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Re: Bitcoin puzzle transaction ~32 BTC prize to who solves it
Does anyone know how to use CUDA for calculations?
use vanity search.
(can't range search)
https://github.com/JeanLucPons/VanitySearch
additional range search
https://github.com/wizard872/VanitySearchRange/releases/tag/v1.1
Re: Bitcoin puzzle transaction ~32 BTC prize to who solves it
I have developed a prediction model based on the @HomelessPhD model for alpha values:
According to this, puzzle 69 starts with 1ba45e.....
i tried found key in range [ 1ba45e000000000000 ~ 1ba45effffffffffff ],According to this, puzzle 69 starts with 1ba45e.....
but not match key.
fail
What did you expect? I hope it wasn't an expensive lesson to learn.
Quote
Only thing here that should be clearly stated is that a model you are using for prognosis should have a number of parameters less than a number of observations - simple polynomial approximation with an order of n will ideally fit the dataset of n points (it will have exactly n roots) just memorizing the data instead of retrieving the logic behind the data - same correct for complex models like SVM or neural networks - they could just memorize (overfit) the data but in a less obvious way - so be carefull using them or you will lose your precious time for mathematical madness.
just searching!
Re: Bitcoin puzzle transaction ~32 BTC prize to who solves it
I have developed a prediction model based on the @HomelessPhD model for alpha values:
https://github.com/HomelessPhD/BTC32
It targets puzzle private keys with a deviation of 0.0000000001 %.
python3 test.py 68
Puzzle 68 Calculation:
Using alpha: 8.692996380248756
Base exponential model: 0.68972090704257297155081468833675824716161023623689914791953650562051467261304 4419972598777231639024182489182413058156841483121232343334326952277688035030318 1311309574260048040167879840662353500733521425500975021869380914054885130184442 40544507079388243203845236809052465256422016436329883051889524096 * 2.00446149587820417640494244738360788449393817055989304597589376020405549833178 4239005365118701510143617670147330576209009738631394722016490414926974514298110 3364613583395133560524755600665108274702675918184495985595413655135405238097981 4864413726673117061462450025066948963321434607944444810622903302^n
Real Value: 219898266213316039825
Predicted Value: 219898266215462633472.00
Percentage error: 0.00000000%
python3 test.py 69
Puzzle 69 Prediction:
Using interpolated alpha: -7.804319
According to this, puzzle 69 starts with 1ba45e.....
I don't have the nerves to improve this for the better.
https://github.com/HomelessPhD/BTC32
It targets puzzle private keys with a deviation of 0.0000000001 %.
Code:
import numpy as np
from mpmath import mp
import argparse
# Set very high precision
mp.dps = 300
# Target numbers (ordinal, value)
target_numbers = [
(1, 1), (2, 3), (3, 7), (4, 8), (5, 21), (6, 49), (7, 76), (8, 224), (9, 467), (10, 514),
(11, 1155), (12, 2683), (13, 5216), (14, 10544), (15, 26867), (16, 51510),
(17, 95823), (18, 198669), (19, 357535), (20, 863317), (21, 1811764),
(22, 3007503), (23, 5598802), (24, 14428676), (25, 33185509),
(26, 54538862), (27, 111949941), (28, 227634408), (29, 400708894),
(30, 1033162084), (31, 2102388551), (32, 3093472814), (33, 7137437912),
(34, 14133072157), (35, 20112871792), (36, 42387769980), (37, 100251560595),
(38, 146971536592), (39, 323724968937), (40, 1003651412950),
(41, 1458252205147), (42, 2895374552463), (43, 7409811047825),
(44, 15404761757071), (45, 19996463086597), (46, 51408670348612),
(47, 119666659114170), (48, 191206974700443), (49, 409118905032525),
