This article is from a submission and does not represent the views of ChainCatcher, nor does it constitute any investment advice.

I. Outlook

1. Macroeconomic Summary and Future Predictions

Last week, the latest report from the U.S. Department of Labor showed that July's employment figures fell far short of expectations, compounded by significant downward revisions to the data from the previous two months. Market confidence was heavily impacted, with investors worried that the momentum of economic recovery is fading. Negative news regarding tariffs and employment has heightened uncertainty about when the Federal Reserve will cut interest rates, and Trump's own questioning of the employment data has further intensified market unease. The macroeconomic trend in the U.S. is clearly characterized by "weak recovery and strong risks." The sharp decline in U.S. stocks on August 1 was driven by worsening employment data and a new wave of large-scale tariff policies. In the future, the economy may continue to face the dual challenges of slowing growth and high inflation, with policy uncertainty continuing to dominate market sentiment.

2. Market Movements and Warnings in the Crypto Industry

The overall crypto market has experienced a sharp correction after a period of high-level fluctuations, with Bitcoin's price dropping from a near $120,000 high to about $112,000, indicating a significant increase in short-term risk sentiment. Ethereum and some unlocked tokens have seen increased volatility due to selling pressure, with institutional funding showing divergence; some large holders are adjusting their positions but still engaging in accumulation. Macroeconomic headwinds such as weak U.S. employment data and escalating tariff policies have intensified market risk aversion, leading to capital flowing out of risk assets. In the short term, it is necessary to be vigilant about the potential for deeper corrections if key support levels are breached, while in the medium to long term, attention should be paid to the continued positioning by institutions and the potential for ecosystem upgrades in quality projects.

3. Industry and Sector Hotspots

Aspecta is a blockchain infrastructure aimed at providing intelligent certification and price discovery mechanisms for trillions of dollars in illiquid assets; Datagram is a Global Hyper-Fabric Network designed to support next-generation real-time connected applications and decentralized physical infrastructure networks (DePIN).

II. Market Hotspot Sectors and Potential Projects of the Week

1. Overview of Potential Projects

1.1. Brief Analysis (Led by YZiLabs) Aspecta---A Network for AI Developer Identities Across Web2/Web3

Introduction

Aspecta is a blockchain infrastructure aimed at providing intelligent certification and price discovery mechanisms for trillions of dollars in illiquid assets. It unlocks liquidity for the entire lifecycle of pre-TGE shares, locked tokens, unlisted equity, and real-world assets (RWA).

Architecture Overview

1. Buildkey: Participating in Quality Asset Liquidity Entry Through Bonding Curves

The operation of BuildKey is as follows:

1. Participate in the issuance of reviewed projects/assets launched by BuildKey;
2. Trade BuildKey through the bonding curve mechanism;
3. Exchange BuildKey for various assets: pre-TGE shares, locked tokens, NFTs, unlisted equity, etc.

A Fair Launch: Early Key Subscription Mechanism

Aspecta has designed a special launch mechanism aimed at providing everyone with a fair opportunity to purchase early BuildKeys and prevent bot abuse.

Key Points: In the first 30 minutes after a project pool launches (specific timing depends on the project), it will be in "Deposit" mode.
During this phase, the system will randomly select transaction executions without considering transaction order, Gas prices, or payment amounts.
Participation in subscriptions, receiving BuildKeys, or refunds requires no permissions; anyone can participate.
To further prevent bots, users verified through Proof of Humanity (PoH) will be eligible to join the premium deposit groups.

Mechanism Explanation

In the first 30 minutes after the project pool launches, the pool will be in "Deposit Mode."
Traders must pre-deposit refundable funds to submit subscription orders.

Each address is limited to submitting 1 order, with a maximum of 20-100 BuildKeys per order (specific numbers depend on the project and the subscription group).

All orders will be randomly selected, sorted, and executed. This means that regardless of the Gas price or submission time, participants will be treated equally.

If subscriptions exceed demand, each winning order will dynamically adjust the number of Keys allocated based on the excess multiplier, allowing more users to have the opportunity to obtain early Keys.

After the subscription ends, users will instantly see:

• Total subscription excess multiplier
• Number of Keys available for collection
• Amount of refundable funds

Claiming Keys and refunds are permissionless and free of charge; users only need to click the "Claim" button on the subscription results page.

To prevent bots from affecting real community users, those verified through PoH can join the Premium Deposit Group. Ways to complete PoH verification include:

• Manually completing PoH tasks,
• Or obtaining an Aspecta community badge,
• Or completing previous Aspecta pre-launch PoH tasks.

B Trading

BuildKey trades using a bonding curve mechanism designed to be "upgradable," benefiting both existing holders and new users.

