1. Forecast

1. Macro-level summary and future forecast

Last week, uncertainty in trade policy will continue to affect market sentiment, especially the direction of the Trump administration's tariff policy. The lawsuit filed by the U.S. Court of International Trade against the Trump administration’s tariff policies and the prospects of trade negotiations between the parties will become key forces affecting market trends.

This week, we need to focus on the May non-farm data to be released on Friday, and the results may become an important reference to affect the pace of future interest rate cuts.

2. Market changes and early warnings in the crypto industry

Last week, Bitcoin suffered profit settlement after breaking through a new high, and the market showed certain adjustments. However, no significant outflow of large amounts of funds has been observed yet, and the overall market sentiment is still relatively stable. At the same time, Ethereum continued to maintain a high consolidation, and its price structure has not yet shown any obvious signs of breaking, showing strong support. However, due to limited overall liquidity in the market, most altcoins suffered a deep pullback last week, and some investors faced certain short-term pressure.

In the future, investors should continue to maintain a high level of caution and pay close attention to subtle changes in macro market sentiment to cope with possible market uncertainties.

3. Industry and track hot spots

Powered by AI, TrendX, a Web3 platform designed to track trends and promote smart trading, develops smarter investment strategies for users; Defi platform XSY is led by Borderless, simplifies users' access to advanced income strategies through flagship product Unity ($UTY).

2. Market hot tracks and weekly potential projects

1. Potential track performance

**1.1. Analyze how TrendX, a Web3 platform powered by AI, aims to track

trends and facilitate smart trading, develop smarter investment strategies for users**

TrendX is a one-stop platform that uses artificial intelligence and big data technology to help users discover investment opportunities. Since its establishment in 2022, TrendX has processed more than 20TB of on-chain and off-chain data, analyzing billions of data points in real time, and mining potential investment opportunities. Adhering to the concept of "change is opportunity", TrendX provides investment advice through an intuitive interface and is committed to standing at the forefront of the global Web3+AI application ecosystem.

TrendX's distributed computing network Owlbot is jointly supported by the TrendX application ecosystem and AI model. It efficiently utilizes idle network resources around the world, significantly reducing the actual cost of training and operation of AI models. Every computer or smartphone with TrendX AI programs installed will contribute computing power to the TrendX network and strive to build the industry's largest distributed computing network. The network is widely used in the Web3 field, providing powerful and sustainable computing services to AI and Web3 companies with computing power demands around the world.

Brief description of the architecture

1. TrendX Chain

With advanced technology and rich industry experience, TrendX is able to effectively deal with the problems facing the industry today. TrendXChain, which is scheduled to be launched in 2025, will rely on its significant advantages to solve these pain points:

Rich data resources : TrendX has a large amount of on-chain data, community data and media data focused on Web3, providing a solid foundation for building AI model training that fits the Web3 ecosystem.

Direct application of AI models for Web3 scenarios : TrendX not only has advanced data processing capabilities, but also directly applies it to actual Web3 scenarios. By integrating smart contracts and automated workflows, the platform supports complex AI operations and real-time decision-making, which is very suitable for the implementation of high-frequency trading and automation strategies.

Deep experience in the field of Web3 and AI : TrendX has been deeply involved in Web3 and AI for many years, accumulated rich technical strength and industry insights, deeply understood the needs and challenges of the Web3 ecosystem, and can help enterprises better respond to future trends with innovative technology solutions.

Innovative decentralized data market and incentive mechanism : TrendX is committed to building a decentralized data market, adopting advanced encryption technology and precise data access control strategies to ensure user privacy and data security. At the same time, through innovative incentive mechanisms, we will stimulate the vitality and creativity of community members and promote the sustainable development of the platform.

Solution :

TrendX meets the above challenges by launching TrendXChain and implementing distributed AI training . TrendXChain not only focuses on data processing and transaction optimization, but also solves the problems of AI training and deployment in a decentralized environment through distributed machine learning technology and consensus mechanism. We adopt Federated Learning , which allows AI models to be trained on multiple nodes, protecting user privacy and reducing the risk of data breaches.

