TRON Industry Weekly Report: Bybit theft triggered a "black swan", and the full-chain VM protocol attracted capital attention
1. Outlook
1. Macro-level summary and future forecasts
Last week, the U.S. stock market was volatile. On February 19, the three major U.S. stock indexes closed slightly higher, and the S&P 500 index continued to hit a record high; but on February 21, the three major U.S. stock indexes fell sharply, with the Nasdaq falling more than 2%. The market is sensitive to a series of economic data, and investors remain cautiously optimistic about the prospects for economic growth and the direction of monetary policy. In the coming period, investors need to pay close attention to changes in economic data, policy dynamics, and geopolitical factors to make wise investment decisions.
2. Cryptocurrency market changes and warnings
Last week, the price of Bitcoin continued to fluctuate in a narrow range. Some people believe that the price of Bitcoin has become complicated, the difficulty of trading has increased, and high-level bulls are no longer chasing in, waiting for a decline or breakthrough. For example, on February 21, the price of Bitcoin was close to the trend line, and the market was concerned about whether it could achieve a real market reversal. If there is no positive stimulus this week, the crypto market may face the risk of further correction.
3. Industry and track hot spots
Mango Network, the L1 public chain linking EVM&MoveVM, received a $13.5 million investment from well-known institutions such as AINFRA and KUCOIN; Polychain Capital led the investment in Fluent, a hybrid execution network linking EVM, SVM and WASM chains; Kaito is a cryptocurrency search engine driven by ChatGPT, which aims to revolutionize crypto research and investment through artificial intelligence, and is now available on Binance and Coinbase.
2. Market hot spots and potential projects of the week
1. Potential track performance
1.1. How did Mango Network, a L1 public chain linking EVM & MoveVM, obtain $13.5 million in investment from well-known institutions such as AINFRA and KUCOIN?
Introduction
Mango Network is a Layer 1 blockchain platform that aims to solve challenges in Web3 applications by combining EVM and MoveVM compatibility. It aims to provide developers and users with a secure, modular and high-performance infrastructure.
Mango aims to be the most accessible smart contract platform, empowering developers to create great user experiences in Web3. To welcome the next billion users, Mango provides developers with a variety of tools to fully leverage the power of the Mango blockchain. The Mango Development Kit (SDK) will enable developers to build in an environment without boundaries.
Mango scales horizontally to meet the needs of applications. As Mango validators increase their processing power, network capacity grows proportionally by adding worker nodes, thereby keeping gas fees low even during peak network traffic. This scalability feature is in stark contrast to the bottleneck design of other blockchains.
Rich on-chain assets support new applications and economic models based on utility, without relying solely on artificial scarcity. Developers can implement dynamic NFTs that can be upgraded, packaged, and grouped according to application needs, such as updating avatars and customizable items based on changes in gameplay. This capability enhances the in-game economy because the behavior of NFTs is fully reflected on-chain, making NFTs more valuable and providing a more interactive feedback loop.
Multi-VM Omni-Chain
Mango Network combines OPStack and MoveVM to create a complete on-chain blockchain network that supports cross-chain communication and pushes the boundaries of technology.
The network adopts a two-layer architecture, with Mango Move as the L1 layer to ensure security and OP-Mango as the L2 layer to increase transaction speed and reduce costs, thereby achieving efficient performance.
Mango Network is flexibly designed and supports multi-virtual machine operations, providing developers and users with a wide range of application scenarios and optimized user experience.
Multi-virtual machine full-chain infrastructure network
Mango Network integrates MoveVM and EVM to create a multi-layer network that supports cross-chain operations, enabling developers to leverage the advantages of both virtual machines to provide a wider range of services and application scenarios.
Multi-virtual machine parallelism and cross-chain technology integration
The synergy of parallel execution, cross-chain communication, and Layer 2 expansion
Multi-VM parallelism
MoveVM optimizes asset management and contract logic, and EVM is combined with OP-Mango to support cross-chain contracts.