Bonding Curve Mechanism Explanation

1. Bonding Curve Upgrade When BuildKey's market capitalization reaches a new milestone, the bonding curve will enter a new phase, creating new price growth opportunities for all users.

2. Asset Exchange Ratio Upgrade As the market capitalization of the target asset grows, the corresponding asset exchange ratio for BuildKey will also increase.
   That is: The more BuildKeys purchased, the higher the asset value that each BuildKey can be exchanged for.
   Once the exchange ratio is upgraded, it will automatically apply to all BuildKeys, including those that have already participated in the exchange.

3. Market Capitalization Calculation Method Current market capitalization of BuildKey = Total circulating supply (total number of Keys held by users) × Current price.

4. Asset Exchange Mechanism

The core idea of BuildKey is to achieve public price discovery of illiquid assets through the trading and holding of Keys. In return, Key holders can gain participation opportunities in the early stages of asset growth.

Project parties and stakeholders can provide assets or rights to BuildKey holders in various ways.

1. Types of Assets

The exchangeable asset forms are diverse, including:

• Pre-TGE Shares
• Locked Tokens
• NFTs
• Private Equities, etc.

As these assets further develop and approach potential liquidity events (such as TGE, IPO, etc.), their value typically increases rapidly.

2. Ways to Obtain Assets

Users can obtain assets from project parties/stakeholders in two ways:

• 2.1 Exchange assets using BuildKey (supporting upgraded Key-asset exchange ratios)
• 2.2 Hold BuildKey to automatically receive holder benefits

Upgradable Asset Redemption (Redeem)

• Redemption Start:
  Click the "Redeem" button to exchange assets using BuildKey. After redemption, BuildKey will be transferred to the project/stakeholder's wallet address.
• Redemption Ends:
  The system will take a snapshot. To ensure users can obtain assets at TGE, redemption usually ends before TGE. Unredeemed BuildKeys can still be traded in the market.
• Asset Collection:
  Users can collect assets on the project page or have them directly transferred from the project to their wallets.
• Redemption Deadline:
  If the Redeem DDL is missed, BuildKey can no longer be exchanged for assets but can still be used for market trading and profit.
• Buying Deadline:
  Some projects may set a buying deadline. After this time, BuildKey can no longer be purchased, but existing BuildKeys can still be used to exchange for assets or trade for profit.
  Exchange ratio upgrades:
  As the market capitalization of the target asset grows, the exchange ratio for the corresponding assets will also upgrade. Already exchanged BuildKeys will also automatically receive the ratio increase.

Comments

Aspecta builds a developer identity network through AI technology, combining data from Web2 (such as GitHub, Twitter) and Web3 (such as wallet addresses) to generate quantifiable developer profiles, supporting over 8,000 skill dimensions and experience highlights, helping individuals gain higher visibility and credit endorsement in decentralized ecosystems. At the same time, Aspecta also achieves price discovery and early participation opportunities for illiquid assets (such as locked tokens, unlisted equity, etc.) through BuildKey and the bonding curve mechanism. Its advantages lie in integrating AI, identity, and asset mechanisms, bridging multi-chain developer social and financing paths;

However, the disadvantages include that PoH verification, KYC, and other compliance requirements may limit participation from some users, and the reliance on AI models for identity assessment also poses risks of accuracy and bias.

1.2. Interpreting Datagram, which raised $4 million---Decentralized Connection Infrastructure for Real-Time Applications Driven by AI

Introduction

Datagram is a Global Hyper-Fabric Network designed to support next-generation real-time connected applications and decentralized physical infrastructure networks (DePIN). Such applications rely on real-world resources like computing power, bandwidth, and storage. Datagram simplifies the launch and scaling of such applications and networks without the need to build complex infrastructure.

The platform rewards Datagram node operators in two main ways:

1. Online Duration and Availability: Operators who keep their nodes online and responsive will be rewarded to ensure the stability and reliability of the entire network.
2. Actual Usage: Nodes that actively contribute resources (such as computing power, bandwidth, etc.) to support real-time applications will receive corresponding rewards based on their contributions.

Datagram is designed for three core user groups:

• Web2 and Web3 Enterprises: Traditional businesses and blockchain projects can access Datagram's decentralized services through simple APIs or SDKs without needing in-depth blockchain technical knowledge.
• Existing DePIN Networks: Projects with established node infrastructure can integrate Datagram through the Datagram Core Substrate (DCS) to expand their bandwidth, computing, and storage capabilities.
• Emerging DePIN Projects: Startups or developers can quickly launch decentralized networks using the one-stop infrastructure provided by Datagram.

Architecture Overview

Datagram is a Layer 1 network built on Avalanche, specifically designed to support real-time applications, providing scalable, decentralized, and cost-effective access to computing, bandwidth, and storage services. Its architecture simplifies the complexity of decentralized systems while retaining the advantages of blockchain (such as security, interoperability, and efficiency), offering a Web2-like user experience.