(50, 611140496167764), (51, 2058769515153876), (52, 4216495639600700),
(53, 6763683971478124), (54, 9974455244496707), (55, 30045390491869460),
(56, 44218742292676575), (57, 138245758910846492), (58, 199976667976342049),
(59, 525070384258266191), (60, 1135041350219496382), (61, 1425787542618654982),
(62, 3908372542507822062), (63, 8993229949524469768),
(64, 17799667357578236628), (65, 30568377312064202855),
(66, 46346217550346335726), (67, 132656943602386256302),
(68, 219898266213316039825), (70, 970436974005023690481)
]
# Provided alpha values
alpha_values = {
20: -5.509999999999367, 21: -4.8099999999993575, 22: 6.690000000000806, 23: 4.309999999879222,
24: -5.109999999999362, 25: -2.4933006701008145, 26: 23.19000000000104, 27: 50.01000000165623,
28: 50.01000000165623, 29: 4.490000000000775, 30: -4.30999999999935, 31: -4.8036514600379405,
32: 2.7900000000007505, 33: 10.110000011655663, 34: 8.496609620547714, 35: 1.6905278399975268,
36: 2.2900000000007434, 37: 6.506918969897979, 38: 1.8912248499932935, 39: 2.9099999998792025,
40: -2.809999999999329, 41: 5.2945524899731184, 42: 5.690000000000792, 43: -4.691035100114615,
44: -3.9099999999993447, 45: 2.5900000000007477, 46: 50.01000000165623, 47: -4.50218804004683,
48: 6.909999999879259, 49: 28.510000011655915, 50: 2.495913989964804, 51: -2.5900000001208756,
52: -2.5099999999993248, 53: 50.01000000165623, 54: 2.4900000000007463, 55: -5.00999999999936,
56: 3.8099999998792153, 57: -2.1943843400942242, 58: 8.890000000000837, 59: -3.009999999999332,
60: -2.2099999999993205, 61: 3.1099999998792054, 62: -6.000280060058447, 63: -2.490000000120874,
64: -2.809999999999329, 65: -19.689999988344763, 66: 2.7931615399815364, 67: -4.709999999999356,
68: 8.692996380248756, 70: -24.301635149307526
}
def calculate_prediction(puzzle_number, alpha):
# Get all previous data points
ordinals = np.array([x[0] for x in target_numbers if x[0] < puzzle_number], dtype=float)
values = np.array([x[1] for x in target_numbers if x[0] < puzzle_number], dtype=float)
if len(ordinals) < 2:
print(f"Not enough data points to make a prediction for puzzle {puzzle_number}")
return None, None, None
log_values = np.array([float(mp.log(val)) for val in values], dtype=float)
weights = np.linspace(0.5, 1, len(ordinals))
coefficients = np.polyfit(ordinals, log_values, 1, w=weights)
a = mp.exp(coefficients[1])
b = mp.exp(coefficients[0])
correction = (((2 ** puzzle_number) - 1) - (2 ** (puzzle_number - 1))) / alpha
predicted_value = (a * mp.power(b, puzzle_number)) - correction
return predicted_value, a, b
def interpolate_alpha(puzzle_number):
"""Interpolate alpha for missing puzzle numbers"""
known_puzzles = sorted(alpha_values.keys())
if puzzle_number < min(known_puzzles) or puzzle_number > max(known_puzzles):
return 0.0 # Default value if outside known range
# Find closest lower and higher puzzle numbers
lower = max([p for p in known_puzzles if p < puzzle_number])
higher = min([p for p in known_puzzles if p > puzzle_number])
# Linear interpolation
alpha_range = alpha_values[higher] - alpha_values[lower]
position = (puzzle_number - lower) / (higher - lower)
return alpha_values[lower] + (alpha_range * position)
def format_mp(value, decimals=None):
"""Format mpmath value with specified decimal places"""
if decimals is not None:
return f"{float(value):,.{decimals}f}".replace(',', '')
return mp.nstr(value, mp.dps)
def main(puzzle_number):
if puzzle_number == 69:
# Special case for puzzle 69 - use interpolated alpha
alpha = interpolate_alpha(69)
predicted_value, a, b = calculate_prediction(69, alpha)
print("\nPuzzle 69 Prediction:")
