I. Outlook

1. Macroeconomic Summary and Future Predictions

Last week's macro summary (2026/7/6--2026/7/12):
Last week, the macro market in Europe and the United States focused on expectations regarding Federal Reserve policy and the pace of economic recovery in Europe. In the U.S., the June FOMC meeting minutes indicated that the Federal Reserve still believes inflation is above the 2% target. Although interest rates were kept unchanged, most officials still lean towards further tightening policy within the year. The market continues to pay attention to the impact of inflation and employment data on future policy paths. Affected by weaker non-farm employment data and a decline in energy prices, market expectations for an immediate rate hike in July have cooled, leading to a stronger performance of risk assets. In Europe, data such as German industrial orders and industrial production show that manufacturing remains weak, but overall inflation pressure in the Eurozone continues to ease. The European Central Bank maintains a cautious wait-and-see attitude, with market expectations that policy will remain stable in the short term.

Future week prediction (2026/7/13--2026/7/19):
Next week, the U.S. will see the release of June CPI, PPI, retail sales, the Federal Reserve's Beige Book, and Federal Reserve Chairman Kevin Warsh's first congressional hearing, which will become the most important pricing factor for global markets. If CPI continues to decline, it will further strengthen market expectations for interest rate cuts or delayed rate hikes within the year, benefiting U.S. stocks and global risk assets. If inflation rebounds beyond expectations, the U.S. dollar and U.S. Treasury yields may strengthen again, putting pressure on risk assets. In Europe, the market will focus on Eurozone industrial production and inflation data. If economic data continues to weaken while inflation continues to decline, it will further solidify the market's judgment that the European Central Bank will maintain interest rates unchanged or even release expectations for easing.

2. Market Changes and Warnings in the Crypto Industry

Last week's summary (2026/7/6--2026/7/12): The crypto market overall showed a volatile recovery trend last week. On July 6, affected by Strategy (formerly MicroStrategy) disclosing the sale of approximately 3,588 BTC (about $216 million), Bitcoin briefly fell to around $61,300. Subsequently, as spot ETF funds flowed back in and buying interest warmed, BTC rebounded to $63,163 on July 7, briefly fell to $62,247 on July 8, and closed at $63,795 on July 11, maintaining a fluctuation around $63,600 on July 12. ETH performed relatively steadily, maintaining a range of $1,760--$1,800 during the week, with the latest price around $1,790. Market hotspots remain focused on AI, RWA, stablecoin infrastructure, and institutional DeFi yield protocols, with overall risk appetite improving compared to the previous week, but trading volume remains cautious.

Future week prediction (2026/7/13--2026/7/19): The market is expected to continue to be driven primarily by macroeconomic data, ETF fund flows, and institutional fund movements. If BTC can stabilize above $64,000, it is expected to further test the resistance zone of $65,000--$66,000; if it falls below $62,000 again, it may retest the support area of $60,000--$61,000. ETH is expected to fluctuate in the range of $1,750--$1,850, with its trend still driven by BTC. In the short term, RWA, stablecoins, AI Agents, on-chain yield, and institutional infrastructure sectors are expected to maintain high attention, but caution is needed regarding market volatility caused by macro events and large institutional fund movements.

3. Industry and Sector Hotspots

From July 6 to July 12, 2026, the crypto industry hotspots mainly revolved around "institutional-level infrastructure, AI, RWA, and stablecoin yields." In terms of financing, Elliptic completed $125 million in strategic financing, further promoting on-chain compliance and blockchain analysis capabilities; KOR Protocol completed $7.5 million in Series A financing, focusing on digital content/IP on-chain infrastructure; Mercado Bitcoin received $20 million in strategic investment, continuing to advance RWA and institutional asset tokenization layout.

In terms of technology, institutional-level yield infrastructure, RWA yield assets, cross-chain liquidity, and AI-driven financial automation remain key directions in the industry. More and more projects focus on connecting traditional financial institution funds with the on-chain yield market through APIs, modular architecture, and automated asset allocation, reflecting that the crypto industry is continuously moving towards institutionalization, asset tokenization, and the integration of AI and DeFi.

II. Market Hotspot Sectors and Potential Projects of the Week

1. Overview of Potential Projects

1.1. Detailed explanation of total financing of $7.3 million, led by Coinbase and TCG, with participation from Strobe, Hashed Emergent, and SAISON—an on-chain credit network connecting global capital with real yields in emerging markets, Jia

Introduction

Jia is a decentralized lending protocol and fintech platform aimed at connecting capital with micro and small enterprises in emerging markets that have real yield potential.

