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.
Ā
Ā
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.
Ā
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.
Ā
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
Ā
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 $600 million hack of the Ronin Ethereum sidechain in 2022. Hyperliquid's situation highlights ongoing concerns about security in the decentralized finance sector.
š¤ AI Won't Destroy Jobs—It Will Create a Labor Shortage! š
While most headlines focus on AI-driven displacement, Groq Founder and CEO Jonathan Ross offers a fascinating, contrarian perspective. He argues that instead of mass unemployment, we are heading toward a massive labor shortage driven by three tectonic shifts:
1. Massive Deflationary Pressure: Efficiency gains from automated farming, robotics, and streamlined supply chains will drive down the cost of everyday essentialsāfrom coffee to housingāmeaning people will ultimately need less money to thrive. āļøš
2. The Great Economic Opt-Out: As living costs drop and productivity skyrockets, humans will choose to work fewer hours, fewer days a week, and retire much earlier because their lifestyles will be easier to support. ā³š“
3. Unimaginable New Industries: Just as agriculture dropped from 98% of the US workforce a century ago to just 2%āpaving the way for entirely new careers like software development and content creationātomorrow's jobs are literally ...
šµ The most important shape nobody talks about šµ
Heinz Hopf discovered this in 1931. Roger Penrose called it "an element of the architecture of our world.' Eric Weinstein brought it up on Joe Rogan - and the silence in the room said everything.
The Hopf fibration maps a 4D hypersphere onto a regular sphere using circles that never intersect but each links through every other exactly once. It shows up in at least 8 areas of physics - including the Bloch sphere geometry that every qubit in a quantum computer lives on.
The bridge between traditional capital markets and the decentralized AI economy is expanding. Through investment vehicles like the Grayscale Bittensor Trust ($GTAO), traditional market participants gain regulated exposure to the native asset powering decentralized machine learning.
No wallet setups, no complex custody hurdlesājust direct tracking of the infrastructure driving open-source intelligence.š
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
šØ BREAKING: XRP JUST GOT PLUGGED DIRECTLY INTO THE U.S. FEDERAL RESERVE’S FEDNOW SYSTEM š³
Volanteās Ripple integration just unlocked $XRP for INSTANT FedNow payments.
Banks can now settle through XRP on the same rails the Fed uses for 24/7 real-time transfers.
This is the quiet infrastructure move nobody saw comingā¦ until now.
Irrespective of which tokens are utilized on the XRP Ledger for FedNow transactions, the underlying mechanism that burns XRP remains constant. š„ This sustained reduction in supply underpins a long-term bullish thesis for XRP holders. ššš
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.
Ā
Now that AI is moving into the physical world, many are asking a bigger question:
Ā
Will these same companies end up controlling robotics too?
Ā
It's a valid concern.
Ā
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.
Ā
That movement is decentralized AI.
Ā
Why Decentralized AI Exists
Ā
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.
Ā
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.
Ā
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.
Ā
Why This Matters for Robotics
Ā
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.
Ā
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.
Ā
This is where decentralized systems become interesting.
Ā
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.
Ā
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.
Ā
That vision is beginning to emerge.
Ā
Bittensor's Move Toward Physical AI
Ā
While many people associate Bittensor (@bittensor) with language models and AI services, parts of the ecosystem are increasingly exploring embodied intelligence and robotics.
Ā
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.
Ā
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.
Ā
In other words, the pieces are starting to appear.
Ā
Not a decentralized robot network yet.
Ā
But the infrastructure that could support one.
Ā
Beyond Bittensor: The Rise of Physical AI Networks
Ā
Bittensor isn't alone.
Ā
Across the industry, researchers and builders are experimenting with decentralized approaches to physical AI.
Ā
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.
Ā
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.
Ā
The goal is not simply decentralization for its own sake.
Ā
The goal is resilience.
Ā
If one server fails, the system continues.
Ā
If one company disappears, the network survives.
Ā
If one participant leaves, innovation continues.
Ā
But Here's the Reality
Ā
Decentralized AI faces the same challenge every decentralized technology faces.
Ā
Big Tech has resources. A lot of resources.
Ā
Training advanced robotics models requires enormous compute budgets, sophisticated simulation environments, access to specialized hardware, and vast amounts of real-world data.
Ā
That's why many robotics startups still partner with major cloud providers and AI companies. It's often the fastest path to deployment.
Ā
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.
Ā
The challenge isn't just decentralizing intelligence.
Ā
It's decentralizing intelligence while maintaining performance.
Ā
That's much harder.
Ā
The Most Likely Outcome
Ā
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.
Ā
Large technology companies will continue providing chips, cloud infrastructure, simulation platforms, and foundational research.
Ā
At the same time, decentralized AI networks will emerge as alternative coordination layers where intelligence, data, and economic value can be shared more openly.
