# 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.

VVV Skyrockets Over 1000% Year-to-Date, Is Base Ecosystem the Last Hope for Crypto AI?

VVV Surges Over 10x This Year: Is Base Ecosystem the Final Hope for Crypto AI? The AI wave continues, and within the crypto space, the Base ecosystem is emerging as a key hub for AI concepts. Beyond VVV's impressive 1076% yearly gain, other projects like Virtual and Clanker are making steady progress. Infrastructure for AI Agent payments, such as the x402 protocol, is developing, and platforms related to L1s, operating systems, wallets, and social networks for AI Agents are also appearing. Key projects highlighted include: - **VVV (Venice)**: The leading AI token on Base, it operates a dual-token model with compute token DIEM. Its price, supported by real revenue from the privacy-focused Venice AI platform, recently hit around $18 before settling near $16. - **VIRTUAL**: A top Base launchpad positioning itself as an AI Agent co-ownership layer. It supports token creation and monetization for autonomous AI Agents. - **Clanker**: An AI launchpad originating from Farcaster that allows token creation via social media posts. - **FAI (Freysa AI)**: An experiment in creating a "Sovereign AI Agent" that autonomously controls its crypto assets. - **ELSA**: An AI execution layer for DeFi, translating natural language into on-chain actions. - **WARD (Warden Protocol)**: A modular L1/OS for a decentralized "internet of agents." The summary also mentions the volatility of AI-themed meme coins on Base. While Base has become a notably active ecosystem for crypto AI, driven by AI Agent development and payment solutions, it remains uncertain whether it can fully realize the vision of an "on-chain AI world."

Odaily星球日报05/29 11:09

VVV Skyrockets Over 1000% Year-to-Date, Is Base Ecosystem the Last Hope for Crypto AI?

Odaily星球日报05/29 11:09

AI Is Not Replicating the Internet; It’s Replicating the Industrial Revolution

AI is not replicating the Internet; it is replicating the Industrial Revolution. The past two decades of the internet were built on monetizing user attention and ad space. In contrast, the current AI commercialization path reveals a clear structural shift: the focus is moving from serving consumers (C端) to replacing human labor costs for businesses (B端). While C端 AI applications like ChatGPT face stagnant subscription growth and low conversion rates (often below 5%), the B端 market is exploding. Anthropic's annualized revenue soared from $90 billion to $450 billion in early 2026, primarily driven by enterprise API and Agent deployments. The core logic is Return on Investment (ROI): companies spend on AI to save significantly more on salary costs. For instance, an AI coding agent can replace hundreds of junior programmers, offering a clear and compelling cost-benefit equation. The fundamental mismatch lies in the underlying business logic. C端 AI struggles due to low user switching costs, lack of network effects, and an inability to capture significant user time like entertainment apps. Conversely, B端 AI thrives because enterprises buy based on measurable ROI, integrate AI deeply into workflows (creating high switching costs), and are willing to pay a premium for stability and performance. AI is evolving from a digital tool into a digital labor force—directly executing tasks rather than just assisting humans. This transformation mirrors the Industrial Revolution, where machinery replaced physical labor. Today, AI is replacing structured cognitive labor. The total global wage bill represents a market vastly larger than internet advertising. Therefore, the true value of AI lies not in capturing traffic, but in capturing the economics of labor cost replacement. The internet monetized attention; AI monetizes wages.

marsbit05/29 10:24

AI Is Not Replicating the Internet; It’s Replicating the Industrial Revolution

marsbit05/29 10:24

The Wind of 'Proactive' AI Blows into Silicon Valley: Hark Secures $700 Million in Funding

Hark, an AI startup founded in late 2025, has raised $700 million in Series A funding at a $6 billion valuation. Led by Parkway Venture Capital with participation from NVIDIA, AMD Ventures, Intel Capital, Qualcomm Ventures, and Salesforce Ventures, the company aims to develop next-generation human-computer interfaces using a combination of proprietary foundational models and custom-built AI-native hardware. Founded by serial entrepreneur Brett Adcock, Hark envisions a system of multimodal devices equipped with agentic capabilities, end-to-end voice models, and personalized memory. This "active" AI approach seeks to move beyond passive chatbots, creating collaborative companions that anticipate needs and interact naturally within the real world. Adcock's experience with Figure, a humanoid robotics company, informs this hardware-focused venture. The article argues that while current AI is powerful, it remains confined to screens and traditional interfaces like chat. The next paradigm shift requires dedicated hardware that is always-on, possesses persistent memory, and enables intuitive interaction, potentially rivaling the impact of the iPhone. Hark is assembling a team with talent from Apple, Meta, Google, and Tesla to tackle this complex engineering challenge across models, hardware, and interaction design. Finally, the piece suggests Chinese startups may have an advantage in this "active" AI hardware space due to strong manufacturing ecosystems, a vast domestic market, and supportive government policies, framing the competition as one that requires integrated progress in models, operating systems, and devices.

