Bitcoin puzzle transaction ~32 BTC prize to who solves it

[citb0in]

Also, question for everyone, if you can solve a 52 bit key, with known pubkey, in 30 seconds, what rate of speed is that?
Simplify the question, if you one is using some version of BSGS and finds a 52 bit key within 30 seconds, what's the speed?

When we talk mathematically about speed, it is calculated as the distance traveled divided by the time it takes to travel that distance (it only has magnitude). It can be defined also in terms of other quantities besides distance, depending on the context. In physics, speed is often defined as the rate of change of position over time, which is commonly expressed as distance traveled divided by the time taken to travel that distance, as mentioned earlier. In certain situations it can also be defined in terms of other quantities such as: angles, phases or displacements.

Your question can therefore not be answered because it is incomplete. Neither the first nor the second.

You could count the steps performed or keys tried and divide the total by the total time required. Then you would have keys/sec or steps/sec as the result, depends on your needs.
But you can't calculate 52bits/30sec and claim that you can work out 0.577 sec per number of bits, and of course you know yourself why that is. Because otherwise you would have already solved the entire puzzle hundreds of times  

[1715348257]

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[1715348257]

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[WanderingPhilospher]

Also, question for everyone, if you can solve a 52 bit key, with known pubkey, in 30 seconds, what rate of speed is that?
Simplify the question, if you one is using some version of BSGS and finds a 52 bit key within 30 seconds, what's the speed?

When we talk mathematically about speed, it is calculated as the distance traveled divided by the time it takes to travel that distance (it only has magnitude). It can be defined also in terms of other quantities besides distance, depending on the context. In physics, speed is often defined as the rate of change of position over time, which is commonly expressed as distance traveled divided by the time taken to travel that distance, as mentioned earlier. In certain situations it can also be defined in terms of other quantities such as: angles, phases or displacements.

Your question can therefore not be answered because it is incomplete. Neither the first nor the second.

You could count the steps performed or keys tried and divide the total by the total time required. Then you would have keys/sec or steps/sec as the result, depends on your needs.
But you can't calculate 52bits/30sec and claim that you can work out 0.577 sec per number of bits, and of course you know yourself why that is. Because otherwise you would have already solved the entire puzzle hundreds of times  

But how do devs calculate their speed when using BSGS?

I see a lot of peta, exa keys per second, I’m trying to figure out how they calculate the speed…and then apply it to my script.

[citb0in]

But how do devs calculate their speed when using BSGS?

I see a lot of peta, exa keys per second, I’m trying to figure out how they calculate the speed…and then apply it to my script.

Well, if you were the person you claim to be (WanderingPhilosopher Keyhunt), you probably wouldn't had put this question.

The source code of Keyhunt is open and you can see how the speed is determined. It is in the nature of the BSGS algorithm that you cannot compare these values with the classic searches. The same applies to Kangaroo, which is a completely different approach. A comparison would be like comparing apples and oranges. These are completely different algorithms, for example: if you run Kangaroo well-tuned and you would rely on the speed rate the tool shows you and then compare it to the speed of let's say BSGS you would be disappointed. Because BSGS will report a much higher rate. But in fact, the  Kangaroo will always run faster than BSGS. Again, you cannot compare them.

In BSGS mode of keyhunt for example the speed shown also depends on the pubkeys used. More keys will result in less speed.

[Cryptoman2009]

after 6 months on first space of puzzle 130
200000000000000000000000000000000:280000000000000000000000000000000

new space (from 25 to 75%)

./keyhunt -m bsgs -f 130.txt -r 280000000000000000000000000000000:380000000000000000000000000000000 -q -S -B random -k 256 -t 8

• Version 0.2.230519 Satoshi Quest, developed by AlbertoBSD
• Quiet thread output
• K factor 256
• Threads : 8
• Mode BSGS random
• Opening file 130.txt
• Added 20000001 points from file
• Range
• -- from : 0x280000000000000000000000000000000
• -- to     : 0x380000000000000000000000000000000
• N = 0x100000000000

130.txt 20M keys:
./keysubtracter -p 03633cbe3ec02b9401c5effa144c5b4d22f87940259634858fc7e59b1c09937852 -n 20000000 -r 0:10000000000000000 >>130.txt

[Tepan]

Also, question for everyone, if you can solve a 52 bit key, with known pubkey, in 30 seconds, what rate of speed is that?
Simplify the question, if you one is using some version of BSGS and finds a 52 bit key within 30 seconds, what's the speed?