By providing loans to micro and small enterprises, Jia aims to bridge the gap in financial service accessibility, offering a more equitable and convenient short-term financing channel for groups long overlooked by traditional financial systems.

On its platform, Jia incentivizes eco-friendly behaviors, collaborates with high-quality data providers, and utilizes a reward token mechanism to provide investors with a continuous and stable source of returns while helping businesses in underdeveloped markets obtain the funding needed for growth.

Brief Description of the Protocol Mechanism

The Jia protocol consists of four core participant types that together build its on-chain credit system aimed at emerging markets.

1. Borrowers

Borrowers are primarily micro and small enterprises (MSMEs) from emerging markets.

These enterprises obtain financing through Jia's lending pool for operating and business development.

Borrowers can be referred to Jia by partners or apply for loans directly. To obtain more favorable loan conditions, borrowers can also provide collateral.

In terms of user experience, borrowers operate through a simple mobile application, with underlying blockchain technology being transparent to users, requiring no prior experience with crypto assets.

2. Lenders

Lenders are investors who provide funds to Jia's lending pool.

After depositing funds into the protocol, they can earn interest from borrowers' repayments, thereby sharing in the real yields generated by the emerging market's real economy.

3. Sponsors

Sponsors hold on-chain assets and use these assets as loan collateral.

For borrowers with insufficient credit histories or higher risks, who would otherwise struggle to pass risk control checks, Sponsors provide additional credit support to help them secure financing opportunities.

Essentially, they play a credit enhancement role within the protocol.

4. Partners

Partners are typically commercial platforms or ecosystem partners that serve borrowing enterprises.

They are responsible for:

• Referring quality borrowers to Jia

• Providing operational and business data

• Assisting with loan risk control and credit assessment

This data will be used for the protocol's loan underwriting, helping to improve risk control accuracy.

Lending Mechanics

Loan Terms

Jia primarily addresses the urgent financing needs of micro and small enterprises in emerging markets—short-term working capital loans.

Standard loan products typically have the following characteristics:

• Loan amount: $100–$5,000

• Loan term: 30–90 days

• Monthly interest rate: 2%–7%

Specific loan conditions will be personalized based on the borrower's credit status, operational situation, and financing needs, and will be continuously optimized as borrowing history accumulates.

Although this interest rate level is relatively high for investors in Europe and the U.S., it is a common commercial loan interest rate level in the emerging markets served by Jia. The shorter loan cycle also helps improve capital turnover efficiency, providing investors with continuous and stable returns.

Jia will evaluate multiple dimensions of data during the loan approval process, mainly including:

Partners' Data

Partners are typically platforms that borrowing enterprises rely on for daily operations.

They can provide:

• Sales data

• Inventory data

• Revenue data

• Operational behavior data

This data helps Jia better understand the operational status of enterprises.

Borrower Application Information

Borrowers need to submit loan applications and disclose:

• Income situation

• Expenditure situation

• Purpose of funds

• Business operational information

This serves as an important basis for credit review.

Third-party Data

Jia will also access external financial data sources, such as:

• Local credit agencies

• Bank data

• Financial service platforms

To further supplement borrowers' credit information.

Underwriting (Smart Risk Control Review)

Unlike most DeFi lending protocols that rely on over-collateralization, Jia's core innovation lies in:

Supporting unsecured or low-collateral credit loans.

To this end, Jia utilizes high-quality financial data from partners, borrowers, and third-party institutions to establish a machine learning-based credit assessment model.

The system comprehensively analyzes the enterprise's:

• Operational capability

• Income stability

• Historical repayment performance

• Cash flow situation

• Credit record

To assess loan risks and determine credit limits and loan conditions.

User Process

JIA Token and Community Incentive Mechanism

Jia not only aims to provide financing for micro and small enterprises but also hopes to make borrowers long-term participants and beneficiaries of the entire ecosystem.

Taking Alice as an example, under the traditional financial system, due to limited financing channels, she often can only obtain high-cost loans, and her relationship with lending institutions is merely a one-way borrowing relationship. Even as her business continues to grow, she cannot share in the financial platform's development benefits.

In the Jia system, once Alice establishes a long-term, good borrowing relationship with the platform, she can earn JIA token rewards by participating in the ecosystem. As she accumulates JIA, her identity evolves from merely being a borrower to gradually becoming a co-builder and stakeholder of the protocol.

After holding JIA, she can:

• Participate in protocol governance

• Vote on the platform's future development direction

• Share in the value brought by the protocol's long-term growth

• Align her interests with those of the entire ecosystem

Jia hopes to upgrade the originally simple "borrowing relationship" to a "co-growth relationship" through this mechanism.

