Bitcoin puzzle transaction ~32 BTC prize to who solves it

[WanderingPhilospher]

Let's say i take one 66 bit address for practice
000000000000000000000000000000000000000000000002be7989dd1a1a63ad | Hash 160 20cb77af1a425c5e74483d9b30cf950911a090de | 13zQNJwpREZogcPSkNJmYQzZ9HZQZS48Hx [TARGET]

result scan :
000000000000000000000000000000000000000000000002b809677889fb1078 | Hash 160 20cb78b594b77cf97259be5cc414f0a49f1bde81 | 13zQNb5x4P7vCjag18rZKCVHkBcqtddaLS | 102.92 sec | 136.0 keys/sec |
00000000000000000000000000000000000000000000000285a7e79fd01fc2a4 | Hash 160 20cbc445f68147eb89314c6710de2a7c5fc2e0fb | 13zQj6btR6awFjac835xsvDqeCtVyioiiW | 112.67 sec | 124.9 keys/sec |
000000000000000000000000000000000000000000000002757de2916bb72c92 | Hash 160 20cbc889d5186984e2189dd818e67d990f992459 | 13zQkFk3v2WXrhLVVhP2NKM1JT4Gbd6VoY | 153.39 sec | 110.4 keys/sec |
000000000000000000000000000000000000000000000002a6bdd8aaca2a5a56 | Hash 160 20cbb6398c3a2a9ad13eec60d2ffd84ed113d96d | 13zQfHTEd9EZncjXmiKMoZV7SqSZP39myL | 159.93 sec | 100.8 keys/sec |


I'm very grateful the result seem make some chance to hit the targeted and correct key
000000000000000000000000000000000000000000000002b809677889fb1078 | Hash 160 20cb78b594b77cf97259be5cc414f0a49f1bde81 | 13zQNb5x4P7vCjag18rZKCVHkBcqtddaLS | 102.92 sec | 136.0 keys/sec |

i make some checkpoint rules and check if at least 10 addresses have similarity in hash160 derived from private key.

It seems like you are doing a search for a partial address/h160 collision? Is this true?

If so, there are GPU tools out there, that do billions of keys per second.

Not to discourage you from further developing your script, but you should be getting a lot more than 100 - 136 keys per second, even with python. I can help you speed it up, but again, you will need luck.

Also, to what citb0in said, there is no correlation between partial matches of addresses/h160. Or at least no one has found one yet.

[Tepan]

Let's say i take one 66 bit address for practice
000000000000000000000000000000000000000000000002be7989dd1a1a63ad | Hash 160 20cb77af1a425c5e74483d9b30cf950911a090de | 13zQNJwpREZogcPSkNJmYQzZ9HZQZS48Hx [TARGET]

result scan :
000000000000000000000000000000000000000000000002b809677889fb1078 | Hash 160 20cb78b594b77cf97259be5cc414f0a49f1bde81 | 13zQNb5x4P7vCjag18rZKCVHkBcqtddaLS | 102.92 sec | 136.0 keys/sec |
00000000000000000000000000000000000000000000000285a7e79fd01fc2a4 | Hash 160 20cbc445f68147eb89314c6710de2a7c5fc2e0fb | 13zQj6btR6awFjac835xsvDqeCtVyioiiW | 112.67 sec | 124.9 keys/sec |
000000000000000000000000000000000000000000000002757de2916bb72c92 | Hash 160 20cbc889d5186984e2189dd818e67d990f992459 | 13zQkFk3v2WXrhLVVhP2NKM1JT4Gbd6VoY | 153.39 sec | 110.4 keys/sec |
000000000000000000000000000000000000000000000002a6bdd8aaca2a5a56 | Hash 160 20cbb6398c3a2a9ad13eec60d2ffd84ed113d96d | 13zQfHTEd9EZncjXmiKMoZV7SqSZP39myL | 159.93 sec | 100.8 keys/sec |


I'm very grateful the result seem make some chance to hit the targeted and correct key
000000000000000000000000000000000000000000000002b809677889fb1078 | Hash 160 20cb78b594b77cf97259be5cc414f0a49f1bde81 | 13zQNb5x4P7vCjag18rZKCVHkBcqtddaLS | 102.92 sec | 136.0 keys/sec |

i make some checkpoint rules and check if at least 10 addresses have similarity in hash160 derived from private key.

