# I. Outlook

## 1. Macroeconomic Summary and Future Predictions

Last week, the three major U.S. stock indices performed steadily, with the S&P 500 index reaching an all-time high. Technology stocks, especially AI-related companies, showed strong performance, driving the Nasdaq index higher. The expectation of a rate cut by the Federal Reserve in July was weakened by strong employment data, with the probability of a rate cut in September at about 75%. The Fed may continue to wait and see, focusing on the impacts of trade and immigration policies. Although U.S. stocks have reached new highs, valuation risks cannot be ignored. Market volatility may increase in the coming months, and investors need to closely monitor the progress of trade negotiations and the dynamics of Fed policy.

## 2. Market Changes and Warnings in the Crypto Industry

Last week, most altcoins continued to consolidate, with market funds increasingly concentrated in Bitcoin, raising BTC's market dominance to 65%, indicating that investors are leaning towards safe-haven assets. Despite Bitcoin's strong price performance, the market has entered a consolidation phase, with technical analysis showing that $109,000 is a key resistance level. In the short term, caution is needed regarding the volatility risks brought by technical resistance and the nature of funds. The performance of altcoins has shown significant differentiation, and investors should remain cautious.

## 3. Industry and Sector Hotspots

Led by OKX and followed by Aptos, the fully on-chain, natively built hybrid Orderbook-AMM DEX Hyperion provides a seamless trading experience for both professional and retail users. The AI smart platform Assisterr, co-invested by Google, aims to enhance customer support efficiency and experience by breaking down the barriers of centralized AI training models through an innovative SLM model.

# II. Market Hotspots and Potential Projects of the Week

## 1. Overview of Potential Projects

1.1. Analysis of the Fully On-Chain, Natively Built Hybrid Orderbook-AMM DEX Hyperion Led by OKX and Followed by Aptos

Introduction

Hyperion is a fully on-chain hybrid Orderbook-AMM decentralized exchange (DEX) built natively on Aptos, leveraging Aptos's excellent throughput and ultra-low latency to provide a seamless trading experience for both professional and retail users. Hyperion aims to become the foundational trading engine on Aptos, offering competitive liquidity options and an outstanding user experience through its hybrid Orderbook-AMM architecture, serving various traders.

++Architecture Overview++

++1. Centralized Liquidity++

++++

In the automated market maker (AMM) model, prices change continuously, while in centralized liquidity protocols, it is slightly different: prices in centralized liquidity are discrete. The price curve is divided into several "ticks," with each tick forming a discrete price space with its adjacent tick. In this price space, each tick increase or decrease results in a price change of 0.01% (one basis point).

Hyperion uses ticks as boundaries for each liquidity position. When a liquidity position is created, the liquidity provider (LP) sets an upper tick and a lower tick, defining the price range covered by their funds.

As prices fluctuate during trading, the smart contract continuously "consumes" all liquidity within the current tick range until the price moves to the next tick. Upon reaching a new tick, the pool contract immediately switches to that tick and activates all "dormant" liquidity within that price range.

Although each trading pool in centralized liquidity protocols has the same number of price ticks, in practice, only a portion of the ticks are considered "active ticks." The spacing of ticks is somewhat related to the fee tier of the trading pair: the lower the fee tier, the denser the ticks can be set. In other words, the higher the fee tier, the wider the spacing of available price ticks in the pool.

For trading pairs that require higher price precision (such as stablecoin trading pairs), narrower tick spacing is more advantageous. In such trades, a tighter tick distribution helps control price impact, achieving smoother price movements, which is a characteristic needed for stablecoin pools.

++2. Fees++

Swap Fees in Hyperion's centralized liquidity protocol are proportionally distributed to all active liquidity positions within the current price range. Only those liquidity positions whose defined price range includes the current spot price are considered valid liquidity and thus qualify for fee income. If the market price moves outside a position's price range, that position becomes inactive and cannot continue to earn fees.

Unlike traditional AMM contracts that automatically allocate swap fees to the liquidity pool, centralized liquidity protocols accumulate fees separately, allowing liquidity providers (LPs) to receive their fee income without withdrawing liquidity.