Tron Comments

Jia's advantage lies in its combination of DeFi funds with the real economic needs of emerging markets, focusing on serving micro and small enterprises that have long been overlooked by traditional finance. By providing unsecured or low-collateral loans through partner data, third-party financial data, and machine learning risk control models, it creates real yields derived from genuine commercial activities while using the JIA token incentive mechanism to allow borrowers, investors, and ecosystem participants to share in the platform's growth value, embodying attributes of inclusive finance and on-chain financial innovation.

However, its disadvantage lies in the fact that the business essentially involves credit lending, facing risks such as borrower defaults, data authenticity, risk control model failures, and macroeconomic fluctuations in emerging markets. It also requires continuous reliance on local partners to obtain high-quality operational data, with operational complexity and compliance requirements far exceeding those of traditional over-collateralized DeFi protocols. Future scaling will also face challenges related to regional regulation and asset quality management.

2. Key Project Details of the Week

2.1. Detailed explanation of total financing unknown, but led by AurumX—building an AI and privacy computing-driven verifiable collaboration network, Manadia

Introduction

Manadia is a Web3 infrastructure platform that deeply integrates AI collaboration and privacy computing, focusing on achieving:

• Verifiable Data Settlement

• Privacy-Enhanced Value Transfer

• Efficient Cross-System Coordination

Its core goal is to build a reliable, secure, and verifiable collaborative environment for high-value scenarios such as finance and asset digitization, breaking down trust barriers between on-chain and off-chain systems without relying on any single trusted third party through standardized technical protocols and tool systems.

Core Analysis of System Architecture

VERITAS ------ Real-World Data Injection and Adjudication Protocol

VERITAS is the core protocol for Manadia to process external world input information, aimed at generating on-chain signals that are resistant to manipulation and can be challenged and verified. It combines multi-source data aggregation, economic penalty mechanisms, and hybrid verification processes, breaking through the limitations of traditional oracles that merely "provide price data."

For high-frequency price data injection, VERITAS adopts a weighted median data aggregation algorithm: the system collects signed data from multiple nodes (pre-selected validators) and generates consensus prices through a deviation detection mechanism (calculating Z-scores and excluding outliers with thresholds greater than 3).

The economic incentive mechanism adopts a staking-slashing model: nodes must lock $MA tokens as collateral. When the data they provide deviates by more than 5%, the slashing mechanism is automatically triggered, with the penalty ratio calculated based on historical reputation and combined with an exponential decay model.

Compared to Chainlink's simple voting mechanism, this solution is more robust and can resist flash loan attacks through a time-lock delay confirmation mechanism.

For example:

In DeFi liquidation scenarios, VERITAS can push ETH/USD prices every 5 seconds, supporting sub-millisecond derivative pricing.

For complex event verification, VERITAS introduces a hybrid model combining AI and human game theory.

First, an integrated large language model (such as Groq series models) parses news APIs or off-chain signals and automatically generates event proposals, outputting structured assertions.

Then, the system opens a fixed challenge window (e.g., 24 hours). During this period, any token holder can submit counter-evidence and initiate challenges while staking an equivalent amount of $MA tokens to participate in the economic game. If the challenge is successful, the challenger can receive the penalized assets as a reward.

Final adjudication is completed by one of the following two methods:

• More than 66% of nodes sign to reach threshold consensus;

• Final adjudication by an arbitration DAO.

This ensures that the results have finality and irreversibility.

Compared to Pyth's market-driven approach, VERITAS's AI-generated proposals can reduce human bias and support non-binary events, such as:

• Probability distribution predictions for election results;

• Verification of complex real-world state changes.

In RWA scenarios, this mechanism can verify changes in real estate status without relying on a single custodial institution.

In addition to price and financial events, VERITAS is also applicable to low-frequency but high-value state event verification, such as:

• Whether participation relationships continue to exist;

• Whether behavioral patterns have undergone substantial interruptions;

• Whether cross-platform signals exhibit coordinated manipulation behavior.

In Potion scenarios, VERITAS is used for multi-source verification and deviation filtering of participation behavior signals provided by external platforms, ensuring that status judgments such as "active," "continuous participation," and "qualification valid" are challengeable and final, thus avoiding systemic risks arising from volume manipulation, script simulation, and single-platform data distortion.

VERITAS's security model is based on an improved Byzantine Fault Tolerance (BFT) mechanism.

Node selection adopts:

• VRF (Verifiable Random Function) random sampling

To reduce the risk of Sybil attacks.

