
@ 958b754a:5c39e88c
2025-02-26 21:29:53
He bought 5 hats
-
@ df3b8a38:40ca8150
2025-02-26 21:29:48
Wednesday #SelfieDay 🤳 Confidence is my best accessory 💖💅 #LoveSelfieCar #ConfidenceIsKey #StyledWithClass https://images2.imgbox.com/f0/82/tSKx0vMa_o.jpg
-
@ 958b754a:5c39e88c
2025-02-26 21:27:08
that’s amethyst isn’t it 🥹
-
@ f03df3d4:a4d4f676
2025-02-26 21:25:48
Block 885460
7 - high priority
7 - medium priority
7 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool
-
@ e7bf8dad:839ef3db
2025-02-26 21:20:48
Block 885460
7 - high priority
7 - medium priority
6 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool
-
@ f03df3d4:a4d4f676
2025-02-26 21:20:48
Block 885460
7 - high priority
7 - medium priority
6 - low priority
2 - no priority
1 - purging
#bitcoinfees #mempool
-
@ f03df3d4:a4d4f676
2025-02-26 21:15:48
Block 885460
6 - high priority
6 - medium priority
5 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool
-
@ f03df3d4:a4d4f676
2025-02-26 21:10:48
Block 885460
6 - high priority
6 - medium priority
5 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool
-
@ e7bf8dad:839ef3db
2025-02-26 21:10:48
Block 885460
6 - high priority
6 - medium priority
5 - low priority
2 - no priority
1 - purging
#bitcoinfees #mempool
-
@ d49a9023:990fa8df
2025-02-26 21:10:17
https://news.sky.com/story/flatplan-13317592
-
@ f03df3d4:a4d4f676
2025-02-26 21:05:48
Block 885459
7 - high priority
7 - medium priority
6 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool
-
@ cbab7074:f9f0bd61
2025-02-26 21:00:49
Block 885459
6 - high priority
6 - medium priority
5 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool
-
@ f03df3d4:a4d4f676
2025-02-26 21:00:49
Block 885459
6 - high priority
6 - medium priority
5 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool
-
@ 1ae011cb:1257a556
2025-02-26 21:00:49
Block 885459
6 - high priority
6 - medium priority
5 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool
-
@ e7bf8dad:839ef3db
2025-02-26 21:00:49
Block 885459
6 - high priority
6 - medium priority
5 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool
-
@ ab7d9170:785ea6f7
2025-02-26 20:56:09
Glorious tree of life that connects all the planets And universes with love and affection
https://i.nostr.build/FTsZzEI3DKvXsNDp.png
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@ f03df3d4:a4d4f676
2025-02-26 20:55:48
Block 885459
6 - high priority
6 - medium priority
5 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool
-
@ ac5c59bc:9afde5b2
2025-02-26 20:55:09
Gun night frens...
Block height: 885457
#Bitcoin price 83757 USD
-
@ e7bf8dad:839ef3db
2025-02-26 20:50:48
Block 885457
7 - high priority
7 - medium priority
7 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool
-
@ f03df3d4:a4d4f676
2025-02-26 20:50:48
Block 885457
7 - high priority
7 - medium priority
7 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool
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@ fa93ee83:08f6848c
2025-02-26 20:50:39
sha256 hashes are very short. 256 bits is just 64 bytes. however network needs to find patterns and possibly complex hidden states. yet, the task may be possible due to the bias in sha256 outputs which reveals something about the algorithm.