When we talk mathematically about speed, it is calculated as the distance traveled divided by the time it takes to travel that distance (it only has magnitude). It can be defined also in terms of other quantities besides distance, depending on the context. In physics, speed is often defined as the rate of change of position over time, which is commonly expressed as distance traveled divided by the time taken to travel that distance, as mentioned earlier. In certain situations it can also be defined in terms of other quantities such as: angles, phases or displacements.

Your question can therefore not be answered because it is incomplete. Neither the first nor the second.

You could count the steps performed or keys tried and divide the total by the total time required. Then you would have keys/sec or steps/sec as the result, depends on your needs.
But you can't calculate 52bits/30sec and claim that you can work out 0.577 sec per number of bits, and of course you know yourself why that is. Because otherwise you would have already solved the entire puzzle hundreds of times  

But how do devs calculate their speed when using BSGS?

I see a lot of peta, exa keys per second, I’m trying to figure out how they calculate the speed…and then apply it to my script.

thank you, I will DM (message) for the source code, wait a minute because I want to add a few things about thread speed, because there is a little bug in the private key search process.
Btw .. this is a very wise answer, sorry I also didn't think to ask like that, I also confused about how the developer can calculate freely for the entire search period with different speeds for each device, and also a different load key search..
but once again.. thank you, this is a wise answer and can be understood more broadly.
repp+ WanderingPhilospher


my code was running build with this formula, to trial and error for setup 66 bit keys.

number of possible 66-bit keys divided with search rate from 30-bit key.

my search rate per hour for 30 bit above is approximately 800,324 keys per hours (like what i said before about leaking the cpu speed and more usage ram when 30 bit above)

Time to find 66-bit key≈1.75×10⁸years

 
​

 

[WanderingPhilospher]

But how do devs calculate their speed when using BSGS?

I see a lot of peta, exa keys per second, I’m trying to figure out how they calculate the speed…and then apply it to my script.

Well, if you were the person you claim to be (WanderingPhilosopher Keyhunt), you probably wouldn't had put this question.

The source code of Keyhunt is open and you can see how the speed is determined. It is in the nature of the BSGS algorithm that you cannot compare these values with the classic searches. The same applies to Kangaroo, which is a completely different approach. A comparison would be like comparing apples and oranges. These are completely different algorithms, for example: if you run Kangaroo well-tuned and you would rely on the speed rate the tool shows you and then compare it to the speed of let's say BSGS you would be disappointed. Because BSGS will report a much higher rate. But in fact, the  Kangaroo will always run faster than BSGS. Again, you cannot compare them.

In BSGS mode of keyhunt for example the speed shown also depends on the pubkeys used. More keys will result in less speed.

Honestly, it’s a simple question, maybe you shouldn’t try to answer.

I’m not trying to compare potatoes and cabbage.

If I am using BSGS and can find a 52 bit key in 30 seconds, what’s the speed? 😂

It really wasn't meant to spurn any controversy. I know others have disagreed in the past, so I was curious to what people had to say.

If I use albertobsd method, I can say that the speed of my single core python script gets roughly, 140,549,854,653,356  Keys/s.
I wasn't trying to say it was fast or anything, just curious as to the actual speed and how different people view it.
I've been working on a low memory BSGS script; this one only uses about 500MB of RAM. Low memory, for various reasons but my reason is because I wrote a server/client script (python) and some of my machines have 8-16 GB max on them so I needed a way to employ them via low memory.

my search rate per hour for 30 bit above is approximately 800,324 keys per hours (like what i said before about leaking the cpu speed and more usage ram when 30 bit above)

Time to find 66-bit key≈1.75×108years

Your code does seem on the slower side of speed, but I was just going to show you some ways/see if we could speed it up (using python only). Not saying it will ever solve a puzzle, but more speed never hurts.

[Tepan]

But how do devs calculate their speed when using BSGS?

I see a lot of peta, exa keys per second, I’m trying to figure out how they calculate the speed…and then apply it to my script.

Well, if you were the person you claim to be (WanderingPhilosopher Keyhunt), you probably wouldn't had put this question.

