you script is slow work because it work only on 1 core CPU or 1 thread
try check on talk manager and show CPU core work

try copy method from this script
https://github.com/Xefrok/BitBruteForce-Wallet/blob/master/seekanddestroy.py

it work on all cpu core by using python multi process multi thread work

good or not if we open new thread post only keyspace that already scan it

like public pool share keyspace scaned

if you scan what keyspace for what puzzle done already post to forum it not found key

I search a lot with prefix like this but can not find any pattern vanity  16jY7qLJ
I think we find to wrong pattern

I think it can not run on ETH mining machine

cuBitcrack can use on NVIDIA CUDA GPU on Windows and Linux only

(Did have any report for use on x86 MAC?)

if you not have GPU and small laptop

don't try with 265 bit range or 256bit key

Kangaroo-256 good for high performance PC with hi end space and GPU
many time I have problem with my small gpu with Kangaroo-256

Recommend to use Kangaroo.exe  JeanLucPons is better for laptop and low space PC

Kangaroo still have limited use with puzzle only success possible, Can not use real key success it 256bit

if you want to try , test setting with keyspace low than 2^70 may be can work

see on image
range too high
dp is high


try test use DP = 10

What is problem detail?

try test compare with binary windows .exe release and you compile version Did both have problem?

Lightning Network for use with bitcoin only right?
Can Lightning Network use with other alt coin?

Can Lightning Network create as new alter coin and fast same?

NVIDIA GeForce RTX 2080

I think driver problem
first try update CUDA driver to 10.2

other try compare old release
https://github.com/brichard19/BitCrack/releases/tag/0.31
https://github.com/brichard19/BitCrack/releases/tag/0.30
https://github.com/brichard19/BitCrack/releases/tag/0.29

Did you have message or image capture

Try test use without use GPU,  may be GPU problem

What is you GPU?

 
I have idea for solve puzzle #64

up to we want to solve puzzle by anyone or keep it for solve puzzle by yourself

if we want to keep it for solve puzzle by yourself don't do anything every one find way yourself (may be next year some one on GPU mining farm try to solve it)

or if want to solve by give away to anyone it easy to solve with in 1 month (30 days)  just want to challenge over old records or not

just idea please ignore
my old idea make bitcrack version #64 build-in public pool (use free firebase database) and promote every one to know will be solve by millions people do it
make like 64bit pool do it but pool split share to who help to highest scan have record keyspace that scan already on database
but this idea is have database record pool but give away to who lucky can scan found this project call bitcrack LOTTO
it like a LOTTO for bitcoin value $28,797.44 USD (price for now post) I believe people over million will try to scan it by bitcrack special version puzzle #64
million people will scan free and one person lucky will win this lotto and get the key . user easy just run bitcrack (lotto) and waiting for lucky. it is FREE lotto no cost to pay just run computer
don't worry still have 78 address leave to solve  (next puzzle 66 may be can use this idea but for next #67 may be too high to do it)
may be add script to make transaction when found key and split for developer 30% (0.2) and who lucky win 70% (0.44)

for me I think leave that idea for every one find the way to crack it themselves  

puzzle is activity for fun
if people think waste of time , no body to crack it, it is boring (no excite)

may be puzzle #64 will last one to can crack because it is to high bits

in.txt  just use notpad on windows

I think you put  puzzle32.txt all in use, that is wrong
you need to split each set of puzzle to new one in.txt   it can you any name

in120.txt
in121.txt
in122.txt
puzzle120.txt
(somting like that)

Don't put new line on the end bottom line too

structure simple just 3 line
1 start keyspace search
2 end keyspace search
first public key search

propose make BitCrack scan key faster?
I know already bitcrack have all process convert key to address on bitcrack include RIPEMD-160 inside

I not sure you not yet understand
bitcrack bruteforce address right?
this idea just bruteforce to RIPEMD-160

however if this method not difference
if you bitcrack work on GPU can scan 1000Mkey/s
and if process use time to do 30% second if do this idea will help to make GPU scan at 1300 Key/s

this idea is help to accelerate spped of bitcrack for scan to high

for use bitcrack solve puzzle #64
address still legacy address and all puzzle is compress address

refer my post with RIPEMD-160

now, we try to optimize bitcrack to can scan address faster and high number right

idea is just cut off process of bitcrack to

I not yet read bitcrack souce code, Did bitcrack convert publickey to RIPEMD-160 and double sha256 or not?
bitcrack convert key to address and check compare gen address and target address right?

