Coin overview
IO
Ended--
Last price (USDT)
418.61K
24h total (USDT)
1.46M
Total vol (USDT)
2024-05-10 10:00:00 ~ 2024-06-11 11:30:00
Trading time (UTC+0)
2024-06-12 04:00:00
Delivery time (UTC+0)
Total supply800.00M
Resources
Introduction
IO.NET is the world’s largest decentralized AI computing network that allows machine learning engineers to access scalable distributed clusters at a small fraction of the cost of comparable centralized services. io.net is uniquely capable of creating clusters of tens of thousands of GPUs, whether they are co-located or geo-distributed, while maintaining low latency for deployers.
Cardano Frozen Out of Wyoming’s Stablecoin Bid
In a first, the state of Wyoming will launch its own dollar-backed stablecoin. Competing blockchains are vying to support the new Wyoming State Token (WST). Charles Hoskinson alleged Cardano was unfairly excluded. Before Donald Trump’s recent pro-crypto push, Wyoming was already leading the charge as one of the most crypto-friendly states, thanks to its progressive tax policies and initiatives to support the digital asset industry. Building on its pro-crypto reputation, Wyoming introduced the Wyoming Stable Token (WST) Act in January 2023, aiming to launch a dollar-backed stablecoin by early 2025. However, Input Output (IO) CEO Charles Hoskinson has revealed that Cardano has been excluded from supporting the project. Concerns Raised Over Tender Process The WST project was initially pitched as a move to accelerate stablecoin payments under the backing of a US state. While Cardano and its developers at IO were involved in the early stages, Hoskinson recently revealed that the blockchain had been excluded from the final phase of the project. Sponsored Hoskinson said IO played a crucial advisory role in the project’s development, helping shape its concept, technical standards, and compliance framework over hundreds of meetings. However, the project’s trajectory shifted when an executive director was hired. The director reportedly pushed for the token to be Ethereum-based, contrary to the bill’s original intent for a multi-chain solution. Hoskinson explained that the tender process, which allowed developers to build prototypes demonstrating their ability to meet the project’s requirements, was abandoned. Instead, the committee and executive director evaluated each blockchain, relying on subjective judgments without public input or opportunities for rebuttal. Cardano Misses Out This approach led to Cardano being excluded from the shortlist, with the commission citing a lack of “certain capabilities,” a claim Hoskinson disputes, arguing that Cardano never got the chance to demonstrate its suitability as it could not submit a prototype. Summing up, Hoskinson described the shortlisting process as “illegal and unconstitutional,” arguing that the committee and executive director bypassed proper procurement procedures, resulting in a subjective and flawed evaluation of blockchains for the WST project. According to Mysten Labs co-founder Evan Cheng, the shortlisted blockchains included Solana, Avalanche, Sui, Stellar, Ethereum, and other EVM-compatible chains. However, the source Cheng cited is no longer accessible, leaving the final selection process shrouded in further controversy. While Hoskinson did not elaborate on IO’s next steps, he hinted at potential action, stating the firm “will pursue options at our discretion.” He also suggested the flawed tender process has raised questions about whether the entire project should be scrapped. On the Flipside WST’s executive director is Anthony Apollo, who worked at Ethereum devs ConsenSys between 2017 and 2018. WST is the first attempt by a public body to compete with private cryptocurrency firms. A US state-backed stablecoin may appeal more to crypto-skeptics. Why This Matters The WST controversy exposes a familiar pattern in government contracts, where public funds and promises of innovation often serve the interests of insiders. Pennsylvanian lawmakers push for a state-level Bitcoin reserve. Pennsylvania Proposes Bill to Establish Bitcoin Reserve Senator Lummis expects a national Bitcoin reserve to halve US debt. Bitcoin Reserve Would Cut US Debt in Half: Sen. Lummis
Charles Hoskinson says Coinbase CEO is the “only one candidate” for Crypto-Czar at the White House
Charles Hoskinson, founder of Cardano, has expressed his support for Coinbase CEO Brian Armstrong to take on the role of Crypto-Czar at the White House. In a detailed post on social media, Hoskinson pointed out that Armstrong has been an influential figure in the cryptocurrency space. He further added that Armstrong’s ability to withstand the pressure from regulators is the major reason why he is suitable for the position. Hoskinson stressed that the U.S. government requires an impartial and informed mediator between the government and the crypto space. He noted that Armstrong has managed to steer the company clear of legal issues and make Coinbase a leading player in the cryptocurrency space. “Coinbase has grown into one of the pillars of crypto and has had to endure the unfair tactics of a government committed to regulation through enforcement,” Hoskinson stated. He said that Armstrong has the potential to bring everyone together and work towards a common goal of attaining the right legislation that will encourage innovation. Hoskinson plans to push for crypto-friendly legislation Hoskinson also mentioned that he would be going to Washington, D.C., to lobby for cryptocurrency policy. He cited his time in Wyoming, where he helped craft 31 pieces of blockchain legislation and laid out new plans to champion regulatory certainty. See also Andrew Tate's DADDY coin surges 20% as the case against the brothers is officially dropped One such initiative is Operation Baseline, which will begin in January through IO Policy. This project proposes to conduct a landscape review of the cryptocurrency industry in the United States in order to understand the problems that exist and potential solutions. The results of the project will be presented to the policymakers and future Crypto Czar to inform the policy and legal frameworks. Hoskinson also stressed the importance of building the right ecosystem for the blockchain and crypto businesses. “The president’s goal is to make America the best place in the world to start and run a cryptocurrency and blockchain business,” he said, calling for unity within the industry to achieve this objective. Trump meets Armstrong to discuss crypto strategy Armstrong’s influence is not limited to Hoskinson’s endorsement. Armstrong was reported to have met with President-elect Donald Trump yesterday to discuss personnel changes in the new administration, as reported by Cryptopolitan. This is the first time that Armstrong and Trump have met since the recent re-election of Trump on November 6. In his second term, Trump has vowed to create a Bitcoin and crypto advisory council to help set up clear rules for the industry within the first 100 days. Although Armstrong did not directly participate in Trump’s campaign, he stated that he was ready to cooperate with the administration. See also Fed chair Powell looks forward to seeing which economic policies president Trump will actually implement Armstrong has also backed Hester Peirce, the SEC Commissioner, as the best candidate to head the commission in case of a leadership change. Recent activities of Trump show that he is determined to deliver on his pre-election promises of making the United States the “crypto capital of the world.” He has reportedly met with several big names in the sector, including the CEO of Ripple, Bradley Garlinghouse, and Circle executives. Trump has also appointed proponents of Bitcoin to important positions, such as Elon Musk, CEO of Tesla, and Vivek Ramaswamy, an entrepreneur who will co-head the new Department of Government Efficiency (D.O.G.E.). A Step-By-Step System To Launching Your Web3 Career and Landing High-Paying Crypto Jobs in 90 Days.
io.net and Zero1 Labs Partner to Advance Decentralized AI Development
io.net, a prominent GPU technology leader, has partnered with Zero1 Labs, a decentralized AI platform. The collaboration seeks to enhance decentralized AI (DeAI) by making it faster, more cost-effective, and accessible for developers. GPU Network io.net Fuels Zero1 Labs’ Blockchain AI Platform Zero1 Labs will use io.net’s GPU compute network to train AI systems for its open platform, Keymaker. The marketplace equips developers with tools to create and deploy autonomous AI agents that perform complex tasks, such as enhancing trading opportunities in Web3 environments. io.net’s GPU clusters enable Zero1 Labs to cut costs and improve efficiency. io.net has been receiving positive community feedback as of late, particularly with its recent market performance. Solana Daily listed io.net among its Top Potential DePin Projects for Bull Run 2025, updated last in August, at rank number three. The same can be said for Zero1, whose $DEAI has been performing fairly over the last week, with a 12% jump from $0.62 to $0.71. “I think $DEAI is one of the most underrated projects that can potentially provide significant profits compared to other coins,” said one enthusiast on X. Community sentiment indicates that enthusiasts have reason to support the coin’s long-term prospects. “gd strength from $DEAI. i know only a few small-mid cap ai plays w charts as strong as this. revisiting 0.6283 not out of the question, but staying above the box is only bullish. @zero1_labs knowing what to do to make their presence known,” one X user observed. Zero1 Labs operates on its Cypher Layer, which combines encryption with blockchain technology to allow secure, private AI computations. Through io.net’s support, Zero1 will accelerate the training and optimization of its AI tools, improving developers’ usability. In addition to providing GPU resources, they will collaborate on several initiatives. The two companies intend to facilitate a global developer community by hosting hackathons and bounty programs. These events will provide developers with opportunities to test and improve their skills. They will also share expertise and resources with strategic partners to strengthen the ecosystem. This partnership empowers the DeAi lab to expand its market share while cutting infrastructure costs. Developers will gain access to better tools and resources for creating decentralized AI applications. Jointly, io.net and Zero1 Labs hope to drive creation in DeAI and open up novel chances for blockchain-based AI systems.
io.net and Zero1 Labs Partner to Accelerate Decentralized AI Development
GPU DePIN io.net has made the announcement that it has formed a partnership with Zero1 Labs , a platform for decentralized artificial intelligence, in order to further the development of decentralized AI (DeAI). As part of the deal, Zero1 Labs will make use of the GPU compute network provided by io.net in order to expedite the training of artificial intelligence agents for Keymaker, the company’s open marketplace. The Zero1 ecosystem is the first Proof-of-Stake-based DeAI ecosystem that is aimed to serve both developers and consumers via apps and tools that are optimized for artificial intelligence performance. The Cypher FHE-EVM Layer, which enables secure and privacy-preserving artificial intelligence computing, lies at the heart of it. Through its partnership with io.net, Zero1 will have the ability to get access to GPU computing that is both demand-based and cost-effective. This will make it simpler for developers to use Zero1’s tools in order to build and deploy decentralized artificial intelligence applications. Keymaker, which is Zero1’s multimodal AI marketplace, creates an open platform for developers of decentralized artificial intelligence (DeAI). It comes with over one hundred different DeAI tools that are meant to simplify the process of developing, accessing, and discovering DeAI applications. It is especially well-liked among developers who are focused on developing very efficient onchain artificial intelligence agents. AI agents have a special utility in web3, where they may be trained to improve trading opportunities. These agents operate as autonomous systems that make use of artificial intelligence to carry out activities without the participation of humans. Additionally, with the assistance of io.net’s GPU computing, developers that are interested in launching artificial intelligence agents across the multichain landscape will have the ability to use expanded Zero1 tools for training and optimization. In accordance with the conditions of the collaboration, io.net will work in a variety of different areas, in addition to providing Zero1 Labs with its decentralized GPU clusters via IO Cloud. The two organizations will collaborate on a number of cooperative projects, including the following: Developer Community: Zero1 Labs and io.net are dedicated to promoting creativity in the artificial intelligence (AI) space by building a developer community via a variety of community activities such as hackathons and bounty programs. The DeAI community will be further enriched as a result of these events, which will give opportunities for developers to collaboratively work together and display their expertise. Strategic Partner Ties: Zero1 Labs and io.net will participate in a strategic exchange of partner resources. This will make it possible for projects that are being developed inside Zero1’s DeAI ecosystem to have access to high-performance resources and sophisticated AI knowledge from io.net. Since Zero1 has formed a cooperation with io.net, the company will be able to increase its portion of the DeAI developer market while simultaneously reducing the expenses of its hardware and infrastructure. Through this method, it will provide builders with the ability to deploy AI agents that enable new onchain use cases, which will in turn drive innovation within the decentralized artificial intelligence field.
