ADA's 24-hour increase exceeded 70%, and its market capitalization ranking rose to eighth; Binance will restrict the trading of non-MiCA compliant stablecoins in the European Economic Area; crypto analyst Eugene said that it is not yet certain whether the market will rebound bullishly or adjust bearishly, and attention should be paid to the direction after March 7.ADA's 24-hour increase exceeded 70%, and its market capitalization ranking rose to eighth; Binance will restrict the trading of non-MiCA compliant stablecoins in the European Economic Area; crypto analyst Eugene said that it is not yet certain whether the market will rebound bullishly or adjust bearishly, and attention should be paid to the direction after March 7.

PA Daily | Trump said that the strategic crypto reserves will include BTC, ETH, SOL, XRP and ADA; ZachXBT said that Ripple still holds about $7.18 billion in XRP

2025/03/03 17:30
16 min di lettura
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Today's news tips:

Binance to restrict trading of non-MiCA compliant stablecoins in the EEA

ZachXBT: Ripple co-founder still holds 2.7 billion XRP, about $7.18 billion

Trump says crypto strategic reserve will include BTC, ETH, SOL, XRP and ADA

Cronos proposes to re-issue 70 billion burned tokens to “create the Cronos Strategic Reserve”

ADA's 24-hour increase exceeded 70%, and its market capitalization ranking rose to eighth

Crypto analyst Eugene: It is not yet clear whether the market is bullish or bearish, and we will focus on the direction after March 7

Community users revealed: Ronaldinho signed a $10 million cooperation agreement with the Shenzhen coin issuance team to promote Meme coin and turned down the original $6 million cooperation agreement

Matrixport: ETF selling pressure appears to have paused for now, and hedge funds may re-evaluate arbitrage opportunities in late March

CZ: STAR10 tokens are limited to the BNB chain. Although I have been a fan of Ronaldinho since 2005, I have no commercial partnership with him.

Regulatory/Macro

Binance to restrict trading of non-MiCA compliant stablecoins in the EEA

Binance announced that in order to comply with the latest EU regulatory requirements on stablecoins, it will adjust the use of non-MiCA (Markets in Crypto Assets) compliant stablecoins in the European Economic Area (EEA). The affected assets include USDT, FDUSD, TUSD, USDP, DAI, AEUR, UST, USTC, and PAXG. MiCA-compliant stablecoin trading pairs (such as USDC and EURI) and fiat currency trading pairs (such as EUR) will continue to be available and unaffected. Binance recommends that users convert their non-MiCA compliant stablecoins (such as USDT) to USDC, EURI or EUR as soon as possible. The custody function of non-MiCA compliant stablecoins will still be retained, and users can deposit and withdraw these assets at any time. The specific adjustment time is as follows: Spot trading: Starting from 07:59 (Beijing time) on April 1, 2025, spot trading pairs of non-MiCA compliant stablecoins will be completely delisted, and users can still sell remaining assets through Binance Convert. Margin trading: Starting from 15:00 on March 27, 2025 (Beijing time), non-MiCA compliant margin trading pairs will be completely delisted, Binance will automatically convert users' related assets and liabilities into USDC and cancel all outstanding orders.

David Sacks, the “Crypto Czar”, confirmed that he sold all his cryptocurrency holdings before taking office in the US government

David Sacks, the White House's head of artificial intelligence and encryption affairs and the "crypto czar", posted on the X platform to confirm that he had sold all cryptocurrencies, including BTC, ETH and SOL, before the start of the administration.

Metaplanet announces an increase in holdings of 156 Bitcoins

According to an official announcement, Japanese listed company Metaplanet announced today that it spent 2.021 billion yen (about 1,344 US dollars) to increase its holdings of 156 bitcoins, bringing its bitcoin holdings to 2,391.

Trump says crypto strategic reserve will include Bitcoin and Ethereum

Trump posted on social media Truth Social: "Obviously, BTC and ETH and other valuable cryptocurrencies will be at the core of the reserves. I also like Bitcoin and Ethereum." Earlier yesterday, it was reported that Trump instructed the presidential working group to advance strategic reserves of cryptocurrencies including XRP, SOL and ADA.

Financing/Sales

Decentralized AI protocol Prime Intellect completes $15 million financing, led by Founders Fund

According to official news, the decentralized AI protocol Prime Intellect announced that it has completed $15 million in financing, led by Founders Fund, and participated by Menlo Ventures, Andrej Karpathy (EurekaAI, Tesla, OpenAI), Clem Delangue (Hugging Face), Dylan Patel (SemiAnalysis), Tri Dao (Together.AI), Balaji Srinivasan (Network School), Emad Mostaque (Stability AI, Intelligent Internet), Jake Medwell (8VC co-founder), Brendan McCord (Cosmos Institute), Sandeep Nailwal (Polygon), etc. This brings its total financing amount to more than $20 million. According to reports, Prime Intellect is building a peer-to-peer protocol for computing and intelligence that will enable collective creation, ownership, and access to sovereign open source AI. The Prime Intellect protocol is already running on the testnet, and it has built a trustless, community-driven ecosystem in which contributors will benefit from the models they help create.

Project News

Grayscale Digital Large Cap Fund Aligns with US Crypto Strategic Reserve Assets

Grayscale pointed out on the X platform that the Grayscale Digital Large Cap Fund (GDLC) is the only publicly traded fund in the United States that holds only the same crypto assets as the U.S. cryptocurrency strategic reserve. As of February 28, the Grayscale Digital Large Cap Fund's holdings include BTC, ETH, XRP, SOL and ADA. Earlier news, Trump announced that the crypto strategic reserve assets include BTC, ETH, XRP, SOL and ADA.

