I. Outlook

1. Macro-Level Summary and Future Predictions

Macro Summary for Last Week (2026/07/06 – 2026/07/12):

The main theme in the US and European macro markets last week revolved around expectations for Fed policy and the pace of the European economic recovery. In the US, the minutes from the June FOMC meeting indicated that the Fed still views inflation as above the 2% target. Despite maintaining interest rates unchanged, most officials still lean towards further tightening within the year. The market continues to focus on the impact of inflation and employment data on the subsequent policy path. Influenced by the weaker non-farm payroll data from the previous month and falling energy prices, market expectations for an immediate rate hike in July have cooled somewhat, leading to a relatively strong performance in risk assets. In Europe, data such as German industrial orders and production showed that the manufacturing sector remains weak. However, overall inflationary pressures in the Eurozone continued to ease. The European Central Bank maintains a cautious wait-and-see stance, and the market expects policy to remain stable in the short term.

Predictions for the Coming Week (2026/07/13 – 2026/07/19):

Next week, the US will see the release of the June CPI, PPI, retail sales, the Fed's Beige Book, and the first congressional testimony by Fed Chair Kevin Warsh, which will be the most important pricing factors for global markets. If the CPI continues to decline, it will further strengthen market expectations for a rate cut or a delayed hike this year, benefiting US stocks and global risk assets. If inflation surprises to the upside, the US dollar and Treasury yields could 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 be weak while inflation continues to fall, it will further solidify the market's judgment that the ECB will maintain interest rates unchanged or even signal easing expectations.

2. Crypto Industry Market Changes and Warnings

Summary for Last Week (2026/07/06 – 2026/07/12): The crypto market showed a volatile recovery trend last week. On July 6, following news that Strategy (formerly MicroStrategy) planned to sell approximately 3,588 BTC (worth about $216 million), Bitcoin briefly dipped to near $61,300. Subsequently, with renewed inflows into spot ETFs and a buying rebound, BTC bounced back to $63,163 on July 7, briefly fell to $62,247 on July 8, closed at $63,795 by July 11, and was trading around $63,600 on July 12. ETH performed relatively steadily, trading within the $1,760–$1,800 range for the week, recently hovering around $1,790. Market hotspots remained concentrated on AI, RWA, stablecoin infrastructure, and institutional-grade DeFi yield protocols. Overall risk appetite improved compared to the previous week, but trading volumes remained cautious.

Predictions for the Coming Week (2026/07/13 – 2026/07/19): The market is expected to be primarily driven by macroeconomic data, ETF flows, and institutional capital movements. If BTC can firmly hold the $64,000 level, it may test the $65,000–$66,000 resistance zone. If it breaks below $62,000 again, it could retest the support area around $60,000–$61,000. ETH is expected to trade in a range of $1,750–$1,850, with its direction still likely to be influenced by BTC. In the short term, sectors like RWA, stablecoins, AI Agents, on-chain yield, and institutional infrastructure are expected to maintain high attention levels, but investors should be cautious of market volatility caused by macro events and large-scale institutional capital flows.

3. Industry and Sector Hotspots

From July 6 to July 12, 2026, the crypto industry hotspots revolved around "institutional-grade infrastructure, AI, RWA, and stablecoin yield." In terms of financing, Elliptic completed a $125 million strategic funding round to further its on-chain compliance and blockchain analytics capabilities; KOR Protocol raised $7.5 million in a Series A funding round, focusing on digital content/IP on-chain infrastructure; Mercado Bitcoin secured a $20 million strategic investment to advance its RWA and institutional asset tokenization roadmap.

On the technology front, institutional-grade yield infrastructure, RWA yield assets, cross-chain liquidity, and AI-driven financial automation remain key industry directions. More and more projects are focusing on connecting traditional financial institution capital with the on-chain yield market through APIs, modular architectures, and automated asset allocation. This reflects the ongoing trend within the crypto industry towards institutionalization, asset tokenization, and the convergence of AI and DeFi.