Layer 2 Scaling
Transactions in Layer 2 are submitted to the main network regularly, and security is ensured through an assertion mechanism.
Inter-VM Communication
Smart contract events are captured and serialized for transmission, enabling contract interoperability between EVM and MoveVM.
Cross-chain asset management
Assets are synchronized to MoveVM through OP-Mango after EVM operation to ensure two-way settlement consistency.
MoveVM and EVM cross-chain communication
Mango Network uses the OP-Mango second-layer network to process transactions through EVM compatibility and P2P synchronization. The sorter sorts transactions and synchronizes the Ethereum state through assertions. Its cross-chain contract achieves interoperability and secure settlement between EVM and MoveVM, optimizing the developer experience.
Reviews
Mango modular blockchain can be as sovereign as Layer 1, resistant to hacker attacks and upgradeable. Mango modular blockchain can be applied to new blockchains, simplifying the process of component uninstallation and reducing deployment time and cost. Mango public chain combines zero-knowledge proof technology, allowing information knowledge to be disclosed without revealing the information itself. Through validity proof, nodes can verify transactions, reduce confirmation time, and increase network throughput. MgoDNS is a distributed domain name solution based on a cross-chain protocol, providing domain name and domain name data analysis services. It helps enterprises and individual users manage valuable data on the chain more efficiently, securely and conveniently, and participate in digital asset transactions.
Mango client uses mechanisms such as resources, distributed locks, and sorting to improve system performance and ensure data consistency and parallelism. It implements multifunctional operations based on efficiency, security, scalability, and quasi-linear capacity increase.
1.2. Analysis of the characteristics of Fluent, a hybrid execution network linking EVM, SVM and WASM chains led by Polychain Capital
Introduction
Fluent is the first hybrid execution network - an Ethereum L2 and framework that fuses Wasm, EVM, and (soon) SVM-based smart contracts into a unified execution environment.
Smart contracts of different virtual machine targets can call each other directly on Fluent. Fluent is currently in the public development network stage and supports applications composed of Solidity, Vyper and Rust contracts.
Fluent's unique value proposition is its ability to:
Simulate multiple virtual machine (VM) execution environments (EEs),
Realize the real-time compatibility of smart contracts related to different virtual machines (such as EVM, SVM, Wasm, etc.),
Supports contracts written in various programming languages (such as Solidity, Rust, etc.),
Runs in a shared state execution environment.
Fluent supports atomic composability between applications targeting different virtual machines, as well as "hybrid" applications consisting of mixed and matched smart contracts. Interactions between different types of contracts supported by the network occur in the background, atomically and in real time.
How does Fluent support hybrid execution?
On Fluent, the functionality of EVM, SVM, and Wasm is "fused" together at the execution layer. This is achieved through rWasm - a low-level intermediate representation (IR) that serves as Fluent's virtual machine. rWasm is actually a state verification function responsible for representing every operation in Fluent's execution layer. Fluent simulates the behavior of EVM, SVM, and Wasm, which are compiled into rWasm for execution. This ensures compatibility and smooth interaction between these different systems, and is optimal from a zk proof perspective, because only one state transition is ultimately proved (unlike multiple state transitions that need to be proved in multi-virtual machine solutions). Fluent's hybrid design is also extensible - more virtual machines can be represented in the future through dedicated AOT/JIT compilers.
Hybrid Execution vs Multiple VMs
MultiVM is an alternative virtual machine (altVM) that uses multiple independent virtual machines within the same network. Each virtual machine runs independently and is suitable for different programming languages or execution environments. Maintaining independent virtual machines brings complexity, especially in terms of state synchronization and cross-virtual machine interaction. This can lead to a fragmented developer and user experience because interactions between different environments require additional coordination. Blended Execution provides a more integrated approach than multiVM. Instead of maintaining independent virtual machines for interaction, hybrid execution merges different virtual machines into a unified execution environment. This allows multiple virtual machines to share the same state and execute seamlessly within the same framework. From a zk proof perspective, this is also optimal because only one state transition is ultimately proved. In practice, the main difference lies in the degree of integration.
