š² Hyperliquid Faces Record Outflows Amid North Korea Hack Allegations š²
Hyperliquid experienced significant net outflows, totaling approximately $250 million, following allegations regarding North Korean hackers being active on its platform.
Hyperliquid experienced significant net outflows, totaling approximately $250 million, following allegations regarding North Korean hackers being active on its platform.
On Monday, Taylor Monahan, a security researcher from MetaMask, shared on social media platform X that she identified several blockchain addresses operating on Hyperliquid linked to North Korea's cyber activities.
DataĀ from Dune Analytics indicated that the platform saw USDC net outflows of $249.1 million on Monday,with an additional $22.2 million recorded on Tuesday.
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Monahan's postĀ included details of blockchain addresses that had been active since Oct. 2, raising concerns about potential threats to the platform's security.
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Monahan reiterated her offer to assist Hyperliquid in bolstering its defenses against these sophisticated threat actors, emphasizing the risks posed by North Korean groups known for their advanced hacking capabilities.
āI am quite concerned that you guys are at increased risk due to the fact we know that these specific threat actors are now intimately familiar with your platform,ā sheĀ statedĀ in a screenshot of a message she says she had written to the Hyperliquid team two weeks prior.
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In responseĀ to the claims, Hyperliquid assured users that all funds were accounted for and stated that no exploit or vulnerability had been detected. The platform emphasized, āThere has been no DPRK exploitāor any exploit for that matterāof Hyperliquid.ā
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The platform's native token, Hype, also experienced volatility, dropping from a peak of $34.5 over the weekend to around $26 on Monday before recovering slightly to $29.63 by the time of reporting.
North Korea's state-sponsored hacking groups have been implicated in some of the largest cryptocurrency thefts, including the $600 million hack of the Ronin Ethereum sidechain in 2022. Hyperliquid's situation highlights ongoing concerns about security in the decentralized finance sector.
RFK Jr: "The Pandemics are coming from labs. ALL OF THEM... Lyme, COVID, RSV, HIV & Spanish Flu came out of a vaccine lab." ā ļø š
"Gain-of-Function Vaccine research has created the worst plagues in our history."
"We can go down the whole list of diseases... Itās just a disaster. Itās given us no benefits. Itās given us everything from Lyme disease to Covid, and many many other diseases. RSV, which is now one of the biggest killers of children, came out of a vaccine lab."
"Thereās strong evidence that even Spanish flu came from vaccine research."
"Thereās plenty of evidence that HIV also came from a vaccine gain-of-function lab program. "
"The 'PANDEMICS' are coming from labs... ALL OF THEM."
šThe industry has gotten incredible at teaching robots
šThe industry has gotten incredible at teaching robots to move, sprint, and imitate body dynamics. But as Michael Parker (@bittensormax) points out in The UMI Thesis, thereās still a massive missing piece in Physical AI: Motion Understanding.
āØ Key Takeaways:
š¹Looking Human vs. Understanding Humans: Robots can execute impressive physical feats, but they still struggle to reliably read non-verbal human cues in context.
š¹Motion is Meaning: A gesture, hesitation, or glance changes completely depending on posture, timing, and surrounding context.
š¹Beyond Pixels: True intelligence requires mapping human intent and sequence across timeānot just processing raw frames.
š¹The UMI Intelligence Layer: As robots enter hospitals, factories, homes, and stores, Bittensorās SN78 @umi_sn78 UMI (Universal Motion Intelligence) aims to own the critical layer that translates human movement into real meaning.
The future of robotics isn't just about how machines moveāit's about how ...
šØ Chutes is being framed as a Hyperliquid-style breakout for decentralized AI inference, with live revenue, verified GPU infrastructure, and a direct challenge to centralized cloud AI šØ
Chutes is gaining attention as a decentralized AI inference platform that claims to combine real usage, cryptographic verification, confidential computing, and open-source infrastructure into a working production system. The thesis is simple: instead of trusting Big Tech clouds with AI workloads, users get a distributed compute layer built around verification and privacy.
š Key points
š¹ Chutes is live in production and reportedly scaled to more than 1,170 active GPU nodes, including large numbers of Nvidia H200s and Blackwell-class hardware.
š¹ The platform says it has processed nearly 38 trillion tokens since launch across 53 deployed applications and more than 700,000 registered users.
š¹ The team reportedly cut unprofitable usage programs, reduced total token volume, and still improved revenue efficiency, with revenue per GPU rising sharply after removing subsidized traffic.
