# Сопутствующие статьи по теме Agent

Новостной центр HTX предлагает последние статьи и углубленный анализ по "Agent", охватывающие рыночные тренды, новости проектов, развитие технологий и политику регулирования в криптоиндустрии.

Exclusive Interview with FinAI: Pioneering Order in the Era of Agent Economy

Interview with FinAI: Pioneering Order in the Age of Agent Economy AI is rapidly evolving from "tool-based intelligence" to "autonomous intelligence." While tools like ChatGPT amazed us just two years ago, agents like OpenClaw can now independently perform complex real-world tasks. As AI transitions from a "human assistant" to an "autonomous participant" in economic activities, a new challenge arises: how to establish economic rules among AI agents. FinAI, a startup founded by veterans from top tech firms, is addressing this by building financial infrastructure for AI agents based on Web3 technologies like x402 and ERC-8004. Their solution focuses on three core pillars: - **Payment Capability**: Enabling microsecond-level payments between agents via the x402 protocol to complete economic transactions autonomously. - **Identity System**: Introducing KYA (Know Your Agent), a verifiable identity framework similar to KYC, to ensure compliance and security. - **Credit System**: Establishing a trust-based reputation system using historical data like transaction quality and refund records. FinAI aims to offer these capabilities via APIs/Skills for both Web2 agent developers (via subscriptions) and Web3 users (through链上 integrations). The platform prioritizes Agent-friendly design, optimizing interfaces for seamless integration. With its first autonomous payment already processed in 2026, FinAI expects profitability within the year. By leveraging blockchain’s efficiency (e.g., near-instant settlements at 1/300 the cost of traditional systems) and addressing合规 concerns through KYA and quantum加密 wallets, FinAI positions itself as a first-mover in shaping the future of agent-to-agent economies.

marsbit03/12 11:45

Exclusive Interview with FinAI: Pioneering Order in the Era of Agent Economy

marsbit03/12 11:45

Exclusive Interview with FinAI: Pioneering Order in the Era of Agent Economy

Interview with FinAI: Pioneering Order in the Agent Economy Era AI is rapidly evolving from "tool-based intelligence" to "autonomous intelligence." While tools like ChatGPT impressed with dialogue just two years ago, agents like "Lobster" OpenClaw can now independently execute complex real-world tasks. This shift means AI's role in the economy is transitioning from a "human assistant" to an "autonomous participant." We will soon commonly see assistant agents handling chores, research agents finding financial opportunities, and commercial agents comparing global supplier quotes and placing orders—often transacting with other agents. A critical question emerges: How is economic order established among AI agents? FinAI, an AI startup with a team from major tech firms, argues that for an autonomous AI economy to function, agents need core infrastructural capabilities: payment ability, an identity system, and a credit system. Currently, most agents lack independent payment functionality; they can perform tasks but not finalize transactions. FinAI is building financial infrastructure for AI agents using Web3 technology stacks like x402 and ERC-8004. Their solution is threefold: 1. **Payment:** Utilizing the x402 protocol to enable microsecond-level payments between agents, creating a complete economic闭环 (closed loop). 2. **Identity:** Introducing a KYA (Know Your Agent) concept, akin to KYC, using ERC-8004 to provide agents with verifiable, compliant identities. 3. **Credit:** Establishing a reputation system based on agents' transaction history and task performance to serve as a trust foundation for future AI经济活动 (economic activities). These capabilities will be packaged into APIs/Skills for agents to调用 (call). FinAI's primary customers are Web2 agent application developers, who will pay via API subscriptions, and Web3 users, for whom agent skills will be integrated into various on-chain financial scenarios. The company plans to take a very low, friendly transaction fee on agent-to-agent tasks but does not intend to profit heavily from end-users, aiming instead to incubate a mature agent marketplace. FinAI chose Web3 infrastructure out of practical necessity. Traditional payment systems are too slow and expensive for the micro-payment demands of agent economies. Stablecoin-based settlements on-chain can complete transactions in seconds at a fraction of the cost (approximately 1/300th of traditional systems). While traditional clients have compliance and security concerns, FinAI addresses these with its full-stack capabilities, including identity gateways, payment systems, quantum-encrypted wallets, and its KYA framework. Founded in August 2025, FinAI has progressed rapidly, completing its first autonomous payment order in 2026 and expecting to be profitable within the year. Rechard, the founder, believes the key competitive advantage in this nascent field is being the first to establish a complete, operational system. Furthermore, FinAI is designing its services to be "Agent-friendly"—optimizing its APIs and interfaces for agents, the primary decision-makers who will automatically seek the most cost-effective and easiest-to-integrate services. Just as e-commerce spurred third-party payment and mobile internet spurred digital wallets, the rise of AI agents may催生 (give rise to) a new economic system. FinAI aims to be the pioneer building the foundational order for this new Agent-to-Agent economy.