Datagram also supports decentralized physical infrastructure networks (DePIN) through its connection layer, Datagram Core Substrate (DCS), with the core mission of providing convenient and available decentralized infrastructure for applications with high-performance connectivity needs. Currently, it supports EVM-based networks and will expand to non-EVM compatibility in the future.

Datagram's architecture consists of four key components that work together to provide low-latency, secure, and high-throughput infrastructure capabilities:

1. Datagram Node Network: The backbone of Datagram, composed of distributed nodes (Datagram Cores) responsible for routing, validation, and data transmission, supporting native and external DePIN projects, providing decentralized computing, bandwidth, and storage resources.

2. Fabric Network: Independent DePIN networks integrated with Datagram infrastructure, maintaining specialized operations while sharing resources to achieve scalability and interoperability.

3. Datagram Core Substrate (DCS): The connection layer that coordinates the computing, bandwidth, and storage resources of the entire network and integrates with the Network Operations Center (NOC) for real-time performance monitoring and reward distribution.

4. Hyper Network Layer: An AI-driven coordination system that intelligently manages routing, load balancing, and resource allocation in real-time, ensuring low latency and fault tolerance.

5. Datagram Node Network

The Datagram node network is the core infrastructure of the entire Datagram ecosystem, composed of globally distributed nodes that provide decentralized computing, bandwidth, and storage resources. It supports both native DePIN projects and external projects without the need to build proprietary infrastructure.

Core Functions:

• Multiple Node Types (Datagram Cores):

• Full Cores: Main nodes responsible for traffic routing and network load optimization, required to hold Core Tokens, supporting high-concurrency scenarios, and rewarded based on performance and resource contributions.

• Partner Cores: Assist main nodes in handling peak traffic, ensuring network stability under pressure.

• Device Cores: Utilize idle IoT device computing power (such as routers, sensors, etc.) to contribute resources, enhancing overall efficiency.

• Hardened Cores: Handle communications with high-security requirements, such as government and enterprise data, equipped with enhanced encryption and data protection.

• Consumer Cores: Run on personal or small organization devices, temporarily contributing resources to support localized services.

• Scalability and Flexibility: Supports rapid deployment and seamless integration into existing DePIN networks without requiring blockchain expertise.

• Chain-Agnostic Integration: Built on Avalanche as a Layer 1, currently supports EVM networks and will support non-EVM networks in the future.

• High-Performance Data Transmission: Nodes collaboratively build a parallel computing network similar to Beowulf clusters, ensuring low latency and fault tolerance, suitable for real-time tasks such as video conferencing and AI computing.

1. Fabric Networks

Fabric Networks are independent DePIN networks that achieve high-performance operation and rapid scaling of their applications by accessing Datagram's infrastructure (computing, bandwidth, storage).

Key Features:

• Interoperability: Seamlessly connects to the Datagram network through Datagram Core Substrate (DCS), enabling resource sharing and cross-network communication.
• High Customizability: Each Fabric Network can customize its operational logic based on its specific scenarios, such as decentralized AI computing, real-time communication, etc., while utilizing Datagram's global infrastructure.
• Independent Execution Environment: Possesses an independent execution system to avoid resource competition and ensure stable performance.
• Plug-and-Play Expansion: Can immediately utilize distributed resources upon integration without building infrastructure from scratch.

Role in the Ecosystem:

Fabric Networks allow DePIN projects to focus on core business logic while relying on Datagram for underlying computing and transmission capabilities. For example, a decentralized video streaming service could operate as a Fabric Network, leveraging resources provided by Datagram for data transmission and storage while retaining its brand and user experience.

3. Datagram Core Substrate (DCS)

DCS is the core connection layer of the Datagram network, providing underlying support for the decentralized scheduling and coordination of computing, bandwidth, and storage resources, helping any DePIN project to quickly launch and scale without building its own network.

Key Features:

• Resource Coordination: Smartly allocates workloads and network traffic based on real-time demand, optimizing performance while maintaining interoperability with the entire Datagram network.
• Network Operations Center (NOC): Integrated with DCS to monitor node online rates, performance, and resource usage, automatically distributing node rewards.
• Modular Architecture: Utilizes containerized deployment (Docker), allowing developers to deploy custom workloads in parallel with the core network.
• Chain-Agnostic Deployment: Supports deployment on both EVM and non-EVM networks (such as Avalanche, Solana, Ethereum L2), enhancing cross-chain compatibility and flexibility.
• Simple Integration: Abstracts the underlying complexity of blockchain, allowing developers to focus on application building without managing cumbersome infrastructure.

Core Functions:

DCS essentially acts as a decentralized service mesh, supporting project parties or developers in deploying custom node networks on top of Datagram's global infrastructure.