It is important to emphasize that the Hyperion protocol is entirely composed of a set of automated smart contracts deployed on Aptos, which users interact with directly by calling contract functions (such as trading or participating in liquidity pools) to achieve decentralized asset interactions among multiple parties. The protocol's deployers act solely as providers of technical tools and do not offer any securities or regulated services, nor do they hold user assets, having no further relationship or control over the operation of the protocol itself.

Fee Tiers in Hyperion's centralized liquidity protocol allow for multiple liquidity pools with different fee tiers for the same trading token pair. Currently, the protocol allows for the establishment of the following four tiers: 0.01%, 0.05%, 0.25%, 1%.

The introduction of a multi-fee rate mechanism helps better meet the actual needs of different types of trading pairs and encourages the market to explore optimal liquidity distribution methods, thus providing greater flexibility for liquidity providers and traders.

It can generally be expected that different types of token pairs will gradually gravitate towards a certain fee tier based on their asset characteristics and the interplay between LPs and traders. For example:

• Low-volatility assets (such as stablecoins) tend to concentrate in the lowest fee tier pool (0.01%) because these assets have smaller price fluctuations, presenting lower risks for LPs, and most traders prefer their trades to be as close to 1:1 as possible.
• Assets with lower liquidity or high volatility are more likely to cluster in higher fee pools (such as 0.25% or 1%) to compensate for the higher holding risks faced by LPs.

Protocol Fees are set at 20% by default from the swap fees of each transaction to maintain the healthy operation of the project's economic model and support the long-term sustainable development of the project treasury.

++3. Swaps++

In the Hyperion protocol, swap operations support both automated market maker (AMM) models and order book models:

• In the AMM model, swap operations are conducted with passive liquidity pools, and liquidity providers earn fee income based on their provided active liquidity ratio.
• In the order book model, swaps follow the first-in-first-out (FIFO) principle, where the time of order submission determines the priority of execution.

This dual model provides users with high flexibility, allowing them to freely choose between automated liquidity provision or active order trading based on their trading strategies.

Slippage

To address the uncertainty of price changes, DEXs have introduced the concept of slippage tolerance. Users can set their acceptable maximum slippage value, indicating the maximum price change they are willing to bear. If the final execution price exceeds the user's set slippage tolerance, the transaction will automatically fail to protect the user's interests.

++4. Fee-based Liquidity Mining++

In centralized liquidity protocols, only liquidity positions within active price ranges will be used for trading, thus generating trading fees. The fee income generated by a liquidity position reflects its effectiveness and actual contribution within the protocol.

Therefore, Hyperion's liquidity mining mechanism has a very unique feature: rewards are distributed based on the user's actual fee performance, rather than simply based on the amount of liquidity provided. This means that if a liquidity provider wishes to earn more fee income and mining rewards, they need to actively participate and optimize the price range of their liquidity positions.

With the linear release of mining rewards, whenever a new trade is executed, the contract will be called to calculate the proportion of fees generated by each liquidity position in the total pool fees since the last call. The released rewards will then be distributed according to the proportion of fees contributed by each position.

This fee performance-based mining mechanism ensures that mining rewards are not diluted by inactive LPs in invalid price ranges or users providing "fake liquidity" solely for rewards.

It significantly reduces the costs for protocol parties and third-party projects in incentivizing liquidity and makes Hyperion's TVL (Total Value Locked) more practically effective and efficient compared to other DEXs.

++Commentary++

Hyperion's advantages lie in its hybrid Orderbook-AMM decentralized exchange (DEX) architecture, natively built on Aptos, combining high throughput and low latency to meet the needs of professional traders for depth and speed while also catering to the ease of use for retail users; its centralized liquidity design and fee-based mining mechanism effectively enhance capital efficiency and incentive precision; multiple fee tiers and automated income calculations also enhance the flexibility of liquidity provision.

On the downside, the complex strategy design and price range management present a higher operational threshold for ordinary users. Additionally, its reliance on the development of the Aptos public chain ecosystem may be limited in the short term by on-chain activity and overall liquidity fundamentals.

1.2. Interpretation of How the AI Smart Platform Assisterr, Co-Invested by Google, Breaks Down the Barriers of Centralized AI Training Models Through Innovative SLM Models to Enhance Customer Support Efficiency and Experience

++Introduction++

AssisterrAI aims to create a collaborative agent system (Mixture of Agents) centered around SLM and provides no-code development tools, empowering users to build small language models tailored to specific task scenarios. By establishing a decentralized free market centered on peer review, model generation, and data validation, Assisterr forms a self-circulating, transparent, and sustainable AI gig economy.