š¹ Chutes is using post-quantum cryptography, trusted execution environments, and Nvidia confidential ...
šØ Chutes is being framed as a Hyperliquid-style breakout for decentralized AI inference, with live revenue, verified GPU infrastructure, and a direct challenge to centralized cloud AI šØ
šØ Chutes is being framed as a Hyperliquid-style breakout for decentralized AI inference, with live revenue, verified GPU infrastructure, and a direct challenge to centralized cloud AI šØ
šØ JPMorgan’s criticism of the CLARITY Act is fueling a fresh power struggle over who gets to write America’s crypto rules šØ
A new clash is emerging between legacy finance and crypto legislation after JPMorgan CEO Jamie Dimon reportedly warned that the CLARITY Act could let crypto firms offer bank-like products without bank-level oversight. The dispute is quickly turning into a larger fight over regulation, competitiveness, and who controls the future architecture of digital finance in the United States.
š Key points
š¹ Jamie Dimon reportedly called the CLARITY Act a threat to the financial system, arguing it could allow crypto firms to offer yield-like products while avoiding the capital, reserve, and oversight burdens traditional banks face.
š¹ Senator Cynthia Lummis pushed back publicly, framing the issue as a global strategic race and warning that if the U.S. does not set digital asset standards, other powers will.
š¹ The core tension is whether the bill creates legitimate regulatory clarity or simply opens the door to regulatory arbitrage for crypto platforms operating outside the traditional banking...
š Coinbase just launched an AI agent for Crypto Trading
Custom AI assistants that print money in your sleep? š
The future of Crypto x AI is about to go crazy.
š Hereās what you need to know:
š 'Based Agent' enables creation of custom AI agents
š Users set up personalized agents in < 3 minutes
š Equipped w/ crypto wallet and on-chain functions
š Capable of completing trades, swaps, and staking
š Integrates with Coinbaseās SDK, OpenAI, & Replit
š What this means for the future of Crypto:
1. Open Access: Democratized access to advanced trading
2. Automated Txns: Complex trades + streamlined on-chain activity
3. AI Dominance: Est ~80% of crypto štxns done by AI agents by 2025
šØ I personally wouldn't bet against Brian Armstrong and Jesse Pollak.
š Coinbase just launched an AI agent for Crypto Trading
š Coinbase just launched an AI agent for Crypto Trading
The future of cybersecurity isn't about blocking yesterday's threatsāit's about defeating tomorrow's before they are even built. š”ļøā”
āThe ultimate competitive edge belongs to defenders who can instantly ingest unique zero-days, automatically generate the attack strains that don't exist yet, and ship bulletproof detections to the front lines ahead of the breach. š®š
āAs legacy security platforms degrade under the weight of novel AI-driven threats, the worldās largest financial institutions won't scatter their bets. They will consolidate on the single provider capable of delivering this predictive defense at global scale. š¦š
āThe Engine Behind the Defense:
āTo stay ahead, that provider needs a constant, unrelenting supply of edge-case attacks. A decentralized subnet is the only architecture built to fuel this machineāpaying a global swarm of attackers to continuously probe, create, and supply novel threats around the clock. ššš„
š Navigating Bittensor yield just got effortless! š§ ā”ļø
If youāve been looking to optimize your $TAO positions across subnets without the headache of manual rebalancing or technical friction, @TrustedStake is changing the game! š
The new Quickstart Guide breaks down how to get seamless exposure to top-tier AI strategies:
šÆ One-Click Index Exposure: Access curated subnet baskets (Universe, Top 15, and specialized sectors) in seconds.
š” Non-Custodial Security: Retain complete asset control while leveraging Substrate proxy accountsākeeping your primary stash safe in cold storage.
š Automated Rebalancing & Yield Optimization: Smart TWAP execution, validator selection, and root reinvestment handle the heavy lifting to maximize your alpha.
š Block-Level Transparency: Real-time PnL metrics and full on-chain visibility right from your dashboard.
šCheck out the full walkthrough to get startedš
...
šÆ Leadpoetās new Arena could change how businesses find their next customers šÆ
Leadpoet is launching an Arena where AI agents compete to identify companies most likely to buy, turning sales intelligence into a measurable performance competition.
š Key points
š¹ Agents compete on lead quality: Systems are evaluated on whether the prospects they identify show genuine buying intent.
š¹ Better than list generation: The goal is to find likely customers rather than produce large volumes of low-quality contacts.
š¹ Real-world signals matter: Agents can analyze company activity, business changes, hiring, technology use, market behavior, and other indicators.
