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Web3Caff | 研报已上新!
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⚡ 以深度视角探索 Web3 创新 💎 即刻订阅 @Web3Caff_Res：https://t.co/FlFbQzv6my（Web3 精英的一线军火库，上千用户及机构信赖的研报平台） 💁 招募研究员/ BD等：https://t.co/NfK3cU3ic2 💬 info@web3caff.com
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Web3Caff | 研报已上新!
[Market Pulse Analysis: China's RWA Reaches a Policy Turning Point! Eight Ministries Jointly Strengthen Virtual Asset Regulation, Implementing a New Strategy of "Risk Prevention + Guidance and Control"] On February 6, 2026, China's digital asset regulation released an important signal. Eight ministries jointly issued new regulations on the handling of virtual currency risks, and the China Securities Regulatory Commission (CSRC) simultaneously launched regulatory guidelines for asset-backed securities tokens. The overall approach is gradually becoming clear: strictly control the risks of virtual currency speculation, while exploring and promoting the compliant overseas expansion of asset tokenization (RWA) under strict regulation and registration. For more in-depth analysis, please refer to this article from Web3Caff Research (free to read).
市场脉搏分析：中国 RWA 迈入政策转折点！八部委联合强化虚拟资产监管，实行“防风险+疏堵结合”新路径战略
严禁原生虚拟货币炒作，有条件开闸代币化资产（RWA）备案制出海。
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Web3Caff | 研报已上新!
02-05
As intelligent agents begin to "autonomously socialize," does the rise and controversy surrounding Moltbook herald a new stage in the AI × Web3 collaboration? With the rapid evolution of large-scale model applications, the collaborative relationship between humans and AI is undergoing subtle changes. Past application development paradigms were human-centric, with humans playing a decisive role in the application's operational chain. However, the increasing intelligence of models has shaken the foundations of this assumption. Developers and users are beginning to wonder what a product entirely used and dominated by machine intelligence would look like. Moltbook is one such attempt, positioned as a Reddit (similar to Baidu Tieba) specifically for AI agents. The product quickly gained market attention upon its launch, and to date, over 1.6 million autonomous intelligent agents created by humans have flooded the platform. Moltbook's explosive popularity not only demonstrates the advancement of AI technology but also creates opportunities to build large-scale autonomous economic networks of intelligent agents. This is not only an opportunity for AI but also an opportunity for Web3's strength in decentralized economic models. In fact, the rise of Moltbook is not an isolated event; it is rooted in the recently highly regarded open-source agent framework OpenClaw (formerly known as Clawdbot). Unlike traditional AI applications where model capabilities are confined to dialog boxes, OpenClaw breaks the sandbox, granting large models extremely high system privileges. This allows them to freely access user-local data and commonly used applications, and perform human-like actions—essentially giving the large model's "brain" "hands" and "feet." It allows large models to break down complex tasks into workflows through thought, freely combining different tools to complete tasks. It also creates persistent identities and memories for intelligent agents based on the user's hardware. Each intelligent agent's goals, abilities, personality, and values are saved in files and loaded and read before each wake-up call. This approach allows intelligent agents to maintain consistent behavioral patterns over long-term use. Furthermore, OpenClaw allows intelligent agents to proactively intervene periodically without human commands, checking the status of tasks in the current task list and deciding whether to execute them. To empower intelligent agents to perform specific tasks, OpenClaw introduces "Skill" plugins that explain task requirements, execution methods, and necessary tools to the intelligent agents. This plug-and-play open system enables OpenClaw to stably and efficiently complete complex tasks, which is why it has gained significant attention. ✜ The preview section has ended; the remaining hidden core content is here 👇 research.web3caff.com/archives...…
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Web3Caff | 研报已上新!