reference this app
http://gobittest.appspot.com/Address

you can see we can convert address to roll back to RIPEMD-160
put address to box 9 - Base58 encoding of 8 and click send
web app will convert back to RIPEMD-160
idea it is just convert address to RIPEMD-160 and cut out work process to short

hope this method help to can scan bitcrack faster up to 30%   (if 30% is process from RIPEMD-160 to address)

just idea please ignore

How long time for bitcrack calculate form RIPEMD-160 and double sha256 then Base58

if use long time

Can possible add function bitcrack to have option --ripemd for search as RIPEMD-160 replace address?

if time from private key to address using time 100%
and time from RIPEMD-160 to address using over 30%

if can search at RIPEMD-160 maybe help search key up to 30%

just idea

openCL  clBitcrack latest version 0.31 some person use have problem

try use  release 0.30 it is no problem for every one
https://github.com/brichard19/BitCrack/releases/download/0.30/clBitCrack.exe

good or not if change 32 to max at 256


Layer (type)                 Output Shape             
=============================
input_1 (InputLayer)         [(None, 256)]           
_____________________________________
multi_category_encoding (Mul (None, 256)               
_____________________________________
dense (Dense)                (None, 32)               
_____________________________________
re_lu (ReLU)                 (None, 32)               
_____________________________________
dense_1 (Dense)              (None, 32)               
_____________________________________
re_lu_1 (ReLU)               (None, 32)               
_____________________________________
regression_head_1 (Dense)    (None, 1)                 
=============================



I will test add more 5 layer
activate I use ReLU


Layer (type)                 Output Shape             
=============================
input_1 (InputLayer)         [(None, 256)]           
_____________________________________
multi_category_encoding (Mul (None, 256)               
_____________________________________
dense (Dense)                (None, 256)               
_____________________________________
re_lu (ReLU)                 (None, 256)               
_____________________________________
dense_1 (Dense)              (None, 128)               
_____________________________________
re_lu_1 (ReLU)               (None, 128)               
_____________________________________
regression_head_1 (Dense)    (None, 1)                 
=============================



I try to use modify sample code for keras to use pubkey dataset
use keras predict titanic and keras predict California housing
just test may be wrong algorithm that should be use

on github have a ot of bitcoin trading bot use neural networks predict price easy than predict elliptic curve

A bit off-topic,

I try Neural Networks with colab

work with dataset of Secp256k1
generate pubkey and save to colab
Limited storage 100GB (system 40gp limited to use 60gb)

under 1 million dataset may be can work with colab
over 1 million is too much for colab

Google colab pro is good to can use Tesla V100 (limited on 24 hour)
Google colab design for work with neural networks
Google colab pro good value for $9.99 cost
name colab "LAB" is already tell for  testing and proof of concept not for production for real work because use time more 24 hour

Kaggle is good for find sample code (most way use github)
I like Google colab more than kaggle

Google colab free you need to finish job on 12 hour work only 1 gpu limited
Google colab pro ($9.99)  finish job on 24 hour
it good for testing job

colab you still need to coding (no GUI)
colab risk to loss you job if disconnect from server or session reset ( do new one again)
colab save file to ssd risk

colab need to use with google drive to use you file on drive is better (but make it work slow than on ssd on colab)

Real work with Neural Networks you should be use you own GPU is better only way
I run test pubkey small dataset with autokeras ()optimize code) use time over 48 hour on low end  gtx 1050 (slow work)

Neural Networks with Secp256k1 use very long time to train it. it use time a lot for try optimize code for get best result

some problem
use pubkey dataset 1000 to 10000 no problem on test
use pubkey dataset  1 million have a lot of error message between training

optimize code on small dataset is fine
but when try with large dataset 1 million

dataset
on test
use dataset by hex (split each)
use dataset by convert to decimal (split each)
use dataset by convert to binary  (binary 10101 not byte code) (split each)
my best result convert to binary 10101

I just on testing on small dataset

agree to try wok with over 1 billion dataset
try on large dataset and fix problem same time
I test on small and change to use large is difference like to try test new one on large dataset

test algorithm Neural Networks
try apply algorithm
use classification is give answer with percent
use predict number is give predict number with out percent

I think it using classification training is better because give result with percent of predict it useful for use decision

I think small neural networks  can not handle with Secp256k1 success problem curve with large number it make very complex
neural networks is small digit work with neurons
problem is still on large number

other idea is create some algorithm to predict will be small and easy than may be correct at 50% only can call success

Google colab is very good you need to subscription $9.99 dollar per month to use colab pro available to use Tesla V100 it is very good deal

colab pro limited use 5 note same time and can use with 24 hour will be re-connect again require to open web browser can not close work page

you need to use with google drive to save you job subscriot more 100gp drive will be good

I just try to use colab pro

great if can do with 1billion dataset

my GPU is small gtx 1050 just 1 millions is slow training
may be need to optimize model code

test at 1000 and 10000 is no problem but 100000 and 1000000 have a lot of error show on training

I can do only test and train
for use may be do by manual use