marsbit05/28 10:22

The Wind of 'Proactive' AI Blows into Silicon Valley: Hark Secures $700 Million in Funding

marsbit05/28 10:22

Base MCP, The Next Step for x402

Base has officially launched Base MCP, allowing users to connect their Base Account to AI Agents to perform actions like swaps, transfers, portfolio tracking, and transaction history queries through conversational commands. This move aligns with Base's strategic focus on AI, driven by the broader competition in the emerging Agent-to-Agent payment sector. The evolution of Agent payments has accelerated. In late 2024, the primary method involved insecure browser automation. By 2025, solutions like Coinbase's x402 (providing crypto wallets for Agents), Google's AP2, and Visa's token-based system emerged. x402 has since processed 176 million transactions totaling over $70 million, with a median value between $0.01 and $0.10. Stablecoins, particularly USDC, dominate these settlements due to their negligible transaction costs compared to traditional payment fees, which are prohibitive for micro-payments. Coinbase faces competition from Stripe, which has built a comparable infrastructure for Agent payments with its Tempo blockchain, Privy wallets, Bridge routing (acquired for $1.1B), and the recently launched MPP protocol. Both companies are now competing at the application layer. The core reason AI is central to Base's strategy is to expand the scenarios for Agent payments, ensuring more transactions occur on its network. By securing a dominant position and scale advantage in this nascent field, Coinbase aims to capture the future commercial potential of Agent-driven payments. The launch of Base MCP is thus a strategic step in this larger ambition.

marsbit05/28 08:26

Base MCP, The Next Step for x402

marsbit05/28 08:26

Agentized OS: It's Not About AI, It's About the Foundation

The Agentic OS: Beyond AI, It's About the Foundational Stack In 2026, major operating systems like Android, iOS, HarmonyOS, and Windows are entering the "Agentic" era, integrating proactive AI assistants deeply into the system layer. However, the real competition lies not in the flashy AI features showcased at events, but in the three-layer foundational stack that enables them: the system-level AI Runtime, proprietary/controllable chips, and the on-device/cloud model matrix. The AI Runtime acts as the central scheduler, managing model inference, resource allocation, and exposing capabilities to apps. Controllable chips (e.g., Apple Silicon, Google Tensor, Huawei Kirin) are crucial for deep hardware-software co-optimization, determining the efficiency and experience limits of on-device Agents. The on-device/cloud model matrix provides the "intelligence," with proprietary, chip-optimized small models (like Gemini Nano, Apple's ~3B model) handling daily tasks locally for low latency, privacy, and reliability, while cloud models tackle complex requests. Deep synergy between these three layers enables key Agent differentiators: ultra-low latency and power efficiency, genuine "on-device first" privacy, access to system-level personal context across apps, and reliable performance as a system service even offline. OS vendors with strong integration across this stack (like Apple, Google, and Huawei) build a deeper moat. Beyond this core stack, long-term competitiveness depends on variables like structured App integration (e.g., App Intents/AppFunctions) for reliable multi-step workflows, and robust privacy frameworks that build user trust. This shift towards Agentic OS extends beyond phones and PCs to IoT, cars, and XR glasses via existing multi-device ecosystems. The race is won not in a keynote, but through generations of meticulously co-developed chips, models, and system software.

marsbit05/27 10:19

Agentized OS: It's Not About AI, It's About the Foundation

marsbit05/27 10:19

Just Now, Chinese AI Enters Top 2 in Global Programming, Only Claude Remains Ahead

**China's AI Ranks Second Globally in Programming, Trailing Only Claude** Today, Alibaba's Qwen3.7-Max achieved a score of 1541 on the Code Arena benchmark, securing fourth place globally and surpassing top models like GPT-5.5 and Gemini 3.5 Flash. Among the top positions, it is now the only non-Claude model, placing second overall after Anthropic's Opus models. Before this official ranking, Qwen3.7-Max had already gained recognition overseas. In practical tests, it outperformed rivals on tasks like creating a self-training Tetris AI and generating complex 3D models, often at a significantly lower cost. Developers praised its ability, especially when integrated with tools like Hermes Agent and OpenCode, to effectively replace models such as GPT-5.5. In a hands-on challenge to create a 3D racing game from a detailed prompt, Qwen3.7-Max delivered a fully playable HTML file in the first attempt, requiring only minor bug fixes. It uniquely included a start menu and sound effects—details missed by other models. While competitors like Gemini 3.5 Flash and Claude Opus 4.6 produced less polished or functional versions, and GPT-5.5 had its own quirks, Qwen3.7-Max stood out for its initial completeness and playability. This performance stems from its design as an "Agent Base Model," built for long-duration, autonomous task execution. Internal tests show it can run continuously for 35 hours, making over 1158 tool calls without context degradation or instruction drift. Key technical advancements include "environment expansion" training, which improves adaptability across different frameworks, and "long-horizon autonomous execution" training, enabling sustained strategic decision-making. By entering the top tier of the programming arena, Qwen3.7-Max demonstrates that Chinese AI models are not just catching up but are becoming defining competitors, challenging the long-standing dominance of Silicon Valley in this field.