What are hybrid apps?
This enables developers to write different parts of an application using the language and execution environment best suited for each component, improving performance, flexibility, and usability.
Reviews
According to the information currently available, Fluent L2 will support two types of applications: shared and dedicated.
Shared Applications: These smart contract applications share state in Fluent’s execution environment. All applications shared on Fluent L2 are written in real-time, even between different VM targets and programming languages (such as Rust and Solidity).
Specialized applications: These applications are customizable, independent state machines that can leverage Fluent for evidence aggregation and verification. Developers can customize independent application runtimes, modular layers (e.g., DA, sequencing), and more.
1.3. What is the origin of Kaito, which is listed on Binance, Coinbase and other leading CEXs? Can crypto information retrieval tools make a breakthrough?
Introduction
Kaito is a cryptocurrency search engine powered by ChatGPT that aims to revolutionize crypto research and investing through artificial intelligence. Kaito uses in-house AI technology to organize and collate terabytes of unstructured information in the crypto space, making it easily accessible to investors, researchers, developers, and the public.
Three major products
1. Portal
It is the ultimate AI-driven Web3 information platform designed to transform terabytes of unstructured information into actionable insights.
Instant opinion search
Search any token, topic, or trend from thousands of high-quality Web3 sources in seconds to get instant insights.
Sentiment Analysis
Gain insights into the drivers of large swings in sentiment and easily interpret complex sentiment data to gain valuable insights with Kaito’s AI-driven analytics.
Real-time intelligent reminder
Set up fully composable real-time intelligent reminders for any project, topic, keyword, event, sentiment change, etc., making monitoring easier than ever before.
Customizable watchlists and dashboards
Track all the latest news, governance proposals, discussions, sentiment changes, upcoming catalysts and events covering any token, project or topic with customized watchlists.
Monitor and benchmark project awareness
Quantitatively and objectively monitor and benchmark changes in perception within a market or specific sector.
Tracking Narrative Turns
Systematically track narrative rotation, understand the momentum of existing narratives, and identify upcoming new narratives in advance.
Catalyst Calendar
Track events and catalysts for over 2,000 crypto tokens in real-time. Including product launches, token economics changes, unlocks, TGEs, governance votes, and more.
Audio Library
Access all podcasts and conference recordings, transcribed by Kaito’s speech-to-text model optimized for Web3, and provided with TLDR summaries to easily get the key information.
The smartest encryption AI assistant
The smartest encryption AI assistant, with the most comprehensive real-time information, improves your work efficiency several times.
2. API
Kaito's API gives you systematic access to our unique internal datasets, helping you make more informed decisions in Web3.
Kaito's API service is aimed at funds, project teams, research institutions, exchanges, etc., providing you with real-time access to the highest quality and most comprehensive knowledge data sets, as well as quantifiable social indicators across our unique database.
Seamless Integration
Easily integrate multiple Kaito functions for systematic decision making, monitoring and analysis. Our API ensures you have access to rich information at all times.
Unique indicators and data sources
Access unique data sets to stay ahead of the market. Our industry-leading methodology, coverage and data integrity help you make informed decisions and stay ahead of the curve.
Comprehensive coverage
Covering 2,000s of tokens and a wide range of Web3 data sources, our API provides unparalleled access to both recent and historical data, helping you make informed decisions in the evolving Web3 landscape.
3. Yaps
A unique ranking of "influential influencers" and the network of connections between them. Unlike other platforms that track influencer posts, Kaito's Yaps product adds a clear path for all the people who have gone through a tweet from the initiator to the @ on the basis of the general functions of such platforms, allowing users to restore the beginning, process and end of an influential tweet.
This feature is the first of its kind in the industry, allowing users in the industry to accurately capture the views of different influencers on an issue or market.
Reviews
Kaito is a next-generation Web3 information platform that indexes a wide range of Web3 content that is difficult to access through traditional search engines. This content includes social media, governance forums, research reports, news, podcasts, conference records, etc. By leveraging advanced AI technology, Kaito redefines the way users discover and interact with blockchain-related information.