In combination with differential privacy technology (Differential Privacy) , we further strengthen our data protection capabilities. By introducing noise into data or model updates, malicious attackers can effectively prevent them from extracting personal data from shared information. This hybrid approach is crucial in Web3's AI mining architecture, ensuring the security and privacy of tasks such as sentiment analysis.

Federated learning is a key distributed AI training technology that allows model training on multiple distributed devices without the need to centralize all data to one server. In the case of distributed sentiment analysis mining , federated learning is crucial because it allows training tasks to be carried out in concert on multiple devices, thereby protecting user privacy and reducing the risk of data leakage. Unlike traditional AI methods that centralize all data to the server or the cloud, federated learning avoids data migration problems by leaving the training process at the source of the data. Even if the data of a certain server is insufficient, overall training can still be carried out efficiently among multiple nodes.

Federal Learning to Innovate Traditional Methods

Federated learning completely overturns traditional machine learning methods by decentralizing the training process to where the data is located, rather than centralizing the data to the training center. This pattern makes machine learning on distributed datasets possible. The basic process is: distribute global model parameters to client nodes (such as smartphones or enterprise servers), each node uses the same model parameters locally for training, and sends the updated model parameters back to the central server. The server then aggregates the models from multiple nodes, thereby gradually optimizing the model and finally obtaining a fully trained model.

During this process, each node participating in the training will also receive corresponding incentives to enhance its participation and contribution to the network.

Application scenarios

• Web3 Big Language Model (Web3 LLM) : TrendXChain builds a rich training dataset by collecting 20 million tweets from over 50,000 key opinion leaders in the Web3 industry (KOLs) over the past two years. Based on the RoBERTa pre-trained model, this model combines social media sentiment classification data for fine-tuning, which can efficiently analyze social media texts. It is an important tool for emotional insight in the Web3 field, greatly improving the accuracy and timeliness of sentiment analysis.
• Web3 AI Miner : Web3 AI Miner is a decentralized sentiment analysis system that distributes sentiment classification tasks to multiple network nodes (i.e. miners) for processing through distributed computing technology. This architecture uses blockchain technology to improve the security and robustness of the system while avoiding single point of failure. An innovative incentive mechanism encourages more nodes to join the network, thereby continuously enhancing the training and reasoning capabilities of AI models.

Incentive mechanism

To promote network participation and promote the development of decentralized networks, TrendXChain designed a blockchain-based incentive mechanism for distributed AI sentiment analysis miners. Miners receive rewards by contributing computing power and completing emotional classification tasks. These mechanisms not only ensure the healthy operation of the network, but also promote the sustainable development of the Web3 ecosystem.

Community Governance

TrendXChain combines the Proof of Stake (PoS) mechanism and the Decentralized Autonomous Organization (DAO) governance model to achieve efficient and transparent network governance, allowing community members to directly share the dividends of network development.

By integrating high-performance blockchain technology, innovative AI model training methods and decentralized data market, TrendXChain is committed to promoting the widespread implementation of Web3. Not only does it provide strong business support for Web3 enterprises, it also ensures the security and stability of the entire system through a global distributed AI node network. The huge on-chain data, community data and media data in the TrendX ecosystem will be used to train large language models customized for the Web3 field.

2. Web3AI

By tracking the latest developments in over 20,000 Web3 projects and 50,000 industry KOLs, combined with AI big data analytics, TrendX is able to tap into the social trends and popularity of each project, helping users make more insightful investment decisions.

With the help of AI sentiment algorithm, we grasp the latest market trends in real time, continuously monitor the latest tweets of all KOLs, record and compare daily sentiment scores and price trends, and deeply analyze the relationship between volume and price fluctuations. Users can directly view the Twitter sentiment profile on the entire network every day, and this indicator has become a key reference for evaluating changes in market sentiment.

AI Emotion Model Training Process

Data collection

• Use the Twitter API : Collect or crawl more than 50,000 tweets related to the Web3 industry KOL through keywords and hashtags (such as #Web3, #Blockchain).
• Real-time monitoring : Establish real-time data flow and continuously analyze current emotional trends.