How to figure out if an AI crypto project is worth investing in
The convergence between artificial intelligence and crypto has been touted as the next big thing in tech. For the past few years, AI crypto tokens have reached market caps of greater than $1 billion. But despite this massive investor interest, there has so far not been a corresponding wave of user demand. Ask the average AI user which program they rely on for everyday use, and they’ll likely mention programs like ChatGPT, Brave’s Leo search app or Microsoft’s Copilot. Rarely will a user state that they use a blockchain or crypto protocol. But is this user demand coming in the future? And will blockchain AI truly revolutionize the world, or is it just the latest fundraising hype? Cointelegraph sat down with executives from some of the leading blockchain AI protocols to ask them this very question. GPU demand is growing Guarav Sharma, chief technology officer of AI project IO, stated that today’s centralized cloud computing systems simply can’t keep up with the demand for graphical processing units (GPUs) that are desperately needed by AI developers, and this provides an opportunity for decentralized blockchain projects. Before working on the project, Sharma was employed in the hotel industry, developing AI models that helped predict which hotels a user was likely to book and what price to charge. But when he asked Amazon for enough GPUs to train his model, it reportedly claimed it didn’t have enough inventory to fulfill his requirements. He stated: “We went to Amazon to be honest, like we first thought of buying it. We couldn't buy it. Then we went to the cloud. We didn't find it there also, and we just had to wait for months to get this inventory from the AWS itself at that time.” The fundamental problem, according to Sharma, is that centralized cloud computing providers take months to set up servers in a particular location and at great cost to the average user. Meanwhile, there may be some GPUs sitting around in exactly the location the customer wants, but because they aren’t owned by the provider, they aren’t offered. For example, if a customer goes to Amazon and asks for 10,000 GPUs in Amsterdam, it’s not going to partner with Google to provide these servers. “That’s not the way they do it, right?” Sharma asked rhetorically. Map of IO GPUs. Source: IO In his view, decentralized protocols like IO can solve this problem by creating a marketplace for GPU power. Clients can come to the platform to find servers, and providers can offer their GPUs on the platform, allowing customers to find GPUs regardless of provider. Given the growing demand for GPUs as AI applications become more popular, he believes this is the only way to efficiently match buyers with sellers. Recent: $100K Bitcoin? 9 analysts share their end-of-year BTC price predictions Even so, Sharma conceded that some AI teams are not offering much value, both in blockchain AI in particular and in the broader AI industry. Some teams claim they are going to create the next big model with just three or five people, when in reality, this takes a much larger team than that. Others have engineers who worked for major companies but have no portfolio to show investors. Sharma suggested that investors should be careful to scrutinize the team behind each project. The ones that are likely to produce good work in the future have likely produced good work in the past. Investors should also demand open-sourcing of code and regular audits to ensure that the public is aware of how much human intervention is involved in the project, he claimed, as some “AI projects” are more human than AI. Prediction markets may need AI According to ORA co-founder Kartin Wong, blockchain prediction markets will need to use AI in the future, and this will necessitate the convergence between the two technologies. Wong pointed to the rise of Polymarket as proof of this need. While Polymarket runs on a blockchain, it “can have no oracle to address and to resolve [the question of who won a bet].” Instead, “it’s based on human judgment most of the time.” But blockchain AI can create oracles that will “answer anything in the world, if this thing happened on [the] internet.” Example Polymarket rule for election betting, relying on a single website instead of an AI model for resolution. Source: Polymarket He also argued that tokenization can facilitate fundraising for AI models. ORA pioneered the idea of an “initial model offering,” allowing untrained AI models to launch tokens. The resulting funds raised can be used to pay for the model’s training, which is highly GPU-intensive and expensive. According to Wong, the models launched on ORA are owned by tokenholders, allowing these holders to profit from their success. They are also open source, which creates transparency for the investing public. Wong claimed that this solves a common problem in AI, which is that most models have to be proprietary in order for their investors to make money. On ORA, model creators are required to abide by the licenses in open-source software, which he claimed prevents developers from merely cutting and pasting code to cannibalize the profits of creators. However, Wong also acknowledged that there are some fake blockchain AI projects or fake AI projects in general. Some models may claim to be generating results from AI, but they may be using humans to check the work produced by a model, and this may make the model superfluous. He suggested that distinguishing between fake and real AI may sometimes be very difficult. However the best way for investors to judge whether a product is really AI is to use it, he stated. He pointed to ChatOLM, a chatbot created through ORA, as an example of a product that is obviously using AI, since it answers questions faster than a human possibly could. Blockchain may allow for “truly autonomous AI” According to Ron Chan, co-founder of blockchain AI project Inference Labs, blockchain provides the only means to attain “truly autonomous AI.” For that reason, humanity won’t be able to do without it in the future. Chan stated that centralized AI “is developed toward the goals of the enterprise.” While this has its place in the world, decentralized AI fulfills a different need. It “is freeform — its development is driven by the participation and speed of market demand,” which “creates the conditions for human-centric innovation with the power to solve great challenges.” He claimed that decentralized AI will develop systems for “proof of inference” or the ability to prove that a particular answer came from a particular AI model. This, he stated, is an “immediate need” for the industry. Chan acknowledged that distinguishing between human and AI projects can sometimes be difficult or even impossible. He pointed to the example of X user Error Error Ttyl, an account that claims to be controlled by an AI model. “How can observers verify there isn’t a human operator making decisions behind the scenes?” he asked rhetorically, pointing out that because both the AI and its creator hold the password to the account, verifying who is generating the posts may be impracticable. Recent: 7 policymakers who are ‘laser focused’ on $100K Bitcoin price The answer is to give the AI exclusive control, verifiable independence and irrevocable delegation, Chan suggested. The AI must have “sole access to the account,” and third parties must be able to verify this fact. In addition, once control over the account is transferred to the AI, it must be impossible for humans to regain this control. Only then can it be known that whatever it does is truly initiated by the AI model and not by a human working behind the scenes. In Chan’s view, this type of provable AI inference is an area where only decentralized protocols can offer a solution. The greatest benefits may come later Cointelegraph asked the interviewees for examples of consumer-facing blockchain AI apps that users can enjoy now rather than in the future. In response, Wong referenced the chat app OLMChat, while Chan discussed an aircraft-tracking AI venture and a liquid staking app created by the Inference Labs team. While these apps may have small user bases compared with superstar software like ChatGPT, the interviewees all had high hopes that blockchain AI really will revolutionize the world, even if its benefits may take a while to be fully realized by end-users.
AI concept tokens generally rise: WLD 24-hour increase of 27.12%
Bitget market data shows that AI concept tokens are performing strongly, among which: WLD has increased by 27.12% in the last 24 hours, currently priced at $2.6923; RENDER has increased by 26.39% in the last 24 hours, currently priced at $7.317; IO has increased by 21.95% in the last 24 hours, currently priced at $2.55.
io.net and NovaNet Collaborate to Launch Zero-Knowledge GPU Verification
An announcement has been made about a strategic relationship between io.net , the DePIN that provides access to high-performance GPU clusters, and NovaNet , which is the decentralized network for zero knowledge proofs (ZKP). Through the partnership, the security and dependability of decentralized computing will be improved, and cryptographic guarantees will be implemented. NovaNet is a decentralized incentive network that uses zero knowledge proofs to provide locally verifiable computations while protecting users’ privacy. The folding scheme-based zkVM that it offers is at the vanguard of ZKP technology. It enables proofs that are safe, transparent, and memory-efficient, therefore protecting user data while still offering strong cryptographic assurances of legitimate computation. An advanced solution that will give cryptographic guarantees that GPU resources on io.net are authentic and perform as claimed will be the primary emphasis of the cooperation. This solution will be known as zero knowledge GPU Identification (zkGPU-ID). Through the use of NovaNet’s powerful ZKPs, it will be possible to guarantee that the computing resources offered by io.net providers are completely verified, safe, and satisfy the performance standards that have been declared. Through the use of zkGPU-ID, io.net and NovaNet will build a system that will verify that the specs of the GPU match or surpass the performance that has been reported. This will allow for increased dependability and transparency within the decentralized computing landscape. Tausif Ahmed, io.net VP of Business Development, said: “Building a permissionless and enterprise-ready decentralized compute network requires optimizing co-ordination and verification across a massive group of distributed GPU suppliers. With NovaNet’s zkGPU-ID, we can increase our ability to continuously validate and test our network of GPUs at a global scale. By partnering with NovaNet and layering their solution on top of our internal verification mechanisms, we are ensuring that our customers can rest assured that the GPUs they are renting from io.net are reliable, trusted and exactly what they asked for.” Wyatt Benno, Technical Co-Founder of NovaNet, added: “For privacy and local verifiability, it is essential that ZKPs can run on many different types of devices and in various contexts. Using NovaNet’s zkVM will support a safe and secure way to identify GPUs using only software. The resulting cryptographic proofs can be cheaply verified by anyone.” The operation of the zkGPU-ID process is accomplished by executing a customized protocol for GPUs across a secure software channel that is supported by zkVM. A ZKP is produced as a result of this, which indicates that the specifications of a GPU meet or surpass the lowest boundaries of its proposed performance. To properly test and identify graphics processing units (GPUs) throughout the io.net platform, this protocol makes use of NovaNet’s zkVM, also known as the zkEngine. Protection against manipulation is provided by the fact that any effort to reroute the process or interfere with it would result in an invalid or degraded evidence.
io.net Partners with YOM to Revolutionize Cloud Gaming Globally with Decentralized Infrastructure
Through the partnership, YOM will be able to take use of io.net’s extensive worldwide network of GPUs. Gamers will be able to enjoy consistent, low-latency gaming experiences regardless of their physical location or network demands. In an effort to increase access to top-notch gaming experiences, leading decentralized GPU infrastructure network io.net has partnered with cloud gaming decentralized network (DePIN) YOM . Through the partnership, YOM will be able to take use of io.net’s extensive worldwide network of GPUs to improve its cutting-edge pixel streaming capabilities. In comparison to conventional centralized server solutions, the DePIN platform will drastically save costs while enabling the smooth, low-latency delivery of AAA gaming experiences to gamers worldwide with the help of io.net. Tausif Ahmed, VP of Business Development at io.net stated: “We are extremely proud to partner with YOM, a cutting-edge project that shares our passion for decentralization – and in particular DePIN. By combining our decentralized GPU infrastructure with YOM’s pioneering cloud gaming infrastructure, we are giving players access to a rich and accessible ecosystem that benefits them in countless ways.” To assist areas where individual nodes have not yet been established and to manage overflow during times of high traffic, YOM plans to make use of the network of io.net. As a consequence, gamers will be able to enjoy consistent, low-latency gaming experiences regardless of their physical location or network demands. Andrew Pringle, CEO of YOM said: “Working with io.net allows us to significantly expand our global reach while maintaining the same high-quality gaming experience users have come to expect. With the gaming market recognized as the biggest consumer entertainment sector by usage and revenue, the ability to quickly match GPU demand in various regions is vital to ensuring optimal UX. Our vision of making AAA gaming experiences accessible to everyone, everywhere, at near-zero prices is coming into clearer focus.” Voice chat, multiplayer functionality, cross-device controls, and extensive analytics are all part of YOM’s extensive feature set. By using a distributed network of gaming machines, the platform successfully removes the need for specialized game consoles (Xbox, Playstation) and platforms like Steam by providing low-latency, near-zero cost cloud gaming to any device and channel.
The performance of Grass Coin is strong, with an increase of 9.3% within 24 hours
In the DePin ecosystem, Grass coin has recently performed outstandingly, with a price increase of 9.3% within 24 hours, and its current price is $1.72. Grass focuses on providing blockchain bandwidth management solutions, and its market performance once made its market value reach $1.8 billion, surpassing IO's market value of $1.2 billion. The market fluctuates greatly, please manage your risk well.
io.net and GAIB Partner to Simplify GPU Access
io.net, a decentralized network for GPU clusters, has teamed up with GAIB, an AI and computing company, to make GPU computing resources easier for everyone to access. According to the press release shared, the collaboration will expand GAIB’s role as a key supplier of GPUs for io.net’s network. They will work together on marketing to reach more users, community engagement, technical integration, and business development. Tausif Ahmed, the VP of Business Development at io.net, said, “This partnership represents a natural evolution of our existing relationship with GAIB.” He also highlighted how both companies can benefit from their combined expertise in GPU technology. Additionally, the two companies will explore shared interests like tokenizing compute resources and working on joint marketing efforts. They will also look for new business opportunities together. Kony Kwong, CEO of GAIB, also shared his excitement about the partnership, saying, “Working with io.net will allow us to further our mission of making AI compute accessible to literally everyone.” This collaboration will help both companies implement their plans successfully. While they have not confirmed specific early activities, they might include technology evaluations and joint publicity efforts. By working together, io.net and GAIB hope to help GPU owners earn funds from their assets. They believe this collaboration will improve GPU computing for businesses and developers, creating new opportunities in the growing AI market. Follow The Crypto Times on Google News to Stay Updated!
io.net and GAIB Forge Strategic Alliance to Democratize GPU Computing Resources
io.net , the premier decentralized physical infrastructure network (dePIN) for GPU clusters, has announced a comprehensive strategic collaboration with GAIB , the economic layer for the AI and computing future. Through the agreement, the two parties will pledge to further their common goal of democratizing access to GPU computing resources. The enlarged relationship will include a number of projects aimed at promoting innovation and enhancing accessibility to GPU computing, building on GAIB’s longstanding position as a major GPU provider to io.net’s decentralized distributed compute network. The organizations will pay special attention to business growth potential, technological integration, community involvement, and strategic marketing. Tausif Ahmed, VP of Business Development at io.net stated: “This partnership represents a natural evolution of our existing relationship with GAIB. Our expertise in decentralized GPU infrastructure, coupled with GAIB’s forward-looking approach to GPU tokenization and financialization, will help unlock exciting new possibilities for both of our communities.” In addition to working together on technological and product synergies such as compute tokenization, cooperative marketing campaigns, and community engagement efforts, the collaboration will investigate a number of shared interests. In addition, there will be collaborative user onboarding campaigns, technological assessments, and concerted business development initiatives to find and seize new market possibilities. Kony Kwong, CEO of GAIB stated: “Working with io.net will allow us to further our mission of making AI compute accessible to literally everyone. We are excited to work hand in glove to push the boundaries of what’s possible.” To guarantee the effective completion of the aforementioned projects, both businesses will make use of their unique resources and areas of competence. Detailed technical assessments, deployment planning, and planned public relations efforts are examples of early actions that have not yet been proven. The partnership represents a major advancement in the efficiency and accessibility of high-performance GPU computing for developers and enterprises, opening up new avenues for GPU owners to profit from their assets.
