Bitcoin puzzle transaction ~32 BTC prize to who solves it

[Akito S. M. Hosana]

sure, here you go:

Private key : 868221233689326498340379183142
Public key: 03d2063d40402f030d4cc71331468827aa41a8a09bd6fd801ba77fb64f8e67e617

Finally, now we are talking. I'm going to be rich! RICH!

Bruh, chill. Your money’s still in Simulation Mode until it hits your bank account 

[nochkin]

Bruh, chill. Your money’s still in Simulation Mode until it hits your bank account 

Why would you have to ruin everything? Now I need to find a simulation bank to be compatible with the simulation coins.

[MrHorse]

@nomachine:

Thank you so much for your software and your effort. Your code has inspired me and others to learn, research and profit. I spent endless hours learning new things from your github. I wish it continued, but like anything it has came to it's end unfortunately.

[Akito S. M. Hosana]

I wish it continued, but like anything it has came to it's end unfortunately.

It's over, but all applications are saved. What fascinates me the most is the WIF-Cracker application. The fuc*** python application solves 12, 13, 14 missing characters.

https://github.com/AkitoHosana/WIF-Cracker

I still don't understand how he managed to do this. I tried to make the AI ​​do this in C++. There is no way to solve 12 characters in C++ in a few minutes even though the speed is 26 M...  

[POD5]

https://github.com/AkitoHosana/WIF-Cracker

So you also deleted your repositories from NoMachine1 ?

@nomachine:

Thank you so much for your software and your effort.

Yes, I agree with you.

[fixedpaul]

Hi guys. Is it possible for someone to have an executable for Windows and GPU with range and prefix search?

Venitysearch doesn't have range. Vanity Bitcrack doesn't work well with prefix, Keyhunt Cuda doesn't have prefix search. Keyhunt have all and work amazing but only for CPU.

Is there one for Windows + GPU + Range + Prefix?

thanks to all

Look VS-bitcrack on my GitHub, It should works also with prefix

[Jorge54PT]

Hi guys. Is it possible for someone to have an executable for Windows and GPU with range and prefix search?

Venitysearch doesn't have range. Vanity Bitcrack doesn't work well with prefix, Keyhunt Cuda doesn't have prefix search. Keyhunt have all and work amazing but only for CPU.

Is there one for Windows + GPU + Range + Prefix?

thanks to all

Look VS-bitcrack on my GitHub, It should works also with prefix

I will
Thanks you so much

[Nodemath]

import time
import hashlib
from coincurve import PrivateKey
from Crypto.Hash import RIPEMD160
import psutil
import os
import signal
import sys
import multiprocessing as mp
from bloom_filter2 import BloomFilter
from google.colab import drive

# Mount Google Drive
drive.mount('/content/drive')

# Config
File_NAME = "Puzzle 71.013.000.csv"
DRIVE_FOLDER = "/content/drive/MyDrive/Puzzle71"
file_path = f"{DRIVE_FOLDER}/{File_NAME}"
SCAN_RANGE = 100_000
TARGET_PREFIX = "f6f543"
BLOOM_CAPACITY = 1_000_000
BLOOM_ERROR_RATE = 0.001

# Load known H160 hashes into Bloom filter
KNOWN_H160S = [
    "f6f5431d25bbf7b12e8add9af5e3475c44a0a5b8",
]
bloom = BloomFilter(max_elements=BLOOM_CAPACITY, error_rate=BLOOM_ERROR_RATE)
for h in KNOWN_H160S:
    bloom.add(h)