Data preprocessing

• Data cleaning : Remove noise such as URLs, special characters, user mentions, etc.
• Language standardization : correct spelling errors and unify slang expressions, so that the text format is standardized and unified.

Model selection and training

• Pre-trained model : Use pre-trained language models such as RoBERTa or BERTweet to further fine-tune them on Web3 field data.
• Emotion classification : Use existing emotion classification structures (such as positive, neutral, negative), and improve classification accuracy through Web3 exclusive data.

Fine-tuning and evaluation

• Data annotation : Manually label some Web3 tweets as training set.
• Model fine-tuning : Fine-tuning the model on the labeled dataset.
• Performance evaluation : Use accuracy, recall, F1 score and other indicators to evaluate model performance.

Deployment and Application

• API deployment : encapsulate the model into an API to facilitate access to various applications.
• Real-time analysis : Integrated into the analytics platform to show real-time emotional trends.

Visualization and reporting

• Emotional Trend Chart : A chart showing the trend of emotions changing over time.
• Word cloud diagram : presents high-frequency positive and negative keywords.

Feedback and optimization

• User feedback : Collect user feedback and continuously optimize model performance.
• Continuous learning : regularly update the model and introduce the latest tweet data for retraining and optimization.

This process ensures the timeliness, accuracy and breadth of the sentiment model, and is especially suitable for real-time monitoring and trend forecasting of Web3 market sentiment.

3. Owlbot

Owlbot is a key component of the TrendX ecosystem and is committed to building a global distributed computing system. By deeply integrating with TrendX's powerful Web3 database and functional systems, Owlbot will drive the development of Web3 industry-specific large language model (LLM) and modular AI models.

TrendX users will become providers of AI computing power, fundamentally reducing computing costs and improving computing efficiency. As more users participate, the availability, accuracy and scalability of the AI ​​model will increase exponentially. This mechanism will promote TrendX to achieve a leap in computing efficiency and scale, and continue to lead the innovative evolution of Web3 and AI technologies.

Owlbot core operating system

Owlbot's business overview explores an in-depth and complex ecosystem that incorporates Federated Learning, advanced AI programs and highly specialized AI models, and is equipped with dynamic incentives to motivate participants and drive continuous innovation.

Together, these elements build a solid framework that not only significantly enhances AI capabilities, but also promotes collaboration and growth within the Web3 community. This innovative approach makes AI robots no longer just tools, but an evolving and dynamic intelligent individual, standing at the forefront of the AI ​​revolution.

Comments

With its rich Web3 big data resources, advanced AI technology and innovative distributed computing network Owlbot, TrendX has powerful data processing and intelligent analysis capabilities, which can provide users with accurate and timely investment insights and sentiment analysis; it adopts federated learning and decentralized incentive mechanisms to effectively protect user privacy and system security and promote the sustainable and healthy development of the ecosystem. However, as an emerging platform, TrendX still faces challenges such as high technical complexity, large demand for model training resources, and fierce market competition. It is necessary to continuously optimize technology and expand its user base to maintain its leading advantage.

1.2. **Brief analysis of Borderless leading investment, the Defi

platform XSY simplifies users ' acquisition of advanced income strategies through flagship product Unity ($UTY)**

XSY is creating the next generation of digital synthetic USD$UTY to unlock the sleepy potential in the blockchain ecosystem in a structured and scalable way.

XSY is redefining the future of blockchain liquidity and solving one of the industry's biggest challenges: the issue of idle capital on-chain. Today, the vast majority of on-chain assets are underutilized, limiting their potential driving force for ecosystem growth. XSY is pioneering a completely new model to transform sleepy value into production capital, thus driving a more efficient and scalable blockchain economy.

Brief description of the architecture

1. Unity ( "UTY")

$UTY, also known as Unity, is a digital synthetic dollar (DSD). Unity is a Delta neutral asset that exists as the core synthetic dollar in the XSY decentralized financial products ecosystem.

Users who pass the whitelist authentication can mint $UTY using a variety of assets. The initially supported asset is AVAX, which is used to build a synthetic Delta neutral collateral system for $UTY.

