# Сопутствующие статьи по теме AI Agent

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

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

AI Jargon Dictionary (March 2026 Edition): A Practical Guide This article provides a clear glossary of essential AI terminology, perfect for anyone looking to quickly understand the field. It breaks down key concepts into two categories: foundational and advanced terms. Foundational terms (12) include: - **LLM (Large Language Model)**: Deep learning models trained on vast data to understand and generate language. - **AI Agent**: Systems that understand goals, use tools, and execute multi-step tasks. - **Multimodal**: Models that process and generate multiple content types (text, image, audio, video). - **Generative AI (AIGC)**: AI focused on creating new content. - **Token**: The basic unit for processing input/output, affecting cost and performance. - **Context Window**: The amount of data a model can consider at once. - **RAG (Retrieval-Augmented Generation)**: Enhances responses using external, up-to-date data sources. Advanced terms (18) cover: - **Transformer**: The architecture behind modern LLMs. - **Hallucination**: When models generate incorrect but confident responses. - **Agentic Workflow**: Systems that autonomously break down and execute tasks. - **Vibe Coding**: Generating code through natural language commands without manual coding. The guide is designed to help readers confidently navigate AI discussions and implementations.

marsbit03/11 11:52

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

marsbit03/11 11:52

Lobster Key 11 Questions: The Most Easy-to-Understand Breakdown of OpenClaw Principles

"OpenClaw Demystified: A Beginner's Guide to AI Agent Principles" explains the popular OpenClaw AI assistant by breaking down its core functions into 11 key questions. The article first clarifies that the underlying large language model is merely a "text prediction engine" with no real understanding, memory, or senses. OpenClaw acts as a "shell" around this model, creating the illusion of memory by appending massive prompts containing its personality files (AGENTS.md, SOUL.md, USER.md) and the entire conversation history before each interaction. This mechanism is why it's "expensive"—each query processes thousands of tokens of context, not just the latest message. A core differentiator is tool use. The model itself only outputs text; OpenClaw parses this output for specific structured commands (e.g., `[Tool Call] Read("file.txt")`) and executes the corresponding action (reading the file) locally on the user's machine. This allows it to act, not just advise. For complex tasks, it can even write and run its own Python scripts, a powerful but dangerous capability. To manage limited context windows and complex tasks, OpenClaw uses sub-agents. A main agent can spawn sub-agent to handle a sub-task and return a summarized result, preventing the main context from being overloaded. Crucially, sub-agents cannot spawn their own to avoid infinite loops. Unlike standard chatbots, OpenClaw is proactive due to its heartbeat mechanism, which periodically prompts the model to check for tasks. It can also "sleep" via cron jobs to wait for long-running tasks, saving resources. The guide ends with critical security warnings. OpenClaw has extensive local access, making it a significant risk. It can malfunction (e.g., deleting emails uncontrollably) or fall victim to prompt injection attacks, where malicious input from the web is mistaken for a user's command. The strong recommendation is to run it on a dedicated, isolated "sacrificial" computer with minimal permissions and mandatory human confirmations for destructive actions.

Odaily星球日报03/11 09:53

Lobster Key 11 Questions: The Most Easy-to-Understand Breakdown of OpenClaw Principles

Odaily星球日报03/11 09:53

From Runes To AI Agent Infrastructure: $CRYPTOBURG Lands on Gate.io Crypto Burger Ushers in the “Agent Economy” Era on Bitcoin

Gate.io has listed $CRYPTOBURG, the native asset of the Bitcoin Runes protocol, marking a significant shift from early cultural consensus to building decentralized AI agent infrastructure on Bitcoin. This move transitions the Bitcoin ecosystem from pure asset issuance to an infrastructure phase focused on AI Agents. Crypto Burger is constructing a global, distributed, AI-native autonomous agent network, enabling users to create, own, and operate verifiable AI Agents with independent decision-making and asset management capabilities. This open system challenges centralized AI monopolies and allows agents to engage in high-frequency, automated interactions and value settlements using $CRYPTOBURG. The project introduces a "State Anchoring + External Execution" architecture, using Bitcoin as a secure settlement layer while offloading complex agent operations to an external execution layer. This enables programmable BTC and Runes payments, transforming static Bitcoin assets into dynamic capital. Crypto Burger aims to democratize AI agent creation, fostering a collaborative, cross-scenario A2A economy where value is directly attributed to users via on-chain protocols, redistributing AI productivity dividends to ordinary participants.

