# 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.

DeAgentAI Announces Establishment of AIA Ecosystem Fund, Focusing on 'AI Agent + Physical AI' Track

DeAgentAI, a leading decentralized AI infrastructure project on SUI and BNB Chain, has announced the establishment of the AIA Ecosystem Fund. The fund will focus on the integrated track of "AI Agent + Physical AI," aiming to incubate and accelerate the next generation of AI applications with autonomous decision-making capabilities and extend AI technology from on-chain intelligence to the real world. The fund will provide comprehensive support in technology, user traffic, and ecosystem resources. Its core investment directions include AI Agent applications with autonomous on-chain execution and multi-agent collaboration capabilities, and Physical AI projects that extend AI inference into the physical world through hardware and computing efficiency. The fund has already made seed-round investments in two projects: - AliceAI: An AI-driven prediction market decision system that compresses fragmented information into verifiable, tamper-proof decision signals, offering a full-cycle solution from signal generation to automated execution via Telegram Bot. - An ASIC AI chip project: A custom hardware solution designed specifically for Transformer-based inference, aiming to reduce token processing costs to less than one-tenth of current GPU solutions while significantly improving energy efficiency and lowering latency. According to DeAgentAI’s founder, the goal is to bridge the gap between on-chain intelligence and the physical world, supporting key protocols that connect users to the future of Physical AI.

marsbit04/14 10:21

DeAgentAI Announces Establishment of AIA Ecosystem Fund, Focusing on 'AI Agent + Physical AI' Track

marsbit04/14 10:21

Hermes Agent Guide: Surpassing OpenClaw, Boosting Productivity by 100x

A guide to Hermes Agent, an open-source AI agent framework by Nous Research, positioned as a powerful alternative to OpenClaw. It is described as a self-evolving agent with a built-in learning loop that autonomously creates skills from experience, continuously improves them, and solidifies knowledge into reusable assets. Its core features include a memory system (storing environment info and user preferences in MEMORY.md and USER.md) and a skill system that generates structured documentation for complex tasks. The agent boasts over 40 built-in tools for web search, browser automation, vision, image generation, and text-to-speech. It supports scheduling automated tasks and can run on various infrastructures, from a $5 VPS to GPU clusters. Popular tools within its ecosystem include the Hindsight memory plugin, the Anthropic Cybersecurity Skills pack, and the mission-control dashboard for agent orchestration. Key differentiators from OpenClaw are its architecture philosophy—centered on the agent's own execution loop rather than a central controller—and its autonomous skill generation versus OpenClaw's manually written skills. Installation is a one-line command, and setup is guided. It integrates with messaging platforms like Telegram, Discord, and Slack. It's suited for scenarios requiring a persistent, context-aware assistant that improves over time, automates workflows, and operates across various deployment environments.

marsbit04/13 13:11

Hermes Agent Guide: Surpassing OpenClaw, Boosting Productivity by 100x

marsbit04/13 13:11

When AI's Bottleneck Is No Longer the Model: Perseus Yang's Open Source Ecosystem Building Practices and Reflections

In 2026, the AI industry's primary bottleneck is no longer model capability but rather the encoding of domain knowledge, agent-world interfaces, and toolchain maturity. The open-source community is rapidly bridging this gap, evidenced by projects like OpenClaw and Claude Code experiencing explosive growth in their Skill ecosystems. Perseus Yang, a contributor to over a dozen AI open-source projects, argues that Skill systems are the most underestimated infrastructure of the AI agent era. They enable non-coders to program AI by writing natural language SKILL.md files, transferring power from engineers to all professionals. His project, GTM Engineer Skills, demonstrates this by automating go-to-market workflows, proving Skills can extend far beyond engineering into areas like product strategy and business analysis. He also identifies a critical blind spot: while browser automation thrives, agent operations are nearly absent from mobile apps, the world's dominant computing interface. His project, OpenPocket, is an open-source framework that allows agents to operate Android devices via ADB. It features human-in-the-loop security, agent isolation, and the ability for agents to autonomously create and save new reusable Skills. Yang believes the value of open source lies not in the code itself, but in defining the infrastructure standards during this formative period. His work validates the SKILL.md format as a portable unit for agent capability and pioneers new architectures for agent operation in API-less environments. His design philosophy prioritizes usability for non-technical users, ensuring the agent ecosystem can be expanded by practitioners from all fields, not just engineers.