Cronos proposes to re-issue 70 billion burned tokens to “create the Cronos Strategic Reserve”

Cronos said on the X platform that a new proposal was posted for voting on the Cronos POS governance forum today, which "aims to restore Cronos to its golden age and will invest huge funds to support the Cronos roadmap (including ETFs) and the United States' ambition to become the global cryptocurrency capital." To achieve this goal, the Cronos Strategic Reserve will be established by reversing the token destruction in February 2021 (i.e. 70 billion CRO). (That is, the total supply is restored to the original 100 billion CRO). The Strategic Reserve will add another 5-year lock-up period to the 5-year lock-up period that has passed since the first Ethereum issuance of CRO, bringing the total vesting period to 10 years.

ADA's 24-hour increase exceeded 70%, and its market capitalization ranking rose to eighth

The market shows that Cardano (ADA) is now trading at $1.12, with a 24-hour increase of 70.3%. In addition, ADA's market value has increased to $40.2 billion, becoming the eighth largest cryptocurrency by market value. Earlier yesterday, Trump instructed the presidential working group to promote the strategic reserve of cryptocurrencies including XRP, SOL and ADA.

Wallets associated with the STAR10 team spent only 80 BNB to purchase 12.24% of the total supply of STAR10 tokens

According to Lookonchain monitoring, the wallet associated with the STAR10 team (@10Ronaldinho) spent only 80 BNB (worth $50,000) to purchase 122.45 million STAR10 tokens (12.24% of the total supply). Address 0x01D9...E14D spent 80 BNB (worth $50,000) to purchase 122.45 million STAR10 tokens, and sold 10 million STAR10 tokens for 433 BNB (worth $270,000), leaving 121.44 million STAR10 tokens (worth $33.5 million, 12.14% of the total supply). Address 0x01D9...E14D transferred 0.15 BNB to the STAR10 team wallet (0x8218...AF37) as a handling fee (gas fee). The address 0x01D9...E14D and the developer wallet (0xb36E...790d) both received funds from Allbridge. Former Brazilian football player Ronaldinho Gaucho announced the launch of his official token "STAR10" on the X platform this morning.

Viewpoint

Crypto analyst Eugene: It is not yet clear whether the market is bullish or bearish, and we will focus on the direction after March 7

Crypto analyst Eugene said that he had held a large number of long positions before the news about Trump, but most of them have been closed. He believes that when Bitcoin reached $80,000 and Solana reached $130, bulls were dominant, but now the market has become neutral. He expects that both long and short sides will face the risk of losses in the short term, and only cautious traders can make profits. He also said that it is not yet clear whether the market will rebound bullishly or adjust bearishly, and plans to wait until after March 7 to make a decision based on market trends. Before that, he will keep a light position to control risks. At the same time, he doubted whether Trump could push legislation related to Solana, Cardano and Ripple through Congress, but also admitted that Trump has the ability to achieve his goals, which still needs to be observed in the future. Earlier news, Tangent co-founder Darryl Wang admitted that Eugene was his trumpet and denied improper behavior such as pumping up shipments and charging for publicity.

Community users revealed: Ronaldinho signed a $10 million cooperation agreement with the Shenzhen coin issuance team to promote Meme coin and turned down the original $6 million cooperation agreement

According to the X platform user R10coin on February 28, Ronaldinho was accused of cooperating with a Shenzhen team to issue tokens and suspected of defrauding investors. R10coin said that its team started cooperation negotiations with Ronaldinho in May 2024, signed a cooperation agreement with a total amount of US$6 million in January 2025, and paid a deposit of US$3 million. However, Ronaldinho subsequently signed a cooperation agreement worth US$10 million with another company, collected a deposit of US$5 million, and began to promote other tokens for the company, seriously violating the original cooperation agreement. R10coin further accused the Shenzhen company of frequently issuing worthless meme tokens, attracting investor funds through false propaganda, and then quickly collapsing and running away with the money, suspected of malicious fraud. R10coin said it would disclose relevant contracts and evidence, and hold Ronaldinho and his partners accountable through legal means, while reminding investors to be vigilant against such fraud and avoid financial losses. *Note: Ronaldinho has not yet responded to the revelation.

Matrixport: ETF selling pressure appears to have paused for now, and hedge funds may re-evaluate arbitrage opportunities in late March

Matrixport analysis pointed out that the scale of fund outflows this month has hit a new high since the launch of the Bitcoin ETF in January 2024, which may be related to hedge funds closing basis trades (long ETFs, short futures). This trend is consistent with the reduction of $8 billion in open interest in CME Bitcoin futures after the Federal Reserve's December 2024 FOMC meeting, a drop of more than 20% of the total ETF inflows. In addition, the expiration and delivery of February futures contracts may also be a source of selling pressure, but this factor has been digested by the market. Matrixport believes that as the impact gradually weakens, hedge funds may reduce ETF selling and re-evaluate arbitrage opportunities in late March. At present, ETF selling pressure seems to have stopped temporarily.

CZ: STAR10 tokens are limited to the BNB chain. Although I have been a fan of Ronaldinho since 2005, I have no commercial partnership with him.

Binance founder CZ issued a statement reminding that football star Ronaldinho's STAR10 token is only issued exclusively on BNB Chain, and the tokens with the same name on other chains are all fake coins, so please be wary of fraud. He pointed out that Meme tokens are extremely volatile, and the trading volume has dropped significantly recently, and the investment risk is relatively high. CZ also said that although he has been a fan of Ronaldinho since 2005 and thanked BNB Chain for choosing to issue the STAR10 token, there is no commercial partnership between BNB Chain and its affiliates and Ronaldinho, and emphasized that this statement is not an endorsement of the token.