The team is committed to revolutionizing the way Web3 information is accessed. It indexes thousands of high-quality Web3 sources, transforming terabytes of unstructured information into searchable and actionable insights to help make more informed decisions.
2. Detailed explanation of the projects of interest this week
2.1. Nvidia supports Solana and Web3 portal protocol Mask and invests in AI Gaming project GamerBoom
Introduction
GamerBoom is an incentive layer and data mining protocol based on mainstream Web2 games, using an AI-driven data annotation system. It aims to revolutionize gaming culture and economy by empowering players to control their digital lives.
Gaming Portal (GamerBoom app) is an application overlaid on Web2 games. It serves as a zero-threshold entrance for players in the ecosystem, connecting Web2 game players with the Web3 universe. Its goal is to smoothly integrate the Web3 incentive mechanism while maintaining the immersive experience of Web2 games. With innovative gamification design, Gaming App is committed to increasing player engagement and encouraging them to actively participate in various activities in the ecosystem.
Technical Analysis
1. Ecosystem Overview
The GamerBoom team is committed to building more than just a gaming platform; the team is building an open gaming ecosystem that allows players to thrive, innovate, and usher in a new era of digital gaming. Join the team and pave a path to the future where games and blockchain technology are closely connected to create a world where every gamer can invest in their own digital life.
GamerBoom's architecture is carefully designed and consists of three core layers:
Data mining layer
This base layer is a gamification incentive layer built on top of Web2 games and is designed for data mining. It provides real-time tracking, reaction, and scoring of in-game player behavior. The system implements an AI tagging system for in-game actions, decision patterns, and engagement.
Future plans include incentivizing users to share game-related social data, thereby enriching the dimensions of game data.
Game Data Layer
At this layer, the processed data shared by players is provided to the tuning node for training AI agents. All AI agents automatically generate a unique bonding pool based on the bonding curve. Sharers select high-quality AI agents from the sharing market and recommend them by adding liquidity to their bonding pools. Part of the revenue generated by AI agents is automatically shared with the sharers. Professional sharers can also use player data and AI agents to develop applications and use cases that better meet business needs, thereby improving profitability.
Application Layer
The application layer is a derivative layer of the game data layer, which is adapted to various usage scenarios, including the upcoming PVP exchange. It uses multi-dimensional real game data to serve multiple purposes such as advising game developers, training game AI, developing applications based on game behavior, or modifying games, in addition to other potential application scenarios.
2. AI-driven data layer
GamerBoom's technical solution aims to leverage a combination of lightweight local models and cloud-based GPT (Generative Pre-trained Transformer) models to facilitate efficient and accurate image recognition tasks in gaming environments. This hybrid approach enables GamerBoom to strike a balance between speed, cost, and complexity, ensuring optimal performance while effectively managing resource utilization.
AI System Architecture
Local model architecture
Our local model architecture consists of a set of lightweight models that are deployed directly in the client-side game application. These models are designed to perform fast and simple image recognition tasks, providing fast responses without incurring significant computational burden.
Hybrid Model Ensemble
Our hybrid model integration strategy combines the advantages of local models with the powerful capabilities of GPT in handling complex image recognition tasks. The approach involves seamless communication between client-side local models and cloud-based GPT models, ensuring comprehensive coverage and accuracy. The integration process includes the following steps:
Local model calls: The local model continuously analyzes the game screen at regular intervals (usually 100 milliseconds) and captures relevant frames.
Simple task solving: The local model quickly handles simple image recognition tasks, provides immediate response, and is suitable for straightforward tasks that do not require a lot of computing resources.
Complex Task Recognition: For tasks beyond the capabilities of local models, such as recognizing complex game elements or scenes, the client application forwards relevant frames to the cloud-based GPT model for further analysis.
AI Processing: The AI model uses advanced deep learning techniques, including image recognition and OCR, to accurately interpret forwarded frames and generate appropriate responses.
