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? The Dinarian on Locals brings you the latest in news, interviews, in-depth conversations, and stories from across the blockchain and global communities—within and beyond cryptocurrency ?. Experts delve into how blockchain technology is reshaping industries, enhancing business networks ?, transforming transaction workflows, and advancing distributed ledger systems ??. We also explore intriguing topics that may venture into the realm of conspiracies—and so much more!
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👇🏻 THETA PATENT PUBLISHED 1/4 👇🏻

#20240005350
👉- Edge Computing Platform Supported by Smart Contract Enabled Blockchain Network with Off-Chain Solution Verification

Credit to @StevensJoe11

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NATO: From Tanks To Tweets

From tonight's sub stream annotating the Rogan episode with receipts.

The World Does Need To See This Crap!

THEY are not giving up yet, The battle for worldwide FREEDOMS reign on!🐺🐑

00:02:26
🔥 Crypto-Saves-Lives 🔥

Still a skeptic? Watch the onchain effect of @Decaf_so, @StellarOrg,
@MoneyGram, and @circle in Colombia.

#Stellar #XLM

00:11:09
📚 Ripple's University Blockchain Research Initiative (UBRI) 📚

Championing blockchain research and development in academia is at the heart of Ripple's University Blockchain Research Initiative (UBRI).

With a mission to inspire and educate the next generation of blockchain builders, UBRI has supported:

⭐️ 1200+ research projects
📚 850+ courses
📍900 on-campus events
🔗 90 projects on the XRP Ledger
✅ 60 students hired

UBRI is empowering students and faculty to shape the future of blockchain technology. And we’re just getting started: https://ripple.com/impact/ubri/

00:02:59
👉 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.

👉 Coinbase just launched an AI agent for Crypto Trading
🇪🇺 EUR Stablecoins surprised a lot in November 🇪🇺

🇪🇺 EUR Stablecoins surprised a lot in November with a result of €2.6b volume in month. This is a 160% increase from October and an 8626% increase from November 2023. This growth would not be possible if it weren't for @circle and its $EURC stablecoin.

About 62% of the total volume for November fell on $EURC on @base. The second is @Tether_to with its $EURt and 26% of the volume. And third were @Celo with their $cEUR - 7%.

From the interesting: In July 2024, @circle was granted a license under MiCA regulations, clarifying the regulatory framework. This allows Circle to offer stable coin services across the EU using MiCA's “passporting” feature, which allows cryptocurrency companies registered in one EU country to operate seamlessly in other countries.

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𝐍𝐄𝐖 𝐑𝐄𝐏𝐎𝐑𝐓: 𝐇𝐨𝐰 𝐭𝐡𝐞 𝐅𝐞𝐝𝐞𝐫𝐚𝐥 𝐆𝐨𝐯𝐞𝐫𝐧𝐦𝐞𝐧𝐭 𝐖𝐞𝐚𝐩𝐨𝐧𝐢𝐳𝐞𝐝 𝐭𝐡𝐞 𝐁𝐚𝐧𝐤 𝐒𝐞𝐜𝐫𝐞𝐜𝐲 𝐀𝐜𝐭 𝐭𝐨 𝐒𝐩𝐲 𝐨𝐧 𝐀𝐦𝐞𝐫𝐢𝐜𝐚𝐧𝐬
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Crypto moving on up!💥

Preach it brother....

The most IMPORTANT thing I want you to get from this, is NOT that crypto is going to keep going up, It is MAKE YOUR MONEY WORK FOR YOU, WHEN ALL IS SAID AND DONE! Namasté 🙏

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Nissan launches web3 rewards, as Nike, adidas take different NFT paths

More than two years have passed since the height of the non fungible token (NFT) boom. Yet Nissan has just announced plans for a new Japanese web3 loyalty program involving NFTs. While NFTs are way past their peak in Europe and the US, they are continuing to prove popular in Japan.

We’re speculating there are cultural reasons for this. Japan is home to the graphic novel and anime cartoons. The visual nature of NFTs is a good match and there’s a deep pool of content to leverage. Japan also has a culture rewards such as Yuutai, the practice where stockholders receive rewards. For example, airlines will provide shareholders with discounted flights. Hence, the combination of the visual nature of NFTs and rewards programs is a good fit in Japan.

Nissan Passport Beta is starting with an NFT lottery that will run until January 14, during which it will give away 5,523 membership NFTs. They’re divided into four types representing futuristic cars, performance cars, classic cars or ‘smart life’. The latter is for those that choose their transport based on practical preferences such as comfort. However, the lottery will allocate the memberships randomly.

In addition to cool pictures and a Discord community, users can earn badges based on activities ranging from posting pictures of cars on social media to providing feedback on the planned reward program. But the ultimate benefits are some attractive potential perks, such as test driving cars on a special course or the ability to take a spin in limited edition cars.