II. Market Sectors and Potential Projects of the Week

1. Potential Project Overview

1.1. Detailed Analysis of Jia – An On-Chain Credit Network Connecting Global Capital with Real-World Yield in Emerging Markets, Total Funding $7.3 Million, Led by Coinbase and TCG, with Participation from Strobe, Hashed Emergent, and SAISON

Introduction

Jia is a decentralized lending protocol and fintech platform designed to connect capital with micro, small, and medium-sized enterprises (MSMEs) in emerging markets that have genuine real-yield potential.

By providing loans to micro and small businesses, Jia aims to bridge the gap in financial service accessibility, offering more equitable and convenient short-term financing channels to groups long overlooked by the traditional financial system.

On its platform, Jia incentivizes ecosystem-beneficial behaviors, partners with high-quality data providers, and utilizes a reward token mechanism to provide investors with a consistent and stable source of yield, while helping businesses in underserved markets access the capital they need to grow.

Brief Explanation of Protocol Mechanism

The Jia protocol consists of four main types of core participants, collectively building its on-chain credit system for emerging markets.

1. Borrowers

Borrowers are primarily micro, small, and medium-sized enterprises (MSMEs) from emerging markets.

These businesses obtain financing through Jia's lending pools to operate and develop their businesses.

Borrowers can be referred to Jia through Partners or can apply for loans directly. To obtain more favorable loan terms, borrowers can also provide collateral.

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

2. Lenders

Lenders are investors who provide capital to Jia's lending pools.

After depositing funds into the protocol, they earn interest income from borrowers' repayments, thereby sharing in the real yield generated by the real economies of emerging markets.

3. Sponsors

Sponsors hold on-chain assets and pledge them as collateral for loans.

For borrowers who lack sufficient credit history or are deemed high-risk and would otherwise find it difficult to pass risk control checks, Sponsors provide additional credit support, helping them secure financing opportunities.

They essentially act as a credit enhancement role within the protocol.

4. Partners

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

They are responsible for:

• Recommending high-quality borrowers to Jia
• Providing operational and business data
• Assisting with loan risk control and credit assessment

This data is used in the protocol's underwriting process, helping to improve the accuracy of risk control.

Lending Mechanics

Loan Terms

Jia primarily addresses the most urgent financing needs of MSMEs 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 terms are adjusted individually based on the borrower's credit profile, business situation, and financing needs, and are continuously optimized as borrowing history accumulates.

While this interest rate level might seem high for investors in Western markets, it is relatively common for commercial loans in the emerging markets that Jia serves. The shorter loan cycles also help improve capital turnover efficiency, providing investors with a steady and consistent stream of returns.

During the loan approval process, Jia evaluates data from multiple dimensions, mainly including:

Partner Data

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

They can provide:

• Sales data
• Inventory data
• Revenue data
• Business behavior data

This data helps Jia gain a more accurate understanding of the business's health.

Borrower Application Information

Borrowers need to submit a loan application and disclose:

• Income
• Expenses
• Purpose of funds
• Business information

This serves as a crucial basis for credit assessment.

Third-party Data

Jia also integrates external financial data sources, such as:

• Local credit bureaus
• Bank data
• Financial service platforms

To further supplement the borrower's credit information.

Underwriting

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

Supporting unsecured or under-collateralized credit lending.

To achieve this, Jia leverages high-quality financial data from partners, borrowers, and third-party institutions to build a credit evaluation model based on machine learning.

The system comprehensively analyzes the business's:

• Operational capability
• Revenue stability
• Historical repayment performance
• Cash flow situation
• Credit history

This is used to assess loan risk and determine credit limits and loan terms.

User Flow

The JIA Token and Community Incentive Mechanism

Jia aims not only to provide financing for MSMEs but also to transform borrowers into long-term participants and beneficiaries of the entire ecosystem.

Take Alice as an example. Under the traditional financial system, due to limited financing channels, she could often only obtain high-cost loans, resulting in a one-way transactional relationship with the lender. Even if her business grew, she could not share in the profits of the financial platform's development.

However, in the Jia system, when Alice establishes a long-term, positive borrowing relationship with the platform, she can earn JIA token rewards through ecosystem participation. As her JIA holdings accumulate, her identity evolves from just a borrower to a co-builder and stakeholder in the protocol.