Response integration: After receiving the response from the GPT model, the client application seamlessly integrates the results into the ongoing game experience, providing relevant insights or actions based on the processed information.
Our hybrid technology approach offers several advantages and considerations:
Advantages:
Speed and efficiency: The use of local models ensures fast responses for simple tasks and reduces latency, thereby improving the overall user experience.
Cost optimization: We optimize the design by outsourcing routine tasks to lightweight local models and only resorting to cloud-based GPT models when faced with complex scenarios. Scalability: The modular architecture facilitates expansion, making it easy to add new models or enhance existing models to adapt to the evolving gaming environment.
Accuracy and flexibility: The combination of local models and GPT ensures the accuracy and flexibility of image recognition in diverse gaming environments, guaranteeing strong performance in different scenarios.
3. AI Agents as Assets
AI Agents as Assets (AIaaA) represents a paradigm shift in the way AI is applied and utilized in various fields. With the convergence of advanced data mining techniques and game data layers, AI Agents are at the forefront of innovation, offering numerous potential applications and promising directions for future development. An Initial Agent Offering (IAO) platform will be launched to accelerate the adoption of AI Agents in the market.
Agent Creator
Agent creators use processed game data to train AI agents for different applications and purposes, beyond just directly trading and monetizing game data.
Once trained, these AI agents will directly provide services to businesses and individual users. These services include but are not limited to API data flow mode or chat conversation mode. Agent creators challenge and compete with each other to improve the performance and services of AI agents.
Potential applications of AI agents
Personalized gaming experience: AI agents can analyze large amounts of gaming data collected from players to create highly personalized gaming experiences. From dynamically adjusting difficulty levels to providing tailored in-game content recommendations, AI agents increase player engagement and satisfaction.
Predictive analytics in games: By leveraging machine learning algorithms, AI agents can predict player behaviors and preferences, facilitating targeted marketing strategies and content development. This predictive capability enables game developers to anticipate trends and adjust products in real time to maximize revenue potential.
Virtual companions for gamers: AI agents can serve as virtual assistants for gamers, providing real-time guidance, tips, and strategies tailored to fit individual play styles and skill levels. Whether assisting with puzzle solving or offering tactical advice in multiplayer battles, AI assistants can enhance the gaming experience.
Anti-cheat mechanisms: AI agents play a vital role in detecting and preventing cheating in games. By analyzing game patterns and identifying anomalies, these agents help maintain fair competition and preserve the integrity of the online gaming environment.
Content Moderation and Safety: AI agents with natural language processing capabilities can monitor in-game communications and detect inappropriate or harmful content. By enforcing community guidelines and filtering out toxic behavior, these agents help create safer and more inclusive gaming environments.
Future development potential
Cross-industry integration: In addition to gaming, AI agents have the potential to revolutionize multiple industries, including education, healthcare, and finance. By adapting their capabilities to suit different domains, these agents can facilitate personalized learning experiences, assist in medical diagnoses, and optimize financial decision-making processes.
Enhanced human-machine collaboration: As AI technology continues to advance, AI agents will become more adept at collaborating with human users. Through natural language interfaces and intuitive interactions, these agents will seamlessly integrate into everyday tasks, augmenting human capabilities and productivity.
Ethical and Responsible AI: As AI agents grow in autonomy and influence, they must follow ethical principles and guidelines to ensure responsible behavior. Future developments will focus on achieving transparent and accountable AI systems that prioritize user privacy, fairness, and societal well-being.
Continued innovation in AI algorithms: The evolution of AI agents relies on continued advances in machine learning algorithms, including deep learning, reinforcement learning, and evolutionary computation. By pushing the boundaries of AI research, developers can unlock new capabilities and expand the potential applications of AI agents.
In summary, AI Agents as a Service (AIaaS) represents a groundbreaking way to leverage the power of artificial intelligence beyond gaming to other industries. With its diverse applications and unlimited potential for innovation, AIaaS heralds a future where intelligent agents will play a central role in shaping our digital experiences and transforming industries.