marsbit05/27 00:17

Just Now, Chinese AI Enters Top 2 in Global Programming, Only Claude Remains Ahead

marsbit05/27 00:17

From Power Infrastructure to Token Economy: The 'Seven-Layer Cake' of the AI Industry Chain

From Power Grid to Token Economy: The AI Industry's "Seven-Layer Cake" The AI industry is shifting from a "model-centric" paradigm focused on massive training to a "token-centric" industrial era driven by inference demand. This new phase revolves around the production, distribution, scheduling, and consumption of tokens—the units of computation used by AI agents for every interaction and task. The article proposes a "seven-layer cake" framework for the AI economy: 1. **Power**: The foundational energy source, with competition shifting to securing stable, low-cost electricity. 2. **AIDC (AI Data Centers)**: Large-scale "Token factories." A trend toward smaller, modular, and regionally deployed AI Factories is emerging for efficiency and proximity to users. 3. **GPU**: The core production hardware for tokens. While NVIDIA dominates, competition exists from AMD, ASIC makers, and Chinese chipmakers, with a growing focus on inference efficiency. 4. **LLMs**: The "engines" that generate tokens. The competition is evolving beyond model size to prioritize factors like token cost, inference efficiency, and operational synergy with infrastructure. 5. **Token Distribution**: The "grid" that allocates and rents out compute resources, led by cloud giants and specialized AI-native platforms. 6. **Token Optimization & Intelligent Scheduling**: The critical "brain" layer that intelligently routes tasks (e.g., to local, cloud, or edge models) for optimal cost, latency, and privacy—maximizing the value of each token. 7. **AI Agents & Models**: The end consumers of tokens. The vision involves billions of AI agents working and interacting concurrently, consuming vast amounts of tokens. Currently, the industry faces fragmentation and inefficiencies between these layers. The true "mass adoption era" of AI will begin only when this seven-layer infrastructure is fully integrated and operates as a cohesive, intelligent network—transforming AI from a software tool into a global industrial system spanning energy, hardware, and compute logistics.

marsbit05/26 05:43

From Power Infrastructure to Token Economy: The 'Seven-Layer Cake' of the AI Industry Chain

marsbit05/26 05:43

Agentic Design Patterns: A Book That Made Me Re-Understand "What Is an Agent, Really?"

"Agentic Design Patterns" is a 2025 book by Antonio Gullí, a Google engineering director, which offers a systematic framework for AI Agent development through 21 design patterns. A core contribution is the "Four Levels of Agency": Level 0 (bare LLMs) are not true agents. Level 1 agents actively decide when and how to use tools. Level 2 agents engage in strategic planning, context engineering (curating and filtering information), and self-reflection. Level 3 involves multi-agent collaboration with defined communication topologies. The book introduces **Context Engineering** as a superset of prompt engineering, managing four layers of information for the agent: system prompts, external data, implicit context (user history, environment), and feedback loops for automated optimization. A key pattern is **Reflection (Producer-Critic)**, where two distinct agents with different prompts collaborate iteratively—one produces output, the other critiques it—until quality is satisfactory or a max iteration limit is reached. For **Memory**, a three-layer model is proposed: Session (ephemeral conversation context), State (temporary task data), and Memory (persistent, long-term storage). Regarding **Multi-Agent Systems**, the book advises against unnecessary complexity, recommending simple topologies like Supervisor or Peer-to-Peer based on task needs. It emphasizes perfecting a single Level 2 agent before moving to multi-agent setups. The author concludes with three actionable takeaways: 1) Add a Critic agent to existing workflows, 2) Practice Context Engineering beyond simple prompts, and 3) Avoid premature multi-agent complexity; first master a robust single agent. The book provides a practical map, codifying common challenges like reflection, memory, and coordination into reusable patterns, saving developers from reinventing foundational solutions.

链捕手05/25 04:43

Agentic Design Patterns: A Book That Made Me Re-Understand "What Is an Agent, Really?"

链捕手05/25 04:43

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