-
@ 637a2f1d:a169dd8f
2025-02-26 20:49:49
https://www.timeout.com/london/news/a-massive-new-46-storey-skyscraper-has-been-proposed-for-the-city-of-london-022525
https://cdn.nostrcheck.me/637a2f1da5d2fc1976c14ec00f8eae4015afe99bb2554f1d537d0086a169dd8f/00db587bcd477b03123e1414e156cbc4833ec4343f0d67e6c7d87c9630794296.webp
-
@ a7a57e6e:a2e75f5f
2025-02-26 20:47:35
here is pytorch implementation to train correct input values from output values of sha256 hashes
//
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import argparse
import hashlib
import random
import os
class NeuralNetwork(nn.Module):
def __init__(self):
super(NeuralNetwork, self).__init__()
self.layer1 = nn.Linear(256, 256)
self.layer2 = nn.Linear(256, 256)
self.output_layer = nn.Linear(256, 256)
def forward(self, x):
x = torch.relu(self.layer1(x))
x = torch.relu(self.layer2(x))
x = self.output_layer(x)
return x
def train_model(model, X_train, y_train, epochs=10, batch_size=32, save_path='model_weights.pth'):
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters())
X_train_tensor = torch.tensor(X_train, dtype=torch.float32)
y_train_tensor = torch.tensor(y_train, dtype=torch.float32)
for epoch in range(epochs):
permutation = torch.randperm(X_train_tensor.size()[0])
for i in range(0, X_train_tensor.size()[0], batch_size):
indices = permutation[i:i + batch_size]
batch_x, batch_y = X_train_tensor[indices], y_train_tensor[indices]
optimizer.zero_grad()
outputs = model(batch_x)
loss = criterion(outputs, batch_y)
loss.backward()
optimizer.step()
torch.save(model.state_dict(), save_path)
print(f"Model weights saved to {save_path}")
def train(args):
random_numbers = [random.randint(0, 2**256) for _ in range(100000)]
random_numbers_bitlists = []
hashes = []
inputs = []
try:
os.remove("trainingdata")
except OSError:
pass
with open("trainingdata", "a") as samplesfile:
for number in random_numbers:
number_bit_list = [int((number >> i) & 1) for i in range(256)]
random_numbers_bitlists.append(number_bit_list)
number_bytes = number.to_bytes(32, byteorder='big')
digest = hashlib.sha256(number_bytes).digest()
hashint = int.from_bytes(digest, byteorder='big')
bit_list = [int((hashint >> i) & 1) for i in range(256)]
samplesfile.write(f"{hex(hashint)[2:]} {hex(number)[2:]}\n")
inputs.append(bit_list)
X_train = np.array(inputs, dtype=np.uint8)
y_train = np.array(random_numbers_bitlists, dtype=np.uint8)
model = NeuralNetwork()
train_model(model, X_train, y_train, epochs=args.num_epochs)
def process_infer_input(input_string):
if len(input_string) == 64:
binary_values = []
for i in range(0, 64, 2):
hex_pair = input_string[i:i+2]
decimal_value = int(hex_pair, 16)
binary_representation = format(decimal_value, '08b')
binary_values.extend([int(char) for char in binary_representation])
return np.array(binary_values).reshape(1, -1)
else:
binary_values = [int(char) for char in input_string]
return np.array(binary_values).reshape(1, -1)
def infer(args):
X_test = process_infer_input(args.input)
X_test_tensor = torch.tensor(X_test, dtype=torch.float32)
model = NeuralNetwork()
model.load_state_dict(torch.load('model_weights.pth'))
model.eval()
with torch.no_grad():
predictions = model(X_test_tensor)
binstr = ''.join(map(str, np.round(predictions.numpy()).flatten().astype(int)))
print(f"bin: {binstr}")
print(f"hex: {hex(int(binstr, 2))[2:]}")
def main():
parser = argparse.ArgumentParser(description='Train or infer with a neural network.')