Each Assisterr model is governed by a DAO (Decentralized Autonomous Organization) and has independent finances. Once a model goes live in the market, it can obtain datasets, computing power, and task resources through crowdsourcing. The on-chain data source system ensures that the data contribution, validation, and incentive processes are traceable and publicly transparent. Ultimately, Assisterr establishes a decentralized SLM factory to power the vertical AI market.

++Architecture Overview++

++Modular Small Language Model Architectures++

To address the limitations faced by agent-based large language models (LLMs), several advanced methods have emerged in recent years, consisting of collaborative agent frameworks made up of multiple small language models (SLMs). By combining SLMs into agentic ensembles, users only need to describe the problem, and the system can analyze, interpret, and ultimately provide the best solution through a series of reasoning processes involving multiple models.

This approach achieves distributed contextual reasoning across multiple mixed, domain-specific models, reducing trade-offs between general reasoning capabilities and deep functional solutions. It provides both breadth (multi-model coverage capability) and depth (domain focus and efficient execution), allowing the system to selectively call upon multiple participating models.

Therefore, this structure is particularly suitable for exploring practical solutions to highly complex or specialized problems.

Currently, two core methods are primarily used in the process of building AI agents from SLM model clusters:

1. Mixtures of Experts (MoE)
2. Mixtures of Agents (MoA)

• Mixtures of Experts (MoE)

When small language models are combined in a MoE structure, the reasoning capabilities of modern SLMs can achieve greater learning flexibility while maintaining functional problem-solving abilities. Through ensemble learning, the system can combine the reasoning capabilities of multiple small models, with each model focusing on different contextual domains, thereby collaboratively solving complex problems.

This structure can produce a hybrid comprehension ability, allowing AI to retain deep processing capabilities. Furthermore, the various "expert layers" of MoE can themselves be composed of multiple MoEs, thereby constructing a hierarchical structure to better address complex contexts and enhance problem-solving abilities.

A typical MoE architecture usually includes a sparse gating layer, which dynamically selects multiple models from parallel networks based on input to generate the most suitable response. To achieve more flexible answers, each "expert" model can be fine-tuned for tasks such as code generation, translation, or sentiment analysis.

More complex MoE architectures may contain multiple such MoE layers and be used in combination with other components. Similar to typical language models, the gating layer of MoE also acts on semantic tokens and requires specialized training.

• Mixtures of Agents (MoA)

When SLMs are organized into a MoA architecture, the reasoning capabilities of the models become more diverse and selectable, allowing AI to execute tasks precisely using the required methodology. Agent models are formed into a collaborative alliance, enhancing task processing efficiency and problem-solving capabilities for complex issues through hierarchical execution protocols, enabling AI to adapt to multi-domain use cases.

A group of agents can collaborate sequentially, iteratively optimizing previous results in each round. The MoA architecture has significantly outperformed large model performance in multiple evaluations. For instance, even among open-source models, MoA's performance exceeded the 57.5% accuracy of GPT-4 Omni on AlpacaEval 2.0.

The mixed agent mechanism (MoA) operates at the model output level rather than at the semantic token level. It does not use a gating layer, but instead sends text prompts in parallel to all agent models for processing.

The output of MoA is not aggregated through weighted summation and normalization, but rather the outputs of multiple agents are concatenated directly, then combined with a synthesize-and-aggregate prompt to be passed to another independent model for generating the final output.

In this architecture, the models are divided into two types of roles:

• Proposers: Responsible for generating diverse preliminary outputs
• Aggregators: Responsible for integrating these results and outputting the final answer

Similar to MoE, the MoA architecture can also stack multiple such hierarchical structures. Since MoA does not rely on a gating layer, it becomes an attractive architectural option—allowing multiple small models to be flexibly combined into more complex systems, with greater modularity and portability.

++AssisterrAI: Technology and Ecosystem++

AssisterrAI is a product of the fusion of two major trends in AI's future.

The first trend is: the shift from expensive, general-purpose large language models (LLMs) to smaller, domain-focused small language models (SLMs). As LLMs gradually reach their innovation peak, SLMs, as their domain-specialized "lightweight" versions, will become the mainstream direction in the future.