State Street Bank: Cryptocurrency ETFs are expected to surpass North American precious metals ETFs by the end of this year

According to the Financial Times, according to the forecast of State Street, the world's largest ETF service provider, the demand for cryptocurrency ETFs has surged, and its total assets are expected to exceed those of North American precious metals ETFs by the end of this year. This change will make digital token ETFs the third largest asset class in the $15 trillion ETF industry, second only to stocks and bonds, and surpassing real estate, alternative investments and multi-asset funds. Frank Koudelka, head of global ETF solutions at State Street, said: "We are very surprised by the growth rate of cryptocurrencies. I expected there would be pent-up demand, but I didn't expect it to be so strong." He expects cryptocurrency ETFs to continue to grow rapidly this year, and pointed out that data shows that more and more investment advisors are interested in cryptocurrencies and include them in their portfolios. Precious metals ETFs have a 20-year first-mover advantage. The world's first physically-backed gold ETF, the $85 billion SPDR Gold Trust (GLD), was launched in 2004 and is still the largest precious metals ETF. However, State Street expects that the total assets of North American precious metals ETFs of $165 billion will be surpassed by cryptocurrency ETFs this year. State Street also predicts that the U.S. Securities and Exchange Commission (SEC) will approve more digital asset ETFs this year. In addition to the existing Bitcoin and Ethereum ETFs, fund management companies have applied to launch ETFs based on a variety of tokens such as SOL, XRP and XRP. State Street expects that by 2025, ETFs based on the top ten tokens by market value will be approved.

Trump's second son Eric Trump: Traditional financial industry better keep up with the pace of cryptocurrency, otherwise it will soon die out

In response to "Trump is advancing the cryptocurrency reserve plan", Trump's second son Eric Trump posted on the X platform: "I like the cleverness of announcing the strategic reserve on Sunday, when the traditional market is closed and Wall Street is dormant. This time, retail investors win. The traditional financial industry better catch up quickly, otherwise it will soon die. The world no longer follows the Monday to Friday, 9 to 5 mode of operation."

Opinion: "Strategic reserves include altcoins" may be Trump's typical negotiation strategy

Udi Wertheimer, founder of Taproot Wizards, wrote on the X platform: "The best view I have seen so far on the strategic reserve is that this is just a typical Trump negotiation tactic. To actually build a reserve, Trump must convince Congress, and he can't decide alone. Whenever Trump needs to convince other stakeholders, he always makes a ridiculous claim first, which he can retract later. So, in Trump's chess language, this just means that he is telling Congress that if you don't agree to the Bitcoin reserve, I will make more outrageous conditions."

David Sacks: Trump is fulfilling his promise to make the United States the "cryptocurrency capital of the world"

David Sacks, the White House AI and cryptocurrency director and "crypto czar," posted on the platform: "President Trump announced a crypto strategic reserve consisting of Bitcoin and other top cryptocurrencies. This is consistent with his first week's Executive Order 14178. President Trump is fulfilling his promise to make the United States the 'crypto capital of the world.' More will be revealed at the White House Crypto Summit on March 7."

GoPlus: STAR10 has serious security risks, and the team can destroy any holder's tokens at will

GoPlus Security warns that Ronaldinho's STAR10 tokens pose a serious security risk. GoPlus found that token owners can destroy any holder's tokens at will. Since ownership has not been relinquished, all tokens are at risk of being destroyed without warning. GoPlus Security calls on Ronaldinho's team to immediately relinquish ownership to protect the community. At the same time, it reminds traders to be highly vigilant about the token and recommends that BNB Chain notify users of the relevant risks.

Important data

ZachXBT: Ripple co-founder still holds 2.7 billion XRP, about $7.18 billion

Crypto sleuth ZachXBT pointed out that XRP addresses associated with Ripple co-founder Chris Larsen still hold more than 2.7 billion XRP (about $7.18 billion). Some of these addresses transferred more than $109 million worth of XRP to exchanges in January 2025. ZachXBT also listed these addresses and mentioned that some of them have not been traded for 6 to 7 years, and it may be that Chris Larsen lost access or the funds were transferred to others in February 2013. In addition, he mentioned that Chris Larsen was hacked last year and lost up to $112 million.

Ronaldinho token STAR10 market value briefly exceeded $400 million, up 54.57% in 1 hour

According to GMGN market data, the market value of STAR10 tokens issued by football star Ronaldinho on BNB Chain exceeded US$400 million in a short period of time, and is currently reported at US$369.6 million, with an increase of 54.57% in 1 hour. *Note: This project has a short release time and a large fluctuation range. It is not an investment advice, DYOR. Earlier news, CZ: STAR10 tokens are limited to BNB chain. Although I have been a fan of Ronaldinho since 2005, I have no commercial cooperation relationship with him.

The Grass team recharged 4.75 million GRASS to Bybit eight hours ago, worth $13.01 million

According to the on-chain analyst @ai_9684xtpa, the Grass team is suspected of shipping, and recharged 4.75 million GRASS to Bybit eight hours ago, worth $13.01 million. After receiving 10 million tokens before TGE, the last large transfer to the exchange was two months ago; after checking, there was no token unlocking or activity.

3 million SOL has been deposited from the pledge account to the Binance cold wallet after 2 months of dormancy

According to OnchainLens, after two months of dormancy, 3 million SOL (worth $510.36 million) have been deposited from the pledge account into the Binance cold wallet. This wallet is likely to belong to Binance itself.