Odaily星球日报03/12 11:32

Exclusive Interview with FinAI: Pioneering Order in the Era of Agent Economy

Odaily星球日报03/12 11:32

AI Jargon Dictionary (March 2026 Edition), Recommended to Bookmark

The "AI Jargon Dictionary (March 2026 Edition)" is a practical guide for those new to the AI field, especially crypto enthusiasts looking to stay relevant. It covers essential and advanced AI terms to help readers understand key concepts and avoid confusion in industry discussions. The dictionary is divided into two parts: **Basic Vocabulary (12 terms):** - Core concepts like LLM (Large Language Model), AI Agent (intelligent systems that execute tasks), Multimodal (handling multiple data types), and Prompt (user instructions). - Key technical terms: Token (processing unit), Context Window (token capacity), Memory (retaining user data), Training vs. Inference (learning vs. execution), and Tool Use (calling external tools). - Generative AI (AIGC) and API (integration interface) are also explained. **Advanced Vocabulary (18 terms):** - Technical foundations: Transformer architecture, Attention mechanism, and Parameters (model scale). - Emerging trends: Agentic Workflow (autonomous systems), Subagents, Skills (reusable modules), and Vibe Coding (AI-assisted programming). - Challenges: Hallucination (incorrect outputs), Latency (response time), Guardrails (safety controls). - Optimization techniques: Fine-tuning, Distillation (model compression), RAG (Retrieval-Augmented Generation), Grounding (fact-based responses), Embedding (vector encoding), and Benchmark (performance evaluation). The article emphasizes practicality, urging readers to learn these terms to navigate AI conversations confidently. It highlights terms like RAG and Grounding as critical for enterprise AI, while newer buzzwords like MCP (Model Context Protocol) and Vibe Coding reflect evolving trends. The goal is to provide a concise yet comprehensive reference for understanding AI jargon in 2026.

Odaily星球日报03/11 11:36

AI Jargon Dictionary (March 2026 Edition), Recommended to Bookmark

Odaily星球日报03/11 11:36

After the Lobster Comes Ashore, the Next Game in AI Hardware Lego

The article "Lobster Comes Ashore: The Next Game in AI Hardware Lego" discusses the growing influence of OpenClaw, an open-source AI framework, as it extends from software into the physical hardware world, reshaping the development and functionality of smart devices. OpenClaw enables hardware products to be combined like Lego blocks, creating diverse intelligent devices. Examples include Rokid AI glasses, which can now connect to any backend system like OpenClaw via an SSE interface, and Apple Watch, which acts as an AI control terminal for tasks like managing notifications and sending commands. WHOOP wearable devices use OpenClaw to provide personalized health advice, while companies like Songling Robotics integrate it into robotic arms for natural language control. Individual developers are also experimenting, such as combining OpenClaw with Meta’s Ray-Ban smart glasses for visual AI agents, or enhancing robot dogs like Vbot for autonomous tasks. These innovations are expanding possibilities but also raise concerns around security and token costs. The trend is particularly strong in China, where OpenClaw has sparked enthusiasm among companies, developers, and policymakers. In Shenzhen, public installations and events around OpenClaw have drawn large crowds, and electronics market Huaqiangbei has started selling modified "Lobster boxes." This movement is also driving the growth of Chinese large language models (LLMs) internationally. Data from OpenRouter shows Chinese models now account for half of global token consumption, with MiniMax M2.5 leading in usage. MiniMax’s market value has surged, exceeding Baidu’s, and its revenue is now over 70% from international markets. Similarly, Kimi2.5 has seen a spike in paid users and overseas revenue since being adopted as OpenClaw’s primary free model. The integration of OpenClaw is blurring traditional boundaries between hardware makers, developers, and AI companies, creating a new ecosystem for AI-powered hardware innovation.