High-Stakes Profits: 3 Altcoins Positioned for Huge Returns This Quarter
The cryptocurrency market is alive with potential this quarter. Three lesser-known coins are attracting attention with their promising outlooks and recent surges. These digital assets show signs that they could deliver significant gains soon. Investors and traders are keen to find out which cryptocurrencies might be on the verge of remarkable growth. Excitement builds as these altcoins position themselves for possible high returns. ZDEX Presale: Once-in-a-Lifetime Opportunity to Crush 2025’s DeFi Market! While many are scrambling to catch up with recent market losses, smart investors are locking down spots in the ZDEX token presale. ZDEX fuels ZircuitDEX, the next-gen DEX running on the ultra-fast Zircuit L2 blockchain. ZurcuitDEX is designed for those who don’t settle for mediocrity. Think almost instant swaps, slippage so small it makes your ex’s apologies look sincere, and fees lower than your last bar tab. Plus, with ZK-proof security, your assets are safer than that secret stash of snacks you hide from your roommates. With concentrated liquidity empowering liquidity providers, ZircuitDEX boosts your capital efficiency by up to 500x compared to any other existing DEXs. And thanks to automated liquidity strategies, you can sit back, relax, and let your gains pile up faster than a Black Friday checkout line. Additionally, ZircuitDEX’s meme coin launchpad gives you front-row access to the next meme coin explosion. Let’s be real – if you missed out on BRETT’s meteoric rise, you probably cried a little inside. But no worries, ZircuitDEX is where meme culture meets serious money! So, whether you’re exploring DeFi opportunities or trading meme coins, ZDEX gives you countless ways to cash in on the 2024 crypto craze. If you’re not in yet, what are you waiting for? Currently, ZDEX is available at a price of just $0.0017 and is primed to deliver 1,000% returns even before the next major crypto rally kicks in! >>>The ZDEX presale is here — get in, or spend 2024 regretting your life choices!<<< Site: ZircuitDEX Twitter: https://x.com/ZircuitDEX Read CRYPTONEWSLAND on google news Telegram: https://t.me/ZircuitDexVerify io.net (IO) Set to Surge: Bullish Momentum Builds Ahead of Altcoin Season io.net is currently trading between $1.55 and $2.02, showing signs of consolidation after an impressive six-month gain of over 2700%. The price hovers near its 10-day and 100-day moving averages, suggesting a potential breakout. The Relative Strength Index is at 46.35, indicating there’s room for upward movement. If IO breaks the nearest resistance at $2.27, it could rally toward the next target of $2.73, marking a significant increase from current levels. With the global crypto market gearing up for a bull run, io.net might be poised for substantial growth, making it a coin to watch in the upcoming altcoin season. Super Trump (STRUMP) Poised for Breakout After Recent Dip Super Trump is trading between $0.0063 and $0.0091, showing consolidation after a 9% dip last week. The price remains above both the 10-day and 100-day simple moving averages, which are close at around $0.0079, indicating strong support. The RSI at 53 suggests bullish momentum, while the high stochastic value of 83 points to potential overbuying. With resistance at $0.0105, a breakout above this level could see STRUMP aiming for the second resistance at $0.0133, which would be an increase of over 40%. Considering the 42% gain in the past month and 139% surge over six months, STRUMP may be set for significant growth in the coming altcoin season. Conclusion IO and STRUMP may offer less short-term potential. ZircuitDEX provides 500X capital efficiency with fast transactions and zero slippage, enhancing capital management. The ZDEX Token is in presale at a 70% discount, offering potential 500% returns at launch. Benefits include early access to new meme coins, lower fees, governance rights, and revenue sharing. Disclaimer and Risk Warning This article is a sponsored press release and is for informational purposes only. Crypto News Land does not endorse or is responsible for any content, quality, products, advertising, products, accuracy or any other materials on this article. This content does not reflect the views of Crypto News Land, nor is it intended to be used for legal, tax, investment, or financial advice. Crypto News Land will not be held responsible for image copyright matters. Readers are advised to always do your own research before making any significant decisions.
Vitalik: Ethereum TPS can be increased by 1.5 times by selectively reducing Gas fees
On October 24th, Ethereum co-founder Vitalik Buterin posted on the X platform stating, "I believe the ideal solution is to deeply but selectively reduce gas fees: reduce the gas cost of all EVM opcodes currently in the 2-5 range to 1, and reduce those in the 6-10 range to 2. Reduce the gas cost of logging operations by 4 times. Reduce the gas cost of precompiled contracts (except those we plan to deprecate). This way, it may be possible to increase TPS by 1.5 times without compromising any "critical worst-case" metrics (such as calldata size, IO)."
Vitalik: Ether TPS Can Be Boosted 1.5x by Selectively Cutting Gas Fees
On October 24, Ether co-founder Vitalik Buterin said in a social media post that I think the ideal scenario would be to make deep but selective cuts to Gas costs: - Reduce the gas cost to 1 for all EVM opcodes currently in the 2-5 range, and to 2 for those in the 6-10 range. - Reduce the gas cost of logging operations by a factor of 4. - Reduce the gas cost of precompiled contracts (except for those we plan to deprecate). In this way, it is possible to improve TPS by 1.5x without compromising any key worst-case metrics (e.g. calldata size, IO).
What Will Happen Next for Bitcoin Price? 5.5 Billion Dollar Shakeout So Close
Bitcoin surged above $69,000 overnight on Sunday, hitting its highest price in three months. The rally capped Bitcoin’s strongest week in two months and added fuel to excitement in a market already gaining momentum. With BTC nearing its all-time high of $73,000, market experts are speculating on the possibility of it breaking a new record this year. Here are what analysts say are the key factors driving Bitcoin’s recent rally. However, today the BTC price experienced a sudden partial decline and is currently trading at $ 67,800. With the decline, there was a liquidation of approximately $ 70 million in the cryptocurrency market, $ 65 million of which were in long positions. Mena Theodorou, co-founder of cryptocurrency exchange Coinstash, attributes the price increase to rising demand for spot Bitcoin ETFs in the U.S. According to Theodorou, institutional investors bought $2.1 billion worth of Bitcoin ETFs last week alone. This massive inflow was fueled by 36,500 Bitcoins mined, significantly exceeding the daily supply. “We are seeing investors buying Bitcoin much faster than it is being mined,” Theodorou said. According to Jonathan de Wet, chief investment officer of crypto trading firm Zerocap, the rising ETF demand is just part of a larger institutional push that could push Bitcoin to new highs. “Institutionalization is the short-term catalyst that is taking us above all-time highs, and monetary policy needs to be eased through 2025 to follow,” De Wet said. He also cited MicroStrategy’s ambition to be a Bitcoin bank and the SEC’s approval of options on Bitcoin ETFs as critical factors contributing to this institutional momentum. Related News MicroStrategy Founder Michael Saylor Makes an Astonishing Prediction About Bitcoin Price Last week, U.S. stocks and gold trading hit all-time highs, while concerns about U.S. public debt, which has risen by nearly half a trillion dollars since last month, prompted investors to seek riskier assets, with Bitcoin being a prime target. Analysts at crypto ETPs provider ETC Group noted that bullish sentiment has returned to the market. They cited the Crypto Asset Sentiment Index, which reached its highest point since March. At its peak in March, the crypto market cap exceeded $2.8 trillion, with Bitcoin trading above $73,000. “Bitcoin is increasingly viewed as an alternative asset to U.S. Treasury bonds, which may have contributed to the recent rally in the price of Bitcoin and other crypto assets,” ETC Group analysts wrote. Despite the optimistic outlook, Bitcoin remains vulnerable to market volatility and geopolitical influences. Analysts note that approximately $5.5 billion worth of Bitcoin options are set to expire on October 25, representing the second-largest Bitcoin option expiration to date. Analysts at CEX.IO, a crypto exchange, warned that if Bitcoin’s upward momentum fades, this key options expiration could send its price down to around $64,000. However, they also suggested that a break above $70,000 could force options sellers to buy more Bitcoin, potentially fueling further bullish momentum. Bitcoin’s historical tendency to perform well in the fourth quarter could also play a role, Theodorou added. “It looks like it’s going to be a big week for crypto markets,” he said. *This is not investment advice.
