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The Satoshi Fortune

At Whale Alert we analyze and report interesting blockchain transactions and for Bitcoin there is no subject more interesting and mysterious than the founder known by the alias Satoshi Nakamoto. We were able to make the most accurate estimate of the number of blocks mined and bitcoins owned by Satoshi: 1,125,150 bitcoin mined up to block 54,316 with an estimated total value of the unspent bitcoin of at least $10.9 billion USD in today’s market. In addition to the size of his fortune we were also able to deduce the purpose of the Patoshi miner: to defend the young network from attacks.

Central to the workings of the Bitcoin network is mining, which ensures transactions are safely executed. In order for a transaction to be processed, one of the many miners needs to bundle it in the block they have mined. The strength of the Bitcoin network is positively correlated to the number of miners: more miners means a safer network that is more reliable and more resilient to attacks. Very simply put, mining is a guessing game in which the participants simultaneously try to guess a huge number. The winner gets to mine the block and claim the block reward (50 bitcoin per block in the early days)and, in addition to the transactions, other details related to the mining process, like the nonce and extranonce values (used by the miner during the mining process), are added to the block. In 2013 Sergio Demian Lerner discovered that there was a distinct pattern visible in this extranonce value in the coinbase transaction (the transaction in which the rewarded bitcoin for mining a block is created) of each block.

Figure1 shows the extranonce values at a block heights below 20,000 with each block represented by a dot and creating a distinct saw like pattern. Lerner argued that the diagonal lines are created by miners using the publicly released standard bitcoin software: the straight lines they leave behind are the result of the miner incrementing the extranonce either during their own mining process or each time a new block is mined on the network. The distinct saw pattern is created by resetting the extranonce value to zero which happens whenever a miner is restarted. The almost vertical lines in the graph have been attributed to a miner that Lerner named Patoshi. We know for certain that Patoshi was operated by Satoshi, because its pattern emerges at the very birth of the network and it mined the block that created the bitcoins sent to Hal Finney. Lerner found additional proof for his claims in the nonces (another value used in the mining process that is also stored in each block, but is different from the extranonce) of the blocks mined by the Patoshi miner: the last byte of the nonce was always within the ranges of 0 to 9 or 19 to 58 whereas all other miners used the full range of 0 to 255. This discovery made it easier to attribute blocks to Patoshi by removing the ones with nonces outside of the range. However, attributing blocks remained difficult at the intersections of Patoshi and standard miner patterns, especially past block 20,000 after which the number of active miners increased greatly.

We suspected that the limited range of the last byte of the nonces was the result of work being divided over a number of computers or CPU cores, with each one using a different number to prevent duplication of work. With this in mind we checked if there was any variation within the range itself as a result of mining capacity being added or removed and we found that it indeed changed over time. For instance, the range [0–9][19–58] was only used during the first 18,015 blocks and was then reduced to [0–9][19–48] until block 21308 with the same happening for later block periods (ranges per block period listed in table below) (Similar findings have been described in this anonymous blog). Our research also revealed some interesting new details: between blocks 21,467 and 25,777 the range [0–9] was only used at the start of each Patoshi chain (each chain being a single run of the miner, and thus a single line in the above graph) and the number 39 was only used sporadically. Between blocks 25,811 and 54,316 the number 29 is missing entirely from some chains. These anomalies could indicate that certain computers or cores were defective or turned off during these periods. These findings allowed us to exclude even more blocks that were not mined by Satoshi and provided us with a clearer image (see figure above) which was especially valuable at higher block heights where the mining activity on the network increased drastically.

The improved attribution of Patoshi blocks allowed us to accurately determine the average mining speed for Satoshi’s miner and surprisingly we found that changes in the nonce ending range did not correlated to changes in mining speed: the average mining speed stayed incredibly constant for long periods of time. For example, for the entire range of blocks between height 2,000 and 16,000 the average number of blocks mined by Patoshi per hour was almost exactly .6 per 10 minutes, even though the number of miners varied greatly during this period. From the data it is apparent that the Patoshi miner adjusted its speed between blocks to maintain the average; when a Patoshi chain was creating more than .6 blocks/10 minutes, the block time for the following Patoshi block was on average lower and vise versa. There are two reasons why Satoshi would want to maintain this average: first, he saw 51% attacks as the biggest threat to the growing network and by maintaining a constant 60% of the processing power he could prevent these from happening while leaving enough blocks for others to mine. As more “honest” miners joined the network and a 51% attack became less likely, Satoshi was able to gradually scale down his mining activities. Second, Satoshi stated that the ideal block time was around 10 minutes and by controlling enough processing power it was possible to artificially keep the block time around this time when there was not enough or too much activity on the network. We suspect that Patoshi was comprised of at least 48 computers, with one machine for coordination and more on standby in case of an attack, which would explain the missing range of [10–18]. As soon as Satoshi deemed the network strong enough he reduced Patoshi’s blocks per 10 min target to give others a better chance at mining a block.