Amber Group is suspected of building a position of 11,000 ETH in the upward trend, worth $27.49 million

According to the monitoring of on-chain analyst @ai_9684xtpa, Amber Group is suspected of building a position of 11,000 ETH in the upward trend, worth $27.49 million. Affected by Trump's "calling", ETH rose by nearly $400 overnight and is now priced at $2,437; Amber proposed this part of the token seven hours ago, with a price of $2,499, but the actual purchase point may be lower.

CME Bitcoin futures set record with gap of more than $10,000

According to Cointelegraph, the Chicago Mercantile Exchange (CME) recorded the largest Bitcoin futures gap in history after US President Trump announced a strategic reserve of cryptocurrency. According to TradingView data, the news added more than $300 billion in trading volume to the spot market and caused a $10,000 gap in CME Bitcoin futures. This record gap exceeds the previous record of slightly above $4,000 in August 2024, which Asymmetric founder Joe McCann observed on March 2. Analyst Rekt Capital pointed out: "Bitcoin has filled the CME gap between $92,800 and $94,000 that was formed when the spot market plummeted last week." He added that Bitcoin successfully filled two CME gaps in a week, but in the process also created a brand new huge gap ranging from $84,650 to $94,000.

Bitcoin spot ETFs saw a net outflow of $2.61 billion last week, with BlackRock IBIT leading the way with a net outflow of $1.17 billion

According to SoSoValue data, last week's trading day (February 24 to February 28, Eastern Time), Bitcoin spot ETFs had a net outflow of $2.61 billion last week. The Bitcoin spot ETF with the largest net outflow last week was Blackrock's Bitcoin ETF IBIT, with a weekly net outflow of $1.17 billion. Currently, IBIT's total net inflow has reached $39.7 billion. The second is Fidelity's Bitcoin ETF FBTC, with a weekly net outflow of $569 million. Currently, FBTC's total net inflow has reached $11.76 billion. As of press time, the total net asset value of Bitcoin spot ETFs is $95.379 billion, and the ETF net asset ratio (market value to Bitcoin's total market value) is 5.71%, and the historical cumulative net inflow has reached $36.942 billion.

Opportunità di mercato
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From Federated Learning to Decentralized Agent Networks: ChainOpera Project Analysis

From Federated Learning to Decentralized Agent Networks: ChainOpera Project Analysis

