TRON Industry Weekly Report: Easing Rate Hike Expectations Push BTC Above 64000, Detailed Analysis of Building AI and Privacy Computing Network Manadia

The hot topics in the crypto industry mainly revolve around "institutional-grade infrastructure, AI, RWA, and stablecoin yields."

Written by: Tron

I. Outlook

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 the expectations of the Federal Reserve's policies and the pace of economic recovery in Europe. In the U.S., the minutes from the June FOMC meeting 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 policies within the year, and the market continues to pay attention to the impact of inflation and employment data on future policy paths. Due to the previous weak non-farm employment data and a decline in energy prices, the market's expectation for an immediate rate hike in July has cooled, leading to a stronger performance in risk assets. In Europe, data on German industrial orders and production show that the manufacturing sector remains weak, but overall inflationary pressure in the Eurozone continues to ease, and the European Central Bank maintains a cautious wait-and-see attitude, with the market expecting stable policies 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 the market's 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 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 be weak while inflation continues to decline, it will further consolidate the market's judgment that the European Central Bank will maintain interest rates unchanged or even release expectations for easing.

Market Movements and Warnings in the Crypto Industry

Last week's summary (2026/7/6--2026/7/12): Last week, the crypto market showed an overall oscillating recovery trend. On July 6, influenced by Strategy (formerly MicroStrategy) disclosing the sale of approximately 3,588 BTC (about $216 million), Bitcoin briefly dropped to around $61,300; subsequently, with the inflow of spot ETF funds and a warming buying sentiment, 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 around $63,600 on July 12. ETH performed relatively steadily, maintaining a range of $1,760--$1,800 throughout the week, with the latest price around $1,790. Market hotspots remain concentrated on AI, RWA, stablecoin infrastructure, and institutional-grade DeFi yield protocols, with overall risk appetite improving compared to the previous week, although trading volume remains cautious.

Future week prediction (2026/7/13--2026/7/19): The market is expected to continue being driven by macroeconomic data, ETF fund flows, and institutional capital 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 oscillate 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 yields, and institutional infrastructure tracks are expected to maintain high attention, but caution is needed regarding market volatility caused by macro events and large institutional capital flows.

Industry and Track Hotspots

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

In terms of technology, institutional-grade yield infrastructure, RWA yield assets, cross-chain liquidity, and AI-driven financial automation remain key directions in the industry. More and more projects are focusing on connecting traditional financial institution funds with on-chain yield markets 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 Tracks 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------Connecting Global Capital with Real Yields in Emerging Markets through On-Chain Credit Network 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 accessibility to financial services, offering a more equitable and convenient short-term financing channel for groups long overlooked by traditional financial systems.

On its platform, Jia incentivizes behaviors that benefit the ecosystem, 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.

Protocol Mechanism Overview

The Jia protocol is mainly composed of four core participants who jointly build its on-chain credit system for emerging markets.

Borrowers
Borrowers are mainly micro and small enterprises (MSMEs) from emerging markets.
These enterprises obtain financing through Jia's lending pool for their operations and business development.
Borrowers can be recommended 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 the underlying blockchain technology being transparent to users, requiring no prior experience with crypto assets.

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, sharing in the real yields brought by the emerging market's real economy.

Sponsors
Sponsors hold on-chain assets and use these assets as loan collateral.
For borrowers with insufficient credit history or higher risks, who would otherwise struggle to pass risk control audits, sponsors provide additional credit support to help them secure financing opportunities. Essentially, they act as a credit enhancement role within the protocol.

Partners
Partners are typically business platforms or ecosystem partners serving borrowing enterprises.
They are responsible for:

• Recommending 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 the borrowing history accumulates.

Although this interest rate level is relatively high for investors in Europe and the U.S., it is considered 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 evaluates multiple dimensions of data during the loan approval process, mainly including:

Partners' Data
Partners are typically the platforms that borrowing enterprises rely on for their 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 operation information
  As important bases 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

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

Supporting unsecured or low-collateral credit loans.

To achieve this, 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 history

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

User Flow

JIA Token and Community Incentive Mechanism

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

Take Alice as an example. In 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 profits.

In the Jia system, when 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 shifts from merely a borrower to a co-builder and stakeholder of the protocol.

After holding JIA, she can:

• Participate in protocol governance
• Vote on the future development direction of the platform
• Share in the value generated from 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 Commentary

Jia's advantage lies in its combination of DeFi funds with the real economic needs of emerging markets, focusing on serving small and micro enterprises that have long been overlooked by traditional finance. It provides unsecured or low-collateral loans through partner data, third-party financial data, and machine learning risk control models, creating real yields derived from genuine business activities. At the same time, the 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 disadvantage is that the business essentially belongs to 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 from regional regulation and asset quality management.

2. Key Projects of the Week Explained

2.1. Detailed Explanation of Total Financing Unknown, Led by AurumX --- Building AI and Privacy Computing Driven Verifiable Collaborative Network Manadia

Introduction

Manadia is a Web3 infrastructure platform centered on the deep integration of AI collaboration and privacy computing, focusing on achieving:

• 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 tool systems, creating a reliable, secure, and verifiable collaborative environment for high-value scenarios such as finance and asset digitization, without relying on any single trusted third party.

