I. Outlook

1. Macro-Level Summary and Future Predictions

Last Week's Macro Summary (2026/7/6–2026/7/12):

Last week, the narrative in the European and American macro markets was primarily centered around the expectations of Federal Reserve policy and the pace of economic recovery in Europe. In the US, the minutes of the June FOMC meeting revealed that the Fed still believes inflation is above the 2% target. Despite maintaining interest rates unchanged, most officials still lean towards further tightening within the year. The market continued to focus on the impact of inflation and employment data on the subsequent policy path. Affected by the previous weakening of non-farm payroll data and the decline in energy prices, market expectations for an immediate rate hike in July have cooled somewhat, leading to relatively strong performance in risk assets. In Europe, data such as German industrial orders and industrial production indicated that the manufacturing sector remains weak. Nevertheless, 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.

Prediction for the Coming Week (2026/7/13–2026/7/19):

Next week, the US will see the release of June CPI, PPI, retail sales, the Fed's Beige Book, and the first congressional testimony by Fed Chair Kevin Warsh, which will become the most important pricing factor for global markets. If CPI continues to decline, it will further strengthen market expectations for a rate cut or a delayed rate hike this year, benefiting US stocks and global risk assets. If inflation rebounds unexpectedly, the US dollar and US 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 remains weak while inflation continues to fall, it will further consolidate the market's judgment that the ECB will maintain its current interest rate or even release dovish expectations.

2. Cryptocurrency Industry Market Changes and Warnings

Last Week's Summary (2026/7/6–2026/7/12): The crypto market overall showed a pattern of volatile recovery last week. On July 6, after Strategy (formerly MicroStrategy) disclosed the sale of approximately 3,588 BTC (around $216 million), Bitcoin briefly fell to near $61,300. Subsequently, as spot ETF funds began to re-enter and buying pressure increased, BTC rebounded to $63,163 on July 7, briefly dipped to $62,247 on July 8, and closed at $63,795 by July 11. On July 12, BTC traded around $63,600. ETH performed relatively steadily, oscillating within the $1,760–$1,800 range during the week, last trading around $1,790. Market hotspots remain concentrated on AI, RWA, stablecoin infrastructure, and institutional-grade DeFi yield protocols. Overall risk appetite has improved compared to the previous week, but trading volumes remain cautious.

Prediction for the Coming Week (2026/7/13–2026/7/19): The market is expected to be primarily driven by macroeconomic data, ETF fund flows, and institutional capital movements. If BTC can firmly hold above the $64,000 level, it is likely to test the $65,000–$66,000 resistance zone. If it falls below $62,000 again, it could pull back to the $60,000–$61,000 support area. ETH is expected to oscillate within the $1,750–$1,850 range, with its trend 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, but caution is warranted regarding macroeconomic events and market volatility caused by large institutional capital flows.

3. Industry and Sector Hotspots

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

Technologically, 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 institutional funds with on-chain yield markets via APIs, modular architectures, and automated asset allocation, reflecting the continued development of the crypto industry towards institutionalization, asset tokenization, and AI+DeFi integration. 

II. Market Hot Sectors and Potential Projects of the Week

1. Potential Project Overview

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

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 possess genuine yield-generating potential.

By providing loans to these businesses, Jia seeks to bridge the gap in financial service accessibility, offering fairer and more convenient short-term financing channels to groups long overlooked by traditional financial systems.

On its platform, Jia incentivizes ecosystem-beneficial behaviors, collaborates with high-quality data providers, and utilizes a reward token mechanism to provide investors with a stable and continuous source of yield, while simultaneously helping businesses in underdeveloped markets obtain the capital support needed for growth.

Protocol Mechanism Brief

The Jia protocol primarily comprises four core participant types, collectively building its on-chain credit system targeting emerging markets.

1. Borrowers

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

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

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

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

2. Lenders

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

By depositing funds into the protocol, they earn interest income from borrower repayments, thereby gaining exposure to the Real Yield generated by the real economies of emerging markets.

3. Sponsors

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

For borrowers with limited credit history or higher risk profiles who might otherwise struggle to pass risk control checks, Sponsors provide additional credit support, helping them access financing opportunities.

Essentially, they act as a credit enhancement role within the protocol.

4. Partners

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

Their responsibilities include:

• 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 loan 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 feature:

• Loan Amount: $100 to $5,000
• Loan Term: 30 to 90 days
• Monthly Interest Rate: 2% to 7%

Specific loan terms are adjusted individually based on the borrower's creditworthiness, business condition, and financing needs, and are continuously optimized as borrowing history accumulates.