print(f"Using interpolated alpha: {alpha:.6f}")
print(f"Base exponential model: {format_mp(a)} * {format_mp(b)}^n")
print(f"Predicted Value: {format_mp(predicted_value, 2)}")
# Compare with n=70
n70_value = next(x[1] for x in target_numbers if x[0] == 70)
growth_rate = n70_value / predicted_value
print(f"Growth to n=70: {float(growth_rate):.6f}x")
print(f"Actual n=70 value: {format_mp(n70_value)}")
return
if puzzle_number not in alpha_values:
print(f"No alpha value available for puzzle {puzzle_number}")
return
alpha = alpha_values[puzzle_number]
predicted_value, a, b = calculate_prediction(puzzle_number, alpha)
if predicted_value is None:
return
real_value = next((x[1] for x in target_numbers if x[0] == puzzle_number), None)
print(f"\nPuzzle {puzzle_number} Calculation:")
print(f"Using alpha: {alpha}")
print(f"Base exponential model: {format_mp(a)} * {format_mp(b)}^n")
if real_value is not None:
print(f"Real Value: {format_mp(real_value)}")
print(f"Predicted Value: {format_mp(predicted_value, 2)}")
if real_value is not None:
error_percentage = abs((predicted_value - real_value) / real_value) * 100
print(f"Percentage error: {format_mp(error_percentage, 10)}%")
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Calculate puzzle values using provided alpha values.")
parser.add_argument("puzzle_number", type=int, help="Puzzle number to calculate")
args = parser.parse_args()
main(args.puzzle_number)
If anyone knows what to do next with this - here you go.from mpmath import mp
import argparse
# Set very high precision
mp.dps = 300
# Target numbers (ordinal, value)
target_numbers = [
(1, 1), (2, 3), (3, 7), (4, 8), (5, 21), (6, 49), (7, 76), (8, 224), (9, 467), (10, 514),
(11, 1155), (12, 2683), (13, 5216), (14, 10544), (15, 26867), (16, 51510),
(17, 95823), (18, 198669), (19, 357535), (20, 863317), (21, 1811764),
(22, 3007503), (23, 5598802), (24, 14428676), (25, 33185509),
(26, 54538862), (27, 111949941), (28, 227634408), (29, 400708894),
(30, 1033162084), (31, 2102388551), (32, 3093472814), (33, 7137437912),
(34, 14133072157), (35, 20112871792), (36, 42387769980), (37, 100251560595),
(38, 146971536592), (39, 323724968937), (40, 1003651412950),
(41, 1458252205147), (42, 2895374552463), (43, 7409811047825),
(44, 15404761757071), (45, 19996463086597), (46, 51408670348612),
(47, 119666659114170), (48, 191206974700443), (49, 409118905032525),
(50, 611140496167764), (51, 2058769515153876), (52, 4216495639600700),
(53, 6763683971478124), (54, 9974455244496707), (55, 30045390491869460),
(56, 44218742292676575), (57, 138245758910846492), (58, 199976667976342049),
(59, 525070384258266191), (60, 1135041350219496382), (61, 1425787542618654982),
(62, 3908372542507822062), (63, 8993229949524469768),
(64, 17799667357578236628), (65, 30568377312064202855),
(66, 46346217550346335726), (67, 132656943602386256302),
(68, 219898266213316039825), (70, 970436974005023690481)
]
# Provided alpha values
alpha_values = {
20: -5.509999999999367, 21: -4.8099999999993575, 22: 6.690000000000806, 23: 4.309999999879222,
24: -5.109999999999362, 25: -2.4933006701008145, 26: 23.19000000000104, 27: 50.01000000165623,
28: 50.01000000165623, 29: 4.490000000000775, 30: -4.30999999999935, 31: -4.8036514600379405,
32: 2.7900000000007505, 33: 10.110000011655663, 34: 8.496609620547714, 35: 1.6905278399975268,
36: 2.2900000000007434, 37: 6.506918969897979, 38: 1.8912248499932935, 39: 2.9099999998792025,
40: -2.809999999999329, 41: 5.2945524899731184, 42: 5.690000000000792, 43: -4.691035100114615,
44: -3.9099999999993447, 45: 2.5900000000007477, 46: 50.01000000165623, 47: -4.50218804004683,