Users use $UTY in XSY's DeFi partner platforms such as Pharaoh and Euler to receive some rewards for early $UTY adopters.

Synthetic Delta neutral assets are a financial position constructed through derivatives, so that changes in the price of the underlying asset have little impact on the value of the overall position. In Unity's design, this mechanism is achieved by dividing the collateral assets into two hedging parts:

• Spot long positions in native protocol assets (such as AVAX) ;
• Perpetual contract short positions in native agreement assets .

Agreement revenue mechanism description:

When a user minted $UTY using native protocol assets, for example, Alice minted $UTY with $1 million worth of $AVAX.

XSY will open a perpetual contract short position on a centralized exchange such as Binance or OKX for an equivalent value of $1 million to match Alice's $UTY minting operation.

Typically, XSY earns an income every 8 hours from holding the short position, which is called the "Funding Rate".

The rate of funds earned by XSY is floating, depending on the market demand for long or short of the native token.

The end result is: Alice received $1 million worth of $UTY, which can be used in partnership agreements such as Pharaoh or Euler; while XSY holds a $1 million neutral hedging position (neither more or less) that earns capital fees, thus bringing ongoing benefits to the agreement.

Description of the underlying derivatives mechanism

In order to maintain the Delta neutrality of $UTY , the agreement will open corresponding short positions in the derivatives market every time the spot long buys. This hedging strategy ensures that the impact of asset price fluctuations is neutralized, thereby stabilizing the underlying value of the assets used in the system.

Derivatives currently used

To achieve this hedging mechanism, for every USD 1 purchase of native agreement assets, the agreement will simultaneously short the asset in the derivatives market in the nominal amount of equivalent value. The current methods are:

• Coin/USD perpetual contract for centralized exchanges

This type of perpetual contract allows us to continue to hold short positions in native agreement assets without the need to regularly move positions and exchange monthly. The current centralized exchanges used for hedging include:

• Binance
• Bybit
• OKX

By executing short positions in these highly liquid derivative markets, the agreement effectively neutralizes directional risks while ensuring efficient allocation of funds.

2. Open market anchoring arbitrage mechanism

The market value of $UTY is maintained by arbitrage mechanism, and when there is a difference between the market price and its relatively fixed casting/redemption price, the arbitrager operates to make a profit. This process naturally keeps $UTY at a close price anchor of $1 during market fluctuations.

Foundry and redemption mechanism: realized by issuing smart contracts

Authorized users can mint or redeem $UTY at a fixed price of approximately $1 by issuing smart contracts. Although trading prices in the secondary market may fluctuate, this stable casting/redemption mechanism provides an opportunity for arbitrageurs to help maintain price stability in $UTY.

Situation 1: Price is less than $1 – by redemption arbitrage

When $UTY is trading below $1 on a decentralized exchange (DEX), arbitrageurs can make profits by:

1. Buy $UTY at a discount on the open market;
2. Redeem $UTY for nearly $1 through smart contracts;
3. Obtain the difference between the buy price and the redemption price as profit while reducing the circulation of $UTY.

This redemption behavior puts upward pressure on market prices, prompting it to return to $1.

Situation 2: Price is higher than $1 – by casting arbitrage

When the market price of $UTY is higher than $1, arbitrageurs can:

1. mint new $UTY at a fixed price of $1 by issuing smart contracts;
2. Sell ​​the newly minted $UTY at a premium on the open market;
3. Obtain the difference between the selling price and the minting price while increasing the market circulation of $UTY.

This new supply brings some selling pressure, which helps market prices fall to around $1.

This arbitrage mechanism is the core means of $UTY maintaining the stability of price anchoring, and uses the spontaneous behavior of market participants to achieve price self-regulation.

Summarize

The core advantage of XSY is that it constructs a structured and scalable digital synthesis dollar mechanism through $UTY, which effectively activates sleeping capital on the chain and realizes liquidity optimization and value reuse within the decentralized ecosystem. Its innovative delta-neutral hedging model combines the CEX derivatives market to continuously bring cash fees to the agreement, and stabilizes $UTY price anchorage through an open market arbitrage mechanism to enhance market confidence and user participation. However, $UTY is not currently open to the public, and its reliance on centralized exchanges may bring systemic risks and regulatory uncertainty to some extent.