ChainOpera leverages Web3-based governance and incentive mechanisms to bring users, developers, GPU/data providers into co-construction and co-governance, allowing AI Agents to not only be "used" but also "co-created and co-owned." Written by 0xjacobzhao In our June research report, "The Holy Grail of Crypto AI: Exploring the Frontiers of Decentralized Training," we mentioned federated learning, a "controlled decentralization" solution situated between distributed and decentralized training. Its core approach is to retain data locally and centrally aggregate parameters, meeting privacy and compliance requirements in healthcare, finance, and other fields. At the same time, we have consistently highlighted the rise of agent networks in previous reports. Their value lies in enabling multi-agent autonomy and division of labor to collaboratively complete complex tasks, driving the evolution from "large models" to "multi-agent ecosystems." Federated learning, with its principle of "data storage within the local machine and incentives based on contribution," lays the foundation for multi-party collaboration. Its distributed nature, transparent incentives, privacy protections, and compliance practices provide directly reusable experience for the Agent Network. Following this path, the FedML team upgraded its open-source nature into TensorOpera (the AI industry infrastructure layer) and then evolved it into ChainOpera (a decentralized agent network). Of course, the Agent Network is not an inevitable extension of federated learning. Its core lies in the autonomous collaboration and task division of multiple agents. It can also be directly built on multi-agent systems (MAS), reinforcement learning (RL), or blockchain incentive mechanisms. 1. Federated Learning and AI Agent Technology Stack Architecture Federated Learning (FL) is a framework for collaborative training without centralized data. Its fundamental principle is that each participant trains the model locally and only uploads parameters or gradients to a coordinating end for aggregation, thereby achieving privacy compliance with "data staying within the domain." Through practical application in typical scenarios such as healthcare, finance, and mobile, FL has entered a relatively mature commercial stage. However, it still faces bottlenecks such as high communication overhead, incomplete privacy protection, and low convergence efficiency due to heterogeneous devices. Compared with other training models, distributed training emphasizes centralized computing power for efficiency and scale, while decentralized training achieves fully distributed collaboration through open computing networks. Federated learning lies somewhere in between, embodying a "controlled decentralization" solution that not only meets industry needs for privacy and compliance but also provides a viable path for cross-institutional collaboration, making it more suitable for transitional deployment architectures within the industry. In the entire AI Agent protocol stack, we divided it into three main layers in our previous research report, namely Agent Infrastructure Layer: This layer provides the lowest-level operational support for agents and is the technical foundation for all agent systems. Core modules: including Agent Framework (agent development and operation framework) and Agent OS (lower-level multi-task scheduling and modular runtime), providing core capabilities for agent lifecycle management. Support modules: such as Agent DID (decentralized identity), Agent Wallet & Abstraction (account abstraction and transaction execution), Agent Payment/Settlement (payment and settlement capabilities). The Coordination & Execution Layer focuses on collaboration among multiple agents, task scheduling, and system incentive mechanisms, and is the key to building the "swarm intelligence" of the agent system. Agent Orchestration: It is a command mechanism used to uniformly schedule and manage the agent lifecycle, task allocation, and execution process. It is suitable for workflow scenarios with central control. Agent Swarm: It is a collaborative structure that emphasizes the collaboration of distributed intelligent agents. It has a high degree of autonomy, division of labor, and flexible collaboration, and is suitable for coping with complex tasks in dynamic environments. Agent Incentive Layer: Builds an economic incentive system for the Agent network to stimulate the enthusiasm of developers, executors, and validators, and provide sustainable power for the intelligent ecosystem. Application & Distribution Layer Distribution subcategories: including Agent Launchpad, Agent Marketplace, and Agent Plugin Network Application subcategories: including AgentFi, Agent Native DApp, Agent-as-a-Service, etc. Consumption subcategory: Agent Social / Consumer Agent, mainly for lightweight scenarios such as consumer social interaction Meme: It is hyped by the Agent concept, lacks actual technical implementation and application landing, and is only driven by marketing. 2. FedML, the Federated Learning Benchmark, and the TensorOpera Full-Stack Platform FedML is one of the earliest open-source frameworks for federated learning and distributed training. Originating from an academic team (USC) and gradually becoming a company-owned product of TensorOpera AI, it provides researchers and developers with tools for cross-institutional and cross-device data collaboration and training. In academia, FedML has become a universal experimental platform for federated learning research, with frequent appearances at top conferences such as NeurIPS, ICML, and AAAI. In industry, FedML has a strong reputation in privacy-sensitive scenarios such as healthcare, finance, edge AI, and Web3 AI, and is considered a benchmark toolchain for federated learning. TensorOpera is FedML's commercialized upgrade into a full-stack AI infrastructure platform for enterprises and developers. While maintaining its federated learning capabilities, it expands to the GPU Marketplace, model serving, and MLOps, thereby tapping into the larger market of the large model and agent era. TensorOpera's overall architecture can be divided into three layers: the Compute Layer (foundation layer), the Scheduler Layer (scheduling layer), and the MLOps Layer (application layer). 1. Compute Layer (bottom layer) The Compute layer is the technical foundation of TensorOpera, building on the open-source DNA of FedML. Its core functions include Parameter Server, Distributed Training, Inference Endpoint, and Aggregation Server. Its value proposition lies in providing distributed training, privacy-preserving federated learning, and a scalable inference engine. It supports the three core capabilities of "Train/Deploy/Federate," covering the entire chain from model training and deployment to cross-institutional collaboration, and serves as the foundation of the entire platform. 2. Scheduler Layer (Middle Layer) The Scheduler layer serves as the computing power trading and scheduling hub, comprised of the GPU Marketplace, Provision, Master Agent, and Schedule & Orchestrate. It supports resource allocation across public clouds, GPU providers, and independent contributors. This layer represents a key milestone in the evolution of FedML to TensorOpera. Through intelligent computing power scheduling and task orchestration, it enables larger-scale AI training and inference, encompassing typical LLM and generative AI scenarios. Furthermore, the Share & Earn model within this layer includes a reserved incentive mechanism interface, potentially enabling compatibility with DePIN or Web3 models. 