48: 6.909999999879259, 49: 28.510000011655915, 50: 2.495913989964804, 51: -2.5900000001208756,
52: -2.5099999999993248, 53: 50.01000000165623, 54: 2.4900000000007463, 55: -5.00999999999936,
56: 3.8099999998792153, 57: -2.1943843400942242, 58: 8.890000000000837, 59: -3.009999999999332,
60: -2.2099999999993205, 61: 3.1099999998792054, 62: -6.000280060058447, 63: -2.490000000120874,
64: -2.809999999999329, 65: -19.689999988344763, 66: 2.7931615399815364, 67: -4.709999999999356,
68: 8.692996380248756, 70: -24.301635149307526
}
def calculate_prediction(puzzle_number, alpha):
# Get all previous data points
ordinals = np.array([x[0] for x in target_numbers if x[0] < puzzle_number], dtype=float)
values = np.array([x[1] for x in target_numbers if x[0] < puzzle_number], dtype=float)
if len(ordinals) < 2:
print(f"Not enough data points to make a prediction for puzzle {puzzle_number}")
return None, None, None
log_values = np.array([float(mp.log(val)) for val in values], dtype=float)
weights = np.linspace(0.5, 1, len(ordinals))
coefficients = np.polyfit(ordinals, log_values, 1, w=weights)
a = mp.exp(coefficients[1])
b = mp.exp(coefficients[0])
correction = (((2 ** puzzle_number) - 1) - (2 ** (puzzle_number - 1))) / alpha
predicted_value = (a * mp.power(b, puzzle_number)) - correction
return predicted_value, a, b
def interpolate_alpha(puzzle_number):
"""Interpolate alpha for missing puzzle numbers"""
known_puzzles = sorted(alpha_values.keys())
if puzzle_number < min(known_puzzles) or puzzle_number > max(known_puzzles):
return 0.0 # Default value if outside known range
# Find closest lower and higher puzzle numbers
lower = max([p for p in known_puzzles if p < puzzle_number])
higher = min([p for p in known_puzzles if p > puzzle_number])
# Linear interpolation
alpha_range = alpha_values[higher] - alpha_values[lower]
position = (puzzle_number - lower) / (higher - lower)
return alpha_values[lower] + (alpha_range * position)
def format_mp(value, decimals=None):
"""Format mpmath value with specified decimal places"""
if decimals is not None:
return f"{float(value):,.{decimals}f}".replace(',', '')
return mp.nstr(value, mp.dps)
def main(puzzle_number):
if puzzle_number == 69:
# Special case for puzzle 69 - use interpolated alpha
alpha = interpolate_alpha(69)
predicted_value, a, b = calculate_prediction(69, alpha)
print("\nPuzzle 69 Prediction:")
print(f"Using interpolated alpha: {alpha:.6f}")
print(f"Base exponential model: {format_mp(a)} * {format_mp(b)}^n")
print(f"Predicted Value: {format_mp(predicted_value, 2)}")
# Compare with n=70
n70_value = next(x[1] for x in target_numbers if x[0] == 70)
growth_rate = n70_value / predicted_value
print(f"Growth to n=70: {float(growth_rate):.6f}x")
print(f"Actual n=70 value: {format_mp(n70_value)}")
return
if puzzle_number not in alpha_values:
print(f"No alpha value available for puzzle {puzzle_number}")
return
alpha = alpha_values[puzzle_number]
predicted_value, a, b = calculate_prediction(puzzle_number, alpha)
if predicted_value is None:
return
real_value = next((x[1] for x in target_numbers if x[0] == puzzle_number), None)
print(f"\nPuzzle {puzzle_number} Calculation:")
print(f"Using alpha: {alpha}")
print(f"Base exponential model: {format_mp(a)} * {format_mp(b)}^n")
if real_value is not None:
print(f"Real Value: {format_mp(real_value)}")
print(f"Predicted Value: {format_mp(predicted_value, 2)}")
if real_value is not None:
error_percentage = abs((predicted_value - real_value) / real_value) * 100
print(f"Percentage error: {format_mp(error_percentage, 10)}%")
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Calculate puzzle values using provided alpha values.")