While Nissan is exploring a new program, Nike is shuttering one.

Nike and adidas take different web3 paths

Nike was one of the first brands to embrace web3 when it acquired RTFKT Studios in 2021. This week it announced plans to shut it down. “RTFKT isn’t ending. It’s becoming what it was always meant to be – an artifact of cultural revolution.” The creative brand was known for artistic sneakers and two NFT collections not directly associated with Nike – CloneX and MNLTH.

Data from 2022 showed it earned $185 million from initial NFT drops and royalties on re-sales. In late 2022 Nike also launched Web3 platform .SWOOSH which is still active.

Also this week, adidas announced its latest collaboration with the STEPN web3 app, which allows people to earn tokens for walking or jogging. The brands have previously made two joint NFT drops. Now they are doing a third, but this time for 1,200 co-branded physical running shoes. Two thirds will be raffled to genesis NFT holders, with the balance raffled to other NFT holders or via contests.

That’s another good example of a reward that fans of the brands will value.

 

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Coinbase exec publishes FDIC letters urging banks to halt or avoid crypto services
Paul Grewal stated that the letters, acquired through FOIA requests, prove that Operation Chokepoint 2.0 existed.

Coinbase chief legal officer Paul Grewal has disclosed letters from the Federal Deposit Insurance Corporation (FDIC) to banks throughout 2022, urging them to halt or avoid crypto-related activities.

The letters, which date back to March 11, 2022, have been dubbed “pause letters” due to their repeated recommendations to suspend or refrain from engaging in crypto services.

FDIC concerns

The FDIC letters cited various concerns, including the agency’s lack of clarity on regulatory requirements for crypto-related activities. One excerpt noted:

“At this time, the FDIC has not yet determined what, if any, regulatory fillings will be necessary for a bank to engage in this type of activity.”

Many sections of the documents were heavily redacted, potentially to protect the proprietary nature of the services or products discussed. The FDIC also emphasized the need for additional information about the banks’ crypto offerings to ensure they would operate “in a safe and sound manner.”

The letters further scrutinized the legal analysis conducted by banks regarding the permissibility of such activities under Part 362 of the FDIC Rules and Regulations, which governs insured state banks. This suggests that some state-chartered banks explored offering crypto-related services in 2022.

Operation Chokepoint 2.0

The release of these documents stems from Coinbase’s Freedom of Information Act (FOIA) request filed on Oct. 18, which sought clarity on an alleged 15% deposit cap imposed on crypto-friendly banks.

Grewal argued that the letters provide evidence of “Operation Chokepoint 2.0,” a purported effort by the Biden administration to stifle the crypto industry. He emphasized that the claims were not a conspiracy theory and criticized the FDIC for withholding significant information through redactions and releasing only a fraction of the relevant documents.

He called for the incoming US administration to reverse what he described as “politically motivated regulatory decisions.”

According to Grewal:

“The incoming administration has the opportunity to reverse so many poor crypto policy decisions, chief among them politically motivated regulatory decisions like Operation Chokepoint 2.0.”

Meanwhile, others in the industry also criticized the letters and raised further concerns about the involvement of the Federal Reserve, which is copied on many of the letters sent to banks.

Caitlin Long, CEO and founder of Custodia Bank, said the Fed’s mention in the letters is evidence that the pause letters were coordinated decisions. She also characterized the so-called pause letters as indefinite directives meant to discourage lawful crypto activities.

She said:

“These weren’t ‘pause letters’ bc the pause was indefinite. These were really ‘cease & desist’ letters cloaked in legalese…designed to crush law-abiding #crypto.”

The pause letters, spanning nearly two years and nine months, suggest a coordinated effort among regulators to limit banks’ participation in cryptocurrency-related activities. Critics argue that such measures undermine the industry’s ability to innovate and expand within the US financial system.

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How Blockchain Is Leveling the AI Playing Field
By Mitch Liu-CEO and co-founder of Theta Labs

In an industry dominated by commercial AI labs, blockchain technology is allowing universities to get more cost-effective access to compute and allowing them to compete.

The rapid advancement of artificial intelligence has created an unprecedented divide between commercial and academic research. While Silicon Valley's tech giants pour billions into developing ever-larger language models and sophisticated AI systems, university labs increasingly find themselves unable to compete. This disparity raises serious questions about the future of AI development and who gets to shape it.

AI Labs are Being Vastly Outspent

In recent years, commercial laboratories have dramatically outspent academic institutions in AI research. In 2021, industry giants spent more than $340 billion globally on AI research and development, dwarfing the financial contributions from governments. For comparison, US government agencies (excluding the Department of Defense) invested $1.5 billion, while the European Commission allocated €1 billion (around $1.1 billion) to similar efforts.