It seems like you are doing a search for a partial address/h160 collision? Is this true?

If so, there are GPU tools out there, that do billions of keys per second.

Not to discourage you from further developing your script, but you should be getting a lot more than 100 - 136 keys per second, even with python. I can help you speed it up, but again, you will need luck.

Also, to what citb0in said, there is no correlation between partial matches of addresses/h160. Or at least no one has found one yet.

sure! that's true, i do partial address/h160 collision, it's like SOLO Mining LOL, need 99%luck.

btw,i make improvements here's before i send the codes to you, take a look puzzle 160 bit

Target 160 bit :
1NBC8uXJy1GiJ6drkiZa1WuKn51ps7EPTv
e84818e1bf7f699aa6e28ef9edfb582099099292

000000000000000000000000bd09c95a35d1f621cfa61ea176f233ea43372317 | Hash 160 e8486774faf19368fb70016092c5258f053b8969 | 1NBCWESVYkRh6kxxuK5hP5pH8jJkWsXkTv | 711.83 sec |

Similiarities Address :

1NBC8uXJy1GiJ6drkiZa1WuKn51ps7EPTv
1NBCWESVYkRh6kxxuK5hP5pH8jJkWsXkTv

Similiarities hash 160 :
e84818e1bf7f699aa6e28ef9edfb582099099292
e8486774faf19368fb70016092c5258f053b8969

it's unique, if i do perform scan range bit '1-'4 it's difficult, if i do 4'0, 4'5, 5'0, 5'5, the puzzle took 4-6 hours to complete with my codes, if want use for larger bits, need math for settings the collision :

                if counter >= 2:
                    # Check if at least addresses have similarity in hash160
                    similarity_count = sum(a == b for a, b in zip(public_key_hash.hex()[:8], target_hash[:8]))
                    if similarity_count >= 7:
                        # At least addresses have similarity, determine bit size of target hash
                        bit_size = len(target_hash) * 4  # Multiply by 4 to convert bytes to bits

                        if bit_size < 160:
                            # Increase search range for smaller bit sizes
                            start_range += 3690000
                            end_range += 3690000
                        else:
                            # Decrease search range for larger bit sizes
                            start_range = max(0, start_range - 100000000 )
                            end_range = max(0, end_range - 100000000)

[Tepan]

i do some Experiment, trial and error for 66 bit

Let's say i take one 66 bit address for practice
000000000000000000000000000000000000000000000002be7989dd1a1a63ad | Hash 160 20cb77af1a425c5e74483d9b30cf950911a090de | 13zQNJwpREZogcPSkNJmYQzZ9HZQZS48Hx [TARGET]

result scan :
000000000000000000000000000000000000000000000002b809677889fb1078 | Hash 160 20cb78b594b77cf97259be5cc414f0a49f1bde81 | 13zQNb5x4P7vCjag18rZKCVHkBcqtddaLS | 102.92 sec | 136.0 keys/sec |
00000000000000000000000000000000000000000000000285a7e79fd01fc2a4 | Hash 160 20cbc445f68147eb89314c6710de2a7c5fc2e0fb | 13zQj6btR6awFjac835xsvDqeCtVyioiiW | 112.67 sec | 124.9 keys/sec |
000000000000000000000000000000000000000000000002757de2916bb72c92 | Hash 160 20cbc889d5186984e2189dd818e67d990f992459 | 13zQkFk3v2WXrhLVVhP2NKM1JT4Gbd6VoY | 153.39 sec | 110.4 keys/sec |
000000000000000000000000000000000000000000000002a6bdd8aaca2a5a56 | Hash 160 20cbb6398c3a2a9ad13eec60d2ffd84ed113d96d | 13zQfHTEd9EZncjXmiKMoZV7SqSZP39myL | 159.93 sec | 100.8 keys/sec |


I'm very grateful the result seem make some chance to hit the targeted and correct key
000000000000000000000000000000000000000000000002b809677889fb1078 | Hash 160 20cb78b594b77cf97259be5cc414f0a49f1bde81 | 13zQNb5x4P7vCjag18rZKCVHkBcqtddaLS | 102.92 sec | 136.0 keys/sec |

i make some checkpoint rules and check if at least 10 addresses have similarity in hash160 derived from private key.