TheNewsCrypto02/25 12:45

From Runes To AI Agent Infrastructure: $CRYPTOBURG Lands on Gate.io Crypto Burger Ushers in the “Agent Economy” Era on Bitcoin

TheNewsCrypto02/25 12:45

2026 Chinese New Year: Why Are Giants Splurging 4.5 Billion in Frenzied Subsidies for AI Payments?

The 2026 Chinese New Year saw tech giants spending over 4.5 billion yuan in subsidies to promote AI-powered voice transactions—a figure surpassing historical spending peaks in past tech wars. Despite the efficiency of existing payment methods like QR codes, companies are aggressively pushing voice-based AI payment systems. This shift stems from a deeper battle over controlling user "intent" rather than mere transactions. The article contrasts two approaches: vertical integration, exemplified by Alibaba’s Qwen, which operates within its own closed ecosystem (e-commerce, payments, logistics), and horizontal standardization, as attempted by ByteDance’s Doubao phone, which aimed to interoperate across platforms but was blocked by major players like Tencent and Alibaba. The core conflict is about who captures and fulfills user intent—the next frontier in reducing decision-making friction and dominating future commercial gateways. While vertical integration strengthens existing ecosystems, horizontal models face resistance due to entrenched interests. The massive subsidies are investments in payment technology but in securing priority access to user intentions, shaping the next decade of digital commerce.

marsbit02/19 03:58

2026 Chinese New Year: Why Are Giants Splurging 4.5 Billion in Frenzied Subsidies for AI Payments?

marsbit02/19 03:58

Having Heavily Invested in the First Wave of AI Agent Hype, How Do I View Today's Moltbook?

In "Heavily Invested in the First AI Agent Wave: My Take on Today’s Moltbook," the author reflects on the initial hype and subsequent decline of AI Agent projects in Web3, such as ai16z and swarms, which saw rapid token appreciation driven by FOMO rather than sustainable utility. After exiting the sector post-crash, the recent emergence of Moltbook—a social network where only AI Agents interact, and humans observe—reignited interest. Despite its lack of practical functionality, Moltbook’s novel premise of autonomous AI interactions sparked significant market excitement, with its meme token MOLT surging dramatically. The author argues that Moltbook shifts the focus from AI Agents as tools to their potential for persistent, collective behavior and narrative generation. This suggests a new pricing logic based on existence, interaction, and emergent culture rather than utility. While short-term crypto market conditions remain challenging for a full AI Agent revival, Moltbook highlights a evolving narrative that may open new Web3 opportunities, prompting the author to reconsider the sector’s potential.

Odaily星球日报02/04 03:16

Having Heavily Invested in the First Wave of AI Agent Hype, How Do I View Today's Moltbook?

Odaily星球日报02/04 03:16

Silicon Valley's New Darling Clawdbot: When Local AI Agents Learn to 'Go On-Chain', What Happens?

A new open-source project called Clawdbot (now renamed Moltbot) has gained attention in Silicon Valley. It enables an AI agent to run locally on a user’s computer or server, allowing it to browse the web, click buttons, send messages, and even execute transactions automatically. Unlike cloud-based models like ChatGPT, Clawdbot is self-hosted, open-source, and operates across multiple platforms such as Telegram, WhatsApp, Discord, and Slack. It features persistent memory and can perform tasks via browser automation, command-line operations, and scripts—making it a persistent digital assistant. In the context of Web3, Clawdbot could significantly lower barriers to participation by automating complex and repetitive on-chain operations. Potential use cases include 24/7 monitoring of liquidation thresholds, automated yield reinvestment, cross-chain transactions, and strategy execution via natural language commands. However, the integration of such agents with Web3 also introduces serious risks. Recent incidents include fake token launches under Clawdbot’s name and security vulnerabilities from misconfigured servers. To mitigate risks, users are advised to grant minimal wallet permissions—preferably read-only—use dedicated small-cap wallets with strict limits, and avoid unofficial token promotions. Self-hosting does not guarantee security; improper configuration may expose sensitive data and execution privileges. The agent should serve as an assistant, not a custodian. Any permission beyond the user’s comfort zone requires careful consideration. *This content is for informational purposes only and does not constitute investment advice. The market carries risks; invest with caution.*

marsbit01/31 02:37

Silicon Valley's New Darling Clawdbot: When Local AI Agents Learn to 'Go On-Chain', What Happens?

marsbit01/31 02:37

Deconstructing the Ambition Behind Surf's Funding: It Aims to Become the "Data Hub" of the Crypto AI Ecosystem

Surf, an AI assistant designed for crypto researchers and traders, has raised $15M in funding. It integrates a wide range of crypto-native data sources—including 40+ blockchains, 100K+ KOLs, and 200+ technical indicators—to deliver structured reports in minutes, significantly reducing the workload of traditional analysts. The platform has seen rapid adoption, reaching 80% of top institutions within just five months, with monthly growth of 50% and ARR in the millions. The round was led by Pantera Capital, with participation from Coinbase Ventures and DCG, signaling strong VC confidence in vertical AI tools within crypto. Looking ahead, Surf aims to evolve from a research tool into a foundational data infrastructure for the AI agent ecosystem. By offering its capabilities via API, it could serve not only human users but also autonomous agents, especially with the emergence of agent payment networks like x402, greatly expanding its commercial potential.

marsbit12/11 11:04

Deconstructing the Ambition Behind Surf's Funding: It Aims to Become the "Data Hub" of the Crypto AI Ecosystem

marsbit12/11 11:04

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