2. Detailed explanation of the project that week

2.1. Detailed explanation of **the Layer1 layer protocol N1 that

eliminates consensus, supports asynchronous and disordered transactions and achieves infinite expansion**

Introduction

N1 is an L1 blockchain designed from scratch, designed to natively support applications and scale-out capabilities. It can be imagined as an Ethereum vision without historical baggage. Its core innovation lies in avoiding the use of consensus mechanisms in most cases - which means that as the number of validators increases, the system can achieve higher scalability.

Unlike traditional practice, N1 does not rely on consensus to sort transactions for all applications, but instead adopts unordered and asynchronous processing by default. Consensus is introduced only when cross-chain interactions between applications.

In addition, N1 natively integrates data availability functions and does not limit the execution environment, giving developers greater freedom to optimize performance.

Technical architecture analysis

1. Settlement

The billing layer of N1 provides the minimum necessary functionality to host execution environment and ensure data availability. We have introduced a new primitive: unordered set replication and adopted a hybrid storage model , which supports ordered logs and unordered sets. Before explaining these concepts, let’s first illustrate it with an example:

Suppose we want to run a zk-rollup on N1 that provides an Ethereum virtual machine (EVM) while implementing bridges between assets inside and outside N1. In order to prove that the operation is honest to the user, we need to do the following for each Ethereum L2 block:

• Ensure that all users can download transaction data;

• Build and verify that an SNARK proves that the block is executed correctly;

1. Prove that the asset bridging operation is correct.

To achieve this we use the broadcast primitives provided by the network. For each block, the operator needs to complete the following steps:

1. Broadcast transaction data

The operator broadcasts the transaction data of the current block to the data availability network. Data is encoded by Erasure Coding to efficiently store shards between validators. In this way, the usage of client bandwidth is proportional to the block size, ensuring maximum efficiency.

2. Collecting proof of data availability

The network then continues to ensure data availability. During broadcasting, the operator will generate a commitment to the data, which binds specific data to facilitate subsequent verification and recovery. This promise is included in the proof of state transition, which any other client can obtain and verify.

3. Execute block

Run the block in zkEVM to generate a "receipt" that proves that the state transition is correct and that the block data is consistent with the data availability commitment. This step binds the execution results to the data availability in the network.

4. Verify and sign the receipt

The operator broadcasts the zk proof again and requests the verifier to verify and sign. These signatures are aggregated into what is called a "state transition certificate". During the verification process, the verifier also checks the receipt of zkEVM to make sure it matches the data availability commitment and follows the correct fork selection rules, thus preventing operators from pushing invalid blocks. Just over 2/3 of the verifier signature is required.

5. Submit certificate to the network

Finally, the operator broadcasts the certificate to the network. Each verifier checks whether the signature is consistent with the current verifier collection and, if correct, store the certificate. This certificate can be extracted and verified by any client. This write operation does not have a sorting constraint with other clients' write operations, so we call it a write to an "unordered collection". The certificate also contains a cash withdrawal list, which will be pushed to the operator's outbox, and its account balance will be updated.

Core features summary:

The above process builds a rollup suitable for EVM. You will notice that throughout the process, validators do not need to execute the traditional "two-stage submission protocol (2PC), which will cause the number of messages to grow quadratically. Instead, operators play a role similar to "leader" and are responsible for handling submission logic. Ultimately, the number of messages is linearly related to the number of validators, with a delay constant and does not block the progress of other clients.

The core primitives required for N1 include:

• Authenticate and store the state transition certificate;

• Ensure the availability of any data;

• Realize cross-chain bridge of assets (the operator has account control);

The only step that requires global sorting is asset bridging. Since most operations are completed internally in L2, there is very little competition for resources between different applications.

Liveness

In the single-operator architecture, how to ensure the activity of the system (liveness)? After all, operators may go down or refuse to generate new blocks.