Core System Architecture Analysis

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

VERITAS is the core protocol of Manadia for processing external world input information, aiming 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 hybrid verification processes, breaking through the limitations of traditional oracles that merely "provide price data."

For high-frequency price data injection, VERITAS employs a data aggregation algorithm based on the weighted median: 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 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, with the slashing 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, integrated large language models (such as Groq series models) parse news APIs or off-chain signals and automatically generate 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 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 slashed assets as a reward.

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

• Over 66% of nodes reach threshold consensus with signatures;
• Final arbitration by the DAO.

This ensures that the results are definitive and irreversible.

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

• Probability distribution predictions of 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 there are coordinated manipulation behaviors across platforms.

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 definitive, thus avoiding systemic risks from data distortion caused by volume manipulation, script simulation, and single platform data.

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.

AI Agent State Management and Coordination Protocol

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

In the actual application of Potion, 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:

• 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
• Behavioral trajectories and other accumulated states

State updates adopt an incremental hash chain mechanism:

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

Decision scheduling employs a reinforcement learning-enhanced rule engine.

Each Agent internally 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 the price fluctuation exceeds 10%, execution will be suspended.

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

Cross-Agent collaboration protocol refers to the A2A standard:

• Tasks are divided into Sub-Commitments
• ECDSA signature verification is used
• Execution failure will trigger penalties
• Consensus adopts Optimistic Rollup
• Disputes are submitted for on-chain arbitration

Economic permissions are realized through a token binding mechanism:

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

System robustness guarantees include:

• Differential privacy noise injection (ε=0.5)
• On-chain auditable logs (Audit Hooks)

All Agent decision records are traceable.

Privacy-Enhanced Settlement and Value Transfer Pathways

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

"Proof of validity without disclosing details."

Core technologies used include:

zk-SNARK (Groth16 scheme)

Users generate proofs (e.g., "position exceeds a certain threshold"), and verifiers only need to validate about 200 bytes of Groth proof without accessing the original 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 processed on-chain

Compliance modules integrate a verifiable credential system similar to Verite:

Users can choose to bind:

• KYC proof
• VC (Verifiable Credential) proof

The system only checks the 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 amount
• 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 worth noting that Manadia's zero-knowledge settlement is not only applicable to amount proofs or position proofs but also importantly supports:

Eligibility Proofs

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

• Original behavioral data
• Platform sources
• Time series information

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

Accumulation and Reuse of Long-Term Participation Data

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

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

Once these trajectories are secured and written into the state tree, they essentially transform into a sustainable reference long-term state asset.

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

But their lifecycle does not end there.

The long-term participation status of the same user can be re-validated and called upon at different points in time and across different applications without needing to re-collect original behavioral data.

This enables the following previously difficult-to-quantify concepts to have a foundation for cross-scenario reuse for the first time:

• Long-Term Activity
• Stable Contribution
• Continuous Support

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

The cost of state generation is only incurred once, while its validation 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 Review

Manadia's advantage lies in its focus not solely on one track among AI, privacy computing, or oracle but integrates trusted data verification (VERITAS), AI Agent collaboration, zero-knowledge privacy settlement, and long-term state assetization into a unified infrastructure, capable of solving trust issues between on-chain and off-chain systems and supporting complex scenarios requiring long-term state verification such as membership systems, RWA, digital identity, DAO governance, and AI autonomous economies; especially its design of assetizing long-term participation qualifications allows user behavior, contributions, and reputation to accumulate and reuse across platforms, demonstrating strong differentiated innovation.

On the other hand, its disadvantage is that the overall architecture is 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 highly depends on real application scenarios and ecological scale, and only after sufficient platforms, developers, and users are onboarded can the long-term state assets and AI Agent network effects truly form, thus facing a prolonged market education and ecological construction cycle.

Industry Data Analysis

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1. Overall Market Performance

1.1. Spot BTC vs ETH Price Trends

BTC

ETH

Macroeconomic Data Review and Key Data Release Nodes for Next Week

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

U.S. service sector data rebounds: June ISM Services PMI returns 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 this year.

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

Key data release nodes 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 the Federal Reserve's interest rate cut expectations).
• July 15 (Wednesday): U.S. June PPI, New York Fed Manufacturing Index, Federal Reserve 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 Confidence Index preliminary value; China will release key economic data such as Q2 GDP and June industrial added value, total retail sales of consumer goods, etc.

Regulatory Policies

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United States

Regulation of the crypto market structure and stablecoins continues to advance: Congress is coordinating 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, and platforms that have not obtained CASP (Crypto Asset Service Provider) licenses are accelerating their exit from the EU market, with regulation officially shifting from "legislation" to "enforcement."

United Kingdom

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

Hong Kong, China

Regulatory preparations for stablecoins continue to advance: Regulatory agencies are continuing 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 promote licensing management for digital payment token (DPT) service providers, enhancing anti-money laundering (AML) and cross-border business compliance requirements.

Japan

Reform of digital asset financialization continues to advance: Regulatory frameworks around 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), promoting the development of institutional-level digital asset businesses.