For instance, a company building a decentralized AI training platform can use DCS to intelligently allocate training tasks to nodes located in different geographical areas while having the NOC monitor node status in real-time and automatically distribute rewards to ensure stable operation.

4. Hyper Network Layer

The Hyper Network Layer is the AI-driven coordination system of the Datagram network, enabling intelligent routing, real-time optimization, and parallel processing across the entire infrastructure. Unlike traditional DePIN networks with static node configurations, it achieves dynamic and efficient infrastructure orchestration.

Key Features:

• Adaptive Traffic Routing: Monitors network status in real-time, automatically directing traffic to optimal nodes to avoid congestion and reduce latency.
• Real-Time Load Balancing: Dynamically allocates computing resources based on actual network load, avoiding bottlenecks caused by static configurations.
• Intelligent Resource Allocation: Predicts network usage patterns and pre-allocates computing, bandwidth, and storage resources to prevent resource congestion.
• Automatic Network Recovery: Can immediately reroute when nodes or subnets fail, ensuring uninterrupted service and enhancing network resilience.
• Large-Scale UDP Optimization: Unlike most decentralized networks that only support TCP, Datagram natively supports large-scale UDP, adapting to real-time high-frequency scenarios such as video streaming, multiplayer gaming, and AI processing, with significant performance advantages.

Ecological Role:

The Hyper Network Layer is a core component that enables Datagram to outperform traditional DePIN architectures, acting as a dynamic AI controller that supports enterprises and project parties in customizing dedicated subnets within its global network while enjoying built-in security, scalability, and integrability.

Comments

Datagram, built on Avalanche, provides scalable, low-latency, decentralized support for computing, bandwidth, and storage for real-time applications. It adopts a modular architecture with a Hyper Network AI scheduling layer, DCS connection layer, cross-chain support, and multiple types of node networks, balancing performance and resilience, suitable for Web2/Web3 enterprises and DePIN projects. At the same time, its abstracted design lowers the development threshold, offering a user experience close to Web2. However, its reliance on AI-driven network management and UDP optimization protocols imposes high technical requirements on overall system coordination and scheduling, making initial deployment and maintenance complex. Additionally, while building on Avalanche Layer 1 enhances performance, it may limit seamless integration with some non-EVM ecosystems, and the actual ecological implementation will still require time to verify its stability and network effects.

2. Detailed Explanation of Key Projects of the Week

2.1. Detailed Analysis of Zypher Network (Raised $7 million, with Hashkey participating) --- Cross-Chain Zero-Knowledge Driven Decentralized AI and Gaming Infrastructure

Introduction

Zypher Network has launched a decentralized cryptographic trust layer specifically designed for autonomous AI agents. By introducing innovative mechanisms such as Proof of Prompt, zkPrompt, zkInference, zkTLS (Zero-Knowledge Transport Layer Security), and Zytron AI Chain, Zypher achieves verifiable, private, and auditable AI interactions across multiple domains.

The network utilizes zero-knowledge proof technology to ensure that AI agents maintain consistency and security without disclosing sensitive data. Its modular infrastructure, including a decentralized prover network and multi-execution engine support, provides excellent composability, scalability, and interoperability with the real world, suitable for various application scenarios such as decentralized finance, gaming, AI compliance, and digital labor.

Feature Analysis

Zypher Network introduces a comprehensive set of cryptographic protocols and infrastructure components aimed at providing verifiability, privacy protection, and integrity assurance for the operation of autonomous AI agents. Each layer of the Zypher tech stack addresses different aspects of AI trust issues: including verifying the integrity of prompts, ensuring the consistency of encrypted communications, implementing scalable proof generation mechanisms, and anchoring these proofs to decentralized infrastructure, thereby building a trustworthy, secure, and auditable AI operating environment.

1. Proof of Prompt Mechanism

Proof of Prompt is the core protocol of Zypher Network, designed to ensure that the prompts sent to AI models and their returned results are verifiable in terms of authenticity, consistency, and integrity. This mechanism allows external observers to verify that a specific AI agent indeed received a particular prompt and generated the corresponding output—without exposing the specific content of the prompt or the output itself.

This mechanism is achieved through a combination of encryption technologies, including:

• Symmetric Encryption (such as AES, ChaCha20): Used for efficient data obfuscation;
• Zero-Knowledge Proofs (ZKP): Used to verify actions without disclosing content;
• Commitment Schemes: Ensure data is tamper-proof.

Ultimately, this forms a lightweight, privacy-protecting verification method that can be widely applied in scenarios such as automated finance, DAO governance, Web3 gaming, and identity management.

Proof of Prompt effectively addresses the challenge of "black box operations" in AI systems—ensuring that the behavior of AI agents indeed originates from a clear, untampered set of instructions, laying a trustworthy foundation for more advanced applications of Zypher.