Web3-AI track panoramic report: in-depth analysis of technical logic, scenario applications and top projects
Written by Geekcartel As the AI narrative continues to heat up, more and more attention is focused on this track. Geekcartel has conducted an in-depth analysis of the technical logic, application scenarios and representative projects of the Web3-AI track to present you with a comprehensive overview and development trends of this field. 1. Web3-AI: Analysis of technical logic and emerging market opportunities 1.1 The integration logic of Web3 and AI: How to define the Web-AI track In the past year, AI narratives have been extremely popular in the Web3 industry, and AI projects have sprung up like mushrooms after rain. Although there are many projects involving AI technology, some projects only use AI in certain parts of their products, and the underlying token economics have no substantial connection with AI products. Therefore, such projects are not included in the discussion of Web3-AI projects in this article. This article focuses on projects that use blockchain to solve production relationship problems and AI to solve productivity problems. These projects provide AI products themselves and use the Web3 economic model as a production relationship tool. The two complement each other. We classify such projects as the Web3-AI track. In order to help readers better understand the Web3-AI track, Geekcartel will introduce the development process and challenges of AI, as well as how the combination of Web3 and AI can perfectly solve problems and create new application scenarios. 1.2 AI development process and challenges: from data collection to model reasoning AI technology is a technology that allows computers to simulate, expand and enhance human intelligence. It enables computers to perform a variety of complex tasks, from language translation, image classification to face recognition, autonomous driving and other application scenarios. AI is changing the way we live and work. The process of developing an AI model usually includes the following key steps: data collection and data preprocessing, model selection and tuning, model training and inference. For example, to develop a model to classify cat and dog images, you need: Data collection and data preprocessing: Collect a dataset containing images of cats and dogs. You can use a public dataset or collect real data yourself. Then label each image with a category (cat or dog) to ensure that the label is accurate. Convert the images into a format that the model can recognize and divide the dataset into training, validation, and test sets. Model selection and tuning: Choose a suitable model, such as a convolutional neural network (CNN), which is more suitable for image classification tasks. Tune the model parameters or architecture according to different requirements. Generally speaking, the network layer of the model can be adjusted according to the complexity of the AI task. In this simple classification example, a shallower network layer may be sufficient. Model training: You can use GPU, TPU or high-performance computing cluster to train the model. The training time is affected by the complexity of the model and the computing power. Model inference: The model training file is usually called the model weight. The inference process refers to the process of using the trained model to predict or classify new data. In this process, the test set or new data can be used to test the classification effect of the model. Usually, indicators such as accuracy, recall rate, and F1-score are used to evaluate the effectiveness of the model. As shown in the figure, after data collection and data preprocessing, model selection and tuning, and training, the trained model is inferred on the test set to obtain the predicted value P (probability) of cats and dogs, that is, the probability that the model infers that it is a cat or a dog. The trained AI model can be further integrated into various applications to perform different tasks. In this example, the AI model for cat and dog classification can be integrated into a mobile app, and users can upload pictures of cats or dogs to get classification results. However, the centralized AI development process has some problems in the following scenarios: User privacy: In centralized scenarios, the AI development process is usually opaque. User data may be stolen and used for AI training without knowing it. Data source acquisition: When small teams or individuals acquire data in specific fields (such as medical data), they may face the limitation that the data is not open source. Model selection and tuning: For small teams, it is difficult to obtain domain-specific model resources or cost a lot to tune models. Obtaining computing power: For individual developers and small teams, the high cost of purchasing GPUs and renting cloud computing power can be a significant financial burden. AI asset income: Data labelers often cannot earn income that matches their efforts, and the research results of AI developers are difficult to match with buyers in need. The challenges existing in centralized AI scenarios can be solved by combining with Web3. As a new type of production relationship, Web3 is naturally adapted to AI, which represents a new type of productivity, thereby promoting the simultaneous advancement of technology and production capabilities. 1.3 Synergy between Web3 and AI: Role Transformation and Innovative Applications The combination of Web3 and AI can enhance user sovereignty, provide users with an open AI collaboration platform, and transform users from AI users in the Web2 era to participants, creating AI that everyone can own. At the same time, the integration of the Web3 world and AI technology can also collide with more innovative application scenarios and gameplay. Based on Web3 technology, the development and application of AI will usher in a brand new collaborative economic system. Peoples data privacy can be guaranteed, the data crowdsourcing model promotes the progress of AI models, many open source AI resources are available to users, and shared computing power can be obtained at a lower cost. With the help of decentralized collaborative crowdsourcing mechanisms and open AI markets, a fair income distribution system can be achieved, thereby motivating more people to promote the advancement of AI technology. In Web3 scenarios, AI can have a positive impact on multiple tracks. For example, AI models can be integrated into smart contracts to improve work efficiency in different application scenarios, such as market analysis, security detection, social clustering and other functions. Generative AI not only allows users to experience the role of artist, such as using AI technology to create their own NFTs, but also to create rich and diverse game scenarios and interesting interactive experiences in GameFi. The rich infrastructure provides a smooth development experience, and both AI experts and novices who want to enter the field of AI can find a suitable entry in this world. 2. Web3-AI Ecosystem Project Map and Architecture Interpretation We mainly studied 41 projects in the Web3-AI track and divided these projects into different levels. The division logic of each layer is shown in the figure below, including the infrastructure layer, the middle layer and the application layer, and each layer is divided into different sections. In the next chapter, we will conduct an in-depth analysis of some representative projects. The infrastructure layer covers the computing resources and technical architecture that support the entire AI life cycle. The middle layer includes data management, model development, and verification reasoning services that connect infrastructure and applications. The application layer focuses on various applications and solutions directly facing users. Infrastructure layer: The infrastructure layer is the foundation of the AI life cycle. This article classifies computing power, AI Chain, and development platform as the infrastructure layer. It is the support of these infrastructures that enables the training and reasoning of AI models, and presents powerful and practical AI applications to users. Decentralized computing network: It can provide distributed computing power for AI model training, ensuring efficient and economical use of computing resources. Some projects provide a decentralized computing power market, where users can rent computing power or share computing power at low cost to gain benefits. Representative projects include IO.NET and Hyperbolic. In addition, some projects have derived new ways of playing, such as Compute Labs, which proposed a tokenization protocol. Users can participate in computing power leasing in different ways to gain benefits by purchasing NFTs representing GPU entities. AI Chain: Using blockchain as the foundation of the AI life cycle, it realizes seamless interaction between on-chain and off-chain AI resources and promotes the development of the industry ecosystem. The decentralized AI market on the chain can trade AI assets such as data, models, agents, etc., and provide AI development frameworks and supporting development tools, with representative projects such as Sahara AI. AI Chain can also promote the advancement of AI technology in different fields, such as Bittensor promoting subnet competition of different AI types through innovative subnet incentive mechanisms. Development platform: Some projects provide AI agent development platforms and can also implement AI agent transactions, such as Fetch.ai and ChainML. One-stop tools help developers create, train and deploy AI models more conveniently, such as Nimble. These infrastructures promote the widespread application of AI technology in the Web3 ecosystem. Middle layer: This layer involves AI data, models, reasoning and verification, and the use of Web3 technology can achieve higher work efficiency. Data: The quality and quantity of data are key factors affecting the effectiveness of model training. In the Web3 world, through crowdsourcing data and collaborative data processing, resource utilization can be optimized and data costs can be reduced. Users can have data autonomy and sell their own data under privacy protection to avoid data being stolen by bad businesses and making high profits. For data demanders, these platforms provide a wide range of choices and extremely low costs. Representative projects such as Grass use user bandwidth to crawl Web data, and xData collects media information through user-friendly plug-ins and supports users to upload tweet information. In addition, some platforms allow domain experts or ordinary users to perform data preprocessing tasks, such as image annotation and data classification. These tasks may require professional knowledge of financial and legal tasks. Users can tokenize their skills to achieve collaborative crowdsourcing of data preprocessing. Representative AI markets such as Sahara AI have data tasks in different fields and can cover data scenarios in multiple fields; and AIT Protocolt annotates data through human-machine collaboration. Model: In the AI development process mentioned above, different types of requirements need to match the appropriate model. Common models for image tasks include CNN and GAN. The Yolo series can be selected for object detection tasks. RNN, Transformer and other models are common for text tasks. Of course, there are also some specific or general large models. Tasks of different complexity require different model depths, and sometimes the model needs to be tuned. Some projects support users to provide different types of models or collaboratively train models through crowdsourcing. For example, Sentient allows users to place trusted model data in the storage layer and distribution layer for model optimization through modular design. The development tools provided by Sahara AI have built-in advanced AI algorithms and computing frameworks, and have the ability of collaborative training. Reasoning and verification: After the model is trained, a model weight file will be generated, which can be used directly for classification, prediction or other specific tasks. This process is called reasoning. The reasoning process is usually accompanied by a verification mechanism to verify whether the source of the reasoning model is correct, whether there is malicious behavior, etc. Web3 reasoning can usually be integrated into smart contracts, and reasoning is performed by calling the model. Common verification methods include ZKML, OPML and TEE technologies. Representative projects such as ORA on-chain AI oracle (OAO) introduced OPML as the verifiable layer of the AI oracle. ORAs official website also mentioned their research on ZKML and opp/ai (ZKML combined with OPML). Application layer: This layer mainly consists of user-oriented applications that combine AI with Web3 to create more interesting and innovative ways to play. This article mainly sorts out projects in the areas of AIGC (AI generated content), AI agents, and data analysis. AIGC: AIGC can be extended to NFT, games and other tracks in Web3. Users can directly generate text, images and audio through prompts (prompts given by users), and even generate customized gameplay according to their preferences in games. NFT projects such as NFPrompt allow users to generate NFTs through AI and trade them in the market; games such as Sleepless allow users to shape the personality of their virtual partners through dialogue to match their preferences; AI agent: refers to an artificial intelligence system that can perform tasks and make decisions autonomously. AI agents usually have the ability to perceive, reason, learn and act, and can perform complex tasks in various environments. Common AI agents include language translation, language learning, image-to-text, etc. In Web3 scenarios, they can generate trading robots, generate meme pictures, and perform on-chain security detection. For example, MyShell, as an AI agent platform, provides various types of agents, including educational learning, virtual companions, trading agents, etc., and provides user-friendly agent development tools, so you can build your own agents without code. Data analysis: By integrating AI technology and databases in related fields, data analysis, judgment, and prediction can be achieved. In Web3, users can be assisted in investment decisions by analyzing market data and smart money trends. Token prediction is also a unique application scenario in Web3. Representative projects such as Ocean have officially set up long-term challenges for token prediction, and will also release data analysis tasks on different topics to encourage user participation. 3. Panoramic analysis of cutting-edge projects in the Web3-AI track Some projects are exploring the possibility of combining Web3 with AI. GeekCartel will sort out the representative projects in this track to lead everyone to experience the charm of WEB3-AI and understand how the projects can achieve the integration of Web3 and AI and create new business models and economic value. Sahara AI: An AI blockchain platform dedicated to the collaborative economy Sahara AI is very competitive in the entire track. It is committed to building a comprehensive AI blockchain platform that covers a full range of AI resources such as AI data, models, agents, and computing power. The underlying architecture safeguards the collaborative economy of the platform. Through blockchain technology and unique privacy technology, decentralized ownership and governance of AI assets are ensured throughout the entire AI development cycle to achieve fair incentive distribution. The team has a deep background in AI and Web3, which enables it to perfectly integrate these two major fields. It has also been favored by top investors and has shown great potential in the track. Sahara AI is not limited to Web3, because it breaks the unequal distribution of resources and opportunities in the traditional AI field. Through decentralization, key AI elements including computing power, models and data are no longer monopolized by centralized giants. Everyone has the opportunity to find a position that suits them in this ecosystem to benefit, and be inspired to be creative and work together. As shown in the figure, users can use the toolkit provided by Sahara AI to contribute or create their own data sets, models, AI agents and other assets, and place these assets in the AI market to make profits while also receiving platform incentives. Consumers can trade AI assets on demand. At the same time, all these transaction information will be recorded on the Sahara Chain. Blockchain technology and privacy protection measures ensure the tracking of contributions, the security of data and the fairness of rewards. In the economic system of Sahara AI, in addition to the roles of developers, knowledge providers, and consumers mentioned above, users can also act as investors, providing funds and resources (GPU, cloud servers, RPC nodes, etc.) to support the development and deployment of AI assets, as operators to maintain the stability of the network, and as validators to maintain the security and integrity of the blockchain. Regardless of how users participate in the Sahara AI platform, they will receive rewards and income based on their contributions. The Sahara AI blockchain platform is built on a layered architecture, with on-chain and off-chain infrastructure enabling users and developers to effectively contribute to and benefit from the entire AI development cycle. The architecture of the Sahara AI platform is divided into four layers: Application Layer The application layer serves as the user interface and primary interaction point, providing natively built-in toolkits and applications to enhance the user experience. Functional components: Sahara ID — ensures secure access to AI assets and tracks user contributions; Sahara Vault — protects the privacy and security of AI assets from unauthorized access and potential threats; Sahara Agent — has role alignment (interactions that match user behavior), lifelong learning, multimodal perception (can process multiple types of data), and multi-tool execution capabilities; Interactive components: Sahara Toolkit — enables technical and non-technical users to create and deploy AI assets; Sahara