# Read decimal numbers from CSV
with open(file_path, 'r') as f:
    lines = [line.strip() for line in f if line.strip()]
if lines[0].lower().startswith('value'):
    lines = lines[1:]
Decimal_numbers = [int(line) for line in lines]

def privatekey_to_h160(priv_key_int):
    try:
        priv = PrivateKey.from_int(priv_key_int)
        pubkey = priv.public_key.format(compressed=True)
        sha256 = hashlib.sha256(pubkey).digest()
        ripemd160 = RIPEMD160.new(sha256).digest()
        return ripemd160.hex()
    except Exception:
        return None

def optimize_performance():
    try:
        p = psutil.Process()
        if hasattr(p, "cpu_affinity"):
            p.cpu_affinity(list(range(os.cpu_count())))
        if hasattr(psutil, "REALTIME_PRIORITY_CLASS"):
            p.nice(psutil.REALTIME_PRIORITY_CLASS)
        else:
            p.nice(-20)
    except Exception as e:
        print(f"[!] Optimization warning: {e}")

def signal_handler(sig, frame):
    print("\n[!] Interrupted. Exiting.")
    sys.exit(0)

def check_key(k):
    h160 = privatekey_to_h160(k)
    if h160 and (h160.startswith(TARGET_PREFIX) or h160 in bloom):
        return ('match', k, h160)
    return ('progress', k, h160)

def process_decimal(decimal):
    start = max(1, decimal - SCAN_RANGE)
    end = decimal + SCAN_RANGE
    keys = list(range(start, end + 1))

    processed = 0
    start_time = time.time()
    last_key = None
    last_h160 = None

    ctx = mp.get_context("fork")
    with ctx.Pool(processes=os.cpu_count()) as pool:
        result_iter = pool.imap_unordered(check_key, keys, chunksize=1000)

        for result in result_iter:
            if result[0] == 'match':
                _, key, h160 = result
                print(f"\n**PREFIX MATCH FOUND!** Private key {hex(key)} produces Hash160: {h160}\n")
            elif result[0] == 'progress':
                _, key, h160 = result
                processed += 1
                last_key = key
                last_h160 = h160

    elapsed = time.time() - start_time
    speed = processed / elapsed if elapsed > 0 else 0
    print(f"\nHash160 of the last processed key {hex(last_key)} -> {last_h160}")
    print(f"[✓] Completed Decimal: {Decimal} - Processed {processed} keys in {elapsed:.2f}s (Speed: {speed:.2f} keys/sec)")

def main():
    signal.signal(signal.SIGINT, signal_handler)
    optimize_performance()

    print(f"\nLoaded {len(Decimal_numbers)} Decimal numbers.")
    print(f"Scanning ±{SCAN_RANGE} around each.\nTarget prefix: {TARGET_PREFIX}")
    print(f"Bloom filter contains {len(KNOWN_H160S)} known H160 hashes.\n")

    for decimal in decimal_numbers:
        process_decimal(decimal)

if __name__ == '__main__':
    main()


Help me out to use rotor cuda gpu modules able run on colab
Private key to hash 160

Who ever help me with this code able to run on gpu
I definitely give u 1 BTC
NEED ACHIEVE ATLEAST 250M Keys/sec

[Bram24732]

Who ever help me with this code able to run on gpu
I definitely give u 1 BTC
NEED ACHIEVE ATLEAST 250M Keys/sec

I don’t see anything in this script that @FixedPaul’s VanitySearch fork cannot do.
It’s using GPU and gives 6.8G keys/sec

based on MCD script
For most cases, Prefix has better average performance.

I can only encourage you to read the dedicated post and it’s conclusions.

[Bram24732]

U will not get what did find
I am just. Looking a way to cover

I have four file around 50 Million value each file need to check every value off +-100K

That its puzzle will be solved

If English is it not your main language please use google translate. We can’t understand what you’re saying

[Jorge54PT]

Hi guys. Is it possible for someone to have an executable for Windows and GPU with range and prefix search?

Venitysearch doesn't have range. Vanity Bitcrack doesn't work well with prefix, Keyhunt Cuda doesn't have prefix search. Keyhunt have all and work amazing but only for CPU.

Is there one for Windows + GPU + Range + Prefix?

thanks to all

Look VS-bitcrack on my GitHub, It should works also with prefix

I will
Thanks you so much

D:\Zdown\VanitySearch-master\VanitySearch-master>VanitySearch.exe -t 0 -gpu -gpuId 0 --keyspace 100000000:1ffffffff 187swFMjz                       Unexpected --keyspace argument

I think keyspace mode (range) not included.