2. zkPrompt (Zero-Knowledge Prompt Verification)

In large language models (LLMs), prompts are typically divided into system prompts and user prompts. The system prompt is set by developers to define the model's context, tone, and behavioral boundaries, making it a key factor in guiding AI performance style and stability. As such, system prompts have become one of the core capabilities in LLM development, and developers are generally reluctant to disclose their content.

However, since many LLMs operate in a "black box" manner, external users find it challenging to verify the consistency of system prompts (i.e., whether the model consistently operates based on the same system prompt), making behavior predictability and trust difficult to guarantee.

To address this, Zypher proposes zkPrompt—a verification mechanism based on zero-knowledge proofs that can verify whether the system prompt used during model initialization is consistent with the original settings without disclosing the content of the system prompt.

The implementation is as follows:

• Construct the model initialization process as a zero-knowledge circuit;
• Add a commitment module in the circuit to generate a commitment value for the system prompt used;
• Verify that the system prompt used in the model is consistent with the commitment value through the circuit, and publicly output that commitment value;
• External users can verify whether the system prompt has been tampered with based on that commitment value, thereby determining whether the model's behavior is consistent.

Cryptographic Commitment is a protocol that allows one party to commit to a value without revealing the specific content to another party until the first party chooses to disclose it. Common commitment mechanisms include hash commitments and Pedersen commitments.

In zkPrompt, the core value of this mechanism is reflected in two aspects:

• Developers can keep the system prompt content confidential (not disclosed to the outside world);
• Once the commitment is completed, the content of the system prompt cannot be changed, thereby ensuring the integrity and trustworthiness of the system prompt.

Overall Process:

1. Developers encrypt the system prompt and send the commitment value to the blockchain;
2. Initialize the zk circuit, generating Prover keys and Verifier keys, with the Verifier key also sent to the blockchain;
3. Users submit user prompts;
4. The large language model generates response content and the corresponding zero-knowledge proof, sending both to the blockchain;
5. The smart contract verifies the zk proof using the original system prompt commitment value;

If the verification passes, it indicates that the system prompt has not been tampered with since submission.

2.1 zkPrompt System Design

The zkPrompt protocol designs an efficient and feasible way to verify the outputs of large language models (LLMs) without users directly interacting with the model service.

The system consists of four key roles: User, Prover, Proxy, and LLM Provider. Even in the case of untrustworthy intermediaries, zkPrompt ensures the authenticity and untampered nature of model responses.

This system verifies whether the Prover faithfully transmits the model output through cryptographic signatures and proof generation mechanisms, while supporting a privacy protection mode that completes verification without disclosing the prompt or response content.

Compared to traditional ZKML frameworks, the zkPrompt architecture significantly reduces computational overhead and aligns more closely with the actual trust models of current mainstream LLM providers.

2.2 zkPrompt Protocol Concept and Comparison

zkPrompt draws on the ideas of zkTLS, embedding zero-knowledge proofs (ZKP) into the interaction process between users and large language models (LLMs) to ensure the authenticity and integrity of response data.

In the protocol, user requests are forwarded to the LLM provider through the Prover and Proxy, with responses returned along the same path. However, TLS communication only occurs between the Prover, Proxy, and LLM, with users unable to directly participate in verification, creating a trust blind spot.

To address this issue, zkPrompt requires the Prover to provide zero-knowledge proofs to prove that the response indeed comes from a genuine LLM and has not been tampered with.

This mechanism significantly enhances the security and credibility of AI agent responses, especially in high-risk scenarios such as on-chain asset management, trading bots, and financial agents.

2.3 Trust and Security Assumptions

The protocol assumes that there is no collusion between the Proxy and the Prover. To enhance the robustness of the system, it is recommended to decentralize the Proxy in actual deployments to prevent single points of failure or abuse.

The current design also assumes that prompts and responses are recorded in plaintext on-chain (a privacy-protecting version will be introduced in the future to address this issue).

2.4 Technical Challenges and Core Solutions

How to ensure that the encrypted response returned by the Prover indeed comes from the LLM and is not fabricated?

Solution: Proxy Signature + Zero-Knowledge Proof

• After receiving the encrypted response from the LLM, the Proxy signs it and sends the encrypted content along with the signature to the blockchain;
• The Prover generates a ZKP (zero-knowledge proof) to prove:
• The decrypted content indeed contains the user's prompt;
• The response content is authentic and untampered.

The core of this process is the correct decryption verification of symmetric encryption, which is efficient and can be implemented based on solutions like Dubhe.

Verification Steps (Three Steps)

1. Verify the Proxy's signature on the ciphertext;
2. Check whether the decrypted content contains the user's prompt;
3. Verify the validity of the zero-knowledge proof provided by the Prover.