The source code of Keyhunt is open and you can see how the speed is determined. It is in the nature of the BSGS algorithm that you cannot compare these values with the classic searches. The same applies to Kangaroo, which is a completely different approach. A comparison would be like comparing apples and oranges. These are completely different algorithms, for example: if you run Kangaroo well-tuned and you would rely on the speed rate the tool shows you and then compare it to the speed of let's say BSGS you would be disappointed. Because BSGS will report a much higher rate. But in fact, the  Kangaroo will always run faster than BSGS. Again, you cannot compare them.

In BSGS mode of keyhunt for example the speed shown also depends on the pubkeys used. More keys will result in less speed.

Honestly, it’s a simple question, maybe you shouldn’t try to answer.

I’m not trying to compare potatoes and cabbage.

If I am using BSGS and can find a 52 bit key in 30 seconds, what’s the speed? 😂

It really wasn't meant to spurn any controversy. I know others have disagreed in the past, so I was curious to what people had to say.

If I use albertobsd method, I can say that the speed of my single core python script gets roughly, 140,549,854,653,356  Keys/s.
I wasn't trying to say it was fast or anything, just curious as to the actual speed and how different people view it.
I've been working on a low memory BSGS script; this one only uses about 500MB of RAM. Low memory, for various reasons but my reason is because I wrote a server/client script (python) and some of my machines have 8-16 GB max on them so I needed a way to employ them via low memory.

my search rate per hour for 30 bit above is approximately 800,324 keys per hours (like what i said before about leaking the cpu speed and more usage ram when 30 bit above)

Time to find 66-bit key≈1.75×108years

Your code does seem on the slower side of speed, but I was just going to show you some ways/see if we could speed it up (using python only). Not saying it will ever solve a puzzle, but more speed never hurts.

hi sir, i'm very motivated with your opinion about search speed.

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.

[entmerkalopa]

Dear puzzle creator:
We, the united super united, wish that you empty all addresses so that all these lost souls can go new ways.
Thank you, puzzle creator.

The game is over for now.
Please clear all addresses up to ~130 bits via exclusive/private mining so that no one can steal the funds by double spending.

[Tepan]

what do you mean bro ?

[mabdlmonem]

But how do devs calculate their speed when using BSGS?

I see a lot of peta, exa keys per second, I’m trying to figure out how they calculate the speed…and then apply it to my script.

Well, if you were the person you claim to be (WanderingPhilosopher Keyhunt), you probably wouldn't had put this question.

The source code of Keyhunt is open and you can see how the speed is determined. It is in the nature of the BSGS algorithm that you cannot compare these values with the classic searches. The same applies to Kangaroo, which is a completely different approach. A comparison would be like comparing apples and oranges. These are completely different algorithms, for example: if you run Kangaroo well-tuned and you would rely on the speed rate the tool shows you and then compare it to the speed of let's say BSGS you would be disappointed. Because BSGS will report a much higher rate. But in fact, the  Kangaroo will always run faster than BSGS. Again, you cannot compare them.

In BSGS mode of keyhunt for example the speed shown also depends on the pubkeys used. More keys will result in less speed.

Honestly, it’s a simple question, maybe you shouldn’t try to answer.

I’m not trying to compare potatoes and cabbage.

If I am using BSGS and can find a 52 bit key in 30 seconds, what’s the speed? 😂

It really wasn't meant to spurn any controversy. I know others have disagreed in the past, so I was curious to what people had to say.

If I use albertobsd method, I can say that the speed of my single core python script gets roughly, 140,549,854,653,356  Keys/s.
I wasn't trying to say it was fast or anything, just curious as to the actual speed and how different people view it.
I've been working on a low memory BSGS script; this one only uses about 500MB of RAM. Low memory, for various reasons but my reason is because I wrote a server/client script (python) and some of my machines have 8-16 GB max on them so I needed a way to employ them via low memory.

my search rate per hour for 30 bit above is approximately 800,324 keys per hours (like what i said before about leaking the cpu speed and more usage ram when 30 bit above)

Time to find 66-bit key≈1.75×108years

Your code does seem on the slower side of speed, but I was just going to show you some ways/see if we could speed it up (using python only). Not saying it will ever solve a puzzle, but more speed never hurts.

hi sir, i'm very motivated with your opinion about search speed.

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.

Could you please share your code

[citb0in]

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.

[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.