š¹ Performance becomes measurable: Agents can be ranked based on precision, relevance, conversion potential, and downstream sales outcomes.
š¹ Competition can improve intelligence: Different agents may identify different signals, creating a wider search across potential customers.
š¹ Sales teams gain prioritization: Businesses ...
Revolut Leak Shows the Cost of Constant ID Collection
Revolutās 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 āunauthorized third partyā 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 agencyās 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 messageās origin and integrity, but do not verify the legitimacy of the legal request itself.
Revolut said that it complied with the request āunder the reasonable belief that it was an authentic government agency requestā ā 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 ālimitedā number of its customers were affected by the data leak, and that the companyās 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.
The reason this is a recurring problem is that companies are keeping highly sensitive information about their customersā identities, and sometimes even financial transactions, for a long time, and this data is then available to be disclosed to third parties ā 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. Revolutās 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 ā for legal reasons.
This means that even if you close your account, your identity documents donāt disappear.
And while the incident with Revolut happened in the financial sector, itās 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 ā 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.
Itās 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 didnāt 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.
This Is The Income A Family Needs To Live Comfortably In Every US State
Hereās the short version of what it takes for a family of four to live comfortably in 2026 by state:
In Massachusetts, youād 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. Thatās a full $142,000 less than what youād need in Massachusetts.
Soā¦ 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 fromĀ SmartAssetĀ (as of February 2026). Theyāre based on the familiar 50/30/20 budget: 50% for necessities, 30% for discretionary spending, and 20% for savings or other goals. These arenāt bare-minimum survival numbersātheyāre what it takes to live pretty well while still putting money aside.
And asĀ Visual CapitalistĀ notes,Ā Massachusetts sits at the very top of that list.Ā 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 theĀ second-smallest populationĀ 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 theĀ wide range in living costsĀ 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.Ā 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.
š¤Can Decentralized AI Stop Big Tech from Owning the Future of Robotics?š¤
The race to build the future of robotics is no longer just about robots. It's about who controls the intelligence behind them.
Ā
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.
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Now that AI is moving into the physical world, many are asking a bigger question:
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Will these same companies end up controlling robotics too?
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It's a valid concern.
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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.
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That movement is decentralized AI.
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Why Decentralized AI Exists
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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.
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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.
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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.
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Why This Matters for Robotics
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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.
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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.
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This is where decentralized systems become interesting.
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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.
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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.
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That vision is beginning to emerge.
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Bittensor's Move Toward Physical AI
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While many people associate Bittensor (@bittensor) with language models and AI services, parts of the ecosystem are increasingly exploring embodied intelligence and robotics.
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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.
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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.
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In other words, the pieces are starting to appear.
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Not a decentralized robot network yet.
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But the infrastructure that could support one.
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Beyond Bittensor: The Rise of Physical AI Networks
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Bittensor isn't alone.
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Across the industry, researchers and builders are experimenting with decentralized approaches to physical AI.
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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.
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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.
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The goal is not simply decentralization for its own sake.
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The goal is resilience.
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If one server fails, the system continues.
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If one company disappears, the network survives.
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If one participant leaves, innovation continues.
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But Here's the Reality
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Decentralized AI faces the same challenge every decentralized technology faces.
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Big Tech has resources. A lot of resources.
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Training advanced robotics models requires enormous compute budgets, sophisticated simulation environments, access to specialized hardware, and vast amounts of real-world data.
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That's why many robotics startups still partner with major cloud providers and AI companies. It's often the fastest path to deployment.
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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.
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The challenge isn't just decentralizing intelligence.
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It's decentralizing intelligence while maintaining performance.
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That's much harder.
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The Most Likely Outcome
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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.
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Large technology companies will continue providing chips, cloud infrastructure, simulation platforms, and foundational research.
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At the same time, decentralized AI networks will emerge as alternative coordination layers where intelligence, data, and economic value can be shared more openly.
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The companies building robots may use NVIDIA hardware.
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Train on Azure.
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Run foundation models from OpenAI.
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But they may also participate in decentralized data networks, decentralized compute markets, and decentralized intelligence protocols.
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The future of robotics could end up looking less like a monopoly and more like an ecosystem.
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The Bigger Question
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The real question isn't whether decentralized AI can eliminate Big Tech.
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It can't.
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At least not anytime soon.
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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.
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As robots become workers, assistants, delivery drivers, factory operators, and even economic agents, that question becomes increasingly important.
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Because the battle for the future of robotics is no longer about hardware.
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