After completing these steps, anyone (such as miners or nodes) can be assured of the response's authenticity.

Privacy Protection

If users wish to protect the privacy of the prompt and response content:

• Only the hash values of the prompt and response are recorded on-chain, rather than the original text:

1. H1 = Hash(prompt)
2. H2 = Hash(response)
3. And add the constraint: the response contains a fragment of the prompt

• The Proxy still records the ciphertext on-chain;

• The Prover provides ZKP to prove:

1. Hash(witness) = H2 (response content is correct);
2. Hash(witness[i:j]) = H1 (the response indeed contains the prompt);
3. Decrypt(ciphertext) = witness (decryption is correct)

This privacy version can complete verification without exposing any sensitive content, making it very suitable for scenarios with high compliance requirements.

3. zkInference Framework: Bringing Verifiability and Fairness to Web3 AI Agents

Currently, many AI agents operate in Web3 as "black boxes," lacking transparent reasoning processes, making their behavior difficult to verify, especially in multiplayer competitive games, where there is a risk of AI collusion and cheating.

3.1 zkInference Solution

The zkInference framework ensures that AI agents operate strictly according to preset models and specifications through zero-knowledge proof algorithms, achieving the following goals without disclosing models and data:

1. Verifiability: Verify whether AI decision-making behavior is genuine and compliant with rules;
2. Anti-Collusion: Prevent multiple agents from colluding, ensuring fairness in games;
3. Unlimited Computing Power: Provide a decentralized computing market to support large-scale verifiable AI computations.

3.2 zkTLS (Zero-Knowledge Transport Layer Security Protocol) zkTLS is a protocol that Zypher uses to verify the content and consistency of TLS sessions through zero-knowledge encryption technology. Traditional TLS is confidential but cannot be externally verified, and zkTLS addresses this issue.
Three deployment modes:

• TEE (Trusted Execution Environment) mode: High fidelity but requires dedicated hardware;
• MPC (Multi-Party Computation) mode: More decentralized but with high performance overhead;
• Proxy mode: High performance, low hardware requirements, suitable for most practical applications.
  Zypher's zkPrompt adopts the proxy mode to ensure that prompts and responses between users and models remain consistent in encrypted sessions.

3.3 Zytron: On-Chain AI Execution Engine Zytron is Zypher's on-chain AI computing and verification layer, responsible for distributed coordination and orchestration of zkPrompt and zkTLS proofs.
Features include:

• Efficient routing and load balancing using Kademlia DHT;
• Cross-node coordination for proof generation and verification;
• Task allocation based on node distance and historical performance;
• On-chain recording of tamper-proof verification interactions and AI decisions.
  Zytron is the core support for Zypher's verifiable AI system.

3.4 Decentralized Prover Network This network supports the scalable generation and verification of zero-knowledge proofs, avoiding reliance on centralized servers and ensuring the trust of the system is decentralized.
Features:

• Any qualified node can contribute computing power;
• Prevent cheating through reputation and staking mechanisms;
• Zytron coordinates task allocation and result verification.
  This network ensures that verification is both efficient and decentralized, supporting internet-level AI verification.

3.5 Composability and Integration Zypher's modular protocol stack facilitates cross-domain integration, allowing developers to flexibly combine components to build trustworthy AI applications.
Application examples:

• Embedding zkPrompt into LLM-based API gateways;
• Using zkTLS as a privacy-protecting data integrity layer for AI assistants;
• Applying Proof of Prompt for AI governance proposal verification in Web3 DAOs;
• Anchoring AI interactions to on-chain smart contracts through Zytron.
  This composability makes Zypher both a foundational protocol layer and a toolkit for developers to build trustworthy AI.

1. Zytron AI Chain

Zytron is the high-performance on-chain AI computing execution layer of Zypher Network, supporting scalable, low-latency, and verifiable AI computations, compatible with the Web3 ecosystem. It supports zk proofs, AI agent logic combinations, and flexible state execution.

4.1 Web3 Compatible Interface Zytron provides a dual-interface system, maintaining compatibility with Ethereum while optimizing transaction latency, state addressing, and proof generation. Developers can interact with Zytron using common tools like MetaMask and Hardhat, enjoying its performance and verification advantages.

4.2 Multi-Execution Engine Support Supports the parallel operation of various smart contract execution models:

• EVM-compatible Solidity contracts;
• UTXO model supporting lightweight parallel transaction flows.
  Different address state groups can be assigned different execution models, with plans to support WASM in the future.

4.3 Zero Gas Transactions Natively supports ERC-4337 account abstraction, allowing zero gas transactions through proxy sponsorship, lowering the interaction threshold between users and AI agents, and supporting customized fee logic.

