👀 Coinbase Reports Issues With U.S. Users Depositing and Withdrawing From Banks 👀

5 hours ago

🚨 Major alpha dropped during yesterday’s @_redteam_ AMA 🚨

#Redteam just revealed they are currently working on 3 contracts with heavyweights in the top 10% of Fortune 500 banking companies. 🏦💼

Why this is massive news for the #Bittensor ecosystem:

💎 Top-Tier Institutional Interest: We aren't talking about mid-market tech startups; these are elite financial powerhouses looking at Bittensor's subnets.

🛡️ Real-World Utility: Redteam’s enterprise-grade security capabilities are proving out decentralized AI’s value in high-stakes, highly regulated financial environments.

📈 Enterprise Acceleration: Moves like this bridge the gap between speculative tech and full-scale institutional adoption.

The enterprise momentum is building fast. Redteam is executing on a whole different level. 👁️⚡🔥

#SN61

Listen 👇

00:03:39

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Dinarian888@dinarian888

8 hours ago

David Schwartz said banks have started using $XRP!

🚨 BIG RIPPLE NEWS: Banks are officially using $XRP! 🏦⚡

Ripple CTO David Schwartz dropped a massive update on the future of cross-border finance.

🔹 Full XRPL Integration: Banks adopting Ripple’s technology aren't just testing the waters—they are set to handle all payment flows directly through $XRP and the XRP Ledger.

🔹 Unlocking Trillions: By swapping out slow, legacy pre-funded accounts (Nostro/Vostro) for instant XRPL settlement, this shift could liberate trillions of dollars in trapped global capital.

🔹 Real-World Utility: We are moving past pure speculation into full-scale enterprise adoption on public ledger rails.

The institutional financial architecture is shifting in real time. 🚀🌐

00:00:36

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Dinarian888@dinarian888

21 hours ago

🤖 Generative AI is evolving into agentic AI—and blockchain could rebuild the financial system 🤖

Sandy Kaul discusses how the transition from generative AI to autonomous agents could create a new financial architecture, replacing legacy systems built around infrastructure from the 1970s.

🔑 Key points

🔹 Generative AI creates content: Today’s models primarily respond to prompts by generating text, images, code, and analysis.

🔹 Agentic AI takes action: AI agents can plan, make decisions, use tools, execute workflows, and operate with greater autonomy.

🔹 Finance needs an infrastructure rebuild: Legacy banking, settlement, identity, custody, and payment systems were not designed for autonomous digital agents.

🔹 Blockchain could provide the coordination layer: Smart contracts, digital identity, tokenized assets, and programmable payments could allow agents to transact within defined rules.

🔹 Agents may become economic actors: AI systems could eventually manage payments, negotiate ...

00:02:44

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Dinarian888@dinarian888

May 31, 2026

🚨 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 🚨

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.

🔑 Key points

🔹 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.

🔹 The platform says it has processed nearly 38 trillion tokens since launch across 53 deployed applications and more than 700,000 registered users.

🔹 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.

🔹 Chutes is using post-quantum cryptography, trusted execution environments, and Nvidia confidential ...

TAODAILY.IO

Chutes Is Doing to AI Inference What Hyperliquid Did to Finance - The TAO Daily

Chutes deliberately cut their GPU fleet from 1,700 H200s down to 700. They killed the free tier on OpenRouter that was driving 20 billion

🚨 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 🚨

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Dinarian888@dinarian888

May 31, 2026

🚨 JPMorgan’s criticism of the CLARITY Act is fueling a fresh power struggle over who gets to write America’s crypto rules 🚨

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.

🔑 Key points

🔹 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.

🔹 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.

🔹 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...

TECHGAGED.COM

JPMorgan’s CLARITY Act Concerns Spark a New Crypto Showdown

The battle over who writes the rules of digital finance just became considerably louder. JPMorgan CEO Jamie Dimon has declared the CLARITY Act a direct threat t

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Dinarian888@dinarian888

October 31, 2024

👉 Coinbase just launched an AI agent for Crypto Trading

Custom AI assistants that print money in your sleep? 🔜

The future of Crypto x AI is about to go crazy.

👉 Here’s what you need to know:

💠 'Based Agent' enables creation of custom AI agents
💠 Users set up personalized agents in < 3 minutes
💠 Equipped w/ crypto wallet and on-chain functions
💠 Capable of completing trades, swaps, and staking
💠 Integrates with Coinbase’s SDK, OpenAI, & Replit

👉 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 👉txns done by AI agents by 2025

🚨 I personally wouldn't bet against Brian Armstrong and Jesse Pollak.

