Shared post - 👀 This Crypto Is up 37,000%

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👀 This Crypto Is up 37,000% – but Is It a Scam? 👀

October 08, 2022

(Dinarian Note: Remind me to STAY FAR AWAY from this project, I have another project where I make 34% APY anyways, which my supporters are fully aware of... this one has way too many RED FLAGS, for my liking)

The 2-year-old cryptocurrency project gives 38% staking rewards. Is the project legitimate or a scam?

The HEX token is based on the Ethereum blockchain. Their website says they are the first blockchain certificate of deposit. Those who stake HEX tokens are given an average of 38% returns. The figure is lucrative because most US banks do not provide more than 2% annual interest.

Who is the founder?

The HEX was founded by Richard Schueler, who adopted the stage name Richard Heart. One of the steps to fundamentally analyze any project in Web3 is to check out the history of its founder.
Richard’s mail archive shows that he used to run a course on How to Spam people. In fact, he was charged with invoking Washington State’s anti-spam law.

His Twitter bio reads that he owns the most expensive Rolex, the world’s fastest Ferrari, and some more boasting about his lifestyle. He has flexed his wealth on multiple occasions on social media. When he was asked why he flexes his wealth, he replied that it was for views and engagement.

The aggressive marketing by HEX focused on price gains.

Twitter’s name of the official account reads the gain in price and high staking rewards. There aren’t many genuine projects that focus on price gains as much as HEX does. Generally, projects care about building the best services for their community. Their social media handles do not market how much the token’s price increased, let alone shout it through their handle’s name.

They also started a campaign #HEXBoughtThis on social media, where shillers or early investors flex their wealth bought from HEX gains.
It is one of the most aggressively marketed crypto projects. The project has been advertised in newspapers, magazines, billboards, and airports. All these campaigns focus on showing how much the token’s price has risen. The approach is the same as any get-rich-quick scheme, to cater to common people’s greed. A Twitter user posted that HEX used customer records from the Ledger hack and sent marketing material by mail to addresses obtained from there. How many Web3 projects do the marketing at par with HEX?

The website claims that investors will make “life-changing wealth.” Genuine projects with strong products do not primarily focus on the increase in their market price for marketing purposes. Bitcoin and Ethereum’s website does not discuss price fluctuations or creating “life-changing wealth” because they serve a purpose. Investors gaining money is a by-product of the greater purpose these projects serve.

The HEX buyers are incentivized to lock up their capital for a certain period. There is a heavy penalty if someone unlocks them before the lock-in period ends. This effectively reduces the supply of the tokens in the market, and demand is brought in through FOMO with the aggressive marketing campaign.

The staking rewards.

The website, as of writing, shows staking rewards of 38%. Who gets the staking reward?
In the proof-of-stake consensus mechanism, the validators deposit a certain amount to the smart contract as collateral to keep the blockchain secure. They are rewarded for their up-time and are slashed for remaining inactive for a long time.

While there is no such thing with HEX, the stakers do not stake their HEX to secure the blockchain or validate the transactions. The only purpose of staking HEX is to reduce the supply, which puts upwards pressure on HEX’s price.

The argument on whether HEX is a scam or not is quite a hot topic of debate amongst the Twitter community. Along with critics, the project has attracted hardcore supporters. Notable industry leaders believe that HEX is a scam going to zero.

HEX made an all-time high in September 2021 at 0.51. It is down by around 94% from all-time highs. Most scam projects in crypto do not survive the test of bear markets. Will HEX survive the current bear market, or will it go to zero? Perhaps, only time can answer this.

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🎬 Elon Musk: The Superintelligence Horizon🎬

🤖 The SI Horizon: Radical Abundance or Loss of Control? 🌌⚡️

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🔑Key Takeaways from the Interview 🧭📊

🔹The Intelligence Gap: Within a decade, humans are unlikely to remain in control, as the cognitive gap between AI and humanity could surpass the gap between humans and chimpanzees.

🔹An Age of Abundance: Despite the risks, Musk views the baseline outcome as an era of radical abundance where goods and services become so efficient that anyone can have virtually anything they want.

🔹The Safety Approach: Rather than trying to halt unstoppable technological momentum, leading AI labs should coordinate safety reviews, allowing competitors to flag issues before major model releases.

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Dinarian888@dinarian888

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⚠️Is AI Just A Bubble?⚠️

⚡️ The Real AI Bottleneck Isn't a Bubble—It's Supply! 🔌🧠

BlackRock CEO Larry Fink highlights a profound truth: the real risk isn't an over-inflated AI bubble, but rather severe, cascading shortages spanning compute, advanced chips, memory, power, and infrastructure as demand drastically outpaces supply.

Traditional centralized tech giants are hitting a wall trying to scale these resources independently. That is precisely where Bittensor ($TAO) comes in. 🌐💎

How Bittensor Solves the Shortage Crisis: Decentralized Compute Pooling: Instead of relying on single-entity mega-datacenters constrained by local power grids, Bittensor aggregates global, distributed compute and idle hardware through an open-market protocol. 🌍⚙️

Incentivizing Efficiency: By aligning economic incentives via blockchain mechanics, the network crowdsources machine learning intelligence and compute power dynamically, bypassing rigid corporate supply bottlenecks. 📈🤖

Open Access ...

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Dinarian888@dinarian888

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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

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🔑 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

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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
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Dinarian888@dinarian888

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🔑 Key highlights:

🔹️ The optimization increased the King model's throughput from 375.4 to 528.9 tokens per second, outperforming the standard SGLang framework.

🔹️ In plain terms: the optimized system can now generate significantly more AI output in the same amount of time.

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Affine (SN120)’s King Model Gets a 40.9% Throughput Boost From Cacheon (SN14) - The TAO Daily

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Dinarian888@dinarian888

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🔑 Key points

🔹 Validators score miners: Their evaluations help determine which miners receive rewards.

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🔹 Lower commissions can attract stake: Copiers may offer lower fees, potentially attracting delegators and increasing their influence over rewards.

🔹 Commit-reveal makes copying harder: The feature hides scores temporarily, but older scores may still be copied after they become public.

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🔑 Key Points:

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🔹 The Partner Program: Ditto has launched a structured partner program aimed at expanding its business reach. This initiative is designed to onboard new collaborators and integrate Ditto's services into a wider commercial ecosystem.

🔹 Key Details: The program focuses on creating mutually beneficial relationships. It provides partners with the tools and incentives needed to promote Ditto's AI marketplace, while also offering them a share of the network's growth.

🔹 ...

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Dinarian888@dinarian888

September 13, 2026

Revolut Leak Shows the Cost of Constant ID Collection

Revolut’s 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 “unauthorized third party” 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 agency’s 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 message’s origin and integrity, but do not verify the legitimacy of the legal request itself.

Revolut said that it complied with the request “under the reasonable belief that it was an authentic government agency request” – 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 “limited” number of its customers were affected by the data leak, and that the company’s 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.

Revolut notice explaining customer identity and financial data was shared after an unauthorized government email request.

The reason this is a recurring problem is that companies are keeping highly sensitive information about their customers’ identities, and sometimes even financial transactions, for a long time, and this data is then available to be disclosed to third parties – 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. Revolut’s 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 – for legal reasons.

This means that even if you close your account, your identity documents don’t disappear.

And while the incident with Revolut happened in the financial sector, it’s 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 – 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.

It’s 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 didn’t 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.

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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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