💥 SIX Digital Exchange goes live with digital asset custody 💥

TheDinarian

News • Business • Investing & Finance

October 11, 2022

Three months ago, the SIX Digital Exchange (SDX) said it was planning an institutional digital asset custody offering and signed a deal with technology provider Fireblocks. Today the SDX Web3 division announced that the custody solution is live, enabling institutions to delegate the operational and technical complexities of cryptocurrency custody to SDX.

The initial target clients are regulated, such as banks, product providers and investment managers, and at first it is focused on Switzerland, with an eye on Europe.

SDX Web3 believes the security standards associated with a major stock exchange group like SIX separates the SDX offering from most others in the marketplace.

SDX’s Stephan Kunz previously spoke about the new crypto custody service, saying, “the advantage of SDX is that it is a riskless counterparty – we are not a financially exposed counterparty.”

Initially, the custody solution supports Bitcoin and Ether, with other tokens such as stablecoins and DeFi-related digital assets to be added in the future based on client demand. In August, SDX announced the launch of non-custodial Ethereum staking services.

SDX spans both cryptocurrencies and the tokenization of real world assets. When it launched last year, SIX issued a tokenized bond using the platform.

SIX isn’t the only big name stock exchange to be associated with crypto custody. Last month Nasdaq announced plans to get into the digital asset custody business, but we believe it’s still early in the journey. Other incumbents associated with custody offerings include Standard Chartered’s Zodia Custody and Nomura’s joint venture Komainu.

The top conventional custodians are all getting in on the act, with BNY Mellon partnering with Fireblocks, State Street Digital with Copper, and BNP Paribas Securities Services with Fireblocks and Metaco.

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Dinarian888@dinarian888

August 23, 2026

🤖 AI Won't Destroy Jobs—It Will Create a Labor Shortage! 📉

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 essentials—from coffee to housing—meaning people will ultimately need less money to thrive. ☕️🏠

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. ⏳🌴

3. Unimaginable New Industries: Just as agriculture dropped from 98% of the US workforce a century ago to just 2%—paving the way for entirely new careers like software development and content creation—tomorrow's jobs are literally ...

00:02:00

Dinarian888@dinarian888

August 22, 2026

🔵 The most important shape nobody talks about 🔵

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.

00:09:42

Dinarian888@dinarian888

August 22, 2026

🌐 Institutional Access Meets Dcentralized AI! 🤖📈

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 hurdles—just direct tracking of the infrastructure driving open-source intelligence.👇

00:05:18

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 🚨

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

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.

Dinarian888@dinarian888

15 hours ago

🚀 ZEC reaches an eight-year high as analysts ask whether TAO could be next 🚀

Zcash has surged to approximately $856, reaching its highest level in eight years and reviving interest in older crypto assets with strong narratives, limited supply, and renewed market demand.

🔑 Key points

🔹 ZEC broke higher: The token’s move to approximately $856 represents a major recovery from its multi-year lows.

🔹 Privacy is back in focus: Renewed concerns around surveillance, financial censorship, and transaction privacy have strengthened interest in privacy-focused assets.

🔹 Limited supply supports the narrative: Zcash’s capped supply gives traders a scarcity-based investment thesis similar to Bitcoin.

🔹 Momentum is attracting attention: Large price increases can draw new capital, increase liquidity, and create a self-reinforcing cycle.

🔹 TAO is being compared with ZEC: Bittensor’s token could benefit from a similar rotation if the market begins rewarding decentralized AI infrastructure.

🔹 TAO ...

TAODAILY.IO

ZEC Hits an Eight-Year High at $876. TAO Could Be Next - The TAO Daily

$ZEC just hit an eight-year high of $856 as Grayscale advances its spot ETF plans. Grayscale has already filed to convert its Bittensor Trust into a spot $TAO ETF

Dinarian888@dinarian888

48 minutes ago

🚀NVIDIA dropped one of the most interesting vision language models 🤖

🚀NVIDIA dropped one of the most interesting vision language models, it's called LocateAnything (3B parameters)
Most models generate bounding boxes token by token, basically typing out the coordinates one at a time.

LocateAnything predicts the whole box at once.

⁃ One pass. Four coordinates. Done.
It's a simple idea, but the speed difference is impressive.

On a single H100, NVIDIA reports 12.7 boxes/sec, compared with 5.0 for Rex-Omni and 1.1 for Qwen3-VL in their setup.

And it doesn't give up localization quality to get there. It performs strongly across dense scenes, GUIs, OCR and document layouts.

One thing I liked is that it doesn't force everything through parallel decoding. That's the default, but it can fal back to autoregressive decoding when the case gets ambiguous or the output becomes irregular.

I've been working in computer vision for 5+ years, and localization is one of those problems that looks easy until you actually have to make it fast and reliable
It does make vou wonder why ...

Dinarian888@dinarian888

1 hour ago

🔄 Vocence pivots to UMI as its alpha token surges 250% in one week 🔄

Vocence has reportedly shifted its subnet strategy toward UMI, triggering a sharp rise in its alpha token. The move shows how quickly capital can rotate when a Bittensor project changes its narrative.

🔑 Key points

🔹 Major strategic pivot: Vocence moved away from its previous direction and began positioning around UMI.

🔹 Alpha token surged 250%: The token reportedly delivered the gain within approximately one week of the pivot.

🔹 Narrative drove the initial move: The price reaction appears to have been fueled primarily by renewed attention, positioning, and expectations around the new direction.

🔹 Bittensor allows rapid repositioning: Subnets can change their products, incentives, and technical focus without creating an entirely new network.

🔹 Capital can rotate quickly: A new thesis can attract stakers and traders even before the updated product is fully operational.

🔹 Token price is not proof of adoption: A sharp ...

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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Dinarian888@dinarian888

July 18, 2026

AI Is Coming for Your Job Title

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 over LinkedIn, where perfectly serviceable occupations now arrive wearing titles such as “forward-deployed and agentic AI architect.” 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 Lab found that the number of postings on Indeed 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 is AI engineer, which ranked No. 1 on LinkedIn’s 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 doesn’t sell.

Indeed’s 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 analysis reported 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.

That produces titles such as AI marketing manager, AI learning specialist, responsible AI counsel and AI transformation lead. These are not always new occupations. Frequently, they are familiar jobs that have discovered a highly effective résumé keyword.

LinkedIn data cited by the World Economic Forum 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.

A Syracuse University review 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 the forward-deployed engineer, an old Palantir title that the AI boom has placed on a rocket sled. These engineers embed with customers, translating an executive’s desire to “do something with AI” into software that works. The Next Web reported that Indeed postings for the role were about 19 times higher in January than a year earlier.

A CTO guide 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. Evals engineers design tests to determine whether models perform reliably. AI red teamers try to make systems fail before customers do. Model behavior engineers study why an AI system responds as it does. AI governance leaders manage risks involving data, bias, security and regulation.

Other titles seem to have escaped from a brainstorming retreat.

There is the Claude Evangelist, whose mission apparently combines product education with the traditional duties of an apostle. There are vibe coders, who build software by describing what they want and accepting AI-generated code with varying degrees of supervision. “Vibe engineer” is the more respectable version, roughly equivalent to putting on a blazer before asking the machine to fix the login page.

“Context engineer” is a real discipline involving the data, instructions, memory and tools supplied to AI models. “Prompt engineer,” 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 “builder,” “AI-native developer,” “RAG engineer,” “agentic AI engineer” and “principal agentic GenAI forward-deployed context architect,” 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 “software developer.”

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.

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