3. MLOps Layer (Upper Layer) The MLOps layer is the platform's direct service interface for developers and enterprises, encompassing modules such as Model Serving, AI Agent, and Studio. Typical applications include LLM Chatbot, multimodal generative AI, and the developer Copilot tool. Its value lies in abstracting underlying computing power and training capabilities into high-level APIs and products, lowering the barrier to entry. It provides ready-to-use agents, a low-code development environment, and scalable deployment capabilities. It is positioned to compete with next-generation AI infrastructure platforms such as Anyscale, Together, and Modal, serving as a bridge from infrastructure to applications. In March 2025, TensorOpera upgraded to a full-stack platform for AI agents, with core products including the AgentOpera AI App, Framework, and Platform. The application layer provides a multi-agent entry point similar to ChatGPT. The framework layer evolved into "Agentic OS" with a graph-structured multi-agent system and Orchestrator/Router. The platform layer deeply integrates with the TensorOpera model platform and FedML to enable distributed model serving, RAG optimization, and hybrid end-to-end cloud deployment. The overall goal is to create "one operating system, one agent network," enabling developers, enterprises, and users to jointly build a next-generation Agentic AI ecosystem in an open and privacy-protected environment. 3. ChainOpera AI Ecosystem Overview: From Co-founder to Technology Foundation If FedML is the technical core, providing the open-source DNA of federated learning and distributed training, and TensorOpera abstracts FedML's research findings into commercially viable full-stack AI infrastructure, then ChainOpera brings TensorOpera's platform capabilities to the blockchain, creating a decentralized agent network ecosystem through an AI Terminal + Agent Social Network + DePIN model, a computing layer, and an AI-Native blockchain. The core shift lies in the fact that TensorOpera remains primarily focused on enterprises and developers, while ChainOpera leverages Web3-based governance and incentive mechanisms to bring users, developers, and GPU/data providers into the co-construction and co-governance of AI agents, allowing them to be not just "used" but "co-created and co-owned." Co-creators ChainOpera AI provides a toolchain, infrastructure, and coordination layer for ecosystem co-creation through the Model & GPU Platform and Agent Platform, supporting model training, intelligent agent development, deployment, and expansion collaboration. The ChainOpera ecosystem's co-creators include AI agent developers (designing and operating intelligent agents), tool and service providers (templates, MCP, databases, and APIs), model developers (training and publishing model cards), GPU providers (contributing computing power through DePIN and Web2 cloud partners), and data contributors and annotators (uploading and annotating multimodal data). These three core components—development, computing power, and data—jointly drive the continued growth of the intelligent agent network. Co-owners The ChainOpera ecosystem also incorporates a co-ownership mechanism, enabling collaborative network building through collaboration and participation. AI Agent creators are individuals or teams who design and deploy new AI agents through the Agent Platform, responsible for their construction, launch, and ongoing maintenance, driving innovation in functionality and applications. AI Agent participants are members of the community. They participate in the lifecycle of AI agents by acquiring and holding Access Units, supporting their growth and activity during use and promotion. These two roles represent the supply and demand sides, respectively, and together form a model of value sharing and collaborative development within the ecosystem. Ecosystem partners: platforms and frameworks ChainOpera AI collaborates with multiple parties to enhance the platform's usability and security, focusing on Web3 integration. The AI Terminal App integrates wallets, algorithms, and aggregation platforms to enable intelligent service recommendations; the Agent Platform introduces multiple frameworks and zero-code tools to lower the development barrier; models are trained and inferred using TensorOpera AI; and an exclusive partnership with FedML supports privacy-preserving training across institutions and devices. Overall, the platform forms an open ecosystem that balances enterprise-level applications with Web3 user experience. Hardware Portal: AI Hardware & Partners Through partners such as DeAI Phone, wearables, and Robot AI, ChainOpera integrates blockchain and AI into smart terminals, enabling dApp interaction, device-side training, and privacy protection, gradually forming a decentralized AI hardware ecosystem. Core Platform and Technology Foundation: TensorOpera GenAI & FedML TensorOpera provides a full-stack GenAI platform covering MLOps, Scheduler, and Compute; its sub-platform FedML has grown from academic open source to an industrial framework, enhancing AI's ability to "run anywhere and scale arbitrarily." ChainOpera AI Ecosystem 4. ChainOpera Core Products and Full-Stack AI Agent Infrastructure In June 2025, ChainOpera officially launched the AI Terminal App and decentralized technology stack, positioning itself as a "decentralized version of OpenAI." Its core products cover four major modules: application layer (AI Terminal & Agent Network), developer layer (Agent Creator Center), model and GPU layer (Model & Compute Network), and CoAI protocol and dedicated chain, covering a complete closed loop from user entry to underlying computing power and on-chain incentives. The AI Terminal app has integrated BNBChain, supporting on-chain transactions and DeFi agent scenarios. The Agent Creator Center is open to developers, offering capabilities such as MCP/HUB, knowledge base, and RAG, with community agents continuously joining. The CO-AI Alliance has also been launched, connecting with partners such as io.net, Render, TensorOpera, FedML, and MindNetwork. According to the on-chain data of BNB DApp Bay in the past 30 days, it has 158.87K independent users and 2.6 million transaction volumes in the past 30 days. It ranks second in the BSC "AI Agent" category, showing strong on-chain activity. Super AI Agent App – AI Terminal (https://chat.chainopera.ai/) As a decentralized ChatGPT and AI social portal, AI Terminal offers multimodal collaboration, data contribution incentives, DeFi tool integration, cross-platform assistants, and support for AI agent collaboration and privacy protection (Your Data, Your Agent). Users can directly access the open-source DeepSeek-R1 model and community agents on their mobile devices, with language tokens and cryptographic tokens transparently transferred on-chain during interactions. Its value lies in enabling users to transition from "content consumers" to "intelligent co-creators," enabling them to leverage a dedicated agent network across scenarios such as DeFi, RWA, PayFi, and e-commerce. AI Agent Social Network (https://chat.chainopera.ai/agent-social-network) Positioned similarly to LinkedIn + Messenger, but for AI agents, it leverages virtual workspaces and agent-to-agent collaboration mechanisms (MetaGPT, ChatDEV, AutoGEN, and Camel) to transform single agents into multi-agent collaborative networks, encompassing applications in finance, gaming, e-commerce, and research, while gradually enhancing memory and autonomy. AI Agent Developer Platform (https://agent.chainopera.ai/) Providing developers with a "Lego-like" creative experience. Supporting zero-code and modular expansion, blockchain contracts guarantee ownership, DePIN + cloud infrastructure lowers barriers to entry, and the Marketplace provides distribution and discovery channels. Its core goal is to enable developers to quickly reach users, transparently record their contributions to the ecosystem, and earn incentives. AI Model & GPU Platform (https://platform.chainopera.ai/) As the infrastructure layer, DePIN combines with federated learning to address the pain point of Web3 AI's reliance on centralized computing power. Through distributed GPUs, privacy-preserving data training, a model and data marketplace, and end-to-end MLOps, it supports multi-agent collaboration and personalized AI. Its vision is to promote a paradigm shift in infrastructure from "companies dominated by large companies" to "community-based collaboration." 