The second trend is: the decentralization of AI training, reasoning, and data ownership, to build a fair and open AI gig economy system.

In addition to these two core innovations, the Assisterr ecosystem will also provide a framework to support users in building agentic AI and passive chatbots. The following diagram illustrates the structure of the Assisterr platform, from model creation to its practical application in the decentralized AI economy.

++SLM Store++

In Assisterr, contributors will be rewarded at every stage of the AI model's life cycle, from inception to release, including data contribution, model creation, validation, and review processes. This revenue-sharing mechanism will be implemented through an SLM tokenization module.

Assisterr's AI Lab will also effectively connect commercial application scenarios with the required data and expertise.

Once a model goes live on the "SLM Store" tab of the Assisterr interface, any user can query it through the chatbot interface. Currently, the SLM Store supports model agent bots covering multiple verticals, including the Web3 ecosystem, healthcare, software development, and finance.

Each model listed in the SLM Store comes with a treasury priced in Assisterr's native token. Each time a user initiates a query, the system deducts a certain amount from their account balance to automatically replenish that model's treasury.

Additionally, users can query models not only through a web user interface connected to a Solana wallet but also via API interfaces, allowing models in the SLM Store to be integrated and called by other applications.

Contributors will be able to create SLMs (small language models), assemble them into agent models, and complete deployment through a no-code interface. This approach provides creators with a rapid market launch cycle and efficient innovation iteration process.

It effectively addresses the challenges faced by independent model developers in distribution and monetization.

As shown in the diagram above, each SLM deployed in the market can participate in the MoA (Mixtures of Agents) architecture.

Since these model clusters can merge reasoning capabilities and problem-solving abilities across multiple models, they bring broader application opportunities. This not only allows models created by contributors to be used as independent solutions but also enables them to become components with specific functions within larger systems.

This mechanism further expands the usage scenarios of models, thereby enhancing contributors' potential to earn rewards.

++Assisterr Treasury Model++

Assisterr's native token is the core medium supporting the operation of the AssisterrAI ecosystem. At every stage of the SLM development process, this token will be used under smart contract protocols to validate user behavior and serve as the transactional unit for interactions.

By using this token, participants within the ecosystem can access various platform functions, such as:

• Using products and services
• Paying related fees
• Participating in the creation, management, and monetization processes of SLMs

The operational foundation of the Assisterr token across the platform's functional modules is the Assisterr Treasury Model (ATM), as shown in the diagram. This model has the following characteristics:

• Adaptable to various application scenarios
• Supports flexible governance mechanisms
• Provides scalable treasury structures
• Achieves a fair incentive distribution system

The execution of this model is divided into three consecutive phases, covering the entire life cycle management of SLMs.

++Summary++

Assisterr's advantages lie in its core focus on small language models (SLMs), building a decentralized, task-driven AI gig economy system that integrates on-chain verification, modular agent architecture (MoA), data crowdsourcing, and governance DAOs, providing an open, transparent, and sustainable collaborative platform for model developers, data contributors, and users. Its no-code tools and token incentive mechanisms significantly lower participation thresholds, accelerating model iteration and commercial landing.

On the downside, as the ecosystem is still in its early stages, the platform's model performance and data quality are highly dependent on the community's self-organization efficiency, and complex mechanisms such as cross-chain operations, MoA architecture, and on-chain verification may present certain understanding and integration barriers for new users or developers.

## 2. Detailed Explanation of Key Projects of the Week

2.1. Detailed Explanation of OneBalance, a Platform Focused on Providing One-Stop APIs and Enabling Developers to Quickly Build One-Click Crypto Products, Funded with $20 Million Led by Cyberfund

++Introduction++

OneBalance is a framework for creating and managing "Credible Accounts." Credible Accounts are an extension of existing account formats (such as externally owned accounts, smart accounts, and stateful accounts) that enable these accounts to make credible commitments without requiring global consensus.

