Retail's cat knight vs. the Wall Street whales. We don't kneel. We don't fold.
Joined January 2026
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猫咪骑士 Cat Knight retweeted
🚨 BREAKING: Hackers are hitting banks in 7 countries at the same time.
This is what Covid looked like in week 1. A few countries. Small numbers. Nobody called it a crisis.
1/ South Korea: hackers used a Chinese AI tool to break into at least 7 financial firms. 68,000 people's data stolen. 16 firms breached by mid-October. This week Daishin Securities reported 38,490 customers' data leaked, bank account numbers included.
2/ Latin America: one gang ran AI agents built on Claude, Grok and Kimi against 400+ targets, banks and payment firms included. They stole passwords and got into customer bank accounts.
3/ Italy: a fake WhatsApp from "the CEO" and an AI-cloned voice got Intesa Sanpaolo's private bank to send out €95 million. €36 million is still MISSING, turned into crypto.
4/ India: small lenders lost ₹300 crore (about $31 million) in 2 months. 150 cases. Hackers copied bank logins and sent the money out as fake loans. One firm was hit twice.
5/ Crypto: $351 million drained from Bitget's hot wallets. North Korean hackers are suspected.
6/ Japan: hackers hit GMO Internet's currency trading service and demanded $50 million in crypto. The prime minister promised an emergency anti-cyberattack package.
7/ Australia: the regulator warns AI attacks put more than A$4.5 trillion in retirement savings in the firing line. Government cyber alerts jumped 80% in a year.
Now zoom out.
AI hacking tools are now easy to get. Smaller firms have weak security. The money moves in minutes. It jumps across banks, through rented accounts, out as cash or crypto. The bosses are often abroad.
Covid locked the streets. Nobody is calling this a crisis yet. But the central banks already plan for the day the hackers get in.
The G7 agreed to run drills that simulate AI attacks on banks. The ECB reportedly gave eurozone banks 4 months to hand in their defence plans.
If your savings sit in a bank, hit follow. I'm tracking where the hackers strike next and how much they take.
North Koreans are too good at this.
They should just open an exchange. It'd be the safest one on earth.
Went green for a second.
Then the market wrecked me. Joy was way too short.
🚨 THIS IS BEYOND ANYTHING CRYPTO HAS SEEN BEFORE.
Binance has been hacked, with estimated stolen assets reaching as high as $6.3 BILLION, making this the largest crypto hack in history.
And now the entire crypto market is crashing.
Bitcoin has crashed below $77,000, dropping more than 6% as panic spreads across the market.
Here's how massive the damage is:
• $6.3 BILLION: Latest reported estimate of assets stolen from Binance across multiple blockchains.
• $170 BILLION: Wiped out from the total crypto market capitalization following the news.
• $1.39 BILLION: Worth of leveraged crypto positions liquidated in just 12 hours.
For perspective, look at the biggest crypto hacks ever recorded:
• Binance (2026): Up to $6.3 BILLION.
• Bybit (2025): $1.5 BILLION.
• Ronin Network (2022): $625 MILLION.
• Poly Network (2021): $611 MILLION.
• BNB Bridge (2022): $570 MILLION.
• Coincheck (2018): $534 MILLION.
• Mt. Gox (2014): Approximately $450 MILLION at the time.
At the highest estimate, Binance's hack is more than FOUR TIMES bigger than Bybit and TEN TIMES bigger than Ronin.
It's also bigger than all SIX of the other major hacks listed above COMBINED.
And this happened at one of the world's largest crypto exchanges.
1. BILLIONS DRAINED FROM MULTIPLE BINANCE WALLETS
On October 29, hackers gained control of several Binance hot wallets and transferred billions of dollars.
• Around $3.3–$3.5 BILLION was drained from Ethereum wallets alone.
• Binance 14 lost approximately $2.5 BILLION in a single transaction.
• That included 167,464 ETH, 1.615 BILLION USDT and dozens of other tokens.
• Binance 16 lost another 28,006 ETH alongside other assets.
• Hot Wallet 34 was also compromised, with approximately $823 MILLION in USDC affected.
• Additional funds were moved from wallets on Arbitrum, Avalanche, Optimism, Bitcoin, Tron and other networks.
The hackers didn't stop after the first transfers.
They continued draining compromised wallets when new deposits arrived.
2. THE HACKERS HAD PREPARED THE ATTACK
The attack began with preparations on October 28–29.
• The hackers funded their operational wallet through Tornado Cash.
• They deployed a malicious contract designed to drain assets.
• They tested the attack on separate wallets before targeting Binance.
• Around 20:20 UTC on October 29, the hackers began transferring billions from Binance wallets.
After the theft, they split the stolen funds across different addresses, swapped stablecoins into ETH and moved assets between blockchains.
3. BINANCE'S WALLET SECURITY WAS COMPROMISED
The hackers gained access to the private keys or signing authority of multiple Binance hot wallets.
They used Ethereum's EIP-7702 feature to give a malicious contract control over Binance 14.
The hackers submitted 50 signed authorizations covering consecutive wallet nonces.
Once the delegation succeeded, they could repeatedly drain assets from that wallet.
Other Binance wallets were drained through normal transactions signed using compromised keys.
This wasn't some network hack.
The hackers gained access to the systems controlling Binance's own wallets.
The exact method used to obtain that access has not been confirmed.