4.4 Native Cross-Chain Bridge The modular sharding architecture supports efficient and secure cross-chain asset transfers. By locking assets through bridge contracts, Zytron shards map and mint native wrapped assets, enhancing security with zero-knowledge proofs, enabling cross-chain AI agent interactions.

4.5 Data Sovereignty Rollup

• Sequencer is responsible for transaction ordering and packaging, forming Layer 2 blocks and batches, using Merkle trees to broadcast data roots, and submitting them to a weak data availability layer, ultimately confirmed by PBFT consensus.
• External Data Availability Layer (DA) is divided into strong and weak categories, with weak DA used for rapid transmission and strong DA for final on-chain execution verification.
• Sequencer Pending State To meet the low-latency requirements of gaming, the Sequencer maintains a local pending state, allowing users to continuously submit transactions for a smooth experience.
• Asset zk-Rollup uses Merkle trees to store asset transactions, supporting deposits, withdrawals, and transfers, employing the UTXO model and zero-knowledge proofs to ensure transactions are instant and valid, avoiding long challenge periods.
• Service Sharding To achieve low-latency gaming services, Zytron executes on-chain contracts through sharding.
• Kademlia Node Data Sharding Based on the Kademlia DHT algorithm, contract execution tasks are allocated based on node distance, with nodes registering and synchronizing states to support decentralized collaboration.
• Address State Groups Execution nodes only store states related to the addresses closest to them, with address state groups updated locally using BFT algorithms to ensure data security for offline nodes.
• Transaction Space Access List Transactions must include state change information (balance, Nonce, storage, etc.) to ensure correct sharding processing; exceeding access limits results in transaction failure.
• Key Space Estimation To help clients mark space access limits, estimation functionality is provided to simulate execution of contracts and generate access ranges.
• Customized Network Optimizes the P2P network, employing a UDP-based KCP protocol to reduce latency, designing a stable dual-layer DHT structure and relay algorithm to ensure stable and low-latency node connections, meeting high-frequency gaming needs.

Summary

Zytron, as a high-performance execution layer for decentralized AI computing and gaming applications, boasts advantages in supporting multiple execution engines (EVM and UTXO), providing a zero gas transaction experience and native cross-chain capabilities, effectively ensuring low latency and data sovereignty while employing innovative sharding and decentralized node architectures to enhance scalability and security. However, its complex distributed design and multi-layer data synchronization mechanisms may pose challenges for implementation and maintenance, resulting in a higher learning cost for developers.

III. Industry Data Analysis

1. Overall Market Performance

1.1. Spot BTC vs ETH Price Trends

BTC

Analysis

Key support this week: $114,000, $111,900

Key resistance this week: $115,100, $116,200

ETH

Analysis

Key support this week: $3,510, $3,360

Key resistance this week: $3,580, $3,680, $3,880

2. On-Chain Analysis

2.1. BTC Layer 2 Summary

This week, BTC Layer 2 has broken through the boundaries of Lightning payments and ecological entrepreneurship:

• GOAT demonstrated the feasibility of low-latency zk-Rollup technology through the BitVM2 testnet;
• LQWD injected capital and infrastructure into the Lightning network, co-building a global BTC payment network;
• The Bitcoin Hyper project had a strong presale, paving an innovative path for the new BTC Rollup ecosystem;
• The coupling of stablecoins and Lightning accelerates the expansion of mainstream application scenarios for BTC Layer 2;
• Lightning public capacity reached a historical high, making the overall ecological structure more robust.

2.2. EVM & Non-EVM Layer 1 Summary

Core Event Analysis

• Injective's mainnet launch is imminent ------ As the first native EVM fully compatible new Layer 1, Injective has launched its mainnet testing environment and successfully processed its first transaction, with the developer ecosystem gradually becoming active. INJ is expected to establish a new generation of high-performance Ethereum-compatible chain market capitalization expectations.

• Shardeum testnet is online ------ Shardeum Unstablenet is its smart contract testing environment, emphasizing elastic scalability and developer-friendly deployment mechanisms. It has currently attracted preliminary application attempts, showcasing a new path for EVM chain construction.

• Solana's ecosystem enters a high-growth phase ------ In July, its stablecoin transfer volume increased by over half year-on-year, reaching $215 billion, indicating Solana's impressive performance in user activity and DeFi scenario reshaping, with the average number of weekly active addresses exceeding Ethereum and other Layer 1s combined.

• Hedera Hashgraph mainnet upgrade completed ------ On July 23, a functional upgrade was completed. Although it was only a minor version, it reflects Hedera's ongoing focus on enterprise-level applications in China and the maintenance of regulatory-compliant links, with a short and smooth maintenance window.