[Nodemath]

I am really asking about the code
Help me out I definitely give u 1 btc

[zion3301]

U will not get what did find
I am just. Looking a way to cover

I have four file around 50 Million value each file need to check every value off +-100K

That its puzzle will be solved

what does these files contains? addresses or public keys or something else?

[Nodemath]

Private key

[fixedpaul]

Look VS-bitcrack on my GitHub, It should works also with prefix

I will
Thanks you so much

D:\Zdown\VanitySearch-master\VanitySearch-master>VanitySearch.exe -t 0 -gpu -gpuId 0 --keyspace 100000000:1ffffffff 187swFMjz                       Unexpected --keyspace argument

I think keyspace mode (range) not included.

You have to use -start and -range, look at the readme, is there on purpose

On your test will be -start 100000000 -range 32

[Jorge54PT]

Look VS-bitcrack on my GitHub, It should works also with prefix

I will
Thanks you so much

D:\Zdown\VanitySearch-master\VanitySearch-master>VanitySearch.exe -t 0 -gpu -gpuId 0 --keyspace 100000000:1ffffffff 187swFMjz                       Unexpected --keyspace argument

I think keyspace mode (range) not included.

You have to use -start and -range, look at the readme, is there on purpose

On your test will be -start 100000000 -range 32

ok thank you again for your suport. i will try

[Valera909]

Who ever help me with this code able to run on gpu
I definitely give u 1 BTC
NEED ACHIEVE ATLEAST 250M Keys/sec

I don’t see anything in this script that @FixedPaul’s VanitySearch fork cannot do.
It’s using GPU and gives 6.8G keys/sec

based on MCD script
For most cases, Prefix has better average performance.

I can only encourage you to read the dedicated post and it’s conclusions.

⚠️ Google Colab is limited
Standard Colab won't give you access to A100 or V100 GPUs by default.

You’ll need Colab Pro+ or your own server with an NVIDIA GPU.

Also, you’ll have to build and run C++ CUDA code, which requires some tricky steps in Colab (mounting Drive, manual compilation, custom execution commands).

[madogss]

Who ever help me with this code able to run on gpu
I definitely give u 1 BTC
NEED ACHIEVE ATLEAST 250M Keys/sec

#include <stdio.h>
#include <cuda.h>
#include "secp256k1.h"
#include "sha256.h"
#include "ripemd160.h"

__global__ void hash(stuff) {

    secp(stuff);
    sha256(stuff);
    ripemd160(stuff);
}

int main() {
     allocate host memory array
     allocate cuda memory
     open file and put in host array

     cuda memcpy host to device

    hash<<<1,1>>>(stuff);

    cuda memcpy device to host

   printf("%X\n", stuff);
}

now just add incremental threading and actual code.

[Nodemath]

Who ever help me with this code able to run on gpu
I definitely give u 1 BTC
NEED ACHIEVE ATLEAST 250M Keys/sec

#include <stdio.h>
#include <cuda.h>
#include "secp256k1.h"
#include "sha256.h"
#include "ripemd160.h"

__global__ void hash(stuff) {

    secp(stuff);
    sha256(stuff);
    ripemd160(stuff);
}

int main() {
     allocate host memory array
     allocate cuda memory
     open file and put in host array

     cuda memcpy host to device

    hash<<<1,1>>>(stuff);

    cuda memcpy device to host

   printf("%X\n", stuff);
}

now just add incremental threading and actual code.