AI Marketplace — For publishing, monetizing, and trading AI assets, with flexible licensing and multiple monetization options. Transaction Layer Sahara AIs transaction layer uses the Sahara blockchain, which is equipped with protocols for managing ownership, attribution, and AI-related transactions on the platform, and plays a key role in maintaining the sovereignty and provenance of AI assets. The Sahara blockchain integrates the innovative Sahara AI native precompilation (SAP) and Sahara blockchain protocol (SBP) to support basic tasks throughout the AI life cycle. SAP is a built-in function at the native operation level of the blockchain, focusing on the AI training/reasoning process respectively. SAP helps to call, record and verify the off-chain AI training/reasoning process, ensure the credibility and reliability of the AI models developed within the Sahara AI platform, and guarantee the transparency, verifiability and traceability of all AI reasoning at the same time. At the same time, faster execution speed, lower computing overhead and gas cost can be achieved through SAP. SBP implements AI-specific protocols through smart contracts to ensure that AI assets and computational results are handled transparently and reliably, including functions such as AI asset registration, licensing (access control), ownership, and attribution (contribution tracking). Data Layer Sahara AIs data layer is designed to optimize data management throughout the AI lifecycle. It acts as an important interface, connecting the execution layer to different data management mechanisms and seamlessly integrating on-chain and off-chain data sources. Data component: includes on-chain and off-chain data. On-chain data includes metadata, ownership, commitments and certifications of AI assets, while data sets, AI models and supplementary information are stored off-chain. Data Management: Sahara AI’s data management solution provides a set of security measures to ensure that data is protected both in transit and at rest through a unique encryption solution. Working with AI licensed SBPs to achieve strict access control and verifiability, while providing private domain storage, users’ sensitive data can achieve enhanced security features. Execution Layer The execution layer is the off-chain AI infrastructure of the Sahara AI platform, interacting seamlessly with the transaction layer and data layer to execute and manage protocols related to AI computations and functions. Depending on the execution task, it securely extracts data from the data layer and dynamically allocates computing resources for optimal performance. Complex AI operations are coordinated through a set of specially designed protocols that are designed to facilitate efficient interactions between various abstractions, and the underlying infrastructure is designed to support high-performance AI computing. Infrastructure: Sahara AIs execution layer infrastructure is designed to support high-performance AI computing, with features such as fast and efficient, elastic and highly available. It ensures that the system remains stable and reliable under high traffic and failure conditions through efficient coordination of AI computing, automatic expansion mechanism and fault-tolerant design. Abstractions: Core abstractions are the basic components that form the basis of AI operations on the Sahara AI platform, including abstractions of resources such as datasets, models, and computing resources; high-level abstractions are built on top of core abstractions, namely the execution interfaces behind Vaults and agents, which can realize higher-level functions. Protocol: The abstract execution protocol is used to execute interactions with Vaults, interactions and coordination of agents, and collaborative computing. The collaborative computing protocol can realize joint AI model development and deployment among multiple participants, support computing resource contribution and model aggregation. The execution layer also includes a low computing cost technology module (PEFT), a privacy protection computing module, and a computing anti-fraud module. The AI blockchain platform that Sahara AI is building is committed to realizing a comprehensive AI ecosystem. However, this grand vision will inevitably encounter many challenges in the process of realization, requiring strong technical and resource support and continuous optimization and iteration. If it can be successfully realized, it will become the mainstay supporting the Web3-AI field and is expected to become the ideal garden in the hearts of Web2-AI practitioners. Team Information: The Sahara AI team is composed of a group of outstanding and creative members. Co-founder Sean Ren is a professor at the University of Southern California and has won honors such as Samsungs annual AI researcher, MIT TR 35 under 35 innovator, and Forbes 30 under 30. Co-founder Tyler Zhou graduated from the University of California, Berkeley, has a deep understanding of Web3, and leads a global team of talents with experience in AI and Web3. Since the creation of Sahara AI, the team has received millions of dollars in revenue from top companies including Microsoft, Amazon, MIT, Snapchat, and Character AI. Currently, Sahara AI is serving more than 30 corporate clients and has more than 200,000 AI trainers worldwide. The rapid growth of Sahara AI has enabled more and more participants to contribute and benefit from the sharing economy model. Financing Information: As of August this year, Sahara Labs has successfully raised $43 million. The latest round of financing was co-led by Pantera Capital, Binance Labs and Polychain Capital. In addition, it has also received support from pioneers in the field of AI such as Motherson Group, Anthropic, Nous Research, and Midjourney. Bittensor: New gameplay under the incentive of subnet competition Bittensor itself is not an AI commodity, nor does it produce or provide any AI products or services. Bittensor is an economic system that provides a highly competitive incentive structure for AI commodity producers, so that producers can continuously optimize the quality of AI. As an early project of Web3-AI, Bittensor has received widespread attention from the market since its launch. According to CoinMarketCap data, as of October 17, its market value has exceeded US$4.26 billion and its FDV (fully diluted valuation) has exceeded US$12 billion. Bittensor has built a network architecture consisting of many subnet networks. AI commodity producers can create subnets with customized incentives and different use cases. Different subnets are responsible for different tasks, such as machine translation, image recognition and generation, language large models, etc. For example, Subnet 5 can create AI images like Midjourney. When excellent tasks are completed, TAO (Bittensors token) will be rewarded. Incentive mechanisms are a fundamental part of Bittensor. They drive the behavior of subnet miners and control the consensus among subnet validators. Each subnet has its own incentive mechanism, where miners are responsible for performing tasks and validators score the results of subnet miners. As shown in the figure, the workflow between subnet miners and subnet validators is demonstrated with an example: The three subnet miners in the figure correspond to UID 37, 42 and 27 respectively; the four subnet validators correspond to UID 10, 32, 93 and 74 respectively. Each subnet validator maintains a weight vector. Each element of the vector represents the weight assigned to a subnet miner, which is determined based on the subnet validators evaluation of the miners task completion. Each subnet validator ranks all subnet miners by the weight vector and operates independently, transmitting its miner ranking weight vector to the blockchain. Typically, each subnet validator transmits an updated ranking weight vector to the blockchain every 100-200 blocks. The blockchain (subtensor) waits for the latest ranking weight vectors from all subnet validators of a given subnet to arrive on the blockchain. The ranking weight matrix formed by these ranking weight vectors is then provided as input to the on-chain Yuma consensus module. The on-chain Yuma consensus uses this weight matrix along with the stake amount associated with the UID on that subnet to calculate rewards. Yuma consensus calculates the consensus distribution of TAO and distributes the newly minted reward TAO to the account associated with the UID. Subnet validators can transfer their ranking weight vectors to the blockchain at any time. However, the subnets Yuma consensus cycle uses the latest weight matrix at the beginning of every 360 blocks (i.e. 4320 seconds or 72 minutes, 12 seconds per block). If the ranking weight vector of a subnet validator arrives after a 360-block cycle, then that weight vector will be used at the beginning of the next Yuma consensus cycle. TAO rewards are issued at the end of each cycle. Yuma consensus is Bittensors core algorithm for achieving fair node allocation. It is a hybrid consensus mechanism that combines elements of PoW and PoS. Similar to the Byzantine Fault Tolerant consensus mechanism, if the honest validators in the network are in the majority, they will eventually reach a consensus on the correct decision. The Root Network is a special subnet, which is Subnet 0. By default, the 64 subnet validators with the most stakes in all subnets are validators in the root network. The root network validators will evaluate and rank according to the quality of each Subnets output. The evaluation results of the 64 validators will be aggregated, and the final emission result will be obtained through the Yuma Consensus algorithm. The final result will be used to allocate the newly issued TAO to each Subnet. Although Bittensors subnet competition model improves the quality of AI products, it also faces some challenges. First, the incentive mechanism established by the subnet owner determines the miners income and may directly affect the miners work enthusiasm. Another problem is that the validator determines the token allocation amount of each subnet, but there is a lack of clear incentives to select subnets that are beneficial to Bittensors long-term productivity. This design may cause validators to prefer subnets with which they have a relationship or those that provide additional benefits. To solve this problem, contributors to the Opentensor Foundation proposed BIT 001: Dynamic TAO Solution, which proposes that the subnet token allocation amount for all TAO stakers to compete should be determined through a market mechanism. Team Information: Co-founder Ala Shaabana is a postdoctoral fellow at the University of Waterloo with an academic background in computer science. Another co-founder Jacob Robert Steeves graduated from Simon Fraser University in Canada, has nearly 10 years of experience in machine learning research, and worked as a software engineer at Google. Financing Information: In addition to receiving funding from the OpenTensor Foundation, a non-profit organization that supports Bittensor, Bittensor has announced in its community announcement that well-known crypto VCs Pantera and Collab Currency have become holders of TAO tokens and will provide more support for the projects ecological development. Other major investors include well-known investment institutions and market makers including Digital Currency Group, Polychain Capital, FirstMark Capital, GSR, etc. Talus: On-chain AI agent ecosystem based on Move Talus Network is an L1 blockchain built on MoveVM, designed for AI agents. These AI agents can make decisions and take actions based on predefined goals, achieve smooth inter-chain interactions, and be verifiable. Users can use the development tools provided by Talus to quickly build AI agents and integrate them into smart contracts. Talus also provides an open AI market for resources such as AI models, data, and computing power, where users can participate in various forms and tokenize their contributions and assets. One of the major features of Talus is its parallel execution and secure execution capabilities. With the entry of capital into the Move ecosystem and the expansion of high-quality projects, Taluss dual highlights of secure execution based on Move and AI agent integrated smart contracts are expected to attract widespread attention in the market. At the same time, the multi-chain interaction supported by Talus can also improve the efficiency of AI agents and promote the prosperity of AI on other chains. According to the official Twitter information, Talus recently launched Nexus, the first fully on-chain autonomous AI agent framework, which gives Talus a first-mover advantage in the field of decentralized AI technology and provides it with important competitiveness in the rapidly developing blockchain AI market. Nexus enables developers to create AI-driven digital assistants on the Talus network, ensuring anti-censorship, transparency, and composability. Unlike centralized AI solutions, through Nexus, consumers can enjoy personalized intelligent services, securely manage digital assets, automate interactions, and enhance daily digital experiences. As the first developer toolkit for on-chain agents, Nexus provides the foundation for building the next generation of consumer crypto AI applications. Nexus provides a range of tools, resources, and standards to help developers create agents that can execute user intent and communicate with each other on the Talus chain. Among them, the Nexus Python SDK bridges the gap between AI and blockchain development, making it easy for AI developers to get started without learning smart contract programming. Talus provides user-friendly development tools and a range of infrastructure, and is expected to become an ideal platform for developer innovation. As shown in Figure 5, the underlying architecture of Talus is based on a modular design, with the flexibility of off-chain resources and multi-chain interactions. Based on the unique design of Talus, a prosperous on-chain smart agent ecosystem is formed. The protocol is the core of Talus and provides the foundation for consensus, execution, and interoperability, on top of which on-chain smart agents can be built to utilize off-chain resources and cross-chain functions. Protochain Node: A PoS blockchain node based on Cosmos SDK and CometBFT. Cosmos SDK has modular design and high scalability. CometBFT is based on the Byzantine Fault Tolerant consensus algorithm, with high performance and low latency. It provides strong security and fault tolerance, and can continue to operate normally in the event of some node failures or malicious behavior. Sui Move and MoveVM: Using Sui Move as the smart contract language, the design of the Move language inherently enhances security by eliminating critical vulnerabilities such as reentrancy attacks, missing access control checks for object ownership, and unexpected arithmetic overflow/underflow. The architecture of the Move VM supports efficient parallel processing, enabling Talus to scale by processing multiple transactions simultaneously without losing security or integrity. IBC (The Inter-Blockchain Communication protocol): Interoperability: IBC facilitates seamless interoperability between different blockchains, enabling smart agents to interact and utilize data or assets on multiple chains. Cross-chain atomicity: IBC supports cross-chain atomic transactions, which is critical for maintaining the consistency and reliability of operations performed by smart agents, especially in financial applications or complex workflows. Scalability through Sharding: IBC indirectly supports scalability through sharding by enabling smart agents to operate on multiple blockchains. Each blockchain can be viewed as a shard that processes a portion of transactions, thereby reducing the load on any single chain. This enables smart agents to manage and execute tasks in a more distributed and scalable manner. Customizability and Specialization: With IBC, different blockchains can focus on specific functions or optimizations. For example, a smart agent might use one chain that allows for fast transactions for payment processing and another chain that is specialized for secure data storage for record keeping. Security and Isolation: IBC maintains security and isolation between chains, which is beneficial for smart agents that handle sensitive operations or data. Since IBC ensures secure verification of inter-chain communications and transactions, smart agents can confidently operate between different chains without compromising on security. Mirror Object: In order to represent the off-chain world in the on-chain architecture, mirror objects are mainly used to verify and link AI resources, such as: resource uniqueness representation and proof, off-chain resource tradability, ownership proof representation or ownership verifiability. Image objects include three different types of image objects: model objects, data objects, and calculation objects. Model Objects: Model owners can bring their AI models into the ecosystem through a dedicated model registry, bringing off-chain models to the chain. Model objects encapsulate the essence and capabilities of the model and build ownership, management, and monetization frameworks directly on top of them. Model objects are flexible assets that can be enhanced through additional fine-tuning processes or completely reshaped through extensive training to meet specific needs when necessary. Data Object: A data (or dataset) object exists as a digital representation of a unique dataset owned by someone. This object can be created, transferred, licensed or converted into an open data source. Computational Object: The buyer proposes a computational task to the owner of the object, who then provides the computational result and the corresponding proof. The buyer holds the key that can be used to decrypt the commitment and verify the result. AI Stack: Talus provides an SDK and integration components that support the development of intelligent agents and their interaction with off-chain resources. The AI stack also includes integration with Oracles, ensuring that intelligent agents can make decisions and react using off-chain data. On-chain smart agent: Talus provides an economy of smart agents that can operate autonomously, make