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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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🔥 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
🇷🇺 Russian President Putin says, "Who can ban Bitcoin?.. Nobody"

Liquidity Liquidity Liquidity or should I say, lack of.

00:00:26
👉 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
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 🙏

Unlocking Opportunity: Ripple & Mercy Corps Ventures 🌍✨

We’re excited to launch Unlocking Opportunity, the latest phase of our #RippleImpact partnership with Mercy Corps Ventures! 💡 Together, we’re investing in emerging market entrepreneurs and funding innovative financial resilience and humanitarian solutions powered by the #XRPL.

So far, this partnership has supported 50+ startups across Africa, Asia, and Latin America 🌏💪. Now, we’re expanding into Web3 innovations like:

🌐 Stablecoins for seamless transactions
🏦 Asset Tokenization for savings and lending
💸 Micropayments for accessibility
🤝 Aid Delivery through blockchain

We can’t wait to see how these innovators harness blockchain to build a brighter future. ✨ Learn more about our work:

https://ripple.com/insights/unlocking-opportunity-ripple-and-mercy-corps-ventures-expand-partnership-to-equip-emerging-market-entrepreneurs

👀 Bitcoin’s 21 Million Cap: It’s Math, Not Magic 🧮✨

Many assume Bitcoin’s 21 million supply limit is hardcoded, but it’s actually the result of a meticulously crafted mathematical system:

🔹 Mining Rewards: Bitcoin started with a reward of 50 BTC per block.
🔹 Halving Cycle: Every 210,000 blocks (~4 years), the reward is halved (50 → 25 → 12.5 → and so on).
🔹 Geometric Series: As rewards decrease, the total sum of all mined Bitcoin converges toward 21 million.

This limit isn’t an arbitrary number—it's a product of Bitcoin’s decentralized design and mathematical brilliance. 🧠🔗

It’s not magic. It’s math. 💡

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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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France Wants Bitcoin HODLers to Pay Taxes on 'Unproductive Wealth'
A Web3 marketer told Decrypt a Bitcoin tax on unrealized gains is “worrying” and “feels pretty out of touch with how volatile crypto markets are.”

Earlier this week French Senator Sylvie Vermeillet proposed classifying Bitcoin and other digital assets as “unproductive,” arguing they should be taxed like luxury items and vacant properties.

The bill in question classifies cryptocurrencies as unproductive assets for the 2025 budget, meaning that taxes will be imposed on unrealized gains if they exceed €800,000.

Paris-based Alice Stork, founder of Web3 public relations agency ICL, told Decrypt that a Bitcoin tax on unrealized gains is “worrying” and “feels pretty out of touch with how volatile crypto markets are.”

What happens when the value of your holdings drops after you've already paid taxes on "gains" you never cashed out, she mused. According to her, the measure could push “innovators and businesses away from France.”

So far, the proposal has passed the Senate in a preliminary vote and been endorsed by the country’s Finance Minister, Laurent Saint-Martin.

The Finance Minister claimed that the change to how Bitcoin gains are taxed makes the system more balanced.

If passed, French cryptocurrency holders would have to report foreign cryptocurrency holdings annually in the Cerfa 3916-bis form. Fines for failing to report crypto holdings abroad would range from €750 to €1,500.

Sébastien Martin, the CEO and co-founder of French crypto risk management firm RAID Square, told Decrypt that because Vermeillet is Vice Président of the Sénat, one of the 2 Chambers of the French Parliament, he’s heavyweight in the nation’s politics.

Martin added that he is also a member of “Parti Radical,” France’s oldest political party and “part of the political parties supporting the policy led by the French Président, Emmanuel Macron.”

France’s interest in levying taxes on Bitcoin HODLers follows other crypto regulation efforts.

As Decrypt reported in early November, France's National Gaming Authority is reportedly planning to block Polymarket. This blockchain-based prediction market platform saw $3.5 billion in trading volume during the U.S. presidential election. But after receiving scrutiny from regulators there, it has blocked French users.

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Bitcoin Price Unfazed as Mt. Gox Shifts Another $352 Million Worth of BTC
Now that repayment has been postponed until 2025, a transfer of $352 million "does not affect the market volatility much anymore," a source told Decrypt.

An address containing a large amount of Bitcoin owned by long-defunct cryptocurrency exchange Mt. Gox just moved funds.

About seven hours before press time, nearly 6,620 BTC worth about $352.7 million were transferred from a Mt. Gox address to an unknown wallet, according to data provided by blockchain intelligence service Arkham Intelligence. The data shows that the exchange is in a heightened level of activity after yesterday’s reports of another Bitcoin transaction worth $2.8 billion.

Market data shows the Bitcoin price has not reacted to the transfer in any evident way, trading laterally mostly between $97,000 and $98,000 after it occurred. At the time of writing, BTC is trading 4.8% lower than it was this time yesterday, according to CoinGecko data.

"Typically, announcements from Mt. Gox have a negative impact on the market, often causing Bitcoin's price to decline," notes Min Jung, an analyst and researcher from Presto Labs, told Decrypt earlier this week about the much larger transfer.

The reduced impact of Mt. Gox transactions is likely to be attributed to the bullish market sentiment and a recent announcement by the Mt. Gox redistribution team that it’s postponing creditor repayment by one year. This means that until then, no transaction on Mt. Gox’s part will result in an immediate increase in sell pressure on Bitcoin or Bitcoin Cash.

Founder of Obchakevich Research Alex Obchakevich told Decrypt that another reason may be that “the market is becoming more mature every year and the capitalization and trading volumes are increasing by billions with the liquidity “growing incredibly fast.”

Therefore, he reasoned, “$353 million does not affect the market volatility much anymore.”

Mt. Gox is shorthand for “Magic: The Gathering Online eXchange,” a name given to the exchange when it still dealt in trading cards at launch in 2010, before Bitcoin was introduced on the platform. After the service pivoted to Bitcoin it captured the almost totality of the market and became the most popular way to buy and sell the world’s first cryptocurrency online.

As a result of a 2014 hack, Mt. Gox lost almost 750,000 BTC owned by its customers and 100,000 of its own. This amount of Bitcoin was equivalent to around 7% of all Bitcoin in circulation at the time.

That’s why in the past, news surrounding Mt. Gox creditor repayment has held a lot of sway over market sentiment. Arkham Intelligence data shows that as of press time the exchange’s wallets still contain 39,705 BTC worth nearly $3.9 billion.

 

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