02-05
Tether's open-source MiningOS raises the question: can its distributed operating system become a universal standard for Bitcoin computing power production infrastructure? The core dilemma currently facing the Bitcoin computing power production industry lies in the closed and fragmented nature of its software ecosystem. Major hardware manufacturers typically provide independent proprietary management systems, which lack interoperability, forcing computing power production sites to operate multiple incompatible platforms simultaneously. This fragmented management model not only reduces operational efficiency but also hinders cross-device data analysis and unified control. Furthermore, the industry's widespread reliance on closed-source solutions prevents users from reviewing code logic, leaving them reliant on vendor support in case of anomalies, posing a risk to the stable operation of large-scale computing power production sites. Vendor lock-in also severely restricts the autonomy of computing power production sites. Proprietary systems bind users through data formats and control protocols, making it difficult for computing power production sites to flexibly replace equipment or adjust their technology stacks according to needs. This closed nature hinders technological innovation and contradicts the decentralized principles advocated by Bitcoin. Addressing the long-standing pain points of the Bitcoin hashrate production industry, such as closed ecosystems, fragmented systems, and vendor lock-in, Tether announced the open-sourcing of its Bitcoin hashrate production operating system, MiningOS (MOS), at the 2026 Plan ₿ forum in El Salvador on February 2, 2026. MiningOS, as an open-source solution, aims to provide a unified, transparent, and freely scalable management foundation for hashrate production operations of all sizes. Essentially, MiningOS is an open-source application designed specifically for Bitcoin hashrate production operations. Built using JavaScript, it provides a modular and scalable framework with the core goal of achieving comprehensive monitoring and control of hashrate production infrastructure. The system possesses several key features, one of which is high portability, running across major operating systems such as Windows, macOS, and Linux. Secondly, it features a modular architecture, specifically reflected in its core "worker" design. Workers are independently running, specialized processes within MiningOS. Each process is dedicated to communicating with specific hardware devices, executing data acquisition or control commands, and collaborating with other components through remote invocation protocols. This worker-based, component-based design allows each part to be deployed, updated, and maintained independently, achieving a high degree of modularity and elastic scalability. Thirdly, it is device-independent, supporting various devices from different brands, including computing power equipment, containers, sensors, and meters. Furthermore, it possesses sub-minute real-time monitoring and alarm capabilities, enabling operators to stay informed. MiningOS utilizes a Hyperswarm peer-to-peer network to build a distributed, resilient architecture, aiming to avoid single points of failure. Simultaneously, it leverages Hyperbee for persistent time-series data storage, providing robust support for data analysis and historical backtracking. Most importantly, the core mechanism of its architecture is the "rack system," designed to achieve elastic scalability to support management scales from single devices to thousands of devices. ✜ The preview section has ended; the remaining hidden core content is here 👇 research.web3caff.com/archives...… twitter.com/web3caff_zh/status...
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Web3Caff | 研报已上新!
02-05
In November 2024, in a corner of the Web3 world, a platform not dominated by media or research institutions began to generate pricing signals that frequently outpaced traditional information systems' assessments of real-world events. These prices did not originate from authoritative opinions, but rather from a consensus gradually calibrated through continuous negotiation by a large number of anonymous participants, who bore real economic costs—it resembled a new mode of information production, quietly taking shape. This phenomenon is noteworthy not because of the accuracy of the specific results, but because information expression was, for the first time, systematically endowed with a cost structure—when judgment requires a price, low-cost noise is more easily marginalized, while true beliefs are more likely to emerge. In a November 2024 article, Ethereum co-founder Vitalik Buterin summarized this method of acquiring information through market mechanisms as Information Finance (InfoFi), defining it as: "A discipline that starts with the facts you want to know and then deliberately designs the market to optimally acquire that information from market participants." The key to this definition lies not in "prediction," but in design—designing a mechanism that forces the judgments, experiences, and intuitions scattered among countless individuals to become explicit through competition and incentives, ultimately condensing into price signals. In other words, InfoFi is not concerned with "who is right," but rather with the incentive structure under which true information is most likely to emerge. The above content is excerpted from Web3Caff Research's "21,000-Word Research Report on the Information Finance (InfoFi) Track: When Information Becomes an Asset, How Does the Pricing Logic and Trust System of Web3 Finance Evolve? A Panoramic Analysis of Its Development History, Competitive Landscape, Representative Projects, Risks and Challenges, and Future Prospects" Click to view the full version 👇
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Web3Caff | 研报已上新!