decisions, execute transactions, and interact with on-chain and off-chain resources. Intelligent agents have autonomy, social capabilities, responsiveness, and initiative. Autonomy enables them to operate without human intervention, social capabilities enable them to interact with other agents and humans, responsiveness enables them to perceive environmental changes and respond in a timely manner (Talus supports agents to respond to on-chain and off-chain events through listeners), and initiative enables them to take actions based on goals, predictions, or expected future states. In addition to the development architecture and infrastructure of a series of intelligent agents provided by Talus, AI agents built on Talus also support multiple types of verifiable AI reasoning (opML, zkML, etc.) to ensure the transparency and credibility of AI reasoning. A set of facilities designed by Talus specifically for AI agents can realize multi-chain interaction and mapping functions between on-chain and off-chain resources. The on-chain AI agent ecosystem launched by Talus is of great significance to the development of the integration technology of AI and blockchain, but it is still difficult to implement. Talus infrastructure enables the development of AI agents with flexibility and interoperability, but as more and more AI agents run on the Talus chain, whether the interoperability and efficiency between these agents can meet user needs remains to be verified. At present, Talus is still in the private testnet stage and is constantly being developed and updated. It is expected that Talus can promote the further development of the on-chain AI agent ecosystem in the future. Team Information: Mike Hanono is the founder and CEO of Talus Network. He holds a bachelors degree in industrial and systems engineering and a masters degree in applied data science from the University of Southern California. He has participated in the Wharton School of the University of Pennsylvania and has extensive experience in data analysis, software development, and project management. Financing Information: In February this year, Talus completed its first round of financing of US$3 million, led by Polychain Capital, with participation from Dao 5, Hash 3, TRGC, WAGMI Ventures, Inception Capital, etc. Angel investors mainly came from Nvidia, IBM, Blue 7, Symbolic Capital and Render Network. ORA: The cornerstone of on-chain verifiable AI ORAs product OAO (On-chain AI Oracle) is the worlds first AI oracle that uses opML, which can bring off-chain AI reasoning results onto the chain. This means that smart contracts can interact with OAO to implement AI functions on the chain. In addition, ORAs AI oracle can be seamlessly combined with the Initial Model Issuance (IMO) to provide a full-process on-chain AI service. ORA has a first-mover advantage in both technology and the market. As a trustless AI oracle on Ethereum, it will have a profound impact on its broad user base, and more innovative AI application scenarios are expected to emerge in the future. Developers can now use the models provided by ORA in smart contracts to achieve decentralized reasoning, and can build verifiable AI dApps on Ethereum, Arbitrum, Optimism, Base, Polygon, Linea, and Manta. In addition to providing verification services for AI reasoning, ORA also provides model issuance services (IMO) to promote the contribution of open source models. ORA’s two main products are: Initial Model Issuance (IMO) and On-Chain AI Oracle (OAO), which work perfectly together to enable the acquisition of on-chain AI models and the verification of AI reasoning. IMO incentivizes long-term open source contributions by tokenizing the ownership of open source AI models, and token holders will receive a portion of the revenue generated by the use of the model on the chain. ORA also provides funding for AI developers to incentivize the community and open source contributors. OAO brings verifiable AI reasoning on the chain. ORA introduces opML as the verification layer of AI oracle. Similar to the workflow of OP Rollup, the verifier or any network participant can check the results during the challenge period. If the challenge is successful, the wrong result is updated on the chain. After the challenge period, the result is finalized and immutable. To build a verifiable and decentralized oracle network, it is crucial to ensure the computational validity of the results on the blockchain. This process involves a system of proofs that ensure the computation is reliable and authentic. To this end, ORA provides three proof system frameworks: AI Oracles opML (currently ORAs AI oracle already supports opML) keras 2c ircoms zkML (mature and high-performance zkML framework) zk+opML combines the privacy of zkML and the scalability of opML to realize future on-chain AI solutions through opp/ai opML: opML (Optimistic Machine Learning) was invented and developed by ORA, combining machine learning with blockchain technology. By leveraging similar principles to Optimistic Rollups, opML ensures the validity of computations in a decentralized manner. The framework allows on-chain verification of AI computations, increasing transparency and promoting trust in machine learning reasoning. To ensure security and correctness, opML employs the following fraud protection mechanisms: Result submission: The service provider (submitter) performs machine learning calculations off-chain and submits the results to the blockchain. Verification period: The verifier (or challenger) has a predefined period (challenge period) to verify the correctness of the submitted results. Dispute Resolution: If a validator finds that a result is incorrect, they initiate an interactive dispute game. This dispute game effectively determines the exact computational step where the error occurred. On-chain verification: Only the disputed computation steps are verified on-chain through the Fraud Proof Virtual Machine (FPVM), minimizing resource usage. Finalization: If no disputes are raised during the challenge period, or once a dispute is resolved, the result will be finalized on the blockchain. ORAs opML enables computation to be performed off-chain in an optimized environment, processing only minimal data on-chain in the event of a dispute. This avoids the expensive proof generation required for zero-knowledge machine learning (zkML) and reduces computational costs. This approach is able to handle large-scale computations that are difficult to achieve with traditional on-chain methods. keras 2c ircom (zkML): zkML is a proof framework that uses zero-knowledge proofs to verify machine learning reasoning results on-chain. Due to its privacy, it can protect private data and model parameters during training and reasoning, thereby solving privacy issues. Since the actual calculation of zkML is completed off-chain, the chain only needs to verify the validity of the results, thus reducing the computational load on the chain. Keras 2C ircom, built by ORA, is the first high-level, battle-tested zkML framework. According to a benchmark of leading zkML frameworks from the Ethereum Foundation ESP funding proposal [FY 23 – 1290], Keras 2C ircom and its underlying circomlib-ml were shown to be more performant than other frameworks. opp/ai (opML + zkML): ORA also proposed OPP/AI (Optimistic Privacy-Preserving AI on Blockchain), which integrates zero-knowledge machine learning (zkML) for privacy with optimistic machine learning (opML) for efficiency, creating a hybrid model tailored for on-chain AI. By strategically partitioning machine learning (ML) models, opp/ai balances computational efficiency and data privacy, thereby achieving secure and efficient on-chain AI services. opp/ai divides ML models into multiple sub-models based on privacy requirements: the zkML sub-model is used to process components with sensitive data or proprietary algorithms, and is executed using zero-knowledge proofs to ensure the confidentiality of data and models; the opML sub-model is used to process components that prioritize efficiency over privacy, and is executed using opMLs optimistic approach to achieve maximum efficiency. In summary, ORA innovatively proposed three proof frameworks: opML, zkML, and opp/ai (a combination of opML and zkML). The diverse proof frameworks enhance data privacy and computing efficiency, bringing higher flexibility and security to blockchain applications. As the first AI oracle, ORA has great potential and broad imagination space. ORA has published a large number of research and results, demonstrating its technical advantages. However, the reasoning process of AI models has certain complexity and verification costs. Whether the reasoning speed of on-chain AI can meet user needs has become a question that needs to be verified. After time verification and continuous optimization of user experience, such AI products may be a great tool to improve the efficiency of on-chain Dapps. Team Information: Co-founder Kartin graduated from the University of Arizona with a degree in computer science and has served as a technical leader at Tiktok and a software engineer at Google. Chief Scientist Cathie holds a masters degree in computer science from the University of Southern California and a doctorate in psychology and neuroscience from the University of Hong Kong. She was a zkML researcher at the Ethereum Foundation. Financing Information: On June 26 this year, ORA announced the completion of a 20 million financing round, with investors including Polychain Capital, HF 0, Hashkey Capital, SevenX Ventures and Geekcartel. Grass: The data layer for AI models Grass focuses on turning public network data into AI datasets. Grasss network uses users excess bandwidth to scrape data from the Internet without obtaining users personal privacy information. This type of network data is indispensable for the development of artificial intelligence models and the operation of many other industries. Users can run nodes and earn Grass points. Running a node on Grass is as easy as registering and installing a Chrome extension. Grass links AI demanders and data providers, creating a win-win situation. Its advantages are: simple installation operations and future airdrop expectations greatly promote user participation, which also provides more data sources for demanders. As data providers, users do not need to perform complex settings and actions, and data capture, cleaning and other operations can be performed without user perception. In addition, there are no special requirements for equipment, which lowers the threshold for user participation, and its invitation mechanism also effectively promotes more users to join quickly. Since Grass needs to perform data crawling operations to reach tens of millions of web requests per minute. These all need to be verified, which will require more throughput than any L1 can provide. The Grass team announced in March that it will build a Rollup plan to support users and builders to verify the source of data. The plan batches metadata through the ZK processor for verification, and the proof of each dataset metadata will be stored on Solanas settlement layer and generate a data ledger. As shown in the figure, clients make web requests, which pass through validators and are ultimately routed to Grass nodes, and the websites server responds to web page requests, allowing its data to be fetched and returned. The purpose of the ZK processor is to help record the source of the datasets fetched on the Grass network. This means that every time a node fetches the network, they can get their rewards without revealing any information about their identity. After being recorded in the data ledger, the collected data is cleaned and structured through the graph embedding model (Edge Embedding) for AI training. In summary, Grass allows users to contribute excess bandwidth to capture network data to earn passive income while protecting personal privacy. This design not only brings economic benefits to users, but also provides AI companies with a decentralized way to obtain a large amount of real data. Although Grass has greatly lowered the threshold for user participation and is conducive to increasing user participation, the project side needs to consider that the participation of real users and the influx of wool parties may bring a large amount of spam, which will increase the burden of data processing. Therefore, the project side needs to set up a reasonable incentive mechanism and price the data to obtain truly valuable data. This is an important influencing factor for both the project side and the users. If users feel confused or unfair about the airdrop allocation, they may distrust the project side, which will affect the consensus and development of the project. Team Information: Founder Dr. Andrej graduated from York University in Canada with a degree in Computational and Applied Mathematics. Chief Technology Officer Chris Nguyen has many years of experience in data processing, and the data company he founded has won many honors, including the IBM Cloud Embedded Excellence Award, Enterprise Technology Top 30, and Forbes Cloud 100 Rising Stars. Financing Information: Grass is the first product launched by the Wynd Network team, which completed a $3.5 million seed round of financing led by Polychain Capital and Tribe Capital in December 2023, with participation from Bitscale, Big Brain, Advisors Anonymous, Typhon V, Mozaik, etc. Previously, No Limit Holdings led the Pre-see round of financing, with a total financing amount of $4.5 million. In September this year, Grass completed its Series A financing, led by Hack VC and participated by Polychain, Delphi Digital, Brevan Howard Digital, Lattice fund and others. The amount of financing was not disclosed. IO.NET: Decentralized computing resource platform IO.NET aggregates idle network computing resources around the world by building a decentralized GPU network on Solana. This enables AI engineers to obtain the required GPU computing resources at a lower cost, easier to obtain, and more flexible. ML teams can build model training and reasoning service workflows on a distributed GPU network. IO.NET not only provides income for users with idle computing power, but also greatly reduces the computing power burden of small teams or individuals. With Solanas high throughput and efficient execution efficiency, it has an inherent advantage in GPU network scheduling. IO.NET has received a lot of attention and favor from top institutions since its launch. According to CoinMarketCap, as of October 17, the market value of its tokens has exceeded US$220 million, and the FDV has exceeded US$1.47 billion. One of the core technologies of IO.NET is IO-SDK, which is based on a dedicated fork of Ray. (Ray is an open source framework used by OpenAI that can scale AI and Python applications such as machine learning to clusters to handle large amounts of computation). Using Rays native parallelism, IO-SDK can parallelize Python functions and also supports integration with mainstream ML frameworks such as PyTorch and TensorFlow. Its memory storage enables fast data sharing between tasks, eliminating serialization delays. Product Components: IO Cloud: Designed for on-demand deployment and management of decentralized GPU clusters, it integrates seamlessly with IO-SDK to provide a comprehensive solution for scaling AI and Python applications. It provides computing power while simplifying the deployment and management of GPU/CPU resources. It reduces potential risks through firewalls, access control, and modular design, and isolates different functions to increase security. IO Worker: Users can manage their GPU node operations through this web application interface, including computing activity monitoring, temperature and power consumption tracking, installation assistance, security measures and revenue status. IO Explorer: Mainly provides users with comprehensive statistics and visualization of various aspects of GPU Cloud, allowing users to view network activity, key statistics, data points and reward transactions in real time. IO ID: Users can view their personal account status, including wallet address activity, wallet balance, and claim earnings. IO Coin: Supports users to view the token status of IO.NET. BC 8.AI: This is an AI image generation website powered by IO.NET, where users can implement the AI generation process of text to image. IO.NET uses idle computing power from cryptocurrency miners, projects like Filecoin and Render, and other projects to aggregate more than one million GPU resources, allowing AI engineers or teams to customize and purchase GPU computing services according to their needs. By utilizing idle computing resources around the world, users who provide computing power can tokenize their earnings. IO.NET not only optimizes resource utilization, but also reduces high computing costs, promoting a wider range of AI and computing applications. As a decentralized computing power platform, IO.NET should focus on user experience, computing power resource richness and resource scheduling and monitoring, which are important chips for winning in the decentralized computing power track. However, there have been disputes about resource scheduling before, and some people questioned the mismatch between resource scheduling and user orders. Although we cannot confirm the authenticity of this matter, it also reminds related projects that they should pay attention to the optimization of these aspects and the improvement of user experience. Without the support of users, the exquisite products are just vases. Team Information: Founder Ahmad Shadid was previously a quantitative system engineer at WhalesTrader and a contributor and mentor to the Ethereum Foundation. CTO Gaurav Sharma previously worked as a senior development engineer at Amazon, an architect at eBay, and worked in the engineering department at Binance. Financing Information: On May 1, 2023, the company officially announced the completion of a $10 million seed round of financing; On March 5, 2024, it announced the completion of a US$30 million Series A financing round, led by Hack VC, with participation from Multicoin Capital, 6th Man Ventures, M 13, Delphi Digital, Solana Labs, Aptos Labs, Foresight Ventures, Longhash, SevenX, ArkStream, Animoca Brands, Continue Capital, MH