Ā
The companies building robots may use NVIDIA hardware.
Ā
Train on Azure.
Ā
Run foundation models from OpenAI.
Ā
But they may also participate in decentralized data networks, decentralized compute markets, and decentralized intelligence protocols.
Ā
The future of robotics could end up looking less like a monopoly and more like an ecosystem.
Ā
The Bigger Question
Ā
The real question isn't whether decentralized AI can eliminate Big Tech.
Ā
It can't.
Ā
At least not anytime soon.
Ā
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.
Ā
As robots become workers, assistants, delivery drivers, factory operators, and even economic agents, that question becomes increasingly important.
Ā
Because the battle for the future of robotics is no longer about hardware.
Navigating the world of blockchain can feel like learning a completely foreign language. Between technical jargon and fast-moving Web3 terminology, getting started can be overwhelming.
Whether you are exploring digital assets, building on-chain, or simply trying to understand decentralized technology, here is your foundational glossary of essential blockchain terms every beginner should know.
šļø 1. Core Architecture: The Base Layer
Blockchain: A distributed, immutable digital ledger that records transactions across a peer-to-peer network of computers. Once data is written to a block and added to the chain, it cannot be altered without altering all subsequent blocks.
Block: A collection of verified transactions grouped together. Once filled, the block is cryptographically linked to the previous one, forming a chronological "chain."
Node: An individual computer connected to a blockchain network that helps validate transactions, store ledger data, and maintain network consensus.
Consensus Mechanism: The set of rules and algorithms that network nodes use to agree on the validity of transactions.
Proof of Work (PoW): Requires miners to solve complex mathematical puzzles using computational power (e.g., Bitcoin).
Proof of Stake (PoS): Requires validators to lock up ("stake") native tokens as collateral to participate in block validation (e.g., Ethereum).
š 2. Ownership & Security: Wallets and Keys
Public Key (Address): An alphanumeric string that acts like your bank account number or email address. It is safe to share publicly so others can send you digital assets.
Private Key: A secret cryptographic passphrase or key that grants full access and control over your wallet assets. Never share your private key or seed phrase with anyone.
Seed Phrase (Recovery Phrase): A sequence of 12 to 24 random words generated when you set up a wallet. It acts as the master backup key to restore your wallet and access your funds on any device.
Hot Wallet vs. Cold Wallet:
Hot Wallet: A software-based crypto wallet connected to the internet (e.g., browser extensions, mobile apps), making it convenient for frequent transactions but higher risk.
Cold Wallet: An offline hardware device (e.g., Ledger, Coldcard) designed to isolate private keys from internet-connected threats.
āļø 3. Execution & Functionality: Smart Contracts and Apps
Smart Contract: Self-executing code stored on a blockchain that automatically enforces agreement terms once predetermined conditions are metāeliminating the need for intermediaries.
dApp (Decentralized Application): Applications built on top of a blockchain network that run via smart contracts rather than centralized cloud servers.
Gas Fees: Network transaction fees paid to validators or miners to cover the computational energy required to process actions on a blockchain.
Layer 1 vs. Layer 2:
Layer 1 (L1): The underlying primary blockchain network (e.g., Bitcoin, Ethereum, Solana) that handles base security and finality.
Layer 2 (L2): Secondary frameworks or companion networks built on top of an L1 to increase transaction speeds and lower gas fees (e.g., Arbitrum, Optimism, Base).
š° 4. Financial & Market Concepts
Tokenomics: The economic design, supply dynamics, utility, and distribution model of a cryptocurrency or token project.
DeFi (Decentralized Finance): Financial servicesāsuch as lending, borrowing, trading, and earning interestābuilt on smart contracts without traditional banks or financial intermediaries.
Liquidity: The ease with which an asset can be bought or sold in a market without significantly impacting its price.
DYOR (Do Your Own Research): A foundational golden rule in the Web3 space reminding users to independently verify technical code, whitepapers, and team backgrounds before making any capital commitments.
š” Quick Cheat Sheet
"Not your keys, not your coins."
If you do not hold the private keys or seed phrase to your digital wallet, you do not truly own the assets inside itāa centralized entity or exchange does. Always prioritize security first as you explore the space.
šTo support my work, Helping to keep the signal high and the noise low:
Artificial intelligence may or may not take your job, but it has already broken into the human resources department and vandalized the org chart.
The evidence is all overĀ LinkedIn, where perfectly serviceable occupations now arrive wearing titles such as āforward-deployed and agentic AI architect.ā That person may be building sophisticated software. They may also be helping a chatbot remember what happened three prompts ago. Either way, somebody approved the business cards.
The expanding AI lexicon offers a useful counterpoint to the darker debate about technology and employment. Most discussion centers on how many jobs AI will eliminate. Hiring data presents a more complicated picture that includes a weak overall labor market containing a small but rapidly growing neighborhood of AI-related work.