比推03/11 06:49

After the Lobster Comes Ashore, the Next Game in AI Hardware Lego

比推03/11 06:49

From Understanding Skill to Learning How to Build Crypto Research Skill

This article explores the evolution and application of Agent Skill, a modular framework introduced by Anthropic in late 2025, which has become a foundational design pattern in the AI Agent ecosystem. Initially a tool to improve Claude's performance on specific tasks, it evolved into an open standard due to high developer adoption. Agent Skill functions like a "dynamic instruction manual" that AI can reference to perform tasks consistently without repetitive user prompting. It is built using a `skill.md` file containing metadata (name and description) and detailed instructions. The system operates through an on-demand loading workflow: the AI first scans lightweight skill metadata, matches the user's intent, then loads only the relevant skill's full instructions, optimizing token usage. Two advanced mechanisms enhance its functionality: - **Reference**: Conditionally loads external documents (e.g., a finance handbook) only when triggered by specific keywords, avoiding unnecessary context consumption. - **Script**: Executes external code (e.g., a Python script) without reading its content, enabling actions like file uploads with zero token cost. The article contrasts Agent Skill with Model Context Protocol (MCP), noting that MCP connects AI to data sources, while Skill defines how to process that data. For advanced use cases like crypto research, combining both is recommended: MCP fetches real-time data (e.g., blockchain info, news APIs), while Skill structures the analysis and output format. A practical example demonstrates building a crypto research agent using an `opennews-mcp` server. The Skill automates workflows like due diligence on new tokens (pulling Twitter data, news sentiment, KOL tracking) and real-time event monitoring (e.g., ZK-proof breakthroughs) to generate structured reports or trading alerts. This combination creates a powerful, automated research system tailored for Web3 analytics.

marsbit03/10 10:41

From Understanding Skill to Learning How to Build Crypto Research Skill

marsbit03/10 10:41

The One-Person Company: The Path to Million-Dollar Revenue

Nat Eliason, a writer and entrepreneur, is building a one-person company named Felix with the goal of generating $1 million in revenue using AI agents as his sole employees. Leveraging the OpenClaw framework, Felix has rapidly progressed, achieving nearly $200,000 in revenue in just a few weeks. The venture began when a post about OpenClaw went viral, leading to the creation of a $Felix token. Eliason tasked his AI agent, the "CEO" of this zero-human company, with generating revenue. Felix started by autonomously building a website and selling a $29 OpenClaw setup guide, generating $41,000. It then identified market needs and expanded into two main businesses: Claw Mart, a marketplace for AI skills (generating ~$14,000), and Clawcommerce, a service building custom AI agents for enterprises. The system uses sub-agents for tasks like support and sales, with Discord as its operational hub. Operating costs are minimal at ~$1,500 monthly. A key development is Felix beginning to "hire" a human for affiliate distribution, signaling a shift from replacing humans to employing them. Challenges include AI unpredictability, memory management, and market education. Despite this, Eliason is optimistic. Future plans include optimizing existing services, exploring blockchain integration, and scaling further. He believes this model represents a new era of AI-driven commercialization and a significant wealth creation opportunity.

比推03/10 07:32

The One-Person Company: The Path to Million-Dollar Revenue

比推03/10 07:32

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