Knowing the changing nonce ranges allowed us to attribute more blocks to Satoshi and with higher certainty: an estimated 22,503 out of the first 54,316 blocks mined. For 50 of these blocks the coinbase transactions (or mining rewards) have been spent, one of which certainly by Satoshi in a transaction of 10 BTC to Hal Finney. 31 of the spent blocks are possible false positives, meaning they matched the Patoshi ‘fingerprint’ by chance and belong to a different miner. We are confident that 18 of the spent blocks belonged to Satoshi, making the total spent by Satoshi 907 BTC (price per BTC was less than $0.01 USD at the time of these transactions) and leaving 1,122,693 BTC unspent.

The goal of this research was not only to find out how much, but also why Satoshi was mining in this particular manner. Did Satoshi stop mining with the Patoshi miner after block 54,316? It is impossible to know whether the mining software was changed and became undetectable as a result or if Satoshi continued mining using the publicly available mining software. There are some anomalies found in higher blocks like the over-representation of nonces ending in [0–9] after block 70,000 and a strange pattern using the same ranges between block 109,500 and 112,500, but at the moment it is safe to say that the Patoshi miner was turned off in May 2010. The timing of the shutdown, the mining behavior, the systematic decrease in mining speed and the lack of spending strongly suggest that Satoshi was only interested in growing and protecting the young network. The bitcoin mined by Patoshi were possibly a mere byproduct of these efforts and it is unlikely that the remainder will ever be spent, although the question remains why Satoshi didn’t simply burn them in this case. Our findings do not exclude the possibility that Satoshi was also running a miner using the publicly released software, if only for testing purposes, and we believe it is likely that at least one of the non-Patoshi patterns belongs to Satoshi as well.

Note: according to our research the following blocks have been mined and spent by Satoshi: 9, 286, 688, 877, 1760, 2459, 2485, 3479, 5326, 9443, 9925, 10645, 14450, 15625, 15817, 19093, 23014, 28593 and 29097.

https://whale-alert.medium.com/the-satoshi-fortune-e49cf73f9a9b

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Follow The Money🎯

Israel Exposed launched 🤯Who Funded The Genocide🤯

A searchable database tracking pro-Israel political money in U.S. politics.

It includes:

👉 11,168 named donors + employers

👉 523 members of Congress + funding

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Revolut Leak Shows the Cost of Constant ID Collection
Revolut’s mistake is the news, but the bigger problem is the growing number of companies being encouraged or required to keep copies of our most sensitive identity documents.

Online bank Revolut has revealed that it gave out sensitive personal and financial information of an undisclosed number of its customers in response to a fake government request.

The information that was handed over to an “unauthorized third party” reportedly includes names, dates of birth, occupations, addresses, phone numbers, account numbers, transaction histories (including Bitcoin), and even copies of government-issued IDs and onboarding verification selfies.

Revolut claims that derived biometric face data was not.

The company said that the data was handed over in response to an email that came from a real government agency’s domain, but was not actually sent or authorized by that agency.

The email passed several authentication checks (SPF, DKIM, and DMARC) that are designed to establish the authenticity of a message’s origin and integrity, but do not verify the legitimacy of the legal request itself.

Revolut said that it complied with the request “under the reasonable belief that it was an authentic government agency request” – and only later found out that it was not.

Revolut said it later realized its mistake, blocked the email address, and reported the incident to the relevant authorities.

Revolut said that only a “limited” number of its customers were affected by the data leak, and that the company’s systems were not hacked, nor was any money stolen.