4. Open sharing network
GamerBoom also provides a decentralized curation network for user-generated AI agents within the ecosystem, which consists of a local curation market, an application launch platform, and a governance module. The liquidity-based curation network is an important part of the GamerBoom ecosystem, providing a reliable and efficient value exchange mechanism and laying a solid foundation for future development.
For each AI agent, a bonding pool based on a bonding curve will be created for liquidity-driven curation and promotion. Curators can add liquidity ($BOOM) for better promotion and earn profit share by holding sharing tokens.
Across the network, all AI agents will be prioritized and recommended based on their liquidity ranking.
Liquidity participants (BLP token holders) in the bonding pool also act as liquidity-based retail curators, materially amplifying the influence of the assets they are linked to. Professional curators also profit from building use cases and applications based on AI agents and player data.
This additional layer of liquidity enables intangible assets (datasets) to create unique bonding pools, earning a share of transaction fees and network incentives by monetizing and tokenizing their influence and intrinsic value.
Our innovative dynamic bonding curve provides an open, transparent and efficient on-chain data curation mechanism that automatically distributes revenue to data miners, tuning nodes, data curators and data consumers. This approach makes the market more efficient and transparent, and the price of tokens is determined by the collective behavior of network participants.
Initial Binding Curve
In the GamerBoom ecosystem, we set the initial value of the reserve ratio K to 1/3, and the value of m is determined when creating a binding pool for a specific asset. With these settings, we can derive the price function for the minting or destruction of BLP tokens (shared tokens of the binding pool) in the asset binding pool as follows:
Price formula:
Reserve formula:
Dynamic Binding Curve
GamerBoom uses a dynamic bonding curve to coordinate the interests of data miners, data curators, and data consumers. If a dataset generates revenue across the ecosystem, part of the revenue will be automatically added to the reserve of the bonding pool.
Assuming that the reserve increases from R 0 to R 0+ΔR, the reserve ratio K will increase from K 0 to K 0+ΔK, then:
If a user sells a total of N BLP tokens, reducing the total supply from S 0 to S 0 −N, the total amount of the parent currency A paid is:
According to the above formula, we get:
When a reserve is added somewhere along the bonding curve, the reserve ratio K will increase (never exceeding 1). For a user who already holds N NLP tokens, this means they will sell more reserve tokens than before the reserve was added.
Summarize
GamerBoom's advantages include: By adopting the UI/UX design of popular launchers such as Steam, Epic Games and Wegame, we allow users to keep their habits without adapting to new workflows. Through an AI-driven data labeling system, popular Web2 games are enhanced with fun and beneficial overlays, smoothly aggregating game data. Introducing bonding curves to build sustainable, decentralized AI agent-driven game economies for billions of players around the world.
Challenges include the need to balance local computing resources with cloud processing needs, which requires careful resource management to avoid performance degradation or excessive costs. Optimizing the integration between local models and GPT, minimizing latency and ensuring accurate results, is critical to maintaining the real-time nature of the gaming experience. When processing sensitive game data (such as screen captures), strong privacy and security measures need to be taken to protect user information and prevent unauthorized access or misuse. Regular maintenance and updates of local and cloud models are required to ensure they remain consistent with the evolving gaming environment and technological advances.
3. Industry data analysis
1. Overall market performance
1.1 Spot BTCÐ ETF
Ethereum spot ETF total net outflow of $10.9256 million (November 1, EST)
1.2. Spot BTC vs ETH price trend
BTC
Analysis
Last week, BTC rebounded as expected but was blocked at the bottom of the $100,000 resistance range. This week, the price trend will most likely continue to test the first-line resistance near $98,000. However, if there is no obvious volume amplification pattern, the possibility of a downward test of $95,000 will increase infinitely. If it falls below $94,000, you can directly focus on the bottom support level of $90,000. If it stabilizes in the $94,000 area or continues to consolidate around the $94,000 ~ $98,000 range, users are reminded to do less and watch more, continue to hold spot goods, and reduce the trading mode of opening contracts.