The solution is to use the L1 block as the "clock" . L1 can be used as an "inbox" that enforces transactions and forces operators to remain active. If the operator ceases to operate, the governance mechanism can step in, take over the operator and force the withdrawal of the remaining funds. In addition, we do not restrict how operators operate: it can also be run by a decentralized collection of nodes to provide stronger activity guarantees.

Bridge

The network native supports the verification primitives required to build cross-chain bridges. We provide native IBC cross-chain bridges connecting Solana and Ethereum .

• Bridge In : The user can bridge the asset to an address controlled by the operator, and provide VM-specific metadata indicating how the asset is routed within the virtual machine. The N1 log maintains a "inbox" that bridges into assets from which operators consume data.
• Bridge Out : When a bridged asset is out, the operator pushes messages to the "outbox", which are enforced by the state transition function.

2. Execution layer

N1 remains completely neutral to the execution environment (agnostic). We encourage developers to be creative and explore specialized paths to performance optimization, such as implementing Lightning Networks or specialized exchanges. N1 provides a default execution environment, as described below.

The default execution environment of N1 is an asynchronous virtual machine network , which we call processes . Each process can run multiple virtual machines, such as TypeScript or Solidity, and can send and receive messages through ordered channels and other processes on the network.

For example:

• A process may be a Perpetuals Exchange built with TypeScript.
• Another process may be some Solidity smart contract.
• The two can interact and collaborate through messages.

The benefits of this architecture are:

• No state contention : Each application runs independently of each other.
• Operates at its own pace : More flexibly optimize performance and improve throughput capabilities.

The Gate application is a special program that processes assets transferred from L1 across chains and routes them to the corresponding applications in the L2 network. This greatly simplifies the topology of the network and allows almost seconds to be achieved between applications .

Unique Properties

You will notice that the N1 default execution network structure is very special. Its design goal is to maximize the capabilities of the underlying Layer 1 and provide a new, high-performance default execution environment. Its core concepts include:

• Dedicated Compute
• Asynchronous Communication
• SNARK Fraud Proofs **
  **

Dedicated Compute

Traditional blockchain runs all applications on a shared virtual machine . This structure will lead to state competition, soaring gas fees, and large fluctuations in delays when load increases, greatly limiting the possibility of building complex applications. This is similar to a shared database cluster in Web2, where shared databases are notoriously poor in scalability.

N1 solves this bottleneck by introducing a "Dedicated Compute Environment, DCE". Each application runs in an independent environment and has exclusive computing resources, so it can be vertically scaled, making full use of its server's computing power resources, and solving performance bottlenecks.

The process of deploying applications is as simple as mainstream blockchains, or even easier. N1 lowers the development threshold by being compatible with existing development toolchains:

• Solidity developers can use Foundry directly
• Rust standard WASM toolchain available to developers
• TypeScript developers can continue to use existing toolsets

This greatly improves the speed of getting started and makes building high-performance applications more efficient.

Instantaneous Communication

Point-to-point communication is performed between each program through an ordered channel , and the message content is in binary format. Each message triggers the payload of the receiver program to execute the message.

Unlike other messaging solutions, the message here is instant, which means that the message will be executed as soon as it arrives. This is different from cross-chain bridges on other blockchains, where messages from both parties must wait for some form of verification in order for the other party to trust the message.

(Snark) Fraud Proof

The execution environment of N1 is unique in that fraud proof based on zero-knowledge proof is non-interactive. That is, once the challenger constructs a zero-knowledge proof, they simply send it to Ethereum, challenging the state of N1's declaration. This is different from some optimistic rollups using interactive solutions, which require multiple steps of interaction between operators (we) and challengers, resulting in a usually 7-day withdrawal waiting period and need to address compatibility issues between on-chain challenge execution and off-chain normal execution.

The process for verifyers on N1 to detect and report fraud is as follows:

1. Replay **
   ** The verifier plays the status on the local machine, based on the transaction data published in the data availability layer;

2. Detection **
   ** The validator found that the status root update submitted to Ethereum does not match the status root obtained through data availability replay;

3. Proof generation **
   ** The verifier executes the program to generate cryptographic credentials with zero-knowledge proof as fraud proof;

4. On-chain verification **
   ** The verifier submits the proof to the on-chain verifier, which verifies the proof based on the program hash;

5. State recovery **
   ** If the proof is successfully submitted and the fraud is proved, the Ethereum contract will enter the state recovery mode, and the operator forces the operator to roll back the fraud status update.