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Dinarian888@dinarian888

8 hours ago

Nasdaq Invests $100M in Kraken Parent Payward $21B Valuation 💰

It shows that maior market operators are starting to treat crypto firms as distribution and infrastructure partners for the next version of securities markets. Nasdag gets a route into on-chain markets. Payward gets a stronger bridge to regulated equities, issuers and institutional liquidity

The important detail is the reported plan for tokenized Nasdag shares to carry the same voting rights as the underlying stock Tokenization has often offered price exposure without ful shareholder participation. That limits its value for investors and makes it harder for issuers to take the model seriously.

In my conversation with Mark Greenberg, Payward's CCO, he argued that tokenized stocks can matter most in markets where opening a brokerage account remains difficult. The ability to buy $5 or $10 of an equity through an app or wallet changes access for users across Asia, Latin America, Africa and Central Asia. Payward is already offering tokenized equities in roughly 80 to 90 countries.

Nasdag's ...

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Dinarian888@dinarian888

11 hours ago

Commercial banks are rapidly upgrading global money rails, moving trillions of dollars from legacy ledger systems to Tokenized Deposits. 🏦⚡

Top Bank Programs

JPMorgan (Kinexys / JPM Coin): The current heavyweight leader, processing ~$7B daily with over $4T in cumulative volume while bridging private chains to public networks like Base. 💰
Citi (Citi Token Services): Focused on 24/7 near-instant USD clearing and global treasury movement across key markets like the US, UK, and APAC. 🌐
DBS Token Services: Driving Asia's 24/7 programmable value transfers and treasury liquidity management on permissioned rails. 🌏
HSBC (Tokenised Deposit Service): Delivering the broadest multi-currency coverage (USD, EUR, GBP, SGD, HKD, CNY) for cross-border corporate treasury. 💱

Major Industry Consortia

The Clearing House (US): Building a shared tokenized deposit network across major US banks, targeting an H1 2027 launch to protect $6.6T in bank deposits. 🇺🇸
UK Finance (GBTD): ...

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Dinarian888@dinarian888

11 hours ago

💸 Verathos undercuts OpenRouter’s GLM-5.2 rates by 27% 💸

Verathos is reportedly offering GLM-5.2 inference at approximately 27% below OpenRouter’s listed rates, adding another price-based advantage to its verified AI-inference network.

🔑 Key points

🔹 27% lower pricing: Verathos’ quoted GLM-5.2 rates reportedly undercut OpenRouter’s comparable pricing.

🔹 Lower inference costs: Developers may be able to reduce spending on model calls, especially for high-volume applications and AI agents.

🔹 Verified inference is included: Verathos aims to provide evidence that the requested model and workload were executed as claimed.

🔹 Decentralized providers supply capacity: The network can route inference across participating compute operators.

🔹 Price is only one comparison: Developers must also evaluate latency, uptime, output quality, rate limits, privacy, and support.

🔹 Model pricing can change: Provider costs, demand, GPU availability, token economics, and marketplace competition can ...

TAODAILY.IO

Verathos Beats OpenRouter's GLM-5.2 Rates by 27% - The TAO Daily

Verathos (SN96) undercuts OpenRouter’s GLM-5.2 rates by 27%, offering $0.25 per million input tokens and $1 output through its decentralized AI compute network.

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Dinarian888@dinarian888

August 28, 2026

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 notes, Massachusetts 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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Dinarian888@dinarian888

August 16, 2026

🤖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.

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Dinarian888@dinarian888

August 09, 2026

Navigating the world of blockchain 🧭

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.

🏛️ 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).

🔑 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.

⚙️ 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 met—eliminating 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).

💰 4. Financial & Market Concepts

Tokenomics: The economic design, supply dynamics, utility, and distribution model of a cryptocurrency or token project.
DeFi (Decentralized Finance): Financial services—such as lending, borrowing, trading, and earning interest—built 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.

💡 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 it—a centralized entity or exchange does. Always prioritize security first as you explore the space.

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Users reveal other challenges

Rank State Income needed for family of four (2026)

Colorado and Vermont Make the Top 10

Just Six States Come in Below $200,000

🏛️ 1. Core Architecture: The Base Layer

🔑 2. Ownership & Security: Wallets and Keys

⚙️ 3. Execution & Functionality: Smart Contracts and Apps

💰 4. Financial & Market Concepts

💡 Quick Cheat Sheet