In my opinion, these puzzles were all manually created by their designer and are not derived from a deterministic wallet. This is because there are no visible patterns in these addresses that are typically found in deterministic wallets. I believe that understanding psychology is more important than cryptography in this case. We need to analyze the personal and individual characteristics of the puzzle creator to determine what pattern might have been in their mind when they designed the puzzles.

The main idea is that computing public keys as fast as possible is even faster if they're not moving around blindly, like what happens in Kangaroo. But no one tried to use this effectively. I think this is because on a CPU there's basically zero performance difference, but on a GPU... the speed rockets (not that it wasn't already freaking fast) because the unknown dynamics disappear. Hence, a speed 3 to 4 times higher.

Here is example:


You don’t need anything else, not even a deterministic wallet.
Just a Google Doc to save the code, and you’ll have all the WIFs.

Whoever came up with this Puzzle knows exactly what he's doing.

I use a trial-and-error method a problem-solving approach where you systematically try different options or solutions until you find one that works or achieves the desired result.

This is like the movie Groundhog Day, about a man reliving the same day over and over again.

All puzzles are created with one or similar random, seed - known to the creator.  The puzzle creator tattooed it on their upper arm.
And there is no pattern. It’s not a timestamp. It can’t be reproduced by going back in time.

I think it's obvious that he has his own custom deterministic wallet with errors = ZERO

There is no limit and way someone can search for a puzzle. It's like art. Mostly worthless art collection.

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 someone is able to find multiple of these prefixes in the range, will it help in any way to reach the target faster?

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

WP did a full tutorial a while back
https://bitcointalk.org/index.php?topic=1306983.msg64379149#msg64379149