parser.add_argument("puzzle_number", type=int, help="Puzzle number to calculate")
args = parser.parse_args()
main(args.puzzle_number)
python3 test.py 68
Puzzle 68 Calculation:
Using alpha: 8.692996380248756
Base exponential model: 0.68972090704257297155081468833675824716161023623689914791953650562051467261304 4419972598777231639024182489182413058156841483121232343334326952277688035030318 1311309574260048040167879840662353500733521425500975021869380914054885130184442 40544507079388243203845236809052465256422016436329883051889524096 * 2.00446149587820417640494244738360788449393817055989304597589376020405549833178 4239005365118701510143617670147330576209009738631394722016490414926974514298110 3364613583395133560524755600665108274702675918184495985595413655135405238097981 4864413726673117061462450025066948963321434607944444810622903302^n
Real Value: 219898266213316039825
Predicted Value: 219898266215462633472.00
Percentage error: 0.00000000%
python3 test.py 69
Puzzle 69 Prediction:
Using interpolated alpha: -7.804319
According to this, puzzle 69 starts with 1ba45e.....
I don't have the nerves to improve this for the better.
i tried found key in range [ 1ba45e000000000000 ~ 1ba45effffffffffff ],
but not match key.
fail
Re: Bitcoin puzzle transaction ~32 BTC prize to who solves it
...
I'm curious about a few things. Bram,
1- Why are you taking such a risk? (Wouldn't it be better to leave it at the top, since you have made a good name?
2- You did not ask anyone (other people) for any money or hardware for the 68th Wallet. Will those who want to invest now invest because you did not include them in the 67th or 68th Wallet? (For 1% chance.)
I read the article you announced a few times. I think the following result will happen. An average of 25% of old investors will not take this risk.
This shows that there is a deficiency of around 100 thousand dollars.
I think you have earned 100 thousand dollars. So would you risk it?
Or,
Would you like to collect this 100 thousand dollars after April 10?
But 75% of old participants will not come. (Because of the risk and because you did not include them in the 68th Wallet.)
In short, the summary of the event is as follows;
No matter how many GPU farms you have.
If Earnings > Capital,
You are gambling.
Your luck You can trust.
Bram, you made a good name for yourself. Now you're turning it into a bad name.
You'll lose people's trust and you'll become a laughing stock.
There's no point in attacking Bram. Everyone is free to do what they want, whether it's renting 100, 1,000, or 10,000 GPUs, or finding a way to decrypt a key with little means.
There are no specific rules to this challenge, and any method is interesting to a certain extent. The main thing is to participate, and no one is forcing anyone to do anything and if Bram wants to involve others, that's his right.
Did you know that around the world there are also groups that play the national lottery together to increase their luck? Crypto mining is exactly the same thing, we mine together and it's the same as finding a private key in this challenge.
There's no need to kill each other here. After all, it's just a game..
yea i think too.
i running my RTX4090 2GPUs for searching priv key
Re: Bitcoin puzzle transaction ~32 BTC prize to who solves it
Grats!
Closest I got was:
BEB4F17D7F675DD5C 1MVDYgVaSNXtr34anQEPicNVrSnyUdNpAA
BEB4F2D9F65D87F04 1MVDYgVaSNMpE4wHjAYS7UqhBs9wKLwg1p
Since those two were so close together, I was searching the keyspace around those, but got beat to it!
I bet Bram will go on to solve all of the remaining puzzles, lol.
Closest I got was:
BEB4F17D7F675DD5C 1MVDYgVaSNXtr34anQEPicNVrSnyUdNpAA
BEB4F2D9F65D87F04 1MVDYgVaSNMpE4wHjAYS7UqhBs9wKLwg1p
Since those two were so close together, I was searching the keyspace around those, but got beat to it!
I bet Bram will go on to solve all of the remaining puzzles, lol.
wow very close!
BEBB394 ~