5. ChainOpera AI Roadmap In addition to the official launch of its full-stack AI Agent platform, ChainOpera AI firmly believes that artificial general intelligence (AGI) will emerge from a multimodal, multi-agent collaborative network. Therefore, its long-term roadmap is divided into four phases: The provider receives revenue based on usage. Phase 2 (Agentic Apps → Collaborative AI Economy): Launch AI Terminal, Agent Marketplace, and Agent Social Network to form a multi-agent application ecosystem; connect users, developers, and resource providers through the CoAI protocol, and introduce a user demand-developer matching system and credit system to promote high-frequency interactions and continuous economic activities. Phase 3 (Collaborative AI → Crypto-Native AI): Implemented in DeFi, RWA, payment, e-commerce and other fields, while expanding to KOL scenarios and personal data exchange; Develop dedicated LLM for finance/encryption, and launch Agent-to-Agent payment and wallet systems to promote "Crypto AGI" scenario applications. Phase 4 (Ecosystems → Autonomous AI Economies): Gradually evolve into an autonomous subnet economy, where each subnet is independently governed and tokenized around applications, infrastructure, computing power, models, and data, and collaborates through cross-subnet protocols to form a multi-subnet collaborative ecosystem; at the same time, it moves from Agentic AI to Physical AI (robotics, autonomous driving, aerospace). Disclaimer: This roadmap is for reference only. The timeline and features may be adjusted dynamically due to market conditions and does not constitute a guaranteed delivery commitment. 7. Token Incentives and Protocol Governance ChainOpera has not yet announced a complete token incentive plan, but its CoAI protocol is centered on "co-creation and co-ownership" and uses blockchain and Proof-of-Intelligence mechanisms to achieve transparent and verifiable contribution records: the input of developers, computing power, data and service providers is measured and rewarded in a standardized manner. Users use services, resource providers support operations, and developers build applications, and all participants share the growth dividend; the platform maintains the cycle with a 1% service fee, reward distribution and liquidity support, promoting an open, fair and collaborative decentralized AI ecosystem. Proof-of-Intelligence Learning Framework Proof-of-Intelligence (PoI) is the core consensus mechanism proposed by ChainOpera under the CoAI protocol, aiming to provide a transparent, fair, and verifiable incentive and governance system for decentralized AI. This blockchain-based collaborative machine learning framework, based on Proof-of-Contribution (PoC), aims to address the challenges of insufficient incentives, privacy risks, and lack of verifiability in practical applications of federated learning (FL). This design, centered around smart contracts and combining decentralized storage (IPFS), aggregation nodes, and zero-knowledge proofs (zkSNARKs), achieves five key goals: 1. Fair reward distribution based on contribution, ensuring that trainers are incentivized based on actual model improvements; 2. Maintaining data locality to protect privacy; 3. Introducing robustness mechanisms to combat malicious trainer poisoning or aggregation attacks; 4. Ensuring the verifiability of key computations such as model aggregation, anomaly detection, and contribution assessment through ZKP; and 5. Efficient and versatile application of heterogeneous data and diverse learning tasks. The value of tokens in full-stack AI ChainOpera's token mechanism operates around five major value streams (LaunchPad, Agent API, Model Serving, Contribution, and Model Training), with the core being service fees, contribution confirmation, and resource allocation, rather than speculative returns. AI users: Use tokens to access services or subscribe to applications, and contribute to the ecosystem by providing/labeling/staking data. Agent/Application Developer: Use the platform's computing power and data for development and receive protocol recognition for the Agents, applications, or datasets they contribute. Resource providers: Contribute computing power, data, or models to obtain transparent records and incentives. Governance participants (community & DAO): participate in voting, mechanism design, and ecosystem coordination through tokens. Protocol layer (COAI): Maintain sustainable development through service fees and balance supply and demand using an automated allocation mechanism. Nodes and validators: provide verification, computing power, and security services to ensure network reliability. Protocol Governance ChainOpera utilizes DAO governance, allowing participants to participate in proposals and voting through token staking, ensuring transparent and fair decision-making. Governance mechanisms include a reputation system (to verify and quantify contributions), community collaboration (proposals and voting to drive ecosystem development), and parameter adjustments (data usage, security, and validator accountability). The overall goal is to avoid centralized power, maintain system stability, and foster community co-creation. 8. Team Background and Project Financing The ChainOpera project was co-founded by Professor Salman Avestimehr and Dr. He Chaoyang (Aiden), both experts in federated learning. Other core team members have backgrounds spanning top academic and technology institutions such as UC Berkeley, Stanford, USC, MIT, Tsinghua University, Google, Amazon, Tencent, Meta, and Apple, combining both academic research and practical industry experience. The ChainOpera AI team has grown to over 40 people. Co-founder: Salman Avestimehr Professor Salman Avestimehr is the Dean's Professor of Electrical and Computer Engineering at the University of Southern California (USC). He serves as the founding director of the USC-Amazon Trusted AI Center and leads the USC Information Theory and Machine Learning Laboratory (vITAL). He is the co-founder and CEO of FedML and co-founded TensorOpera/ChainOpera AI in 2022. Professor Salman Avestimehr received his PhD in EECS from UC Berkeley (Best Paper Award). As an IEEE Fellow, he has published over 300 high-level papers in information theory, distributed computing, and federated learning, with over 30,000 citations. He has received numerous international honors, including PECASE, NSF CAREER, and the IEEE Massey Award. He led the creation of the FedML open-source framework, which is widely used in healthcare, finance, and privacy-preserving computing, and forms the core technology foundation of TensorOpera/ChainOpera AI. Co-founder: Dr. Aiden Chaoyang He Dr. Aiden Chaoyang He is the co-founder and president of TensorOpera/ChainOpera AI. He holds a PhD in Computer Science from the University of Southern California (USC) and is the original creator of FedML. His research interests include distributed and federated learning, large-scale model training, blockchain, and privacy-preserving computing. Prior to starting his own business, he worked in R&D at Meta, Amazon, Google, and Tencent. He also held core engineering and management positions at Tencent, Baidu, and Huawei, leading the implementation of multiple internet-grade products and AI platforms. Aiden has published over 30 papers in both academia and industry, with over 13,000 citations on Google Scholar. He has also been awarded the Amazon Ph.D. Fellowship, the Qualcomm Innovation Fellowship, and Best Paper Awards at NeurIPS and AAAI. The FedML framework, which he led in development, is one of the most widely used open-source projects in the federated learning field, supporting an average of 27 billion requests per day. He was also a core author on the FedNLP framework and hybrid model parallel training method, which are widely used in decentralized AI projects such as Sahara AI. In December 2024, ChainOpera AI announced the completion of a $3.5 million seed round, bringing its total raised with TensorOpera to $17 million. The funds will be used to build a blockchain L1 platform and AI operating system for decentralized AI agents. This round was led by Finality Capital, Road Capital, and IDG Capital, with participation from Camford VC, ABCDE Capital, Amber Group, and Modular Capital. The company also received support from prominent institutional and individual investors, including Sparkle Ventures, Plug and Play, USC, and EigenLayer founder Sreeram Kannan and BabylonChain co-founder David Tse. The team stated that this round of funding will accelerate the realization of its vision of "a decentralized AI ecosystem co-owned and co-created by AI resource contributors, developers, and users." 