What result are you talking about? I'm sorry if I can't see the wood for the trees, but I see absolutely nothing here that can be helpful in any way that relates to the topic of finding the puzzle. I see you have defined a target and I see you have listed four private keys and the corresponding addresses. There is no relation between them, how could there be, it wouldn't make any sense.

So what exactly did you find out or what makes you think that you're on the right track? Please don't misunderstand me, but I only see random data here without any relation to anything.

Did you know the work of bitcoin address, my codes to search the puzzle to sink the large private key range bro, random data ? it's calculated, the private key generate public key, and hash160 was from public key of address, the "20cb78" of hash 160 from 13zQNfHTEd9EZncjXmiKMoZV7SqSZP39myL is on range "2a6bdd8aaca2a5a56".

hash 160 of 13zQNJwpREZogcPSkNJmYQzZ9HZQZS48Hx is "20cb77af1a425c5e74483d9b30cf950911a090de", take a look "20cb78" and "20cb77" It doesn't look that far away from the hash160 generated from the private key.

if you change the 78 into 78 it make lot of changes on address.
20cb77af1a425c5e74483d9b30cf950911a090de : 13zQN JwpREZogcPSkNJmYQzZ9HZQZS48Hx
20cb78af1a425c5e74483d9b30cf950911a090de : 13zQN agqxpgJMoqzrF3SUvYVq82ENKem4J


so the work of codes is when found that similiarities it will change the range of private key e.g 2a6bdd8aaca2a5a56 to 3ffffffffffffffff

the technical i learn is from https://learnmeabitcoin.com/technical/address, you change some number/letter on hash160 it's generate the different Bitcoin Address, but in other ways, we don't know what coresponding private key from that changes.

so what i do is collision hash160, it's like changes the private key start range to end range, when similiarities found.

i will preview the codes, the most unique is i make something new for search proccess and work on puzzle 40 bit within 18 second with that codes, it's not completed by speed, but by math from collision hash160.
and if u ask ? why not complete the 66 puzle, it's hard because i need to configure anything on that codes, because if there's mistaken value input for search, the proggress can be exhausted.

but thank you for your response, even though you don't help anything in the development I do, it's just criticism regarding someone's proggress in this community.

[satashi_nokamato]

@Tepan, could you implement KNN algorithm to your script?  it could somewhat help you in predicting a clearer pattern in order to create a map of similar hashes in certain ranges.

You could scan 39 to 40 bit range and categorize similarly found hashes,  do that with different hash prefixes in different bit ranges,  you might figure out the average probability of certain hashes existing in specific ranges.

Note that KNN algo is a great tool for statistics.  it stands for k-nearest neighboring.

[WanderingPhilospher]

you might figure out the average probability of certain hashes existing in specific ranges.

I think you could figure out the probability before scanning any range, especially the first x amount of leading characters/"prefixes". A rough, but close estimate.

Take the range size, say 2^40, and merely divide by the size of leading characters (in bits). So if you are wondering about the h160 prefix, "20cb78", and consider each position/character 4 bits, so 6 characters x 4 = 24 bits.

Then in a range size of 2^40, you would roughly find 2^40/2^24 = 2^16 h160s starting with "20cb78".

[Tepan]

@Tepan, could you implement KNN algorithm to your script?  it could somewhat help you in predicting a clearer pattern in order to create a map of similar hashes in certain ranges.

You could scan 39 to 40 bit range and categorize similarly found hashes,  do that with different hash prefixes in different bit ranges,  you might figure out the average probability of certain hashes existing in specific ranges.

Note that KNN algo is a great tool for statistics.  it stands for k-nearest neighboring.