++Features Analysis++

++A. Toolkit Integration++

++++

In most cases, when using the OneBalance toolkit, applications only need to specify a high-level intent, and the toolkit will convert it into actual transaction payloads and provide them to clients for signing. The routing in chain abstract intents includes:

• Determining the best spending chain based on the user's balance distribution across different chains
• Assessing whether cross-chain bridging is needed to fulfill the intent or if it can be executed on the same chain
• Executing the minimum bridging amount required for the intent
• Identifying which solver/packager can provide the best execution price
• Determining whether a payer is needed or if the user will cover the transaction fees themselves

The OneBalance toolkit is compatible with embedded signing providers (such as Turnkey and Privy) and also works with applications that directly access signing keys (such as Web3 wallets and centralized exchanges).
The toolkit uses gas fee abstract accounts compatible with all chains, so users do not need to worry about paying gas fees. For more details on specific account options, please refer to the relevant sections.

Resource Locks and Fast Paths
The concept of resource locks introduced by OneBalance in early 2024 significantly enhances multi-chain transaction efficiency.
Specifically, it allows for the asynchronous execution of multi-chain intents, separating the completion of intents from settlement, making multi-chain transactions feel like same-chain transactions for users.
Once resource locks are enabled through the toolkit, all user transactions will be co-signed (queued) by OneBalance, preventing double spending during the asynchronous multi-chain execution process.
For detailed workings of resource locks and their impact on cross-chain bridging speed, please refer to the resource lock concept page. Terminology definitions can be found in the glossary.

Monetization
From the outset, applications can define flexible user transaction fees, supporting configuration by chain and transaction type. The fees paid by users are used to:

• Offset the gas fees subsidized by the application
• Generate a stable and flexible source of income, with fees settled directly into the application's wallet alongside each user transaction.

++Core Elements++ ++Transaction Lifecycle++

• Transaction Types and Attributes
• Swap and Transfer
  Swap and transfer any tokens on any supported chain.
• Contract Call
  Execute smart contract calls on any chain, using aggregated balances for payment while keeping the sender unchanged.
• Fast Path and Standard Path
  Fast paths are used for cross-chain execution, eliminating delays from source chain transactions.
  Standard paths are the basic sequential execution method for cross-chain transactions.

• Execution Process Details

1. Initial Request Phase
   The user submits an intent (for example, swapping ETH aggregated across chains for SOL on Solana).
   OneBackend processes the request, executing routing and other optimizations.
   Note that funds can be spent from multiple chains within the same intent without affecting the process. Our service will assess the available fund amounts on each chain and whether they are suitable for fast path or standard path execution.
2. Providing Quotes
   The solving system provides a quote that includes prices covering gas fees and swap fees.
   After receiving the quote, the user decides whether to proceed.
   The user signs the operation with their session key and returns it to OneBackend.
3. Verification and Security Phase
   OneBackend forwards the signed operation to our Resource Lock (RL) service.
   The RL service verifies the user's request and provides a security proof.
   Based on the security status, the transaction will follow one of the following paths:

• Fast Path: Same-chain execution

• Fast Path: Cross-chain execution (instant execution with security guarantees)

• Standard Path: (Executed after hosted transaction confirmation)

• Execution Phase

OneBackend submits the operation and security guarantees to the solver, and the execution process will follow one of the three paths:

Fast Path (Same-chain)
Fast Path (Cross-chain)
Standard Path

The standard path follows traditional sequential execution and is used when fast path conditions are not met. Because it requires waiting for final confirmation from the source chain, the standard path is typically at least twice as slow as the fast path.

The execution process is as follows:

• OneBackend executes the operation on the source chain
• The solver verifies the execution result on the source chain
• The solver executes the operation on the target chain
• OneBackend verifies the execution result and confirms success to the user

Fast Path Execution Conditions
Multi-chain intents can use the fast path if they meet any of the following conditions:

• Routing is on the same chain, or
• All of the following conditions are met simultaneously:
• The user has sufficient confirmed on-chain balances that are not pre-approved (cross-chain aggregation), and that balance has not been locked (spent but not settled)
• There are no swaps on the source chain during the execution process
• The Resource Lock (RL) service, according to risk policies, does not exceed the total unsettled position limits

++b. Resource Locks++

Resource locks are OneBalance's innovative solution that achieves fast cross-chain transactions by eliminating final confirmation wait times and preventing double spending, thus bringing key improvements in speed, reliability, and cost efficiency.

Resource locks enable cross-chain transactions to execute at the speed of the target chain while providing cryptographic guarantees and reducing operational costs through optimized settlement models.