4. AROUND $3 BILLION IN STOLEN ASSETS FROZEN
• Tether froze more than $1.56 BILLION in USDT linked to the hack.
• Circle froze approximately $823 MILLION in USDC.
• Ripple reportedly froze another $200 MILLION.
• Arbitrum's Security Council secured approximately $30.5 MILLION.
• Other projects also froze assets connected to the hackers.
Total frozen funds were estimated at roughly $2.5–$3 BILLION.
However, a large amount of ETH, BTC and other assets remained outside those freezes.
Binance temporarily suspended deposits and withdrawals following the hack.
The exchange confirmed it was investigating the security breach and said all customer funds were safe.
The estimated $6.3 billion theft is equivalent to nearly 4.4% of Binance's $144 billion in assets under custody.
Even the lower estimate of $3.3 billion would make this more than TWICE the size of the Bybit hack.
The final losses are still being calculated because billions of dollars have been frozen.
But the consequences are already extending far beyond Binance.
This is one of the most devastating security failures in crypto history.
Why are North Korean hackers this good?
Are they gods or something?
猫咪骑士 Cat Knight retweeted
Dear Chinese people,
Mbappe is right, Mao Zedong is a dictator and China is a totalitarian state.
My name is Diogo Dalot and I play for Manchester United.
Maybe ordinary people in China are selling to show how unhappy they are with the policy.
This is so bad.
Who's going to stop these power-hungry players?We just want a peaceful world.
NEW: Russia is actively supporting Houthi operations to disrupt global energy markets by providing the Houthis with satellite imagery of Saudi oil and military infrastructure. Russian satellites have routinely been positioned above major Saudi energy and military sites in the days prior to and after Houthi attacks on those sites since July 2026. (1/2)
Yeah. Desk jobs are running out of meaning.
I hit the gym every day now. Future's unclear, but staying strong still pays.
⚡️ AI is beginning to separate legal authority from the human expertise that historically justified it.
For centuries, organizations needed lawyers because understanding the rules was expensive. Interpreting contracts, regulations and liability required scarce human judgment, giving the legal profession enormous economic leverage.
Now that expertise is moving directly into corporate workflows.
Employees can consult systems that understand their company’s contracts, policies, regulatory obligations and risk thresholds without routing every question through expensive professionals.
Law begins shifting from a department you consult into an environment you operate inside.
And that creates a remarkable inversion.
A legal department can shrink dramatically in headcount while gaining unprecedented influence over corporate behavior.
A small group establishes the rules. Machines apply those rules across thousands of decisions. Human experts intervene when something exceeds the organization’s permitted boundaries.
One legal professional can govern more activity than entire departments previously supervised.
But the same architecture concentrates risk.
Historically, legal mistakes were distributed across different people and transactions. A shared AI system can apply the same mistaken interpretation to thousands of contracts before anyone notices.
The cost of legal reasoning collapses while the potential consequences of a single error multiply.
That creates a new scarcity: accountable judgment.
Someone still has to determine which risks the institution accepts, who can authorize exceptions and who bears responsibility when machine-generated decisions fail.
And this extends far beyond law.
Once sophisticated reasoning becomes embedded directly into business operations, organizations begin questioning why so much of their structure was built around waiting for experts.
Accounting, consulting, finance, compliance and management all face versions of the same transformation.
Institutions can become more tightly governed while becoming less dependent on the professionals who historically performed that governance.
Authority concentrates while routine human intervention declines.
And that brings us to the larger question.
Who gets to write the rules that millions of machine-mediated decisions will obey?
Whoever controls that layer gains leverage over something far more consequential than professional services.
They gain influence over the operating logic of institutions themselves.
猫咪骑士 Cat Knight retweeted
⚡️ AI is beginning to separate legal authority from the human expertise that historically justified it.
For centuries, organizations needed lawyers because understanding the rules was expensive. Interpreting contracts, regulations and liability required scarce human judgment, giving the legal profession enormous economic leverage.
Now that expertise is moving directly into corporate workflows.
Employees can consult systems that understand their company’s contracts, policies, regulatory obligations and risk thresholds without routing every question through expensive professionals.
Law begins shifting from a department you consult into an environment you operate inside.
And that creates a remarkable inversion.
A legal department can shrink dramatically in headcount while gaining unprecedented influence over corporate behavior.
A small group establishes the rules. Machines apply those rules across thousands of decisions. Human experts intervene when something exceeds the organization’s permitted boundaries.
One legal professional can govern more activity than entire departments previously supervised.
But the same architecture concentrates risk.
Historically, legal mistakes were distributed across different people and transactions. A shared AI system can apply the same mistaken interpretation to thousands of contracts before anyone notices.
The cost of legal reasoning collapses while the potential consequences of a single error multiply.
That creates a new scarcity: accountable judgment.
Someone still has to determine which risks the institution accepts, who can authorize exceptions and who bears responsibility when machine-generated decisions fail.
And this extends far beyond law.
Once sophisticated reasoning becomes embedded directly into business operations, organizations begin questioning why so much of their structure was built around waiting for experts.
Accounting, consulting, finance, compliance and management all face versions of the same transformation.
Institutions can become more tightly governed while becoming less dependent on the professionals who historically performed that governance.
Authority concentrates while routine human intervention declines.
And that brings us to the larger question.
Who gets to write the rules that millions of machine-mediated decisions will obey?
Whoever controls that layer gains leverage over something far more consequential than professional services.
They gain influence over the operating logic of institutions themselves.