By holding JIA, she can:

• Participate in protocol governance
• Vote on the future direction of the platform
• Share in the value created by the protocol's long-term growth
• Align her interests with the entire ecosystem

Through this mechanism, Jia aims to upgrade the simple "borrower-lender relationship" into a "co-growth relationship".

Tron's Take

Jia's advantage lies in its ability to combine DeFi capital with real economic demand in emerging markets, specifically targeting MSMEs long neglected by traditional finance. By utilizing partner data, third-party financial data, and machine learning risk models to offer unsecured or under-collateralized loans, it generates yield derived from real economic activities (Real Yield). Furthermore, the JIA token incentive mechanism allows borrowers, investors, and ecosystem participants to share in the platform's growth value, blending inclusive finance with on-chain financial innovation.

However, its disadvantage is that the business is fundamentally credit-based lending, facing risks such as borrower default, data authenticity issues, model failure, and macroeconomic volatility in emerging markets. It also requires continuous reliance on local partners for high-quality operational data, resulting in operational complexity and compliance requirements far exceeding traditional over-collateralized DeFi protocols. Future scaling will also face challenges related to regional regulations and asset quality management.

2. Key Project Deep Dive for the Week

2.1. Detailed Analysis of Manadia – A Verifiable Collaboration Network Driven by AI and Privacy Computing, Total Funding Amount Unknown, Led by AurumX

Introduction

Manadia is a Web3 infrastructure platform deeply integrating AI Collaboration with Privacy Computing, focusing on enabling:

• Verifiable Data Settlement
• Privacy-Enhanced Value Transfer
• Efficient Cross-System Coordination

Its core objective is to break down trust barriers between on-chain and off-chain systems through standardized technical protocols and toolkits. Without relying on any single trusted third party, it aims to build reliable, secure, and verifiable collaboration environments for high-value scenarios such as finance and asset digitization.

Core System Architecture Analysis

VERITAS – Real-World Data Injection and Adjudication Protocol

VERITAS is the core protocol for processing external world input information on Manadia. It aims to generate on-chain signals that are resistant to manipulation and can be challenged and verified. It combines multi-source data aggregation, economic penalty mechanisms, and a hybrid verification process, going beyond the limitations of traditional oracles that merely "provide price data."

For high-frequency price data injection, VERITAS uses a data aggregation algorithm based on Weighted Median. The system collects signed data from multiple nodes (pre-selected validators) and generates a consensus price through a deviation detection mechanism (calculating Z-score, eliminating outliers with a threshold 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. The slashing ratio is calculated based on historical reputation combined with an exponential decay model.

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

Example:

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

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

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

Subsequently, the system opens a fixed challenge window (e.g., 24 hours). During this period, any token holder can submit a counter-evidence and initiate a challenge, simultaneously staking an equivalent amount of $MA tokens to participate in the economic game. If the challenge succeeds, the challenger receives the slashed assets as a reward.

The final adjudication is completed by one of the following two methods:

• A threshold consensus reached by over 66% of node signatures.
• A final ruling by an Arbitration DAO.

This ensures the outcome is final and irreversible.

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

• Probability distribution predictions for election outcomes.
• Complex real-world state change verification.

In an RWA scenario, this mechanism can verify changes in real estate status without relying on a single custodian.

Beyond prices and financial events, VERITAS is also suitable for verifying low-frequency but high-value state-based events, such as:

• Whether a participation relationship continues to exist.
• Whether a behavior pattern has had a material interruption.
• Whether there is collusive manipulation of cross-platform signals.

In the Potion scenario, VERITAS is used to perform multi-source verification and bias filtering of participation behavior signals provided by external platforms. This ensures that status judgments like "active," "continuously participating," and "eligible" are challengeable and final, thereby mitigating systemic risks arising from inflated activity, script simulation, and data distortion on a single platform.

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

Node selection employs:

• VRF (Verifiable Random Function) random sampling

To reduce the risk of Sybil attacks.

The system's target throughput is:

1000 TPS

And it achieves gas optimization through a batch proofing mechanism similar to Rollups.

2. AI Agent State Management and Coordination Protocol

Manadia's technical architecture is not designed for