marsbit04/13 01:29

When AI's Bottleneck Is No Longer the Model: Perseus Yang's Open Source Ecosystem Building Practices and Reflections

marsbit04/13 01:29

Stop Staring at GPUs: CPUs Are Becoming the 'New Bottleneck' in the AI Era

In the AI era, while GPUs have long been the focus for computational power, the narrative is shifting as CPUs are increasingly becoming the new bottleneck. By 2026, system performance is more dependent on execution and scheduling capabilities, with CPUs playing a critical role in enabling AI operations. A supply crisis is emerging, with server CPU prices rising about 30% in Q4 2025 due to high demand and production constraints, as GPU orders compete for limited semiconductor capacity. Companies like Google and Intel have deepened collaborations, and Elon Musk is investing in custom CPU solutions for his ventures, highlighting the strategic importance of CPU infrastructure. The shift is driven by the rise of agentic AI, where CPUs handle tasks such as multi-step reasoning, API calls, and data I/O, accounting for 50–90.6% of total latency in intelligent workloads. Expanding context windows in AI models further strain GPU memory, necessitating CPU offloading for key-value cache management. Major players are adopting varied strategies: Intel is strengthening its Xeon processor line and partnerships; AMD is benefiting from increased demand, with server CPU revenue surpassing 40%; and NVIDIA is designing CPUs like Grace to optimize GPU-CPU synergy through high-speed interconnects. The industry is witnessing a rebalancing of compute infrastructure, with CPUs gaining prominence as essential enablers of scalable AI agent systems. By 2030, the CPU market is projected to double to $60 billion, driven largely by AI demands. The focus is now on overcoming system-level bottlenecks to maximize the efficiency and economic viability of AI deployments.

marsbit04/13 00:57

Stop Staring at GPUs: CPUs Are Becoming the 'New Bottleneck' in the AI Era

marsbit04/13 00:57

When AI Becomes an Economic Entity, Why is Ethereum the Optimal Financial Solution for Agents?

The article argues that Ethereum is the optimal financial infrastructure for AI Agents, which are emerging as autonomous economic actors. It highlights through the example of an AI Agent named Felix, which generated significant revenue but was constrained by traditional financial systems requiring human identity. In contrast, Ethereum enables AI Agents to access decentralized finance (DeFi) services—such as lending, borrowing, yield generation, and fundraising—without human intervention, thanks to its permissionless, low-cost, and composable nature. Key points include: Agent-to-Agent transactions are already thriving on protocols like x402, with millions of micro-payments processed. Ethereum’s DeFi ecosystem offers unmatched maturity, deep liquidity, institutional adoption (e.g., BlackRock’s BUIDL), and reliability, making it the preferred platform for low-risk financial operations. As AI Agents grow in number and autonomy, they will drive demand for block space and ETH-based gas fees, while locking ETH as collateral. Despite potential challenges like gas abstraction or competition, Ethereum’s network effects and structural advantages position it as the foundational financial layer for the machine economy.

marsbit04/11 03:48

When AI Becomes an Economic Entity, Why is Ethereum the Optimal Financial Solution for Agents?

marsbit04/11 03:48

Base Official Selection: 12 Promising Projects Chosen from 1,175 Applications

Base has officially selected 12 standout projects from 1,175 applications for its accelerator program, signaling a strategic focus on high-potential on-chain ecosystems over independent unified stacks outside the OP Superchain. The chosen projects span key sectors including DeFi, prediction markets, AI agents, and payments. Highlights include: BlockRunAI (AI agent infrastructure with x402 pay-per-use payments), a stealth prediction market with novel leverage primitives, 0x4Mica (micropayment clearing for AI agents), Opal DEX (privacy-focused perpetual DEX), TradeOnsight (gamified social prediction platform), Credifi (uncollateralized lending via zkTLS), Tomorrow Loans (creator economy lending), AgentlyHQ (AI agent coordination layer), Nivo Finance (FX hedging for SMEs), JPEGapp (image-based prediction market), Floe Labs (AI agent credit infrastructure), and Liminal (AI-native neobank). Base is positioning itself as a major hub for on-chain innovation this cycle.

marsbit04/10 02:14

Base Official Selection: 12 Promising Projects Chosen from 1,175 Applications

marsbit04/10 02:14

Just Now, Zuckerberg's $14.3 Billion "Avocado" Is Here: Challenging GPT-5.4 Head-On, Silicon Valley's Most Expensive Chinese Debut