Indeed Hiring LabĀ found that the number of postings onĀ IndeedĀ mentioning AI surged 134% from its February 2020 level by the end of 2025, even as total postings stood only 6% above that benchmark. AI appeared in a record 4.2% of Indeed postings in December.
AI, in other words, is not merely changing work. It is adding syllables to it.
The Titles Employers Actually Want
The undisputed champion isĀ AI engineer, which ranked No. 1 onĀ LinkedInās 2026 Jobs on the Rise list. The ranking, based on growth during the previous three years, also highlighted AI consultants and strategists, AI and machine-learning researchers and data annotators.
The title is popular partly because it is wonderfully accommodating. An AI engineer might build applications around large language models, connect corporate data to an AI system, improve model performance or spend Thursday afternoon persuading a customer service bot not to offer refunds for products the company doesnāt sell.
Indeedās data showed the terminology spreading beyond Silicon Valley. Nearly 45% of data and analytics postings contained an AI-related term at the end of 2025, along with roughly 15% of marketing postings and 9% of human resources listings. A more recent Indeed analysisĀ reportedĀ by Business Insider found that the number of frequently advertised job titles explicitly referencing AI rose from 264 in 2022 to 822 in the first quarter of 2026. Nearly two-thirds were outside traditional technology fields.
LinkedIn data cited by theĀ World Economic ForumĀ estimated that AI investment has supported 1.3 million positions, including AI engineers, data annotators and forward-deployed engineers, plus more than 600,000 AI-enabled data center jobs. The server racks, unlike the chatbots, still need electricians.
The Jobs With the Science-Fiction Salaries
At the upper end, AI has created a compensation market that resembles professional sports, except the competitors wear hoodies and discuss inference latency.
AĀ Syracuse University reviewĀ put chief AI officer compensation between $200,000 and more than $500,000, while specialized roles can exceed $400,000 after bonuses and equity. Frontier research engineers, AI infrastructure specialists and engineers who can train or deploy advanced models command some of the largest packages.
Then there is theĀ forward-deployed engineer, an oldĀ PalantirĀ title that the AI boom has placed on a rocket sled. These engineers embed with customers, translating an executiveās desire to ādo something with AIā into software that works.Ā The Next WebĀ reported that Indeed postings for the role were about 19 times higher in January than a year earlier.
AĀ CTO guideĀ from the blog Signal Through the Noise placed forward-deployed engineer compensation between $238,000 and $700,000, research-engineering packages as high as $1.4 million and chief AI officer compensation above $1 million in some cases. It also made a less flattering observation: Many lavishly differentiated titles describe the same three basic functions. People build AI products, train models or keep the infrastructure from catching fire.
The Department of Unnecessary Titles
AI has created some genuinely new work.Ā Evals engineersĀ design tests to determine whether models perform reliably.Ā AI red teamersĀ try to make systems fail before customers do.Ā Model behavior engineersĀ study why an AI system responds as it does.Ā AI governance leadersĀ manage risks involving data, bias, security and regulation.
Other titles seem to have escaped from a brainstorming retreat.
There is theĀ Claude Evangelist, whose mission apparently combines product education with the traditional duties of an apostle. There areĀ vibe coders, who build software by describing what they want and accepting AI-generated code with varying degrees of supervision. āVibe engineerā is the more respectable version, roughly equivalent to putting on a blazer before asking the machine to fix the login page.
āContext engineerā is a real discipline involving the data, instructions, memory and tools supplied to AI models. āPrompt engineer,ā once advertised as a possible six-figure profession for gifted chatbot whisperers, is increasingly treated as one skill inside a broader AI role.
The CTO guide also identified ābuilder,ā āAI-native developer,ā āRAG engineer,ā āagentic AI engineerā and āprincipal agentic GenAI forward-deployed context architect,ā the last of which appears to require both technical proficiency and exceptional lung capacity.
Has AI created entirely new jobs? Absolutely. Some occupations, including AI safety, evaluation and model governance, exist because modern generative systems introduced new technical and business problems. However, many job titles are old jobs with fresh vocabulary, higher salary bands and a sudden aversion to the words āsoftware developer.ā
That may be the safest prediction about AI and employment. The machines will automate some tasks, generate others and force companies to rethink the division of labor. Before any of that is settled, however, corporate America will form a steering committee, appoint a chief agentic transformation evangelist and schedule a meeting to determine what that person does.
Sign Up for free to see more from this community or subscribe to TheDinarian for $5/month to support TheDinarian for more interaction and exclusive content.
Welcome to the Dinarian on Locals, where we discuss everything blockchain and digital asset related. We are here to learn from one another as this is a new and ever evolving space. Please post and share what you like, but be respectful to others as they are here to learn as well.
Knowledge is power, using that knowledge can be extremely powerful,
The Dinarian