I tried it's showing me a lot errors on it

[mcdouglasx]

Code:

import time
import hashlib
from coincurve import PrivateKey
from Crypto.Hash import RIPEMD160
import psutil
import os
import signal
import sys
import multiprocessing as mp
from bloom_filter2 import BloomFilter
from google.colab import drive

# Mount Google Drive
drive.mount('/content/drive')

# Config
File_NAME = "Puzzle 71.013.000.csv"
DRIVE_FOLDER = "/content/drive/MyDrive/Puzzle71"
file_path = f"{DRIVE_FOLDER}/{File_NAME}"
SCAN_RANGE = 100_000
TARGET_PREFIX = "f6f543"
BLOOM_CAPACITY = 1_000_000
BLOOM_ERROR_RATE = 0.001

# Load known H160 hashes into Bloom filter
KNOWN_H160S = [
    "f6f5431d25bbf7b12e8add9af5e3475c44a0a5b8",
]
bloom = BloomFilter(max_elements=BLOOM_CAPACITY, error_rate=BLOOM_ERROR_RATE)
for h in KNOWN_H160S:
    bloom.add(h)

# Read decimal numbers from CSV
with open(file_path, 'r') as f:
    lines = [line.strip() for line in f if line.strip()]
if lines[0].lower().startswith('value'):
    lines = lines[1:]
Decimal_numbers = [int(line) for line in lines]

def privatekey_to_h160(priv_key_int):
    try:
        priv = PrivateKey.from_int(priv_key_int)
        pubkey = priv.public_key.format(compressed=True)
        sha256 = hashlib.sha256(pubkey).digest()
        ripemd160 = RIPEMD160.new(sha256).digest()
        return ripemd160.hex()
    except Exception:
        return None

def optimize_performance():
    try:
        p = psutil.Process()
        if hasattr(p, "cpu_affinity"):
            p.cpu_affinity(list(range(os.cpu_count())))
        if hasattr(psutil, "REALTIME_PRIORITY_CLASS"):
            p.nice(psutil.REALTIME_PRIORITY_CLASS)
        else:
            p.nice(-20)
    except Exception as e:
        print(f"[!] Optimization warning: {e}")

def signal_handler(sig, frame):
    print("\n[!] Interrupted. Exiting.")
    sys.exit(0)

def check_key(k):
    h160 = privatekey_to_h160(k)
    if h160 and (h160.startswith(TARGET_PREFIX) or h160 in bloom):
        return ('match', k, h160)
    return ('progress', k, h160)

def process_decimal(decimal):
    start = max(1, decimal - SCAN_RANGE)
    end = decimal + SCAN_RANGE
    keys = list(range(start, end + 1))

    processed = 0
    start_time = time.time()
    last_key = None
    last_h160 = None

    ctx = mp.get_context("fork")
    with ctx.Pool(processes=os.cpu_count()) as pool:
        result_iter = pool.imap_unordered(check_key, keys, chunksize=1000)

        for result in result_iter:
            if result[0] == 'match':
                _, key, h160 = result
                print(f"\n**PREFIX MATCH FOUND!** Private key {hex(key)} produces Hash160: {h160}\n")
            elif result[0] == 'progress':
                _, key, h160 = result
                processed += 1
                last_key = key
                last_h160 = h160

    elapsed = time.time() - start_time
    speed = processed / elapsed if elapsed > 0 else 0
    print(f"\nHash160 of the last processed key {hex(last_key)} -> {last_h160}")
    print(f"[✓] Completed Decimal: {Decimal} - Processed {processed} keys in {elapsed:.2f}s (Speed: {speed:.2f} keys/sec)")

def main():
    signal.signal(signal.SIGINT, signal_handler)
    optimize_performance()

    print(f"\nLoaded {len(Decimal_numbers)} Decimal numbers.")
    print(f"Scanning ±{SCAN_RANGE} around each.\nTarget prefix: {TARGET_PREFIX}")
    print(f"Bloom filter contains {len(KNOWN_H160S)} known H160 hashes.\n")

    for decimal in decimal_numbers:
        process_decimal(decimal)

if __name__ == '__main__':
    main()

Help me out to use rotor cuda gpu modules able run on colab
Private key to hash 160

Codes must be inserted properly; otherwise, you are creating an unreadable thread.