Data Availability
Data availability refers to the N1 network exposing the entire transaction data history it receives to the node. The node can use these historical data to reconstruct the state transfer and verify whether the state on N1 is legal. This is the key to preventing operators from hiding data to prevent fraud proof generation. In the case where the state transition is invalid, the node can use the transaction data to construct fraud proofs to roll back the chain state and punish the verifier who submits the fraud state update.

N1 is built on high throughput data availability, because the throughput of any program is limited by the bottleneck of data availability bandwidth. Our modular architecture allows arbitrary bandwidth to be allocated on demand for specific programs, which is critical to implementing dedicated computing.

3. Trading life cycle
   Here are some example life cycles for deposits and withdrawals. The purpose is to show how transactions involving the most system components run, thereby helping to understand the overall system architecture. We include the processes on both sides of L1 (Layer 1) and L2 (Layer 2) in the system.

• deposit
  The deposit is first bridged, then enters the gate application in the network, and then sent to the specified application (app).

• Withdrawal
  The withdrawal must first reach the target app, then pass through the gate application, and finally enter the settlement layer at the next settlement.

• Cross-application transfer
  Cross-application transfer is similar, consisting of two transfer operations.

Summarize

The advantage of N1 is its innovative non-consensus default design, which greatly improves scale-out capabilities and performance, supports asynchronous communications and dedicated computing environments, reduces state contention and latency, and has flexible execution environment compatibility (such as supporting multiple virtual machines). In addition, built-in data availability and SNARK-based fraud proof mechanism enhance security and trust assurance. The disadvantage may be that its optimistic model relies on the honesty of the operator, and the fully decentralized and efficient SNARK proof system is not yet mature, bridging and cross-application interactions still require more complex coordination, and the overall ecology and tool chain are still in the development stage.

3. Industry data analysis

1. Overall market performance

ETF, November 1, ET) Total net outflow of Ethereum Spot ETF 10925,600 USD

1.1. Spot BTC vs ETH Price Trend

BTC

Analysis

Core: BTC announced that the upward trend from $74,000 after falling below key support of $107,000 was completely destroyed

Focus: In the short term, pay attention to whether the rebound can quickly stand above $107,000. If successful, it is expected to test the previous high again. Otherwise, it means that the price will continue to test the lower edge of the 2-stage consolidation zone, that is, support around $100,600.

Conclusion: If you stand firm at the $100,000 mark and rebound quickly break through $107,000, the medium-term rise is still expected. Otherwise, the trend may officially start a downward trend, and the price will continue to fall until the first stage consolidation zone.

ETH

Analysis

Core: ETH is only in the second stage of consolidation after the rise begins, and from the early consolidation from the end of April to mid-May and the second stage of consolidation from June to the present, ETH has accumulated more momentum of upward momentum, so ETH has strong support at US$2,320.

Focus: The effectiveness of the first-line support of US$2470 and the second-line support of US$2320

Conclusion: As long as it stabilizes at US$2320, it will remain bullish in the medium and long term, otherwise it will enter a downward range. The new support area needs to go to the US$1600~1800 range.

2. Public chain data

2.1. BTC Layer 2 Summary

Analysis

1. Stacks (STX)

Stacks continues to maintain a leading position in the Bitcoin smart contract space. Its Nakamoto upgrade, launched in 2024, shortened block time to about 5 seconds and introduced decentralized Bitcoin-anchored asset sBTC, enhancing integration with the Bitcoin main chain. Currently, Stacks' total lock-in value (TVL) exceeds US$190 million, supporting multiple DeFi platforms and NFT markets, such as Velar, Bitflow, and Alex.