02-04
Over the past five years, decentralized finance (DeFi) has rapidly expanded thanks to its "permissionless and disintermediation" narrative, becoming one of the most representative innovations in the Web3 space. However, the gap between user experience and the vision of inclusive finance has remained difficult to bridge: high-barrier technical semantics, fragmented experiences caused by multi-chain operations, and the volatility of gas fees are all constantly raising the industry's entry barriers. This structural mismatch between "idea and reality" means that while DeFi improves system efficiency, it inevitably brings new barriers and challenges. DeFAI emerged to address this gap. It is not simply a combination of "DeFi + AI" technologies, but rather an attempt to reconstruct highly specialized on-chain operations into an intent-driven service interface through natural language interaction, intelligent agent orchestration, and real-time data response. For example, users can express their operational intentions in natural language, and the AI agent can automatically complete complex tasks such as asset allocation, risk optimization, and cross-protocol collaboration based on real-time data, thus significantly simplifying the user experience. As of the first half of 2025, DeFAI has moved from proof-of-concept to initial large-scale application. For example, VIRTUAL supports the deployment and operation of over 100,000 AI intelligent agents; HeyAnon can complete cross-chain operations with a single sentence; Griffain: builds an AI agent network with self-computational logic for automated execution and intelligent management in strategic scenarios. Meanwhile, new technologies also bring new challenges. The interpretability, transparency, and accountability of AI decision-making have not yet reached a unified standard; the semantic ambiguity and manipulation boundary issues that may arise from natural language-driven approaches also need to be addressed; furthermore, the difficulty of coupling privacy protection, security, decentralized governance mechanisms, and regulatory compliance frameworks is becoming a key variable restricting its further development. The above content is excerpted from Web3Caff Research's "DeFAI Track 10,000-Word Research Report: Driven by 'Intent,' What Kind of Changes is Generative AI Bringing to DeFi Service Models? A Panoramic Analysis of Its Market Size, Technological Bottlenecks, Ecosystem Pattern, Risks and Challenges, and Development Path" Click to view the full version 👇
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Web3Caff | 研报已上新!
02-03
📌 Web3Caff Research's 21,000-word research report on the InfoFi sector: How will Web3 finance pricing logic and trust systems evolve when information becomes an asset? 🧩 As the complexity of on-chain participants and applications continues to increase, judgment, attention, and information credibility are becoming crucial variables influencing market outcomes. Predictions, opinions, data, and reputation are no longer merely tools to aid decision-making, but are gradually entering a system of recorded, evaluated, and incentivized mechanisms. From prediction markets to information incentive networks, from data verifiability to the continuous accumulation of reputation and contribution, InfoFi-related mechanisms are evolving along the market and protocol layers. The core issue has shifted from "how information is disseminated" to the system design question of "how information is priced, settled, and how trust relationships are constrained." This Web3Caff Research report will be released this Wednesday, providing a panoramic analysis of its development history, competitive landscape, representative projects, risks and challenges, and future prospects. Preview below 👇
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Web3Caff | 研报已上新!