>> test.py 18ZRLg9BMfAg9WYVWqVDtYdT59QV1PzrJe 18Z 52ea8235174b368df646576fc04eb0d2737057b6 1a000000000000000 1afffffffffffffff

000000000000000000000000000000000000000000000001a6f7e55277418662 | Hash 160 52ffb5fad1e2964e0d62d0fb1fba1b2a51959602 | 18ZrjqFukCHggGmF9NHvTHnypz8CWUiesG | 600.90 sec |
000000000000000000000000000000000000000000000001a698bf0141abba89 | Hash 160 52faf3bd9c45c9788dd66c1c3247b9861149342c | 18Zm3BwAm4CNym3KuUUZu3n3vytQ6TXRoU | 700.29 sec |
000000000000000000000000000000000000000000000001a61adda168bf1479 | Hash 160 5304d1f3f9a3e5d191a70656b8cd49f242d91e8e | 18ZxrqALETxJS5eQ834BD8xdLBuLzDztqA | 700.61 sec |
000000000000000000000000000000000000000000000001a64271593ec75f14 | Hash 160 52df324fdd87f86fb67bd415db3fd3c63d70d5a2 | 18ZBnhRYcHxdw4fSDByCb7tMqnCA1n9UxC | 700.87 sec |
000000000000000000000000000000000000000000000001a678bde0e606d586 | Hash 160 52ea8235174b368df646576fc04eb0d2737057b6 | 18ZRLg9BMfAg9WYVWqVDtYdT59QV1PzrJe | 800.39 sec |
000000000000000000000000000000000000000000000001a7d696f300cda2fc | Hash 160 52db4611541a1d382a06279c0a32b45bd1a0bf2b | 18Z768mmb7LCGDjU5xsF5YtPiSvRo5pVvR | 900.63 sec |
Similar hash160 found: 52ea8235174b368df646576fc04eb0d2737057b6

=========[Address Found]===========
Private Key: 000000000000000000000000000000000000000000000001a678bde0e606d586
Hash 160: 52ea8235174b368df646576fc04eb0d2737057b6
Address: 18ZRLg9BMfAg9WYVWqVDtYdT59QV1PzrJe
0x1a678bde0e606d586
Compressed Public Key: 02f46cb94bfecf5daf63cd54353c73eb3f7e148ac0f9b8af46e3a94ee8b60e1260
Time taken to find: 0.0000 seconds
Speed of keys: 0.0000 keys per second
=========[Address Found]===========

thankyou for advice, i make the KNN algorithm work with my previous code but have some trouble with other codes, some confusion call the rest codes to work with scan range, but it's work.
i test on 65 bit.

mightbe push my luck for 66 for couple weeks.

[zahid888]

@Tepan, could you implement KNN algorithm to your script?  it could somewhat help you in predicting a clearer pattern in order to create a map of similar hashes in certain ranges.

You could scan 39 to 40 bit range and categorize similarly found hashes,  do that with different hash prefixes in different bit ranges,  you might figure out the average probability of certain hashes existing in specific ranges.

Note that KNN algo is a great tool for statistics.  it stands for k-nearest neighboring.

>> test.py 18ZRLg9BMfAg9WYVWqVDtYdT59QV1PzrJe 18Z 52ea8235174b368df646576fc04eb0d2737057b6 1a000000000000000 1afffffffffffffff