subparsers = parser.add_subparsers(dest='command')
train_parser = subparsers.add_parser('train', help='Train the model')
train_parser.add_argument('num_epochs', type=int, nargs='?', default=1, help='Number of epochs to train')
train_parser.set_defaults(func=train)
infer_parser = subparsers.add_parser('infer', help='Infer with the model')
infer_parser.add_argument('input', type=str, help='Path to input data for inference')
infer_parser.set_defaults(func=infer)
args = parser.parse_args()
if 'func' in args:
args.func(args)
else:
parser.print_help()
if __name__ == "__main__":
main()
-
@ f03df3d4:a4d4f676
2025-02-26 20:45:48
Block 885457
6 - high priority
6 - medium priority
6 - low priority
2 - no priority
1 - purging
#bitcoinfees #mempool
-
@ e7bf8dad:839ef3db
2025-02-26 20:40:48
Block 885456
8 - high priority
7 - medium priority
7 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool
-
@ f03df3d4:a4d4f676
2025-02-26 20:40:48
Block 885456
8 - high priority
7 - medium priority
7 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool
-
@ f03df3d4:a4d4f676
2025-02-26 20:35:48
Block 885456
7 - high priority
7 - medium priority
6 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool
-
@ d49a9023:990fa8df
2025-02-26 20:30:59
lol goddamn you bears 🤣
nostr:note1yq3s59avsphvpc6e7s64q09s8ez0q2kh4u2s5730auf87j9j2j6qjqv65x
-
@ cbab7074:f9f0bd61
2025-02-26 20:30:49
Block 885455
10 - high priority
8 - medium priority
7 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool
-
@ e7bf8dad:839ef3db
2025-02-26 20:30:49
Block 885455
10 - high priority
8 - medium priority
7 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool
-
@ f03df3d4:a4d4f676
2025-02-26 20:30:49
Block 885455
10 - high priority
8 - medium priority
7 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool
-
@ df3b8a38:40ca8150
2025-02-26 20:30:04
Wednesday #SelfieDay 🤳 My kind of luxury 🚗💎 #LoveSelfieCar #LuxuryLifestyle #LuxuryCars https://images2.imgbox.com/f5/41/KdKPNCG2_o.jpg
-
@ f6c49e1b:89c41ac9
2025-02-26 20:28:58
Looks like the Byzantine general’s problem
-
@ 21335073:a244b1ad
2025-02-26 20:27:58
I think that Carl and his ex gf are getting back together.
-
@ 958b754a:5c39e88c
2025-02-26 20:26:21
Facts
-
@ f03df3d4:a4d4f676
2025-02-26 20:25:48
Block 885455
9 - high priority
8 - medium priority
6 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool
-
@ e7bf8dad:839ef3db
2025-02-26 20:20:49
Block 885455
7 - high priority
6 - medium priority
5 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool
-
@ f03df3d4:a4d4f676
2025-02-26 20:20:48
Block 885455
7 - high priority
6 - medium priority
5 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool
-
@ f03df3d4:a4d4f676
2025-02-26 20:15:48
Block 885455
7 - high priority
6 - medium priority
5 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool
-
@ f03df3d4:a4d4f676
2025-02-26 20:10:48
Block 885455
5 - high priority
5 - medium priority
5 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool
-
@ e7bf8dad:839ef3db
2025-02-26 20:10:48
Block 885455
5 - high priority
5 - medium priority
5 - low priority
2 - no priority
1 - purging
#bitcoinfees #mempool
-
@ 958b754a:5c39e88c
2025-02-26 20:10:27
https://m.primal.net/PMXe.mov
-
@ f03df3d4:a4d4f676
2025-02-26 20:05:48
Block 885454
5 - high priority
5 - medium priority
5 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool
-
@ 1ae011cb:1257a556
2025-02-26 20:00:49
Block 885453
5 - high priority
5 - medium priority
5 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool
-
@ cbab7074:f9f0bd61
2025-02-26 20:00:49
Block 885453
5 - high priority
5 - medium priority
5 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool
-
@ e7bf8dad:839ef3db
2025-02-26 20:00:49
Block 885453
5 - high priority
5 - medium priority
5 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool
-
@ f03df3d4:a4d4f676
2025-02-26 20:00:48
Block 885453
5 - high priority
5 - medium priority
5 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool
-
@ f03df3d4:a4d4f676
2025-02-26 19:55:48
Block 885452
6 - high priority
6 - medium priority
5 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool
-
@ f03df3d4:a4d4f676
2025-02-26 19:50:49
Block 885452
5 - high priority
5 - medium priority
5 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool
-
@ f85e27b0:d9cc58a7
2025-02-26 19:50:48
What're you gonna use for? Maybe a dehydrated broth mix?
-
@ e7bf8dad:839ef3db
2025-02-26 19:50:48
Block 885452
5 - high priority
5 - medium priority
5 - low priority
4 - no priority
2 - purging
#bitcoinfees #mempool