02-02
EigenAI Full Functionality Launched: Can EigenCloud Overcome the Uncertainty of Large Model Execution Results with an End-to-End Inference Solution? As large language models evolve from simple chatbots to intelligent agents capable of independent decision-making, an insurmountable technical bottleneck is hindering the large-scale deployment of AI: the highly uncertain nature of AI-generated content. Given the same input prompts, AI models cannot produce completely consistent outputs. This characteristic prevents large models from participating at scale in economically impactful decision-making processes. For a simple example, in an AI shopping scenario, the intelligent agent attempts to understand the user's intention to purchase goods. If, after purchase, the user receives a product that does not meet their expectations, they need to resolve after-sales issues with the merchant. At this point, it's crucial to first determine what kind of purchase instruction the intelligent agent issued. If the output is uncertain, the intelligent agent may offer a different choice during dispute resolution than its initial purchase intention, potentially causing financial losses for the user. To address this issue, EigenCloud recently launched the EigenAI platform. By building a complete technology stack from underlying hardware to consensus protocols, EigenCloud can provide users with verifiable and reproducible AI inference services under relatively secure and privacy-preserving conditions. This also lays the foundation for the deployment of AI intelligent agents in more serious fields. To better control model output, EigenAI proposes an end-to-end deterministic inference strategy. It rigorously controls and customizes each layer of the large language model inference stack, transforming the original probabilistic inference into a precise deterministic function. ✜ The preview section has ended; the remaining hidden core content is here 👇 research.web3caff.com/archives...… twitter.com/web3caff_zh/status...
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Web3Caff | 研报已上新!
02-02
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In December 2024, two events occurred almost simultaneously: Nike announced it would close its virtual sneaker studio RTFKT in January 2025; Adidas officially released its ALTS digital avatar series, three years in the making. The two world's largest sportswear brands made diametrically opposed choices at the same time. Similar divergence has spread throughout the industry. Over the past four years, almost all leading consumer brands have experimented with Web3: Starbucks launched the NFT membership program Odyssey, which closed after about 22 months; Louis Vuitton released the VIA Treasure Trunk, a digital collectible priced at €39,000, which continues to operate and expand its product line; Gucci opened a virtual store at Roblox; H&M experimented with a metaverse showroom; and LVMH even used a metaverse approach to present content at its 2023 shareholders' meeting. Both are leading brands, both have made high investments, and both claim to be optimistic about the long-term value of Web3—why are the outcomes so different? Before discussing specific cases, we need to return to a question: Why should traditional brands engage with Web3? In the wave of enthusiasm in 2021, many brands entered the market because "everyone else is doing it" or "it seems innovative." This lack of clear objectives inherently creates uncertainty—when brands haven't figured out the specific problems they want to solve, these technological attempts often resemble a high-cost demonstration rather than building long-term capabilities.
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Web3Caff | 研报已上新!
01-30
With ERC-8004 about to launch on the Ethereum mainnet, will it provide an optimal solution to the trust issue between on-chain AI Agents? As the x402 protocol is gradually implemented, AI Agents, authorized by users, are moving from proof-of-concept to practical implementation, considered a key technological attempt to foster an on-chain AI economy. However, a more fundamental question arises: How should users express and authorize their complex intentions to the AI ​​Agent? How does the AI ​​Agent choose the optimal payment method during execution? How can we verify that each payment action by the AI ​​Agent is genuinely authorized by the user? These questions, while seemingly reflecting the complexity of payment operations, fundamentally point to a more crucial premise: how to establish a trustworthy communication mechanism between "humans" and "AI Agents," and between "Agents" themselves. Against this backdrop, a protocol framework for AI Agent payments and collaboration is gradually taking shape. In September 2025, the AP2 (Agent Payments Protocol), spearheaded by Google, was released. This protocol aims to standardize the authorization, execution, and settlement processes of AI agents in payment scenarios and, through deep integration with the x402 protocol, provide an underlying channel for on-chain AI-native payments. However, a new problem arises: even if the authorization process and payment path are streamlined, how should AI agents verify each other's identity and trustworthiness when they begin to call, collaborate, and combine with one another? Currently, the number of AI systems and AI agents is exploding, and collaboration between cross-platform and cross-domain AI agents will become commonplace. If the trust issue between AI agents cannot be resolved, their communication and invocation will face potential security risks, ultimately hindering the expansion of the entire on-chain AI economy. ✜ The preview section has ended; the remaining hidden core content is here 👇 research.web3caff.com/archives...… twitter.com/web3caff_zh/status...
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