The story broke on September 11 when Revolut customers started receiving an email notice about a data leak, and the news was picked up by media outlets the following day.

Revolut notice explaining customer identity and financial data was shared after an unauthorized government email request.

The reason this is a recurring problem is that companies are keeping highly sensitive information about their customers’ identities, and sometimes even financial transactions, for a long time, and this data is then available to be disclosed to third parties – either in response to valid legal requests, or, as in the case of Revolut, fake ones.

One reason for this is know your customer (KYC) and anti-money laundering (AML) rules. Revolut’s current UK customer privacy notice spells it out: the company generally keeps personal data of UK customers for no more than seven years after the relationship ends, and sometimes longer – for legal reasons.

This means that even if you close your account, your identity documents don’t disappear.

And while the incident with Revolut happened in the financial sector, it’s by no means the only one that requires customers to hand over sensitive identity information. Discord, a popular chat service, said in an October 9, 2025 security update that government ID photos of approximately 70,000 users may have been exposed after a third-party customer service provider got hacked.

This was not a financial service, nor the same type of attack. But the result was similar – because the underlying business process was the same: requiring and storing sensitive identity documents. In the case of Discord, these were used to review age-related appeals.

It’s hard to do anything about a copy of your old passport, or a photo of your face, or a record of your past transactions. These can be used to identify and profile you, and can be used to carry out targeted fraud. And this can happen even if the initial disclosure didn’t result in financial loss.

The more companies are forced to collect and store such information, and the more of it they have, the more opportunities there are for this data to be leaked, either by the company itself or a third party it works with. That's what makes governments' push for more ID checks just to access ordinary parts of life so reckless.

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This Is The Income A Family Needs To Live Comfortably In Every US State

Here’s the short version of what it takes for a family of four to live comfortably in 2026 by state:

In Massachusetts, you’d need nearly $330,000 a year - the highest figure in the entire country. Only three states clear the $300,000 mark: Massachusetts, Hawaii, and California. At the other end of the spectrum, Mississippi is the most affordable at about $188,000. That’s a full $142,000 less than what you’d need in Massachusetts.

So… how much does a family of four need in your state?

This map shows the pre-tax income a household with two working adults and two kids needs to live comfortably in every U.S. state.

The numbers come from SmartAsset (as of February 2026). They’re based on the familiar 50/30/20 budget: 50% for necessities, 30% for discretionary spending, and 20% for savings or other goals. These aren’t bare-minimum survival numbers—they’re what it takes to live pretty well while still putting money aside.

And as Visual Capitalist notesMassachusetts sits at the very top of that list. Massachusetts tops the ranking, with a family of four needing $329,555 per year to meet the 50/30/20 benchmark.

Hawaii follows at $313,165, while California ranks third at $302,682.

Rank State Income needed for family of four (2026)

  • 1 - Massachusetts - $329,555
  • 2 - Hawaii - $313,165
  • 3 - California - $302,682
  • 4 - Connecticut - $298,189
  • 5 - New Jersey - $295,110
  • 6 - New York - $291,533
  • 7 - Colorado - $283,213
  • 8 - Washington - $281,798
  • 9 - Oregon - $280,966
  • 10 - Vermont - $280,384
  • 11 - Alaska - $272,064
  • 12 - New Hampshire - $267,904
  • 13 - Rhode Island - $264,659
  • 14 - Minnesota - $263,078
  • 15 - Maryland - $257,837
  • 16 - Maine - $250,931
  • 17 - Montana - $249,434
  • 18 - Pennsylvania - $247,936
  • 19 - Illinois - $244,109
  • 20 - Virginia - $242,944
  • 21 - Nevada - $242,278
  • 22 - Indiana - $241,696
  • 23 - Wisconsin - $238,451
  • 24 - Arizona - $236,870
  • 25 - Utah - $235,789
  • 26 - Delaware - $228,134
  • 27 - Ohio - $226,221
  • 28 - Idaho - $226,054
  • 29 - Florida - $223,392
  • 30 - New Mexico - $223,142
  • 31 - Nebraska - $223,059
  • 32 - Missouri - $217,734
  • 33 - Georgia - $214,573
  • 34 - Michigan - $214,323
  • 35 - South Carolina - $212,909
  • 36 - North Carolina - $212,410
  • 37 - Wyoming - $212,410
  • 38 - Oklahoma - $211,910
  • 39 - North Dakota - $210,496
  • 40 - Kansas - $207,917
  • 41 - Iowa - $204,422
  • 42 - Texas - $203,424
  • 43 - West Virginia - $202,592
  • 44 - South Dakota - $201,760
  • 45 - Alabama - $198,931
  • 46 - Louisiana - $197,933
  • 47 - Tennessee - $197,267
  • 48 - Arkansas - $195,437
  • 49 - Kentucky - $194,854
  • 50 - Mississippi - $187,533