Ventures, Sandbox Games, etc. MyShell: An AI agent platform connecting consumers and creators MyShell is a decentralized AI consumer layer that connects consumers, creators, and open source researchers. Users can use the AI agents provided by the platform, or build their own AI agents or applications on MyShells development platform. MyShell provides an open market for users to freely trade AI agents. In MyShells AIpp store, you can see a variety of AI agents, including virtual companions, trading assistants, and AIGC-type agents. As a low-threshold alternative to various types of AI chatbots such as ChatGPT, MyShell provides a wide range of AI functional platforms, lowering the threshold for users to use AI models and agents, enabling users to get a comprehensive AI experience. For example, users may want to use Claude for document organization and writing optimization, while using Midjourney to generate high-quality images. Usually, this requires users to register multiple accounts on different platforms and pay for some services. MyShell provides a one-stop service, providing free AI quotas every day, and users do not need to register and pay fees repeatedly. In addition, some AI products have restrictions on certain regions, but on the MyShell platform, users can generally use various AI services smoothly, which significantly improves the user experience. These advantages of MyShell make it an ideal choice for user experience, providing users with a convenient, efficient and seamless AI service experience. The MyShell ecosystem is built on three core components: Self-developed AI models: MyShell has developed multiple open source AI models, including AIGC and large language models, which users can use directly; you can also find more open source models on the official Github. Open AI development platform: Users can easily build AI applications. The MyShell platform allows creators to leverage different models and integrate external APIs. With native development workflows and modular toolkits, creators can quickly transform their ideas into functional AI applications, accelerating innovation. Fair incentive ecosystem: MyShells incentive method encourages users to create content that meets their personal preferences. Creators can receive native platform rewards when using self-built applications, and can also receive funds from consumers. In MyShells Workshop, you can see that it supports users to build AI robots in three modes. Both professional developers and ordinary users can match the appropriate mode. Use the classic mode to set model parameters and instructions, which can be integrated into social media software; the development mode requires users to upload their own model files; using the ShellAgent mode, you can build AI robots in a code-free form. MyShell combines the concept of decentralization with AI technology, and is committed to providing an open, flexible and fair incentive ecosystem for consumers, creators and researchers. Through self-developed AI models, open development platforms and multiple incentive methods, it provides users with a wealth of tools and resources to realize their creativity and needs. MyShell has integrated a variety of high-quality models, and the team is also continuously developing many AI models to improve the user experience. However, MyShell still faces some challenges in use. For example, some users reported that some models support for Chinese needs to be improved. However, by looking at the MyShell code repository, you can see that the team is continuously updating and optimizing, and actively listening to feedback from the community. I believe that with continuous improvement, the user experience will be better in the future. Team Information: Co-founder Zengyi Qin focuses on voice algorithm research and holds a Ph.D. from MIT. While pursuing a bachelors degree at Tsinghua University, he has published several top conference papers. He also has professional experience in robotics, computer vision, and reinforcement learning. Another co-founder, Ethan Sun, graduated from Oxford University with a degree in computer science and has many years of work experience in the AR+AI field. Financing Information: In October 2023, it raised $5.6 million in seed round financing. Led by INCE Capital, Hashkey Capital, Folius Ventures, SevenX Ventures, OP Crypto and others participated in the investment. In March 2024, it received $11 million in financing in its latest Pre-A round of financing. The financing was led by Dragonfly, and participated by investment institutions such as Delphi Digital, Bankless Ventures, Maven 11 Capital, Nascent, Nomad, Foresight Ventures, Animoca Ventures, OKX Ventures and GSR. In addition, this round of financing also received support from angel investors such as Balaji Srinivasan, Illia Polosukhin, Casey K. Caruso, and Santiago Santos. In August this year, Binance Labs announced an investment in MyShell through its sixth season incubation program, with the specific amount not disclosed. IV. Challenges and considerations that need to be addressed Although the track is still in its infancy, practitioners should think about some important factors that affect the success of the project. Here are some aspects to consider: Balance between supply and demand of AI resources: For Web3-AI ecological projects, it is extremely important to achieve a balance between supply and demand of AI resources and attract more people with real needs and who are willing to contribute. For example, users who have model, data, and computing power needs may have become accustomed to obtaining AI resources on the Web2 platform. At the same time, how to attract AI resource providers to contribute to the Web3-AI ecosystem, and how to attract more demanders to obtain resources and achieve a reasonable match of AI resources is also one of the challenges facing the industry. Data challenge: Data quality directly affects the model training effect. Ensuring data quality during data collection and data preprocessing, and filtering out the large amount of junk data brought by wool users will be important challenges faced by data projects. Project owners can improve the credibility of data through scientific data quality control methods and more transparently display the effect of data processing, which will also be more attractive to data demanders. Security issues: In the Web3 industry, it is necessary to use blockchain and privacy technologies to achieve on-chain and off-chain interactions of AI assets to prevent malicious actors from affecting the quality of AI assets and to ensure the security of AI resources such as data and models. Some project parties have proposed solutions, but the field is still under construction. As the technology continues to improve, higher and proven security standards are expected to be achieved. User Experience: Web2 users are usually accustomed to traditional operating experience, while Web3 projects are usually accompanied by complex smart contracts, decentralized wallets and other technologies, which may have a high threshold for ordinary users. The industry should consider how to further optimize user experience and educational facilities to attract more Web2 users to enter the Web3-AI ecosystem. For Web3 users, establishing an effective incentive mechanism and a continuously operating economic system is the key to promoting long-term user retention and healthy development of the ecosystem. At the same time, we should think about how to maximize the use of AI technology to improve the efficiency of the Web3 field and innovate more application scenarios and gameplay combined with AI. These are all key factors that affect the healthy development of the ecosystem. As the development trend of Internet+ continues to evolve, we have witnessed countless innovations and changes. At present, many fields have been combined with AI. Looking forward to the future, the era of AI+ may blossom everywhere and completely change our way of life. The integration of Web3 and AI means that the ownership and control of data will return to users, making AI more transparent and trustworthy. This integration trend is expected to build a more fair and open market environment and promote efficiency improvement and innovative development in all walks of life. We look forward to industry builders working together to create better AI solutions. References https://ieeexplore.ieee.org/abstract/document/9451544 https://docs.ora.io/doc/oao-onchain-ai-oracle/introduction https://saharalabs.ai/ https://saharalabs.ai/blog/sahara-ai-raise-43m https://bittensor.com/ https://docs.bittensor.com/yuma-consensus https://docs.bittensor.com/emissions#emission https://twitter.com/myshell_ai https://twitter.com/SubVortexTao https://foresightnews.pro/article/detail/49752 https://www.ora.io/ https://docs.ora.io/doc/imo/introduction https://github.com/ora-io/keras2c ircom https://arxiv.org/abs/2401.17555 https://arxiv.org/abs/2402.15006 https://x.com/OraProtocol/status/1805981228329513260 https://x.com/getgrass_io https://www.getgrass.io/blog/grass-the-first-ever-layer-2-data-rollup https://wynd-network.gitbook.io/grass-docs/architecture/overview#edge-embedding-models http://IO.NET https://www.ray.io/ https://www.techflowpost.com/article/detail_17611.html https://myshell.ai/ https://www.chaincatcher.com/article/2118663 Acknowledgements There is still a lot of research and work to be done in this emerging infrastructure paradigm, and there are many areas that are not covered in this article. If you are interested in related research topics, please contact Chloe. Many thanks to Severus and JiaYi for their insightful comments and feedback on this article. Finally, thanks to JiaYi for her cat love appearance.
io.net and ParallelAI Collaborate to Boost Decentralized Compute for Generative AI
ParallelAI is going to greatly expand upon the GPU compute that it presently accesses via IO Cloud. The decentralized GPU computing capabilities of io.net will be thoroughly integrated into ParallelAI’s platform. GPU DePIN io.net has made the announcement that it has entered into a strategic relationship with ParallelAI , which is the leading provider of solutions for optimizing parallel processing for artificial intelligence developers. Through the partnership between the two organizations, the decentralized GPU computing capabilities of io.net will be thoroughly integrated into ParallelAI’s platform. ParallelAI is going to greatly expand upon the GPU compute that it presently accesses via IO Cloud in the form of A100s by forming a partnership with io.net. Because of this, ParallelAI will be able to grow its platform in order to provide artificial intelligence developers with the computational resources they demand for operations like as LLM training, conducting inference on trained models, and distributed deep learning activities. Additionally, in accordance with the conditions of the collaboration, io.net and ParallelAI will work together on research and development. By combining their individual skills and areas of expertise, the partners will work toward the goal of pushing the technological boundaries of GPU cloud computing and developing solutions that establish new standards for both performance and efficiency. ParallelAI is a tool that helps speed the development of artificial intelligence by enabling developers to write high-level code before entrusting ParallelAI with the responsibility of managing parallel computing across several GPUs and CPUs. This may cut the amount of time needed for calculation by up to twenty times and greatly cut expenditures. Parallel AI will be able to grow its company without encountering bottlenecks or disruptions in service if it is able to have access to decentralized GPU clusters on demand using IO Cloud. As a consequence of this, customers of ParallelAI are able to take advantage of the assurance of access as well as the capability to access compute in order to effectively handle intense AI workloads. Computing power for artificial intelligence use cases is provided by IO Cloud, which offers savings of up to 90 percent in comparison to typical cloud services. io.net’s cooperation with ParallelAI will make it possible for the company to increase its share of the market for AI/ML developers while simultaneously reducing the amount of money spent on hardware and infrastructure maintenance. Furthermore, it will be a driving force behind innovation within the decentralized artificial intelligence field by means of the creation of collaborative technologies between io.net and ParallelAI. These technologies have the potential to fuel the subsequent wave of AI solutions that are revolutionary. Because it is a decentralized distributed compute network, io.net makes it possible for machine learning engineers to deploy a GPU cluster of any size in a matter of seconds at a fraction of the cost that is required by centralized cloud providers. There are numerous places from which io.net obtains computing resources, and then it deploys those resources into a single cluster at a vast scale. The training, fine tuning, and inference processes for a broad variety of machine learning models have been effectively handled by io.net. To make better use of available computing resources, ParallelAI is an advanced artificial intelligence language platform. ParallelAI was developed specifically for companies that are experiencing performance challenges. It employs cutting-edge parallel processing methods to promote efficiency, so assisting organizations in doing more while simultaneously decreasing the need for massive infrastructures.
Could a Bitcoin Price Correction Loom as Futures Open Interest Soars Amid Declining Trading Volumes?
Recent analysis suggests a potential downturn in Bitcoin prices as the futures market sees unprecedented open interest levels. Experts express concern over the juxtaposition of increasing leverage with a notable decrease in trading activity. Illia Otychenko from CEX.IO highlights the fragility of the current market setup, suggesting that even minor fluctuations could lead to significant price corrections. As Bitcoin approaches a critical price threshold, analysts warn of a potential correction amidst rising leverage and declining trading volumes, urging traders to reassess their strategies for the upcoming market movements. Open Interest Peaks Amidst Declining Trading Volume Bitcoin’s open interest in the futures markets has reached an all-time high for 2024, raising alarms among traders and analysts alike. Reports indicate that while the open interest escalates, trading volumes are not keeping pace, resulting in a precarious market environment. Illia Otychenko, Lead Analyst at CEX.IO, notes the troubling implications of this trend: “A lack of active participation, combined with rising leverage, heightens the risk of abrupt corrections,” he explained. “Market sensitivity is at an all-time high; any unexpected sentiment shift could initiate liquidations across heavily leveraged positions.” The Role of Leverage in Market Fragility According to data from CoinGlass, Bitcoin’s recent price fluctuations demonstrate the potential outcomes of this fragile state. After soaring to $67,922, the price swiftly retreated to $65,160, liquidating approximately $302.25 million worth of leveraged positions in a matter of moments. Otychenko emphasizes that “the combination of reduced trading activity and growing leverage is alarming. It’s a scenario that could easily evolve into a downward spiral if traders opt to lock in profits on long positions.” The current market dynamics thus present a race against time for many traders hoping to navigate potential volatility. Technical Analysis Signals Caution Technical indicators further corroborate the cautious sentiment pervasive in the market. Bitcoin is presently testing critical resistance at $68,000—a level it has struggled to breach on multiple occasions. Moreover, the asset is nearing the upper boundary of the Bollinger Bands, which often precedes price corrections. The RSI and MACD indicators reveal bearish divergences, indicating a potential loss of upward momentum. Analysts suggest that the lack of solid support for the ongoing rally calls for heightened vigilance from traders. “The market environment is ripe for a pullback given the current indicators,” noted a market strategist. Institutional Interest and Market Sentiment The shifting landscape of Bitcoin’s price action is also colored by institutional involvement. Analyst Valentin Fournier from BRN pointed to substantial institutional flows contributing to the market’s psychology. “Recent ETF inflows totaling $371 million demonstrate strong institutional support despite the volatility. The Fear and Greed Index has climbed to 73, indicating increasing confidence among participants,” Fournier remarked. However, he cautioned that the market is now perched on a precarious edge, with looming selling pressure that could prompt sharp corrections if sustained bullish movement doesn’t materialize soon. Political Sentiment and Its Impact on Bitcoin The interplay between political sentiment and market dynamics could also influence Bitcoin’s trajectory. With Donald Trump’s odds of winning the 2024 presidential election climbing to 58.9%, market participants are acutely aware of the potential ramifications of political shifts. As election day draws near, the prospect of policy changes could further exacerbate Bitcoin’s volatility, leading traders to hedge their positions in anticipation of unforeseen outcomes. Otychenko aptly highlights this synergy, stating, “As election scenarios evolve, we may witness altered trading strategies based on expectations surrounding political events.” Conclusion As Bitcoin navigates through a multi-faceted landscape characterized by rising open interest and decreasing trading volumes, traders are advised to remain vigilant as market conditions remain unstable. While institutional participation and political developments add layers of complexity, the fundamental elements of leverage and trading volume suggest that caution may be warranted. Market participants must closely monitor these dynamics to mitigate risks and take informed positions based on ongoing market conditions. In Case You Missed It: Could Increasing Political Support for Bitcoin Influence Future Investments?