While this interest rate level might seem relatively high to investors in European or American markets, it is quite common for commercial loans in the emerging markets Jia serves. The shorter loan cycles also help improve capital turnover efficiency, generating continuous and stable returns for investors.

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

Partner Data

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

They can provide:

• Sales data
• Inventory data
• Revenue data
• Operational behavior data

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

Borrower Application Information

Borrowers must submit a loan application, disclosing:

• Income situation
• Expenditure situation
• Purpose of funds
• Business operational information

This serves as crucial basis for credit evaluation.

Third-party Data

Jia also integrates external financial data sources, for example:

• Local credit bureaus
• Bank data
• Financial service platforms

To further supplement the borrower's credit information.

Underwriting (Intelligent Risk Control)

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

Supporting unsecured or under-collateralized credit loans (Unsecured Lending).

To achieve this, Jia utilizes 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 allows it 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 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 could often only obtain high-cost loans, resulting in a one-way borrowing relationship with the lender. Even if her business grows, she cannot share in the profits from the financial platform's development.

However, within the Jia ecosystem, when Alice establishes a long-term, healthy borrowing relationship with the platform, she can earn JIA token rewards by participating in the ecosystem. As her JIA holdings accumulate, her identity transforms from being merely a borrower to becoming a co-builder and stakeholder in the protocol.

By holding JIA, she can:

• Participate in protocol governance
• Vote on the future development 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 hopes to upgrade the simple "borrowing relationship" into a "shared growth relationship."

Tron Review

Jia's strength lies in combining DeFi capital with real economic demand in emerging markets, specifically serving MSMEs long ignored 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 Real Yield derived from genuine commercial activities. Furthermore, its JIA token incentive mechanism allows borrowers, investors, and ecosystem participants to share in the platform's growth value, embodying both inclusive finance and on-chain financial innovation.

However, its weakness lies in the fact that its business is fundamentally based on credit lending, exposing it to risks such as borrower default, data accuracy 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 significantly higher than traditional over-collateralized DeFi protocols. Future scaling will also face challenges related to regional regulations and asset quality management.

2. Detailed Breakdown of Key Projects This Week

2.1. Detailed Look at Manadia: An AI and Privacy Computing-Driven Verifiable Collaboration Network, Led by AurumX (Undisclosed Total Funding)

Introduction

Manadia is a Web3 infrastructure platform deeply integrating AI Collaboration with Privacy Computing. It focuses on enabling:

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

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

Core Architecture Analysis

VERITAS – Real-World Data Injection and Adjudication Protocol

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

For high-frequency price data injection, VERITAS uses a Weighted Median data aggregation algorithm: the system collects signed data from multiple nodes (pre-selected validators) and generates a consensus price through a deviation detection mechanism (calculating Z-scores, removing outliers with a threshold greater than 3).

The economic incentive model employs a Staking–Slashing mechanism: nodes must lock $MA tokens as collateral. When the deviation of the data they provide exceeds 5%, a 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 resist flash loan attacks through time-locked delayed confirmation mechanisms.

Example:

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

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

First, an integrated large language model (e.g., 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, 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 exceeding 66% of node signatures; or
• A final ruling by an Arbitration DAO.

This ensures the result has finality and is irreversible.

Compared to Pyth's entirely 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;
• Verification of complex real-world state changes.

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, for example:

• Whether a participation relationship persists;
• Whether a behavior pattern has materially ceased;
• Whether there is coordinated manipulation of signals across platforms.

In the Potion scenario, VERITAS is used for multi-source verification and deviation filtering of participation behavior signals provided by external platforms, ensuring that judgments like "active," "continuously participating," or "eligible" are challengeable and have finality, thereby mitigating system risks from fake volume, script simulation, and data distortion from a single platform.

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

Node selection employs:

• Verifiable Random Function (VRF) random sampling

To reduce the risk of Sybil attacks.

The system targets a throughput of:

• 1000 TPS

ยินดีต้อนรับเข้าร่วมชุมชนทางการของ Odaily

กลุ่มสมาชิก：

https://t.me/Odaily_News

กลุ่มสนทนา：

https://t.me/Odaily_GoldenApe

บัญชีทางการ：

https://twitter.com/OdailyChina

กลุ่มสนทนา：

https://t.me/Odaily_CryptoPunk

บทความแนะนำ

AI投资逻辑变了：云厂进入「收租」时代，基础设施产业链承压

1955年以来首次，标普500盈利高出长期趋势14%

币股风向标丨Strategy减持1638枚BTC套现1.05亿美元；Michael Saylor重申本人未出售任何比特币，Strategy买卖行为与个人持仓无关（8月4日）

จาก「投机資產」到「下一代金融底層」，Crypto 正長出一套新的 TradFi 世界？