How Resource Locks Work
Resource locks are managed by OneBalance's RL service, responsible for the following four key functions:

1. Verification
   Validating the user's on-chain balance and locked balance
2. Joint Signing
   Adding signatures to operations to ensure control over account state changes and prevent double spending
3. Providing Guarantees
   Issuing security proofs to the solver for instant delivery, despite delays in settlement
4. Tracking Positions
   Ensuring that unsettled balances comply with risk policies, maintaining system integrity

This system supports asynchronous execution, separating the fulfillment of intents from settlement, significantly enhancing user experience.

Key Advantages

• Speed: Near-instant execution
  Traditional cross-chain transactions require waiting for final confirmation from the source chain (which typically exceeds 12 minutes on Ethereum). Resource locks provide instant execution guarantees through cryptographic joint signing, eliminating this wait.
• Reliability: Guaranteed Settlement
  Unlike intent-based systems that may fail due to solver unavailability or market conditions, resource locks mathematically ensure that operations can be successfully completed.
• Cost Efficiency: Optimized Economics
  Resource locks support batch processing of transactions and optimal routing decisions, significantly reducing the total costs for users and applications conducting cross-chain operations.

Types of Resource Locks
Resource locks are mainly divided into two categories:

1. Account-based locks ------ Implemented at the account level, seamlessly compatible with existing user workflows and wallet interactions.
2. Custodial locks ------ Instead of locking accounts, these are implemented through custodial smart contracts, where users deposit tokens into the contract to support cross-chain operations.

Currently, OneBalance primarily employs account-based locks to achieve the best user experience and compatibility.

Once resource locks are enabled, transactions can only be sent through the OneBalance Toolkit; otherwise, double spending cannot be prevented in the asynchronous execution environment.

++c. Account Model++

Understanding OneBalance's account model and its functionalities.

EVM-Compatible Configurations (See Table Below)

OneBalance supports a modular architecture designed to be compatible with various account types and is happy to support other account versions based on demand or recommend the most suitable account types for your application.

++d. Account Component Description++
The OneBalance account system consists of several key components that can be combined in different ways:

• Validators responsible for identity verification, access control, and permission management
  Validators are a module of smart accounts
• Account Versions optimized for different deployment methods
  Smart contract implementations
• Deployment Types how accounts are initialized
  Directly affects user deposit (account) addresses
• Resource Locks supporting fast path cross-chain execution
  Require gating at the account/signature level

Supported Validator Types
Validators are responsible for validating transactions and managing account access permissions:

• ECDSAValidator (default) uses the standard Elliptic Curve Digital Signature Algorithm (ECDSA) for authentication, compatible with most existing wallet infrastructures.
• RoleBasedValidator allows the establishment of a user_admin role (similar to a user cold wallet), coexisting with the signer role for key rotation and executing trustless "rage quit" (quick exit) in emergencies.

Supported Smart Account Versions

• Kernel 3.1 compatible with: ECDSAValidator and RoleBasedValidator
  A multifunctional version supporting various validator types, widely compatible with different configurations.
• Kernel 3.3 optimized for: EIP-7702 deployment using ECDSAValidator
  A dedicated version designed to enhance gas efficiency by leveraging the latest EIP-7702 standard.

++e. Fees and Monetization++

OneBalance enables applications to monetize services through a transparent and flexible fee structure. This page explains how fees work, who pays them, and how to configure fees for your application.

Users pay a consolidated fee to the application, and the Toolkit helps the application handle the underlying gas fees and paymaster costs.

++f. Aggregated Assets++

Aggregated assets are OneBalance's solution for representing the same token across multiple blockchain networks as a single unified asset. Instead of managing USDC on Ethereum, USDC on Polygon, and USDC on Arbitrum separately, you can directly use an aggregated asset ds:usdc.