9. Analysis of the Federated Learning and AI Agent Market Landscape There are four main representative federated learning frameworks: FedML, Flower, TFF, and OpenFL. FedML is the most comprehensive, combining federated learning, distributed large-scale model training, and MLOps, making it suitable for industrial deployment. Flower is lightweight and easy to use, with an active community, and is oriented towards teaching and small-scale experiments. TFF, deeply dependent on TensorFlow, has high academic research value but weak industrialization. OpenFL focuses on healthcare and finance, emphasizes privacy compliance, and has a relatively closed ecosystem. Overall, FedML represents an industrial-grade, all-round approach, Flower focuses on ease of use and education, TFF is more focused on academic experiments, and OpenFL has advantages in compliance with vertical industry regulations. At the industrialization and infrastructure level, TensorOpera (the commercialization of FedML) inherits the technical expertise of open-source FedML, providing integrated capabilities for cross-cloud GPU scheduling, distributed training, federated learning, and MLOps. Its goal is to bridge academic research and industrial applications, serving developers, small and medium-sized enterprises, and the Web3/Decentralized Infrastructure (Decentralized Infrastructure) ecosystem. Overall, TensorOpera is like "Hugging Face + W&B for open-source FedML," offering a more comprehensive and versatile full-stack distributed training and federated learning platform, distinguishing it from other platforms focused on community, tools, or a single industry. Among the innovation-tier representatives, ChainOpera and Flock are both attempting to integrate federated learning with Web3, but their approaches differ significantly. ChainOpera builds a full-stack AI agent platform encompassing four layers: access, social networking, development, and infrastructure. Its core value lies in transforming users from "consumers" to "co-creators," enabling collaborative AGI and community-building ecosystems through its AI Terminal and Agent Social Network. Flock, on the other hand, focuses more on blockchain-enhanced federated learning (BAFL), emphasizing privacy protection and incentive mechanisms within a decentralized environment, primarily targeting collaborative verification at the computing and data layers. ChainOpera prioritizes application and agent network implementation, while Flock focuses on strengthening underlying training and privacy-preserving computing. At the agent network level, the most representative project in the industry is Olas Network. ChainOpera, derived from federated learning, builds a full-stack closed loop of models, computing power, and agents, and uses the Agent Social Network as a testing ground to explore multi-agent interaction and social collaboration. Olas Network, rooted in DAO collaboration and the DeFi ecosystem, is positioned as a decentralized autonomous service network. Through Pearl, it launches a directly implementable DeFi revenue scenario, demonstrating a distinct approach from ChainOpera. 10. Investment Logic and Potential Risk Analysis Investment Logic ChainOpera's advantage lies first in its technological moat: from FedML (a benchmark open source framework for federated learning) to TensorOpera (enterprise-level full-stack AI Infra), and then to ChainOpera (Web3 Agent network + DePIN + Tokenomics), it has formed a unique continuous evolution path that combines academic accumulation, industrial implementation and encryption narrative. In terms of application and user scale, AI Terminal has already established an ecosystem with hundreds of thousands of daily active users and thousands of Agents. It ranks first in the AI category on BNBChain DApp Bay, demonstrating clear on-chain user growth and real transaction volume. Its multimodal coverage of crypto-native applications is expected to gradually expand to a wider range of Web2 users. In terms of ecological cooperation, ChainOpera initiated the CO-AI Alliance, and joined forces with partners such as io.net, Render, TensorOpera, FedML, MindNetwork, etc. to build multilateral network effects such as GPU, model, data, and privacy computing; at the same time, it cooperated with Samsung Electronics to verify mobile multimodal GenAI, demonstrating the potential for expansion to hardware and edge AI. In terms of tokens and economic models, ChainOpera distributes incentives around five major value streams (LaunchPad, Agent API, Model Serving, Contribution, and Model Training) based on the Proof-of-Intelligence consensus, and forms a positive cycle through a 1% platform service fee, incentive distribution, and liquidity support, avoiding a single "coin speculation" model and improving sustainability. Potential risks First, the technical implementation is quite challenging. ChainOpera's proposed five-layer decentralized architecture spans a wide range of domains, and cross-layer collaboration (especially in large-scale distributed inference and privacy-preserving training) still faces performance and stability challenges. It has yet to be verified in large-scale applications. Secondly, the ecosystem's user stickiness remains to be seen. While the project has achieved initial user growth, it remains to be seen whether the Agent Marketplace and developer toolchain can maintain long-term activity and high-quality supply. The currently launched Agent Social Network primarily relies on LLM-driven text conversations, and user experience and long-term retention still need further improvement. If the incentive mechanism is not carefully designed, there is a risk of high short-term activity but insufficient long-term value. Finally, the sustainability of the business model remains to be determined. Currently, revenue relies primarily on platform service fees and token circulation, and stable cash flow has yet to be established. Compared to more financial or productivity-focused applications like AgentFi or Payment, the commercial value of the current model requires further verification. Furthermore, the mobile and hardware ecosystems are still in the exploratory stages, leaving market prospects uncertain.
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PANews2025/09/19 11:00
Ondo Partners with Pantera Capital to Launch $250 Million Investment Program for RWA Tokenization Projects

Ondo Partners with Pantera Capital to Launch $250 Million Investment Program for RWA Tokenization Projects

PANews reported on July 4 that according to Coindesk, Ondo Finance is working with Pantera Capital to launch a $250 million "Catalyst" investment plan to invest in physical asset tokenization
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PANews2025/07/04 07:50