000000000000000000000000000000000000000000000001a6f7e55277418662 | Hash 160 52ffb5fad1e2964e0d62d0fb1fba1b2a51959602 | 18ZrjqFukCHggGmF9NHvTHnypz8CWUiesG | 600.90 sec |
000000000000000000000000000000000000000000000001a698bf0141abba89 | Hash 160 52faf3bd9c45c9788dd66c1c3247b9861149342c | 18Zm3BwAm4CNym3KuUUZu3n3vytQ6TXRoU | 700.29 sec |
000000000000000000000000000000000000000000000001a61adda168bf1479 | Hash 160 5304d1f3f9a3e5d191a70656b8cd49f242d91e8e | 18ZxrqALETxJS5eQ834BD8xdLBuLzDztqA | 700.61 sec |
000000000000000000000000000000000000000000000001a64271593ec75f14 | Hash 160 52df324fdd87f86fb67bd415db3fd3c63d70d5a2 | 18ZBnhRYcHxdw4fSDByCb7tMqnCA1n9UxC | 700.87 sec |
000000000000000000000000000000000000000000000001a678bde0e606d586 | Hash 160 52ea8235174b368df646576fc04eb0d2737057b6 | 18ZRLg9BMfAg9WYVWqVDtYdT59QV1PzrJe | 800.39 sec |
000000000000000000000000000000000000000000000001a7d696f300cda2fc | Hash 160 52db4611541a1d382a06279c0a32b45bd1a0bf2b | 18Z768mmb7LCGDjU5xsF5YtPiSvRo5pVvR | 900.63 sec |
Similar hash160 found: 52ea8235174b368df646576fc04eb0d2737057b6

=========[Address Found]===========
Private Key: 000000000000000000000000000000000000000000000001a678bde0e606d586
Hash 160: 52ea8235174b368df646576fc04eb0d2737057b6
Address: 18ZRLg9BMfAg9WYVWqVDtYdT59QV1PzrJe
0x1a678bde0e606d586
Compressed Public Key: 02f46cb94bfecf5daf63cd54353c73eb3f7e148ac0f9b8af46e3a94ee8b60e1260
Time taken to find: 0.0000 seconds
Speed of keys: 0.0000 keys per second
=========[Address Found]===========

thankyou for advice, i make the KNN algorithm work with my previous code but have some trouble with other codes, some confusion call the rest codes to work with scan range, but it's work.
i test on 65 bit.

mightbe push my luck for 66 for couple weeks.

Can i take a look at your code ?

[Tepan]

@Tepan, could you implement KNN algorithm to your script?  it could somewhat help you in predicting a clearer pattern in order to create a map of similar hashes in certain ranges.

You could scan 39 to 40 bit range and categorize similarly found hashes,  do that with different hash prefixes in different bit ranges,  you might figure out the average probability of certain hashes existing in specific ranges.

Note that KNN algo is a great tool for statistics.  it stands for k-nearest neighboring.

>> test.py 18ZRLg9BMfAg9WYVWqVDtYdT59QV1PzrJe 18Z 52ea8235174b368df646576fc04eb0d2737057b6 1a000000000000000 1afffffffffffffff

000000000000000000000000000000000000000000000001a6f7e55277418662 | Hash 160 52ffb5fad1e2964e0d62d0fb1fba1b2a51959602 | 18ZrjqFukCHggGmF9NHvTHnypz8CWUiesG | 600.90 sec |
000000000000000000000000000000000000000000000001a698bf0141abba89 | Hash 160 52faf3bd9c45c9788dd66c1c3247b9861149342c | 18Zm3BwAm4CNym3KuUUZu3n3vytQ6TXRoU | 700.29 sec |
000000000000000000000000000000000000000000000001a61adda168bf1479 | Hash 160 5304d1f3f9a3e5d191a70656b8cd49f242d91e8e | 18ZxrqALETxJS5eQ834BD8xdLBuLzDztqA | 700.61 sec |
000000000000000000000000000000000000000000000001a64271593ec75f14 | Hash 160 52df324fdd87f86fb67bd415db3fd3c63d70d5a2 | 18ZBnhRYcHxdw4fSDByCb7tMqnCA1n9UxC | 700.87 sec |
000000000000000000000000000000000000000000000001a678bde0e606d586 | Hash 160 52ea8235174b368df646576fc04eb0d2737057b6 | 18ZRLg9BMfAg9WYVWqVDtYdT59QV1PzrJe | 800.39 sec |
000000000000000000000000000000000000000000000001a7d696f300cda2fc | Hash 160 52db4611541a1d382a06279c0a32b45bd1a0bf2b | 18Z768mmb7LCGDjU5xsF5YtPiSvRo5pVvR | 900.63 sec |
Similar hash160 found: 52ea8235174b368df646576fc04eb0d2737057b6