How Aggregated Assets Work
When you operate an aggregated asset (such as ds:usdc), OneBalance automatically:

• Aggregates your balances of that token across all supported chains
• Optimizes routing to spend from the chain with the highest transaction efficiency
• Automatically handles bridging when cross-chain operations are needed
• Provides unified pricing and fiat value calculations

Supported Aggregated Assets
Each aggregated asset includes:

• Unique ID: such as ds:usdc or ds:eth
• Symbol and Name: easily recognizable identifiers
• Decimal Places: the precision of the aggregated asset
• Constituent Assets: the specific on-chain tokens that make up the aggregated asset

++Summary++

OneBalance's advantages lie in its cross-chain aggregated asset management and unified account system, greatly enhancing user convenience and efficiency, supporting automatic aggregation of multi-chain assets and optimized routing, reducing the complexity and costs of cross-chain operations; its innovative resource lock mechanism achieves fast and secure cross-chain transactions, preventing double spending, enhancing user experience and system reliability. Additionally, the flexible account model and transparent fee structure provide highly customizable support and profitability for application parties.

On the downside, OneBalance's complex technical architecture presents a high integration and understanding threshold, and some features are still under development, which may affect short-term stability and applicability.

# III. Industry Data Analysis

1. Overall Market Performance

As of November 1 (Eastern Time), the total net outflow of Ethereum spot ETFs was $10,925,600.

1.1. Price Trends of Spot BTC vs ETH

BTC

Analysis

This week’s support levels to watch: $108,300 first line, $107,300 second line, $105,200 third line.

This week’s resistance levels to watch: $110,400 first line, $112,000 second line.

ETH

Analysis

This week’s support levels to watch: $2,530 first line, $2,470 second line, $2,380 third line.

This week’s resistance levels to watch: $2,630 first line, $2,680 second line.

2. Public Chain Data

2.1. BTC Layer 2 Summary

Analysis

1. Botanix Mainnet Officially Launched

• Botanix Labs launched its Bitcoin Layer-2 network mainnet on July 1, reducing Bitcoin's block confirmation time from about 10 minutes to 5 seconds and achieving EVM-equivalent smart contract support for the first time.
• The network is managed by a decentralized consortium of 16 node operators, including Galaxy Digital and Fireblocks, aimed at ensuring highly decentralized governance from the outset.
• The mainnet launch coincided with the simultaneous release of several DeFi applications, such as the decentralized lending trading protocol Dolomite, the perpetual contract platform GMX, and BTC-supported stablecoin Palladium and DEX Bitzy.
• This release is significant for three reasons: it introduces the concept of "BTCFi" (supporting DeFi on BTC), significantly enhances transaction efficiency, and provides a comprehensive EVM-compatible experience.

1. Bitcoin Hyper Pre-sale Launched

• A new Bitcoin Layer-2 solution called Bitcoin Hyper is currently in pre-sale, having raised approximately $2 million to build a Layer-2 network that supports speed improvements and smart contracts.
• Current project information is not comprehensive, and interested users should carefully evaluate its white paper and team background.

1. Interoperable Bridge Technology Research: Union Bridge

• A recent research paper introduced a trust-minimized bridging protocol called Union Bridge suitable for Rootstock, achieving secure asset flow from BTC to Layer 2 through an improved BitVMX optimistic proof mechanism.
• Key innovations include: reusable security binding design, participant management mechanisms, lightweight client support frameworks, and efficient timelock mechanisms, significantly enhancing the capital efficiency and security of bridging solutions.

2.2. EVM & Non-EVM Layer 1 Summary

Analysis

EVM Layer 1

1. Injective Launches Native EVM Testnet
   Injective announced that its native EVM testnet will go live on July 3, allowing developers to run Ethereum-compatible dApps directly on the chain without relying on external bridging layers.
2. Cosmos Launches EVM Upgrade
   The Cosmos network released its EVM module on the mainnet this week, enhancing cross-chain interoperability and further solidifying its position in the multi-chain ecosystem.
3. Shardeum Officially Launches Mainnet
   The community-driven EVM sharded chain Shardeum announced its mainnet launch, adopting a linearly scalable architecture, promising low fees and no MEV, emphasizing its decentralization and high performance.

Non-EVM Layer 1

1. Aave Expands to Aptos (First Non-EVM Deployment)
   Aave DAO voted to deploy Aave V3 on Aptos (a Move language chain), with the first supported assets including APT, USDC, USDT, and sUSDe, marking its first foray into the non-EVM ecosystem.
2. Strong Developer Growth on Sui Network
   The Move language chain Sui reported a 54% increase in developers over the past two years, making it one of the strongest growth performers among top Layer-1 projects, currently ranking in the top five for developer activity.
3. Algorand's Non-EVM Asset Native Ecosystem Steadily Developing
   Algorand continues to solidify its position as a non-EVM Layer-1, with its native ASA asset standard supporting efficient asset issuance and management, particularly suitable for institutional and compliance scenarios.

