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🌐USTC Revitalization and Incremental Peg to $1 through the implementation of a Peg Divergence Tax on Transactions both on and off chain🌐
January 23, 2023
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(Dinarian Note: This is by far the BEST re-Peg Plan I have seen, it will take time, but this would eventually re-peg the USTC to market value, giving utility back to the network, and eventually back to $1 USD. It's a Lottery ticket worth investing a little bit in... (NOT FINANCIAL ADVICE))

USTC Revitalisation and Incremental Peg to $1 through the implementation of a Peg Divergence Tax on Transactions both on and off chain

Nearly 8 months have passed since the collapse of Terra Luna (LUNC) and USTC last May, and while we’ve made great strides in updating the blockchain and keeping it secure we have been severely lacking in terms of developing on-chain utilities and repegging USTC.

Repegging USTC is no easy task, while on-chain transactions and swaps can be maintained algorithmically in a de-peg scenario, without off-chain implementation USTC relied on arbitragers maintaining the peg. If arbitragers failed to hold the peg USTC then relied on TFL(Luna Foundation Guard) having enough funds in reserve to defend the peg. When both these defence mechanisms failed the price of USTC crashed causing LUNC to mint into oblivion. This leaves us in the situation we are today with LUNC, a hugely inflated token that’s utility derived from the stability of USTC now gone and 0 capital in reserve to buyback or defend the peg.

There’s also a misconception that a stablecoins utility comes from its ability to be stable at $1USD. This is not the case, whether it’s one cent or one dollar, its utility comes from it being stable at a set value. So, I’m proposing we peg USTC to around its current market value by applying a peg divergence tax on both on and off-chain transactions and incrementally pull peg back to $1USD over time. Not only does this bring instant utility, it incentives dApps to use USTC over any other stablecoin as they stand to make more profit.

The Peg Divergence Tax

The peg divergence tax works by taxing slightly more than the spread (the difference between the selling price and the peg price). The taxes retained by the protocol are then used to buy back USTC to maintain the peg. In the beginning the tax is always on the seller side to incentivise buying and it always accumulates the more desirable asset. To work it needs to be applied to both on and off-chain transactions. At the beginning the depeg events may occur until the protocol has retained enough assets to buy back to the set peg.

Mathematically how the algorithm will operate:

For the worked examples I’ll be using a peg of 1USD and a profit multiplier of 1.1.

X = (Target Peg Price)

Y = (Market Price)

T – (Tax/Transaction Fee)

M – (Profit Multiplier)(optional)

Scenario 1:Ā Where the price is below 1USD or (Y<X)
T = ((X-Y)*M)

Worked example : X = $1.00, Y = $0.99 , M = 1.1

T = (($1-$0.99)*1.1)

T = $0.011

Example: Seller creates a sell order at $0.99. Buyer pays $0.99 BUSD. Seller receives $0.979 BUSD. Protocol retains $0.011 BUSD.

Scenario 2:Ā Where the price is above 1USD or (Y>X)

T = ((Y-X)*M)

Worked example : X = $1.00, Y = $1.01 , M = 1.1

T = (($1.01-$1.00)*1.1)

T = $0.011

Example: Seller creates sell order at $1.01. Buyer pays $1.01… Profits are dependant on protocol purchase price. LUNC holders are incentivised to swap into USTC (using liquidity pool of USTC bought back by protocol)

Scenario 3:Ā Where the market price equals the peg
If (X)=(Y) let T=0

In this instance the tax does not come into effect and is dormant.

Implementation

Phase 1:Ā We re-peg USTC to around its current market value. This gives instant utility to the network by allowing dApps to immediately begin selling services/products on the Terra network in USD values.

Phase 2:Ā We start the incremental pull back to 1USD peg by disabling the tax mechanism where the price is above peg, and just apply the tax to any deviation below. What this does is allows the price to deviate above peg but not below. As the price of USTC increases we repeg at incrementally higher values until we reach 1USD.

This incremental peg makes USTC more desirable than any other stablecoin to build on/invest in. The reason for this is when service/product providers convert their profits from stablecoin to FIAT its redeemable for the same value, USTC will be the only stable that will have the ability to 50x.

Phase 3:Ā Use the USTC obtained by the protocol during buybacks to provide a liquidity pool which would allow unidirectional LUNC>>>USTC swaps without any minting. This serves to reduce LUNC supply without increasing USTC supply.

Phase 4:Ā Once we’ve reached 1USD peg, we relax the algorithm’s parameters to allow for limited arbitrage to occur again and increase utility further. The algorithm is not switched off, it’s just lying dormant, and will reactivate in the event arbitragers fail and prevent any further de-peg scenario.

The Profits

The profits generated by the algorithm should be used to further develop the Terra Classic ecosystem and speed up both the re-peg to 1USD and to burn down the supply of LUNC.