The system's target throughput is:

• 1000 TPS

And achieves gas optimization through a batch proof mechanism similar to Rollup.

2. AI Agent State Management and Coordination Protocol

Manadia's technical architecture is not designed for high-frequency trading or one-off interaction scenarios but focuses on long-term, cross-platform, interruptible yet recoverable state relationship management.

In Potion's practical application, these states manifest as ongoing participation relationships between users and content, platforms, or ecosystems lasting for months or even years.

Its core challenges are not transaction throughput but:

• Continuity

• Anti-Manipulation

• Verifiable Evolution

Therefore, Manadia introduces at the protocol level:

• State Trees

• Persistent Agent Execution Mechanisms

• Eligibility Proofs

Making "whether a long-term condition is met" itself a settleable object, rather than just a one-time action or instantaneous data.

Manadia views AI Agents as autonomous economic entities.

Through persistent state trees and scheduling algorithms, it achieves long-term online collaboration.

Each Agent maintains a Merkle Patricia Trie (MPT) state tree anchored on IPFS to record:

• Decision history

• Credit scores

• Accumulated behavioral trajectories

State updates use an incremental hash chain mechanism:

Each round of interaction generates a new root hash, broadcasting only the difference proof, thus reducing bandwidth overhead.

Decision scheduling employs a reinforcement learning-enhanced rule engine.

Each Agent runs a lightweight Actor-Critic model (based on the Torch framework).

Inputs include:

• VERITAS signals

• Historical states

• External task queues

Outputs include:

• Rate of equity release

• Scheduling parameters and other decision results

For example:

In liquidation scenarios, Agents can dynamically adjust execution thresholds based on market fluctuations:

If price fluctuations exceed 10%, execution is paused.

At the same time, long-term returns are optimized through Q-Learning.

Cross-Agent collaboration protocols reference A2A standards:

• Tasks are split into sub-commitments

• Use ECDSA signatures for verification

• Execution failures trigger slashing

• Consensus adopts Optimistic Rollup

• Disputes are submitted for on-chain arbitration

Economic permissions are achieved through a token binding mechanism:

Agents hold "Agency Warrants" similar to ERC-721, authorizing them to conduct limited value transfers, thus avoiding unlimited risk exposure.

System robustness guarantees include:

• Differential privacy noise injection (ε=0.5)

• On-chain queryable audit logs (Audit Hooks)

All Agent decision records are traceable.

3. Privacy-Enhanced Settlement and Value Transfer Pathways

Manadia's settlement system relies on zero-knowledge proof circuits and conditional contracts to achieve:

"Proving validity without disclosing details."

Core technologies include:

• zk-SNARK (Groth16 scheme)

Users generate proofs (e.g., "position greater than a certain threshold"), and verifiers only need to verify about 200 bytes of Groth proof without accessing raw data.

The system also combines:

• Ring Signatures

To achieve multi-party anonymous transfers without exposing identities.

Automatic settlement is achieved through state channels:

• Pre-signed transaction trees (similar to Lightning Network)

• VERITAS triggers off-chain settlements

• Disputes are submitted for on-chain processing

Compliance modules integrate a verifiable credential system similar to Verite:

Users can choose to bind:

• KYC proofs

• VC (Verifiable Credential) certificates

The system only checks AML blacklist status without exposing the complete transaction graph.

For example:

In cross-border payment scenarios, Manadia can prove:

• The source of funds is legitimate

While hiding:

• Transfer amounts

• Transaction details

Performance optimizations include:

• Recursive SNARK batch proofs

• Single transaction gas consumption below 100k

Security audits focus on:

• Constant-time computation

• Side-channel attack protection

It is noteworthy that Manadia's zero-knowledge settlement applies not only to amount proofs or position proofs but, more importantly, supports:

Eligibility Proofs

Users only need to prove that they meet certain long-term participation conditions without disclosing:

• Raw behavioral data

• Platform sources

• Time series information

These mechanisms together form the trustworthy settlement infrastructure of Manadia in complex scenarios.

4. Accumulation and Reuse of Long-Term Participation Data

The data collected and maintained by Manadia does not serve a single application but continuously accumulates around "long-term participation relationships."

The value of this data lies not in the single actions themselves but in the stable behavioral trajectories formed across time and platforms.

Once these trajectories are secured and written into state trees, they essentially transform into a sustainable reference for long-term state assets.

In Potion scenarios, these states are initially used for automating membership rights and eligibility settlements.

However, their lifecycle does not end there.

The long-term participation status of the same user can be re-verified and invoked across different time points and applications without needing to re-collect raw behavioral data.

