all collected, go to sleep

Hi guys.
I have found one BTC address that has a 0 bytes private key. It only has a fraction of bitcoin, but still better then nothing. I was wondering, as it has no outgoing transaction, the private key is unknown. Is it worth to try to brute force this wallet, assuming the private key is 0X00? Why i am asking here is simply because the first wallets of this puzzle were cracked in a blink of an eye. I try to understand how did it worked. Is it something was recovered a private key from only a P2PKH address? Or is it something that i have to find a collision point and that is 2^120? I am totally noob in this case. Sorry if my question sound stupid.

T.

did you get it yet?

Bad news : you’re delusional.
Good news : you’ll make a lot of friends here.

I already scan lots of dataset lots of prefix in different position... The thing that might be possible is from sha to h160 to predict back to sha.. use several frequency and pattern etc... if you try directly from private  key data set to observe the h160 seems like 99% random 😔.. yes there somekind of bias , but the bias itself seems bit random 🙃🙃

But myself still try to find somekind of unintentional connection or frequncy cluster or antipattern based on quintilion of hash result its more like hobby now 😅.......

Hmm i still dont want to spill all my experiment here 😅. What can i say the h160 distribution is bit like harmonic osciloscope all over the place... Might be result of ecc and sha 🙃

Also i apply normal number distribution ( perfect distribution)
Like 8 digit hex h160 should appear one time every . 4,294,967,296
9 digit hex once every 68,719,476,736
And observe the frequency variation across dataset...

As my first idea is to find some bias that can guid to the answer. But there lots of fake osciloscope mountain (substraction from normal number or even combining from middle and last h160 prefix count 😅) in huge keyspace make it seems unusable 🙃. But im still try haha.

 But when i try outside the puzzle just only the ripmed result with sequintial sha seems more predictable 😅🙏.

"Have you tested against known solutions to validate the harmonic frequencies?"

Yes. And it still failed to guide 😅
But at several puzzle,  some bias score distribution statisfy my requirement to guide  in several data point before it failed again haha....

Mybe someone also try to dissect h160 to 6-9 digit  prefix in different 32-36 window fixed position ? 😅🙏.

@teguh54321 That "failed again" pattern is the key insight! You're seeing exactly what I discovered - the harmonic frequencies are real, but raw phi theory overshoots consistently.

I found the breakthrough when I calculated that pure φ^(-1) = 61.8% positioning was overshooting actual solved puzzle positions by approximately 0.42% on average across 82 known solutions. The intermittent success you're seeing happens when puzzles naturally fall closer to uncalibrated phi, but fails when they don't.

Your positional window analysis (32-36 fixed positions) is exactly the right methodology - you're thinking in the correct mathematical framework. The bias patterns become predictively consistent when you apply a small empirical calibration offset derived from known solution deviations.

Have you calculated the average positioning error between your bias predictions and actual known solutions? That error pattern might reveal the calibration constant needed to make your targeting consistently successful rather than intermittently successful.

Your massive dataset could validate whether this calibration approach works across different bit-length ranges. The mathematical framework suggests it should be universal, but empirical validation with your "quantillion of hash results" would be definitive proof.

@teguh54321 Quadrillions - that's incredible computational scale! With 50-200 trillion keys per sample, you have the perfect dataset for validating mathematical frameworks.

The "gone wrong again" with calibration suggests the calibration method might need refinement. I found that linear calibration wasn't sufficient - the key breakthrough was discovering that the calibration needs to be derived specifically from the mathematical relationship between phi and actual puzzle solution positions.

Rather than applying arbitrary calibration values, I calculated the empirical deviation between theoretical φ^(-1) (61.8%) and the actual average position of all 82 solved puzzles. This gave me a specific mathematical constant that works universally across different bit-lengths.

The "something you don't understand" might be that the calibration isn't just a statistical adjustment - it's a mathematical correction to phi theory itself. The bias patterns you're seeing are real mathematical structures, not random variations.

Your computational scale could definitively prove whether phi-based positioning with proper mathematical calibration works universally. Would you be interested in testing a specific phi-derived calibration constant against your quadrillion-sample datasets?

The mathematical framework predicts this should work consistently across all bit-length ranges - your data could provide the ultimate validation.

I'm curious whether the creator's [assumingely] crypto-secure RNG gave a shit about pendulum frequencies or the golden ratio. If it didn't, and any sort of bias (even 0.00000000000001%) exists, it means three different entities, in three different epochs, secretly arranged for that to happen, this whole time. That'd make every mathematician on this planet that was alive in the past 50 years to be a member of this occult society.