Connecticut, New Jersey, and New York aren't far behind, bringing the number of states with comfortable-income thresholds above $290,000 to six.

Colorado and Vermont Make the Top 10

As expected, many of the highest income thresholds are concentrated in the Northeast and along the West Coast.

However, Colorado has the seventh-highest threshold in the country at $283,213, ranking above Washington and Oregon.

Vermont rounds out the top 10 at $280,384, despite having the second-smallest population of any U.S. state. Meanwhile, nearby states like New Hampshire, Maine, and Rhode Island all fall outside the top 10.

Just Six States Come in Below $200,000

Despite the wide range in living costs across the country, only six states have a comfortable-income threshold below $200,000 for a family of four.

Mississippi ranks lowest at $187,533, followed by Kentucky. The states of Arkansas, Tennessee, Louisiana, and Alabama also fall below the $200,000 mark.

The gap between Massachusetts and Mississippi exceeds $142,000 per year, meaning the Massachusetts benchmark is about 76% higher.

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🤖Can Decentralized AI Stop Big Tech from Owning the Future of Robotics?🤖
The race to build the future of robotics is no longer just about robots. It's about who controls the intelligence behind them.
 
Over the last three years, a small group of companies has emerged as the backbone of the AI revolution. Microsoft provides cloud infrastructure. NVIDIA supplies the chips. Google, OpenAI, Anthropic, Meta, and others develop the models. Together, they control much of the compute, data, and software stack powering modern AI.
 
Now that AI is moving into the physical world, many are asking a bigger question:
 
Will these same companies end up controlling robotics too?
 
It's a valid concern.
 
The latest generation of robots relies on enormous amounts of compute, simulation, training data, and foundation models. Many robotics startups today are built on infrastructure provided by large technology companies. NVIDIA's Omniverse is becoming a key simulation environment for robot training. Microsoft Azure is powering the training of robotics foundation models. Physical AI startups increasingly depend on hyperscale cloud infrastructure to train and deploy intelligent systems. Recent partnerships across the industry show just how central Big Tech has become to robotics development.
But while Big Tech is building the highways, another movement is trying to ensure it doesn't own every destination.
 
That movement is decentralized AI.
 
Why Decentralized AI Exists
 
The idea behind decentralized AI is simple. Instead of a handful of companies owning the models, compute infrastructure, data pipelines, and intelligence networks, these resources are distributed across thousands of participants.
 
This means anyone can contribute compute, contribute models, validate outputs and can participate.
The most visible example today is the decentralized AI network known as Bittensor (@bittensor). The network has evolved into a large ecosystem of specialized AI markets called subnets, where participants compete to provide useful machine intelligence and are rewarded based on performance. Rather than relying on a single company, intelligence is generated and validated by a distributed network of miners and validators.
 
Think of it as an attempt to build an open marketplace for AI instead of a world where intelligence is rented from a few centralized providers.
 
Why This Matters for Robotics
 
Robotics has a unique problem. Unlike chatbots, robots operate in the physical world. They need to perceive environments, make decisions, move safely and they need to learn continuously.
 
The challenge is that collecting and training on real-world robotic data is incredibly expensive. That's one reason large companies have such an advantage. They can afford the compute, simulation environments, and data infrastructure needed to train robotics models at scale.
 
This is where decentralized systems become interesting.
 
Instead of one company collecting all the data and training all the models, decentralized networks could allow thousands of contributors to participate in building robotic intelligence.
 
Imagine a future where:
  • Warehouse robots contribute operational data.
  • Delivery robots contribute navigation data.
  • Factory robots contribute manipulation data.
  • Developers contribute models.
  • Validators evaluate performance.
The resulting intelligence becomes a shared network rather than a proprietary asset.
 