2. Bitlayer

Bitlayer provides a trustless scaling solution through its BitVM architecture and OP-DLC bridging. The project has established strategic cooperation with multiple networks such as Base, Starknet and Arbitrum to promote Bitcoin’s liquidity in the multi-chain DeFi ecosystem.

3. Mezo

Mezo introduced the "Proof of HODL" consensus mechanism to encourage users to gain profits by locking in BTC. The platform has attracted more than 1,059 BTC of deposits and plans to implement a dual token model (BTC/MEZO) to decentralize validator incentives.

2.2. EVM &non-EVM Layer 1 Summary

Analysis

EVM Layer 1 Blockchain

Ethereum

Pectra upgrade : The Ethereum Foundation activates the Pectra upgrade on the Sepolia test network, which is an important step to improve Ethereum's scalability and user experience. The upgrade content includes account abstraction (EIP-7702), increased verifier staking ceiling (EIP-7251), and improved optimization of Rollup scalability (EIP-7691).

Base

Flashblocks and Appchains : Base has launched Flashblocks to improve transaction confirmation speed; it also launched Appchains, allowing developers to create customized Layer 3 solutions to enhance user engagement and developer flexibility.

Non-EVM Layer 1 Blockchain

Injective

Native EVM Integration : Injective introduces native Ethereum virtual machine support on its Layer 1 blockchain, enabling Ethereum-compatible decentralized applications to run on Injective and enhance its ecosystem capabilities.

2.3. EVM Layer 2 Summary

Analysis

Overall market performance

• Total Value Lock (TVL): TVL in the Ethereum L2 ecosystem has exceeded US$51 billion, a year-on-year increase of more than 205%.
• Active Address: The number of active addresses on the L2 platform has reached about 10.9 million per week, about 5 times that of the Ethereum main network.
• Trading volume: The annual transaction volume of Base Network has exceeded 10 million, with an average daily transaction number of approximately 1 million, showing its strong appeal among users and developers.

4. Macro data review and key data release nodes next week

截至5月24日当周，美国初请失业金人数为24万人，高于预期的23万人，但总体仍处于低位。此前值为22.6万人，显示就业市场保持韧性。美国4月核心PCE物价指数年率为2.5%，创下2021年3月以来的新低。这一数据符合市场预期，前值由2.6%修正为2.7%。

本周（6月2日-6月6日）重要宏观数据节点包括：

6月4日：美国5月ADP就业人数

6月5日：美国至5月31日当周初请失业金人数

6月6日：美国5月失业率；美国5月季调后非农就业人口

五. 监管政策

美国：比特币战略储备与监管改革

• 比特币战略储备 ：副总统JD Vance 强调比特币在美中竞争中的战略作用，并重申特朗普政府推动建立比特币战略储备的举措。
• 监管转向 ：美国证券交易委员会（SEC）撤销对币安的诉讼，显示监管环境正在变得更为合作。同时，众议院推出《数字资产市场透明法案》（CLARITY Act），为加密资产建立正式监管框架。
• 稳定币立法 ：参议院推动《GENIUS法案》，对稳定币实施更严格的审计要求和反洗钱规定。

英国：计划将加密货币纳入主流金融体系

• 立法提案 ：改革党领导人奈杰尔·法拉奇（Nigel Farage）宣布“加密革命”计划，拟对加密货币收益征收10%资本利得税，并在英格兰银行设立国家比特币储备。该党还计划接受加密货币形式的政治捐款。

巴基斯坦：成立国家级加密货币委员会

• 监管机构建立 ：巴基斯坦成立了“巴基斯坦加密委员会”（PCC），旨在推动区块链技术与数字资产的发展，并发布了该国首个政府主导的战略比特币储备。

印度：即将发布加密资产政策框架

• 政策讨论文件 ：印度财政部计划于2025年6月发布加密资产监管政策的讨论文件，显示出对数字金融采取稳健推进的策略。

欧盟：对稳定币与货币主权的担忧

• 主权风险 ：稳定币的兴起在欧洲引发了对货币主权的担忧。由于美元支持的稳定币在国际上发展迅猛，而欧元支持的稳定币仍未成熟，可能导致“美元化”加剧，从而削弱欧元区的货币主权。