This gives rise to concepts that were previously difficult to quantify, now having a basis for cross-scenario reuse:

• Long-Term Activity

• Stable Contribution

• Continuous Support

This design fundamentally avoids the problem of "one-time data consumption."

The cost of state generation is incurred only once,

While its verification and use can continue for years.

For developers:

There is no need to build user profiles and risk control systems from scratch.

For users:

Long-term participation behavior is no longer limited to a single platform but can gradually accumulate into portable, verifiable, and sustainably accumulated digital qualification assets.

Tron Comments

Manadia's advantage lies in its integration of trusted data verification (VERITAS), AI Agent collaboration, zero-knowledge privacy settlement, and long-term state assetization into a unified infrastructure. It can address trust issues between on-chain and off-chain systems and support complex scenarios requiring long-term state verification, such as membership systems, RWA, digital identity, DAO governance, and AI autonomous economies. Particularly, its design for assetizing "long-term participation eligibility" allows user behavior, contributions, and reputation to accumulate and reuse across platforms, demonstrating strong differentiated innovation.

On the other hand, its disadvantage lies in the overall architecture being relatively complex, involving multiple technical modules such as AI, TEE, ZK, oracles, and state management, leading to high engineering implementation and operational costs. At the same time, the project's value heavily relies on real application scenarios and ecosystem scale. Only after sufficient platforms, developers, and users are onboarded can long-term state assets and AI Agent network effects truly form, thus facing a lengthy market education and ecosystem building cycle.

III. Industry Data Analysis

1. Overall Market Performance

1.1. Spot BTC vs ETH Price Trends

BTC

ETH

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

Macroeconomic Data Review (2026/7/6--2026/7/12)

• U.S. service sector data warms up: The June ISM Services PMI returned to the expansion zone, indicating that service consumption remains resilient, alleviating market concerns about a rapid slowdown in the U.S. economy. The Federal Reserve released the minutes of the June meeting, continuing to emphasize that the future interest rate path will depend on economic data, and the market maintains cautious expectations for interest rate cuts within the year.

• Market performance: Major U.S. stock indices rose overall this week, with market risk appetite rebounding, as investors began to shift their focus from macro data to the upcoming second-quarter earnings season.

Key Data Release Points for Next Week (2026/7/13--2026/7/17)

• July 14 (Tuesday): U.S. June CPI, Core CPI (the most important data this week, directly affecting Federal Reserve rate cut expectations).

• July 15 (Wednesday): U.S. June PPI, New York Fed Manufacturing Index, Federal Reserve Economic Conditions Beige Book.

• July 16 (Thursday): U.S. June Retail Sales, Initial Jobless Claims, Real Estate-related Data.

• July 17 (Friday): U.S. June Industrial Production, Housing Starts Data, July Michigan University Consumer Sentiment Index preliminary value; China will release key economic data such as Q2 GDP and June industrial added value, total retail sales of consumer goods.

V. Regulatory Policies

United States

• Regulation of crypto market structure and stablecoins continues to advance: Congress continues to coordinate around the digital asset market structure and stablecoin regulatory framework in preparation for subsequent legislative votes, with regulatory focus still on stablecoin reserves, issuer access, and SEC/CFTC responsibilities.

European Union

• MiCA enters full enforcement phase: Member states continue to implement MiCA, with platforms that have not obtained CASP (Crypto Asset Service Provider) licenses accelerating their exit from the EU market, with regulation officially shifting from "legislation" to "enforcement."

United Kingdom

• Ongoing discussions on digital pound and crypto regulation: The Bank of England reiterated that the digital pound (CBDC) policy has not changed due to external lobbying and continues to evaluate regulatory arrangements for digital currencies and stablecoins.

Hong Kong, China

• Preparations for stablecoin regulation continue to advance: Regulatory agencies continue to promote stablecoin issuer licenses and supporting regulatory arrangements in preparation for the formal implementation of stablecoin regulatory systems.

Singapore

• Institutional access and licensing regulation continue to strengthen: MAS continues to advance licensing management for digital payment token (DPT) service providers, enhancing anti-money laundering (AML) and cross-border business compliance requirements.

Japan

• Financialization reform of digital assets continues to advance: Regulatory frameworks for crypto asset financial products, tax reforms, and institutional investor participation rules continue to be improved.

United Arab Emirates (Dubai)

• VARA continues to improve the virtual asset regulatory system: Continuing to optimize licensing and compliance requirements for virtual asset service providers (VASP) and promote the development of institutional-level digital asset businesses.