=========[Address Found]===========
Private Key: 000000000000000000000000000000000000000000000001a678bde0e606d586
Hash 160: 52ea8235174b368df646576fc04eb0d2737057b6
Address: 18ZRLg9BMfAg9WYVWqVDtYdT59QV1PzrJe
0x1a678bde0e606d586
Compressed Public Key: 02f46cb94bfecf5daf63cd54353c73eb3f7e148ac0f9b8af46e3a94ee8b60e1260
Time taken to find: 0.0000 seconds
Speed of keys: 0.0000 keys per second
=========[Address Found]===========

thankyou for advice, i make the KNN algorithm work with my previous code but have some trouble with other codes, some confusion call the rest codes to work with scan range, but it's work.
i test on 65 bit.

mightbe push my luck for 66 for couple weeks.

Can i take a look at your code ?

sure, i'll dm you, btw your codes looks cool and more advanced!
wow!, i'm beginner can you teach ?!

#EDIT
from my codes provide chance to find the 66 puzle is on 3000000000000000:37fffffffffffffff , below that it's just give same result hash and different 13zb1 address derived from private key. (but it's just speculation.)

[zahid888]

sure, i'll dm you, btw your codes looks cool and more advanced!
wow!, i'm beginner can you teach ?!

Sure, I'll see what else i can do to improve your code.

[WanderingPhilospher]

sure, i'll dm you, btw your codes looks cool and more advanced!
wow!, i'm beginner can you teach ?!

#EDIT
from my codes provide chance to find the 66 puzle is on 3000000000000000:37fffffffffffffff , below that it's just give same result hash and different 13zb1 address derived from private key. (but it's just speculation.)

Tepan, you seem hesitant to share your code, but trust me, what you are doing has already been done, in a much faster programming language, C++; CPU and GPU versions, where you can provide a h160 or address prefix and it will find EVERY collision in a 40 bit range, in less than a minute (depending on GPU setup).

Your python script may have some different settings/search criteria, but the bottom line is the same.

People are trying to help you speed up your code, in python, because your code is extremely slow. If your python code isn't checking at least 250,000 keys per second, then it desperately needs an overhaul. I can create a python script to do what yours is doing, that will check at least 250,000 keys per second, python only, single core, in less than 10 minutes.

But hey, you do you.

Zahid888, is the expert at prefix searches...probably has the world's largest prefix database, he can attest to what I am saying to you.

[Tepan]

sure, i'll dm you, btw your codes looks cool and more advanced!
wow!, i'm beginner can you teach ?!

#EDIT
from my codes provide chance to find the 66 puzle is on 3000000000000000:37fffffffffffffff , below that it's just give same result hash and different 13zb1 address derived from private key. (but it's just speculation.)

Tepan, you seem hesitant to share your code, but trust me, what you are doing has already been done, in a much faster programming language, C++; CPU and GPU versions, where you can provide a h160 or address prefix and it will find EVERY collision in a 40 bit range, in less than a minute (depending on GPU setup).

Your python script may have some different settings/search criteria, but the bottom line is the same.

People are trying to help you speed up your code, in python, because your code is extremely slow. If your python code isn't checking at least 250,000 keys per second, then it desperately needs an overhaul. I can create a python script to do what yours is doing, that will check at least 250,000 keys per second, python only, single core, in less than 10 minutes.

But hey, you do you.

Zahid888, is the expert at prefix searches...probably has the world's largest prefix database, he can attest to what I am saying to you.

no, i'm not hesitant to share my codes, because don't have much time, i know what i'm working on it clearly someone’s has been done before.
 i'll dm you and zahid888, soon.

[acrom]

Wow I just did the math to see how long it would really take if you went from start to finish with no stride for puzzle 66
With the start and end params of 20000000000000000...3ffffffffffffffff and a 3080 at 1100 MKeys/s or 1.1 billion keys a second
I got
33,539,534,679 seconds
558,992,244 minutes
9,316,537 hours
388,189 days
1,063 years

[mcdouglasx]

it is more likely to find a privatekey-publickey pattern
than a pattern between privatekey-h160.
For mathematical reasons, pubkeys are in their most "basic" section a residue of P, so you could go in scale reducing the search range where you get more matches.
This is a vague idea of a possible search.