• NEAR proposes core economic model reform ------ The NEAR community initiated a proposal to set the original inflation rate at 5%, now suggesting a reduction to 2.5% to enhance the resilience and sustainability of the token model, which may impact staking yield mechanisms and long-term holder behavior.

2.3. EVM Layer 2 Summary

1. Linea: Airdrop and Deflationary Economics Rising

• Total supply of approximately 7.2 billion, with airdrop accounting for 9% (Voyage active community users) + 1% (strategic dapp and community builders), with actual investment accounts filtered for Sybil risk and no smooth lock-up set;
• Accounting for about 22% of the initial circulation, combined with ecological fund locking, future accompanying cost-reduction and burning mechanisms (20% ETH, 80% LINEA) may drive value capture and token burning resonance.

1. ZKsync Accelerates Upgrade Track

• Core v29.0.0 technical improvements mainly target current branch compatibility, such as CLI access methods and bridge model integration;
• Inclusion of Tokenized Asset Coalition (TAC) marks its response to institutional-level asset tokenization signals, with future potential to connect RWA, synthetic assets, and other financial products;
• Era and ZKsync Gateway become a native interoperability layer, significantly reducing transaction friction between various Era-Chains, laying the groundwork for applications;
• Security Council proposal reduces annual budget expenditures by half, introducing algorithmic mechanisms to control mint cap (related to token market capitalization), demonstrating governance willingness.

1. Optimism's Market Temperature Test

• Listing on Upbit supports KRW/OP trading pairs led to a rapid influx of local Korean users, forming the main driving force behind OP's recent rise;
• However, OP unlocking is set for July 31, with a significant unknown proportion of total supply still requiring close monitoring of on-chain liquidity and price-volume relationships during this period;
• The market response represented by OP's recent rise indicates that the Optimism ecosystem still has attention that can be reached at both retail and institutional levels; however, it remains to be seen whether selling pressure will emerge after the unlocking.

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

In July, non-farm payrolls added only 73,000 jobs, significantly below market expectations. At the same time, employment data for May and June was significantly revised down, totaling a reduction of 258,000 jobs. The unemployment rate rose from 4.1% to 4.2%, with the number of long-term unemployed increasing to 1,826,000, indicating a substantial deterioration in the overall employment market.

Important macroeconomic data nodes for this week (August 4 - August 8) include:

August 7: Initial jobless claims in the U.S. for the week ending August 2.

V. Regulatory Policies

United States: The White House Releases Digital Asset Policy Report, SEC Launches "Project Crypto"

• The White House policy team released a 160-page blueprint for crypto policy, proposing a legislative roadmap for digital assets, stablecoin regulatory reforms, promoting the exploration of a U.S. central bank digital currency (CBDC), and calling for the passage of the "GENIUS Act" and "CLARITY Act" within the next six months.

• The chairman of the U.S. Securities and Exchange Commission (SEC) announced the launch of "Project Crypto," aimed at simplifying regulatory pathways by clarifying token attributes and trading roles, supporting the compliant landing of tokenized securities, and improving market uncertainty.

• The House of Representatives has passed the "GENIUS Act" (stablecoin regulation), the "CLARITY Act" (clarification of token regulation), and related bills opposing the federal issuance of CBDCs.

• Among them, the "GENIUS Act" has received support from the Senate and is expected to enter the formal signing process in August, establishing a unified federal framework for stablecoin issuance within the U.S.

European Union: ESMA Promotes the Formal Implementation of the MiCA Framework

• The European Securities and Markets Authority (ESMA) released an assessment, urging member states to strengthen the consistency of licensing reviews under the MiCA framework to avoid regulatory arbitrage.

• At the same time, ESMA issued a consumer protection announcement, warning that non-compliant products must not be misleadingly advertised as "regulated assets."

• The EU also updated cybersecurity and KYC technical standards related to digital assets as pre-compliance requirements for MiCA.

Hong Kong: Stablecoin Regulatory Ordinance Officially Takes Effect

• The Hong Kong Legislative Council passed the "Stablecoin Ordinance," which took effect on August 1. The ordinance requires stablecoin issuers to be locally registered, maintain 100% reserves, prohibit the issuance of algorithmic stablecoins, and includes a six-month transition buffer period.

• The Hong Kong Monetary Authority (HKMA) released draft implementation details, with the first batch of issuance licenses expected to be granted in early 2026.

Indonesia: Major Restructuring of Cryptocurrency Taxation System

• Starting August 1, Indonesia has raised tax rates related to cryptocurrency transactions, including: transaction tax increased from 0.1% to 0.21%, miner VAT rate increased to 2.2%, and cross-border seller tax rate reaching 1%.

• The new system also includes cryptocurrency traders in the regular tax collection framework, incorporating them into the corporate income tax or personal income tax system, marking the formal inclusion of cryptocurrency assets into a comprehensive tax framework.