This enormous gap in spending has given commercial labs a clear advantage, especially in terms of access to vital resources like computing power, data and talent. With these assets, companies are leading the development of advanced AI models at a scale that academic institutions struggle to match. Industry AI models are, on average, 29 times larger than those developed in universities, showcasing the stark difference in resources and capabilities.

The sheer size and complexity of these industry-driven models highlight the dominance of commercial labs in the race to develop cutting-edge artificial intelligence, leaving academic research labs trailing far behind.

The reasons for this disparity extend beyond simple economics. While commercial AI labs can operate with long-term horizons and significant risk tolerance, academic researchers must navigate complex grant cycles, institutional bureaucracies and limited budgets

Perhaps most critically, academic institutions often lack access to the massive computing infrastructure required for cutting-edge AI research. Training large language models can cost millions in computing resources alone – a prohibitive expense for most university departments. This creates a troubling dynamic where potentially groundbreaking research ideas may never see the light of day simply due to the high cost of compute

This cost is growing exponentially. One study by the Stanford Institute for Human-Centered Intelligence showed that OpenAI’s GPT-3 and Google’s PaLM cost less than $10M to train while the most recent GPT-4 and Google Gemini Ultra cost $78M and $191M respectively. This rate of 10x per year is estimated to persist over the next few years with new foundational models soon costing in the billions. 

The 2024 AI Index Report from Stanford HAI reinforces this trend, highlighting the skyrocketing costs of training AI models, the potential depletion of high-quality data, the rapid rise of foundation models and the growing shift towards open-source AI—all factors that further entrench the dominance of well-resourced companies and challenge academic institutions in keeping pace.

However, new solutions are emerging that could help level the playing field. Distributed computing infrastructure, built on decentralized architecture powered by blockchain technology, is beginning to offer researchers alternative paths to access high-performance computing resources at a fraction of traditional costs. These networks aggregate unused GPU computing power from thousands of participants worldwide, creating a shared pool of resources that can be accessed on demand.

On Decentralized Networks

Recent developments in this space are promising. Several major research universities in South Korea, including KAIST and Yonsei University, have begun utilizing Theta EdgeCloud, our decentralized computing network of over 30,000 globally distributed edge nodes, for AI research, achieving comparable results to traditional cloud services at one-half to one-third of the costs. Their early successes suggest a viable path forward for other academic institutions facing similar resource constraints.

The implications extend far beyond cost savings. When academic researchers can compete more effectively with commercial labs, it helps ensure that AI development benefits from diverse perspectives and approaches. University research typically prioritizes transparency, peer review and public good over commercial interests in the form of open-source models and public data sets – values that become increasingly important as AI systems grow more powerful and influential in society.

Consider the current debate around AI safety and ethics. While commercial labs face pressure to rapidly deploy new monetization capabilities, academic researchers often take more measured approaches, thoroughly examining potential risks and societal impacts. However, this crucial work requires significant computational resources to test and validate safety measures and sift through vast amounts of data. More affordable access to computing power could enable more comprehensive safety research and testing.

We're also seeing promising developments in specialized AI applications that might not attract commercial investment but could provide significant societal benefits. Researchers at several universities are using distributed computing networks to develop AI models for ultra-rare disease researchclimate science and other public interest applications that might not have clear profit potential.

Openness and Transparency

Beyond the question of resources, academic institutions offer another crucial advantage: transparency and public accountability in their research. While commercial AI labs like OpenAI and Google Brain produce groundbreaking work, their research often occurs within closed environments where methodologies, data sources and negative results may not be fully disclosed. This isn't necessarily due to any misconduct – proprietary technology and competitive advantages are legitimate business concerns – but it does create limitations in how thoroughly their work can be examined and validated by the broader scientific community.

Academic research, by contrast, operates under different incentives. Universities typically publish comprehensive methodologies, open-source their models, share detailed results (including failed experiments) and subject their work to rigorous peer review. This openness allows other researchers to validate findings, build upon successful approaches and learn from unsuccessful ones. When KAIST AI researchers recently developed improvements to Stable Diffusion’s open-source text-to-image generative AI models for virtual clothing e-commerce applications, for example, they published complete technical documentation, public domain training data sets and methodology, enabling other institutions to replicate and enhance their work.

The distributed computing networks now emerging could help amplify these benefits of academic research. As more universities gain access to affordable computing power, we're likely to see an increase in reproducible studies, collaborative projects and open-source implementations. Many South Korean and other universities around the globe are already sharing their AI models and datasets through these networks, creating a virtuous cycle of innovation and verification.

This combination of computational accessibility and academic transparency could prove transformative. When researchers can both afford to run ambitious AI experiments and freely share their results, it accelerates the entire field's progress.

 

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