Code:

#@mcdouglasx
import secp256k1 as ice
import random

print("vanity search...")

#03633cbe3ec02b9401c5effa144c5b4d22f87940259634858fc7e59b1c09937852
Target_A = "633cbe3e"

       
min =   600000000000000000000000000000000000000
max =  1370000000000000000000000000000000000000

while True:

    r =  random.randint(min, max)
    A0 = ice.scalar_multiplication(r)
    A1 = A0.hex()
    dupuba = A1[2:66]
    puba = str(dupuba)


    if puba.startswith((Target_A)):

            print("Match")
            A2 = ice.to_cpub(A1)
            data = open("vanity.txt","a")
            data.write("Pk:"+" "+str(r)+"\n")
            data.write("cpub:"+" "+str(A2)+"\n"+"\n")
            data.close()

[zahid888]

Zahid888, is the expert at prefix searches...probably has the world's largest prefix database, he can attest to what I am saying to you.

Yes, perhaps I have the world's highest number of prefixes for puzzle66.
And maybe I have conducted the most experiments on prefixes worldwide, whether it be in the form of base58 or hash160.
Through these experiments, I have consistently encountered a 50-50 probability of outcomes.
Sometimes, a few bytes of a private key match, and at other times, they do not match at all.
So, there is no difference between a 'private keys with matched prefixes' and a 'totally random private keys'.
I save addresses with prefixes only as a 'proof of work'; beyond that, these addresses serve no further purpose.

If your objective is solely to locate addresses with matching prefixes, it would be advisable to opt for the vanity search implemented by WanderingPhilosopher, which is based on multi-GPU counting, rather than attempting to enhance speed in Python, which never ever compete with GPU-counting.

Edit :
Anyway, I have scanned about 6.5% of puzzle 66 keys individually, I need your best wishes, and a lot of good wishes from my side to all of you.

[Tepan]

sure, i'll dm you, btw your codes looks cool and more advanced!
wow!, i'm beginner can you teach ?!

Sure, I'll see what else i can do to improve your code.

i already make some display looks clean and more likely like your display codes on terminal



i'll send the python files, i hope you can help me , thanks!

[NotATether]

Has anyone ever figured out what the #64 and #120 keys were, up to now?

[zahid888]

Has anyone ever figured out what the #64 and #120 keys were, up to now?

Obviously not, this was just my wishful thinking when I had less experience ⬇

16jY7qLJXAVFd7AJXJ5N8xT9DEs24NDaXV  F7051F24C01D5BB2
16jY7qLJnxb7CHZyqBP8qca9d51gAjyXQN  F7051F27B09112D4

feeling too much unlucky  I should have taken these green letters seriously.

I hope Tepan has some new idea

[vklimin]

For educational purposes. Can anyone provide Python code for iterating through private keys in a specified range that works with CUDA? Or a link to such a resource.
Thank you.

[AndrewWeb]

Wow I just did the math to see how long it would really take if you went from start to finish with no stride for puzzle 66
With the start and end params of 20000000000000000...3ffffffffffffffff and a 3080 at 1100 MKeys/s or 1.1 billion keys a second
I got
33,539,534,679 seconds
558,992,244 minutes
9,316,537 hours
388,189 days
1,063 years

Then 1000 billion keys a second would take one year (31,536,000 seconds) ?

[nomachine]

For educational purposes. Can anyone provide Python code for iterating through private keys in a specified range that works with CUDA? Or a link to such a resource.
Thank you.

It is 20 times faster in Rust  then Python/Numba

https://github.com/Rust-GPU/Rust-CUDA

https://github.com/Rust-GPU/Rust-CUDA/blob/master/guide/src/guide/getting_started.md


But there is a 0 puzzle script written in Rust.
There is this one here as an example
https://bitcointalk.org/index.php?topic=1306983.msg63539973#msg63539973