ETH
Analysis
ETH continued to fluctuate along the Fibonacci retracement line of $2,600 to $2,900 last week, so the trend is relatively easy to judge. Whenever a stagflation pattern appears near $2,900, it means that the small-scale rebound is over and the downward trend continues. Support can continue to focus on around $2,600. What can be determined with a high probability is that if the fundamentals do not fluctuate too much, then the probability of continuing to fluctuate in the above range this week is relatively high. Before falling below $2,520, it is not advisable to be bearish in the long term.
1.3. Fear & Greed Index
Between February 17 and February 23, 2025, the Crypto Fear & Greed Index dropped from 47 to 38, showing a shift in sentiment from neutral to fearful. This suggests that investors have become more cautious during this period. Factors that may have contributed to this change include:
Market Volatility: Increased price volatility can lead to higher investor uncertainty, which can cause market sentiment to shift toward fear.
Market Correction: A drop in cryptocurrency prices could cause investors to worry about the future direction of the market, which could in turn lead to fear.
Negative news: Reports of security breaches, exchange hacks (Bybit theft), or other adverse events may undermine investor confidence.
2. Public chain data
2.1. BTC Layer 2 Summary
Analysis
Between February 17 and February 23, 2025, there were several important developments in the field of Bitcoin's second layer solutions (Layer 2), highlighting the growing focus on improving Bitcoin's scalability and efficiency.
Main progress:
Nic Carter Discusses Bitcoin Rollups with David Seroy: On February 17, 2025, Nic Carter spoke with David Seroy of Strata/Alpen about Bitcoin Rollups and the upcoming “Bitcoin Layer 2 Season.” The conversation highlighted the growing interest in second-layer solutions designed to improve transaction efficiency and scalability on the Bitcoin network.
Market reaction: Bitcoin price rose slightly after the announcement of progress on Bitcoin's second-layer solution. Bitcoin price rose to $65,420 at 12:00 UTC on February 17, 2025, from $65,100 at 9:00 UTC. In addition, trading volume increased by 10% to 23.5 million BTC within three hours of the announcement, showing a high level of attention from traders.
Stacks Launches Nakamoto Upgrade: Stacks, a leading Bitcoin second-layer platform, announced the launch of the Nakamoto upgrade, designed to improve transaction speed and security. This upgrade is expected to enhance the Bitcoin economy by supporting faster and more secure applications and smart contracts.
These developments reflect the growing focus on improving Bitcoin’s scalability and efficiency through second-layer solutions, a period during which related discussions and technological advances have received much attention.
2.2. EVM &non-EVM Layer 1 Summary
Analysis
Between February 17 and February 23, 2025, several important developments occurred in the field of EVM-compatible and non-EVM Layer 1 blockchains:
EVM is compatible with Layer 1 blockchains:
Monad Testnet Launch: Monad, a high-performance EVM-compatible Layer 1 blockchain, launched its testnet on February 19. The network aims to reach speeds of over 10,000 transactions per second through parallel execution, striving to compete with Ethereum and Solana in terms of speed and scalability. Developers can now participate in the testnet using new browsers and wallets, and obtain testnet tokens from the Monad faucet.
Orderly integrates with Monad: On February 19, Orderly, a permissionless liquidity layer, integrated its cross-chain liquidity infrastructure with Monad. This move provides the Monad ecosystem, including decentralized exchanges, with deep liquidity across multiple blockchains, improving users’ trading experience.
Sonic Labs Performance Milestone: Sonic Labs, an EVM Layer 1 blockchain, reports that it has processed over 25 million transactions, attracted 758,000 unique addresses, and deployed 58,000 smart contracts. The network operates at 10,000 transactions per second, with low costs and a rapidly growing ecosystem.
Non-EVM Layer 1 Blockchain:
Aptos Integration Proposal with Aave: The Aptos Foundation proposes to integrate the Aave Protocol v3 into its mainnet, the first time the Aave liquidity protocol has been deployed on a non-EVM blockchain. The proposal seeks community feedback and aims to expand Aave's influence beyond EVM-compatible networks.