A percentage of the profits should be retained for the following purposes:

  • To provide a liquidity pool so that we can re-enable swaps between USTC and LUNC but without minting. Swaps would be available while liquidity is sufficient.
  • A liquidity pool to buy back USTC at times of low taker and high maker volume. This will allow capital to move more freely and incentivise network utilisation.
  • CEXs should be compensated with a percentage of the profits generated. If they implement our code and help save USTC they deserve to be compensated.
  • To compensate developers, app builders and incentivise further development and building of community owned utilities.
  • To buy back and burn both LUNC and USTC and speed up recovery of our network.

Case Study : Binance BUSD/USTC Trading Last Quarter 2022

To prove it would beneficial for both LUNC/USTC holders and the CEXs to implement the protocol and give some indication to its feasibility I modelled it against the last 3 months trading between the BUSD/USTC trading pair on Binance. Binance has the highest trading volume of both USTC and LUNC and for the last quarter USTC has had an average price of approximately $0.025 BUSD. I’ve modelled the date based on a rate multiplier of 1.1 and three different peg levels of $0.02, $0.0225 and $0.025. Fees shown are those retained by Binance as they are currently implemented at a rate of 0.002%.

The above values show profits retained by the protocol with divergence tax applied to trading data as though it had no effect on the trading volume or prices and did not buyback of USTC. We should assume that the tax will have a massive reduction on trading volume below peg and to reduce these figures accordingly. During the month of October the price of USTC never fell below 0.025 so the protocol would not have come into effect and would have retained no profits. If we were toĀ assume a 90% reduction in trading volumeĀ below the set peg levels we would still have retained over $52,000 at a peg of $0.02, over $1,593,000 at $0.0225 and $5,448,00 at $0.025 over 3 months of trading. If we then gave even 10% of these fees back to Binance they would have increased profits of approximately 16%, 38% and 67% for the respective peg levels over the same time period.

Conclusion

Given that we are going to whitelist Binance wallets I believe we should capitalise on the close relationship and be the first blockchain to have their capital controls implemented algorithmically both on and off chain. But this is not a utility and should not be viewed as such its more of a failsafe.

This approach should appeal to both USTC and LUNC holders alike and allow us to slowly but safely collateralise USTC with BUSD. It also doesn’t require any minting, funds from the Oracle/Community Pools or an external liquidity provider so there shouldn’t be any major initial negative price action.

It doesn’t interfere with any of the other proposal that have been put forward so far by the community and if anything it should augment them.

This proposal requires more work and discussion around buyback rates and tax multiplier rates.

Negatives:

  • Most likely will have a dramatic reduction on trading levels below peg.
  • If the markets react badly and without and current onchain utilities to drive demand it could result in stale/no trading.
  • If there isn’t sufficient demand at/above peg and you need to sell you will be taxed quite aggressively offramping.

Has to be implemented by everywhere USTC is traded including off chain.

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šŸ¤–Can Decentralized AI Stop Big Tech from Owning the Future of Robotics?šŸ¤–
The race to build the future of robotics is no longer just about robots. It's about who controls the intelligence behind them.
Ā 
Over the last three years, a small group of companies has emerged as the backbone of the AI revolution. Microsoft provides cloud infrastructure. NVIDIA supplies the chips. Google, OpenAI, Anthropic, Meta, and others develop the models. Together, they control much of the compute, data, and software stack powering modern AI.
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Now that AI is moving into the physical world, many are asking a bigger question:
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Will these same companies end up controlling robotics too?
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It's a valid concern.
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The latest generation of robots relies on enormous amounts of compute, simulation, training data, and foundation models. Many robotics startups today are built on infrastructure provided by large technology companies. NVIDIA's Omniverse is becoming a key simulation environment for robot training. Microsoft Azure is powering the training of robotics foundation models. Physical AI startups increasingly depend on hyperscale cloud infrastructure to train and deploy intelligent systems. Recent partnerships across the industry show just how central Big Tech has become to robotics development.
But while Big Tech is building the highways, another movement is trying to ensure it doesn't own every destination.
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.
Ā 
It's about who owns the intelligence.
Ā 
And that battle is just getting started.
Ā 
Ā 

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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.

šŸ›ļø 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).

šŸ”‘ 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.

āš™ļø 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).

šŸ’° 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.

šŸ’” 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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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 overĀ LinkedIn, where perfectly serviceable occupations now arrive wearing titles such as ā€œforward-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 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 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.

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 rƩsumƩ keyword.

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 executive’s desire to ā€œdo 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. ā€œVibe engineerā€ is the more respectable version, roughly equivalent to putting on a blazer before asking the machine to fix the login page.

ā€œContext engineerā€ is a real discipline involving the data, instructions, memory and tools supplied to AI models. ā€œPrompt 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 ā€œbuilder,ā€ ā€œAI-native developer,ā€ ā€œRAG engineer,ā€ ā€œagentic AI engineerā€ and ā€œprincipal 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 ā€œsoftware 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.

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