Meta has unveiled Muse Spark (codenamed Avocado), its first AI model from the Meta Superintelligence Lab (MSL), positioning it as a direct competitor to leading models like GPT-5.4 and Gemini 3.1 Pro. With a score of 52 on the Artificial Analysis benchmark, it significantly outperforms its predecessor, Llama 4 Maverick (18 points), and ranks among the top models. Key features include native multimodal perception, tool use, visual chain-of-thought reasoning, and multi-agent collaboration via a "Contemplating Mode." Muse Spark excels in multimodal tasks, health-related queries (trained with input from 1,000+ clinicians), and agent-based applications, though it lags slightly in coding and complex reasoning tasks. The model is highly token-efficient, using significantly fewer tokens than competitors like Opus 4.6 and GPT-5.4 for similar performance. It is integrated into Meta's platforms (meta.ai, Facebook, Instagram, WhatsApp) and is free to use, though it remains closed-source. The development involved a complete rebuild of Meta's AI stack, reducing pre-training compute needs to one-tenth of Llama 4's requirements. The project, led by Chief AI Officer Alexandr Wang and a team of researchers from OpenAI and DeepMind, marks Meta's re-entry into the top tier of AI development.

marsbit04/09 07:49

Just Now, Zuckerberg's $14.3 Billion "Avocado" Is Here: Challenging GPT-5.4 Head-On, Silicon Valley's Most Expensive Chinese Debut

marsbit04/09 07:49

Cursor 3 Released: The IDE Becomes Irrelevant, Agent Console Takes Over, The VS Code Era Begins to Fade

Cursor 3, codenamed Glass, represents a fundamental shift in AI-assisted development by replacing the traditional code editor with an agent management console as the primary interface. While engineers can still write code, the core design philosophy now centers on users spending most of their time directing AI agents, reviewing their outputs, and deciding which tasks to deploy. Key features include multi-repository support, a unified sidebar for all agents (local and cloud), and Cloud Handoff, which allows seamless movement of agent sessions between local and cloud environments. This release is part of Cursor's accelerated response to competitive pressure from tools like Anthropic's Claude Code. The company also recently launched Automations for triggering agents automatically, Composer 2 (its proprietary model claiming superior performance to Claude Opus), and self-hosted cloud agents for enterprise customers. The transition signals a broader industry paradigm shift where agent orchestration becomes the new control plane, similar to how cloud consoles replaced SSH for infrastructure management. This challenges the decades-long dominance of IDEs like VS Code, suggesting that software engineering roles are evolving toward overseeing AI agents rather than directly editing code. The architectural debate now centers on whether this orchestration layer should exist inside the IDE (Cursor, Google), as a separate tool (Anthropic, OpenAI), or be omnipresent.

marsbit04/08 10:16

Cursor 3 Released: The IDE Becomes Irrelevant, Agent Console Takes Over, The VS Code Era Begins to Fade

marsbit04/08 10:16

Industry Experts Gather, Reflections and Breakthroughs in the AI Agent Era

Industry experts gathered to discuss the challenges and opportunities in the AI Agent era. The event, co-hosted by several organizations, addressed key questions about model selection, token resource sustainability, and strategies for individuals and businesses to adapt. Conflux's Chief Architect highlighted the current trend of granting AI more autonomy, noting that its limitations in complex scenarios stem from difficulties in capturing and retaining key contextual constraints. Future advancements should focus on enhancing external memory, continuous learning, and domain-specific applications. Speakers from Tencent Cloud and Biteye shared practical insights. Tencent's WorkBuddy leverages multi-agent collaboration for tasks like resume screening and report generation, emphasizing enterprise-grade security. Biteye’s founder discussed mitigating AI hallucinations through rigorous code review processes, managing token consumption, and using platforms like Discord for agent coordination. Legal risks were also addressed, with a partner from Mankun Law advising on liability isolation, intellectual property protection, and mitigating platform dependency risks. Investors noted that AI is still in its early stages, with technology rapidly evolving. They emphasized investing in foundational layers like compute power and exploring AI-Web3 convergence. The discussion concluded that AI should be viewed as a productivity tool rather than a threat. Customizable agents can significantly enhance efficiency, but successful implementation requires careful engineering, security measures, and human oversight to integrate AI into complex workflows effectively.

marsbit04/08 05:51

Industry Experts Gather, Reflections and Breakthroughs in the AI Agent Era

marsbit04/08 05:51

Whale.io Launches the First AI Agent MCP for Crypto Casino

Whale.io has launched the first AI Agent MCP (Model Context Protocol) for the crypto casino industry, accompanied by a two-week competitive campaign. The MCP enables AI agents to autonomously interact with the platform—placing bets, playing games, and managing real cryptocurrency funds. The campaign features a live leaderboard, performance-based rewards, and a $10,000 USDT prize pool. Aimed at developers and the vibe coding community, it allows participants to deploy AI models—compatible with Claude, OpenAI, LangChain, and others—to test strategies in a real-world crypto casino environment without human intervention.

TheNewsCrypto04/07 19:39

Whale.io Launches the First AI Agent MCP for Crypto Casino