That vision is beginning to emerge.
 
Bittensor's Move Toward Physical AI
 
While many people associate Bittensor (@bittensor) with language models and AI services, parts of the ecosystem are increasingly exploring embodied intelligence and robotics.
 
One example is Kinitro, a subnet focused on incentivizing the training and evaluation of embodied AI systems. The goal is to create competitive environments where developers build robotic intelligence and are rewarded based on performance.
 
The broader Bittensor ecosystem has also expanded into compute marketplaces, distributed inference systems, bandwidth infrastructure, and AI coordination layers that could eventually support robotics workloads. Several subnets now focus on decentralized compute, confidential inference, data transfer, and model training, critical components for future robotic systems.
 
In other words, the pieces are starting to appear.
 
Not a decentralized robot network yet.
 
But the infrastructure that could support one.
 
Beyond Bittensor: The Rise of Physical AI Networks
 
Bittensor isn't alone.
 
Across the industry, researchers and builders are experimenting with decentralized approaches to physical AI.
 
New research published in 2026 introduced the concept of DAO-enabled decentralized physical AI, or DePAI. The idea combines robotics, decentralized infrastructure, AI models, governance systems, and human oversight into a single framework. Instead of centralized control, robots and physical infrastructure could be coordinated through transparent rules and distributed ownership models.
 
At the same time, developers are exploring decentralized operating systems for robots that allow machines to communicate directly with each other and with distributed compute resources. These architectures are designed to make robotic systems more resilient and less dependent on a single cloud provider.
 
The goal is not simply decentralization for its own sake.
 
The goal is resilience.
 
If one server fails, the system continues.
 
If one company disappears, the network survives.
 
If one participant leaves, innovation continues.
 
But Here's the Reality
 
Decentralized AI faces the same challenge every decentralized technology faces.
 
Big Tech has resources. A lot of resources.
 
Training advanced robotics models requires enormous compute budgets, sophisticated simulation environments, access to specialized hardware, and vast amounts of real-world data.
 
That's why many robotics startups still partner with major cloud providers and AI companies. It's often the fastest path to deployment.
 
And there are legitimate concerns about whether decentralized networks can maintain quality, reliability, and security at the scale required for industrial robotics. Even researchers studying decentralized AI systems have highlighted risks around concentration, incentives, governance, and network security.
 
The challenge isn't just decentralizing intelligence.
 
It's decentralizing intelligence while maintaining performance.
 
That's much harder.
 
The Most Likely Outcome
 
The future probably won't be fully centralized. And it probably won't be fully decentralized either. Instead, we're likely heading toward a hybrid model.
 
Large technology companies will continue providing chips, cloud infrastructure, simulation platforms, and foundational research.
 
At the same time, decentralized AI networks will emerge as alternative coordination layers where intelligence, data, and economic value can be shared more openly.
 
The companies building robots may use NVIDIA hardware.
 
Train on Azure.
 
Run foundation models from OpenAI.
 
But they may also participate in decentralized data networks, decentralized compute markets, and decentralized intelligence protocols.
 
The future of robotics could end up looking less like a monopoly and more like an ecosystem.
 
The Bigger Question
 
The real question isn't whether decentralized AI can eliminate Big Tech.
 
It can't.
 
At least not anytime soon.
 
The real question is whether decentralized AI can prevent a future where a handful of companies control every robot, every model, every dataset, and every decision made by the machines operating around us.
 
As robots become workers, assistants, delivery drivers, factory operators, and even economic agents, that question becomes increasingly important.
 
Because the battle for the future of robotics is no longer about hardware.
 
It's about who owns the intelligence.
 
And that battle is just getting started.
 
 

🙏To support my work, Helping to keep the signal high and the noise low:

👉 Cashapp: $thedinarian

👉 Buy me a coffee: https://buymeacoffee.com/thedinarian

👉 PayPal: Scan the QR code below 📲 or Click Here

👇 Crypto Donations 👇

XRP: r9pid4yrQgs6XSFWhMZ8NkxW3gkydWNyQX
XLM: GDMJF2OCHN3NNNX4T4F6POPBTXK23GTNSNQWUMIVKESTHMQM7XDYAIZT
XDC: xdcc2C02203C4f91375889d7AfADB09E207Edf809A6

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