.io domains may disappear: the ups and downs of domain names changed by geopolitics
Original author: Gareth Edwards Original translation: TechFlow Introduction If you observe carefully, you will find that many projects in the cryptocurrency world have website domain names ending with .io. Not only in the cryptocurrency world, .io domain names are also often favored by emerging technology companies; from github.io to many innovative start-ups, .io seems to have become a trend symbol in the technology world. The British government recently announced that it would transfer sovereignty of the Chagos Archipelago to Mauritius, a seemingly distant diplomatic decision that could lead to the disappearance of .io domains. This article will take you deep into this incident, revealing the little-known connection between the digital world and real politics, and its possible profound impact on the technology industry. The original text is as follows: Gareth Edwards, who usually chronicles the forgotten history of Silicon Valley in his column, “Crazy People.” When the British government announced last week that it was transferring sovereignty of an island in the Indian Ocean to Mauritius, Gareth immediately recognized the digital implications: the end of the .io domain extension. In this article, he explores how geopolitical shifts are unexpectedly disrupting the digital world. His exploration of historical precedents—like the collapse of the Soviet Union—provides valuable context for tech founders, users, and observers. Read this article to learn about the unexpected intersection of international relations and internet infrastructure. On October 3, the British government announced that it would relinquish sovereignty over a group of tiny tropical atolls in the Indian Ocean called the Chagos Islands. The islands will be handed over to the neighboring island nation of Mauritius, about 1,100 miles off the southeast coast of Africa. This story didn’t make tech news, but maybe it should have. The decision to transfer the islands to new owners will result in the loss of one of the top-level domains preferred by the tech and gaming industries: .io. Whether it’s Github.io, gaming site itch.io, or Google I/O (arguably a trend that started in 2008), .io has always been a fixture in the tech lexicon. Its popularity can sometimes be explained by the fact that it stands for “input/output,” or any data that a system receives and processes. But what people often fail to acknowledge is that it’s more than just a playful domain. It’s a country code top-level domain (CCCID) associated with a country, which means it involves politics that extend far beyond the digital world. The United Kingdom and the United States have operated a major military base on the Chagos Archipelago (formally known as the British Indian Ocean Territory) since 1968, but neighboring Mauritius has long disputed British sovereignty. The Mauritian government has long argued that Britain illegally retained control when Mauritius gained independence. The dispute, which lasted more than 50 years, has finally been resolved. In return for a 99-year lease for the military base, the islands will become part of Mauritius. Once the treaty is signed, the British Indian Ocean Territory will cease to exist. Various international bodies will update their records. In particular, the International Organization for Standardization (ISO) will remove the country code IO from its specifications. The Internet Domain Name Authority (IANA), which creates and delegates top-level domains, uses this specification to determine which top-level country domains should exist. Once IO is deleted, IANA will reject any new .io domain name registrations. It will also automatically begin the process of deactivating existing ones. (There is no official count of the number of existing .io domain names. Officially, .io and countless websites will disappear. At a time when some domains can be worth millions of dollars, it’s a sobering reminder that forces beyond the internet still shape our digital lives. When domain names outlive countries Its extremely rare to remove an entire country or territory from a world map, so one might ask why the process of removing a domain name is so clearly documented. The answer is simple: history. There are two organizations responsible for domains and Internet addresses. IANA decides what should and shouldnt become top-level domains, such as .com, .org, .uk or .nz. The organization originated at the University of Southern California, but wasnt formally established until 1994, when it won a contract in the United States. As the Internet grew, it became clear that a more formal setup was needed. By 1998, IANA became part of a new organization: the Internet Corporation for Assigned Names and Numbers (ICANN). Based in the United States, ICANN was given broader responsibilities, overseeing the operational stability of the Internet and ensuring that international interests were represented. The two organizations may seem to play mundane roles, but they find themselves making some of the toughest decisions on the global internet. On September 19, 1990, IANA created the top-level domain .su and delegated it to the Soviet Union. Less than a year later, the Soviet Union collapsed. At the time, no one thought about what would happen to the .su domain - the Internet as we know it was still many years away from developing. As a result, the .su domain was handed over to Russia to operate alongside Russias own domain (.ru). The Russian government agreed that it would eventually be shut down, but there were no clear rules surrounding its governance or when that should happen. But ambiguity is the worst thing that can happen to a top-level domain. Unwittingly, this decision created an environment that made .su a digital Wild West. Today, it is a mostly unpoliced top-level domain, a home for deniable Russian dark operations, and a place for supremacist content and cybercrime. A few years later, in 1992, IANA learned a similar painful lesson at the end of the Balkan Wars, when Yugoslavia broke up into several smaller countries. In the aftermath, Serbia and Montenegro tried to adopt the name Federal Republic of Yugoslavia. Slovenia and Croatia objected, claiming that this meant that Serbia and Montenegro were the legal successors of Yugoslavia. The two countries protested to the United Nations. Throughout the early 90s, the international issue of the names of Serbia and Montenegro raged, and IANA was still unsure who should control .yu, the top-level domain for Yugoslavia. Email access and the Internet were now integral to research and international discussion, and IANA’s ambiguity led to an extraordinary academic espionage operation. According to journalist Kaloyan Kolev, Slovenian academics traveled to Serbia in late 1992. Their destination was the University of Belgrade in the countrys capital. Upon arrival, they broke into the university and stole all the hosting software and the domain records for the top-level domain .yu, everything they needed to seize control. For the next two years, the .yu domain was informally operated by ARNES (Slovenian Academic and Research Network), which repeatedly denied its involvement in the initial heist. ARNES rejected all requests for new domains from Serbian institutions, severely limiting the countrys ability to participate in the growing Internet community. The situation became so chaotic that in 1994, IANA founding manager Jon Postel personally stepped in and overturned the IANA regulations, forcing ownership of the .yu domain back to the University of Belgrade. In 2006, Montenegro declared independence from Serbia. With the digital revolution firmly underway, IANA was determined not to let chaos reignite. It created two new top-level domains: .rs for Serbia and .me for Montenegro. Both releases required that .yu would be officially terminated. That didnt happen until 2010, but IANA finally got its way. In the aftermath, the organization established a new, stricter set of rules and timelines for top-level domain expiration that exist today. These rules will soon apply to .io domains. They are firm, they are clear. Ideally in three to five years, once the country code ceases to exist, the domain name must also cease to exist. Just like a tenant is told that their landlord is selling the house and they must move, every person and company using a .io domain name will be told the same thing. The persistence of real-world history .io is popular among startups, especially those involved in cryptography, which often subscribe to one of the original principles of the internet: the independence that cyberspace confers on its users. Yet the long tail of real-world history may force them to make significant changes. IANA could probably fudge its own rules to allow .io to continue to exist. Money talks, and a lot of money is tied up in .io domains. However, Soviet and Yugoslav history still looms large, and IANA may feel that playing fast and loose with top-level domains will only come back to haunt it. Whatever happens, the warning to future technology founders is clear: be careful when choosing your top-level domain. Physical history is never as separate from our digital future as we think.
Cardano Price Forecast: ADA Could Unlock $1.2 Billion in Profits as It Eyes Key Resistance at $0.37
Last updated: October 10, 2024 09:20 EDT The Cardano price has dipped by 1% in the past 24 hours, dropping to $0.339 as the crypto market as a whole loses 2.5% today. ADA is also down by 1% in a week and by 13.5% in a fortnight, although the altcoin holds on to a 35% gain in a year. It may have underwhelmed in recent months, but its In/Out of the Money Around Price indicator currently suggests that a relatively modest rise to $0.37 would see around 3.5 billion ADA tokens – worth over $1 billion – enter profit. This highlights Cardano’s considerable potential, with the coin having some of the strongest – and most untapped – fundamentals in the market. Cardano Price Forecast: ADA Could Unlock $1.2 Billion in Profits as It Eyes Key Resistance at $0.37 Apart from a brief flurry a few days ago, ADA’s relative strength index (purple) has spent much of the past week under 50, a sign of overselling. Source: TradingView This is even more noticeable with the coin’s 30-period moving average (orange), which has been well below the 200-period average (blue) for quite some time. In other words, ADA’s technicals signal that the coin is selling substantially below a ‘fair’ value, with traders likely to make a tidy profit if they buy the token now. This is also the gist of the aforementioned In/Out of the Money Around Price indicator, which shows that traders have bought around $1.2 billion in ADA between its current price and the $0.37 resistance level. Source: IntoTheBlock What this means is that, if ADA rises to $0.37 in the next few weeks, the holders of these tokens will be in profit. Of course, given that they bought at a price between $0.339 and $0.37, they will not make $1.2 billion in profits in total. Assuming that they purchased at exactly $0.339, they would make a combined profit of around $109 million. Regardless, there are plenty of reasons to expect the Cardano price to rise in the near future. Not only are its technicals promising, but Cardano continues to grow steadily as a layer-one network. Its total value locked in has reached $206 million in recent weeks , while its platform now has 1,376 projects building on it . The Cardano ecosystem keeps growing 🌐. 🔎As of September 27, 2024, IO is aware of 1,376 projects building on Cardano🛠️. 🔎 The number of delegated wallets increased by 1,000. 🔎Plutus scripts grew by 13,611 to a total of 88,340. 🔎The number of token policies increased by… pic.twitter.com/71gIGAPYpW — Input Output (@InputOutputHK) October 7, 2024 Meanwhile, developers continue to make progress in developing Cardano’s scaling solution, Hydra, which the community claims will have uncapped transactions per second . Given all of this, the Cardano price could reach $0.37 in the next few weeks, and $0.50 by the end of November. Alternative Alts for Big Returns Traders who find ADA a little too steady for their liking may prefer to investigate newer tokens, of the kind that can post market-beating rallies during their early growth spurts. Identifying such coins early can be extremely tricky, but one way of finding candidates with good potential is to look for successful presales. And one coin having a good sale right now is Crypto All-Stars (STARS), a new ERC-20 token that has raised over $2.1 in its offering. Crypto All-Stars is unique because it’s the only token in the market that provides ‘MemeVault’. The latter enables users to stake and earn rewards from any meme token whatsoever, with holders receiving more rewards the more STARS they hold. By making use of the ERC-1155 multi-token standard, Crypto All-Stars can even tokenize coins not on the Ethereum blockchain. And because users staking DOGE or SHIB, for example, will receive more rewards if they hold more STARS as well, the MemeVault could incentivize massive demand for the new coin. It will have a max supply of 42.069 billion, with 20% of going to its presale , 20% to staking, and another 25% to the MemeVault ecosystem. Investors can participate in the STARS sale by going to the official Crypto All-Stars website , where they can buy the coin at a price of $0.0014947 per token. This price will rise in just over two days, while it could rise much, much higher once the token lists. And with Crypto All-Stars already having a large community and over 16,000 followers on X , it has every chance of rallying towards the end of the year. Visit Crypto All-Stars Now Disclaimer: Crypto is a high-risk asset class. This article is provided for informational purposes and does not constitute investment advice. You could lose all of your capital.
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