Entangle’s Solana Integration: Entangle has expanded its cross-chain support by integrating Solana into its Photon messaging protocol. This integration enables trustless cross-chain messaging between EVM and non-EVM Layer 1 networks (such as Solana), facilitating secure interactions between different blockchain ecosystems.
2.3. EVM Layer 2 Summary
Analysis
Between February 17 and February 23, 2025, several important developments occurred in the Ethereum Virtual Machine (EVM) second-layer ecosystem:
Berachain mainnet is launched on Bitget wallet, providing airdrop rewards
On February 7, 2025, the Berachain mainnet was launched on the Bitget wallet, and users can receive additional rewards through the BERA airdrop.
Ramp Network Launches Direct Withdrawal Functionality for Ethereum Layer 2 via MetaMask
On January 22, 2025, Ramp Network announced that users can now sell cryptocurrency directly from the Ethereum Layer-2 network through MetaMask.
SOON Raises $22M for SVM-Powered Ethereum Layer 2 via NFT Sale
In January 2025, Solana Optimistic Network (SOON) raised $22 million through NFT sales for its Ethereum Layer-2 network based on the Solana Virtual Machine (SVM).
4. Macro data review and key data release nodes next week
The consumer confidence index released by the University of Michigan dropped sharply from 71.7 in January to 64.7 in February, the lowest level since August 2024. Consumers' confidence in future economic conditions has clearly weakened.
The latest Fed meeting minutes released last week showed that officials believed that inflation was still slightly above target and that there was uncertainty about the economic outlook, but that the risks to employment and inflation targets were roughly balanced. The market is generally paying attention to the core PCE data to be released this week to further determine the direction of the Fed's monetary policy.
Important macro data nodes this week (February 24-February 28) include:
February 26: U.S. EIA crude oil inventories for the week ending February 21
February 27: U.S. initial jobless claims for the week ending February 22
February 28: US January core PCE price index annual rate
V. Regulatory policies
There were many major events in the crypto industry during the week, including Argentine President Milley's attempt to clarify the relationship with LIBRA, Bybit's hackers stealing about $1.5 billion in cryptocurrencies, etc., all of which showed the urgency and necessity of the crypto industry and regulators to explore appropriate regulatory methods. After the United States relaxed its high-pressure regulation, Hong Kong launched a regulatory roadmap in a timely manner, with a very clear intention to build a friendly crypto center, but how it will be promoted in the future remains to be seen.
USA
The U.S. Securities and Exchange Commission (SEC) announced the creation of the Cyber and Emerging Technology Unit, CETU, to protect retail investors. Laura D'Allaird was appointed as the head of the new unit, which replaces the Crypto Assets and Cyber Unit and consists of approximately 30 fraud experts and lawyers from multiple SEC offices. This new unit will complement the work of the Crypto Task Force led by Commissioner Hester Peirce. Importantly, the new unit will also allow the SEC to deploy enforcement resources wisely. CETU will be responsible for combating fraud involving blockchain technology and crypto assets, fraudulent disclosures by public issuers related to cybersecurity, and other misconduct.
Nigeria
According to Reuters, court documents show that Nigeria has filed a lawsuit to force Binance to pay $79.5 billion to make up for the economic losses caused by its operations in the country and pay $2 billion in back taxes. Previously, Binance said it was working with the Nigerian Federal Inland Revenue Authority to resolve potential historical tax issues.
India
According to CoinDesk, the Indian Enforcement Directorate (ED) revealed that India has seized about $190 million in cryptocurrencies, cash and a Lexus car in the BitConnect fraud investigation. BitConnect founder Satish Kumbhani has been wanted in both India and the United States since 2023.
Hong Kong
On February 19, the Hong Kong Securities and Futures Commission (SFC) released a new virtual asset regulatory roadmap, the "ASPI-Re" roadmap, which aims to enhance the security, innovation and growth of Hong Kong's virtual asset market. The roadmap has five pillars: access, safeguards, products, infrastructure and relationships.