2.3. EVM Layer 2 Summary

Analysis

1. Bitcoin eSports Level L2 --- Botanix Mainnet Launch

Botanix is an EVM-compatible Layer-2 network built specifically for Bitcoin, and its mainnet has officially launched. This network reduces BTC's block time from 10 minutes to 5 seconds while being compatible with Ethereum smart contracts. This development marks a new era for the Bitcoin ecosystem as it moves towards DeFi applications.

2. XRPL Launches EVM Sidechain Mainnet

Ripple has launched an EVM-compatible sidechain mainnet based on the XRP Ledger, allowing developers to deploy Ethereum dApps on this network. The sidechain achieves cross-chain interoperability through the Axelar bridge and uses XRP as the gas token, expanding the application scenarios of the XRP ecosystem.

3. Vitalik Buterin: Decentralization Cannot Just Be a Slogan

Ethereum co-founder Vitalik stated at this week's Ethereum community conference that Layer-2 networks and DeFi protocols must prioritize the security of user assets. He emphasized that if projects rely on "instant upgrade buttons" or opaque mechanisms, the so-called "decentralization" will become an empty promise.

4. MEV Behavior in Optimistic Rollups Raises Concerns

Recent academic research revealed significant MEV (Maximum Extractable Value) activities in Layer-2 networks such as Arbitrum, Base, and Optimism. These transactions accounted for over half of the gas used, but contributed less than a quarter of the fees, exposing potential issues of resource allocation inequity and efficiency.

# IV. Macroeconomic Data Review and Key Data Release Nodes for Next Week

The U.S. economy has shown some resilience, with 147,000 non-farm jobs added in June, exceeding expectations, and the unemployment rate dropping to 4.1%, indicating a strong labor market. Overall, the June employment data was stronger than expected, showing that the U.S. labor market remains resilient, which has nearly eliminated market expectations for a rate cut by the Fed in July.

Important macroeconomic data release nodes for this week (July 7 - July 11) include:

July 9: U.S. EIA crude oil inventories for the week ending July 4

July 10: U.S. initial jobless claims for the week ending July 5

# V. Regulatory Policies

United States

• The upcoming "Crypto Week" from July 14 to 18 will review the CLARITY Act (clarifying asset classification and exchange responsibilities), the Anti-CBDC Surveillance State Act (opposing central bank digital currency privacy infringements), and the GENIUS Act (establishing a federal regulatory framework for stablecoins to ensure one-to-one asset backing and transparent disclosure).
• Congress plans to introduce regulatory bills for digital assets and stablecoins within two weeks to clarify the legal framework and market structure.
• The Senate has passed the GENIUS Act, requiring stablecoin issuers to back their coins with highly liquid assets and disclose reserves regularly.
• Coinbase's acquisition of Liquifi is seen as a strategic move for the future easing of token issuance regulations in the U.S.

Europe and the UK

• While the EU is reviewing the postponement of MiCA II, it plans to introduce regulations for transparency and security in DeFi by 2026.
• Spain has introduced new regulations requiring virtual asset service providers (VASPs) to report transaction and holding data of Spanish users, allowing tax authorities to seize non-compliant assets.

Asia and Africa

• Hong Kong plans to implement a stablecoin licensing mechanism on August 1 to prevent fraudulent activities.
• Japan is undergoing regulatory reforms, planning to standardize cryptocurrency taxation and promote the legalization of Bitcoin ETFs to enhance market friendliness.
• Several African countries have introduced cryptocurrency regulatory frameworks aimed at attracting international institutions and regulating local markets.

U.S. States

• The California Assembly is discussing whether to allow crypto payments and has initiated research on related regulatory frameworks.
• Connecticut state government agencies have prohibited the holding, trading, or investing in crypto assets internally.

Trends in Other Regions

• The Swedish Minister of Justice announced that over $8.3 million in crypto assets related to criminal cases have been seized since 2024.
• The Cook Islands plans to empower authorities to trace suspicious crypto assets through new legislation, raising concerns about excessive surveillance.