# Сопутствующие статьи по теме Agents

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

60 Curated Skills, Workflows & Open-Source Projects: The Ultimate Claude Advanced List

This comprehensive guide curates 60 essential AI tools, frameworks, and resources to help developers, creators, and builders move from using AI to constructing robust AI systems. The list is organized into nine key categories: AI Programming Agents & IDEs (e.g., Claude Code, Cursor), Agent Frameworks (e.g., OpenClaw, LangGraph), MCP Services & Tool Integrations (e.g., Tavily, Context7), Claude Skills (e.g., PDF Processing, Frontend Design), Local AI & Model Runners (e.g., Ollama, Open WebUI), Workflow & Automation (e.g., n8n, Langflow), Search, Data & RAG (e.g., GPT Researcher, Chroma), API & Infrastructure (e.g., FastAPI, Portkey Gateway), and Learning Resources (e.g., Awesome Agents, PromptingGuide). The author emphasizes a structured approach, recommending specific starting toolkits based on the user's role—developer, creator, product builder, or learner—to avoid overload and build practical, production-ready workflows.

marsbit03/30 11:10

60 Curated Skills, Workflows & Open-Source Projects: The Ultimate Claude Advanced List

marsbit03/30 11:10

The Time of Machines: When Agents Consume Stablecoins

"The Age of Machines: When Agents Consume Stablecoins" explores the convergence of AI and cryptocurrency, focusing on the emerging narrative of AI agents as economic actors. The author argues that while AI is rapidly advancing into production and consumption, crypto, particularly stablecoins, is struggling to find its role beyond financialization. The piece begins by reflecting on how AI-powered bots are evolving from nuisances to become autonomous economic entities, potentially even developing a "dislike" for humans. This shift creates a sense of desperation in the crypto community, which is now trying to prove its value to AI by promoting stablecoins as the preferred medium of exchange for agents. A core tension is highlighted: AI is mastering both production and the new "relations of production" by replacing human labor, while crypto remains confined to a narrow financial role. Previous attempts by crypto to capture AI use cases—through decentralized storage, compute, or GPU lending—have largely failed. The author warns that compliant, bank-issued stablecoins on networks like Canton could ultimately prevail over native crypto stablecoins. The emergence of payment protocols for machines, like Stripe's MPP, is noted, but these efforts are seen as integrating machines into the existing traditional financial system rather than creating a new crypto-native one. The crypto industry's strategy of selling stablecoins to AI based on technical merits like cheapness and speed is portrayed as a weak, last-resort effort. The article then pivots to a more promising path for crypto: leveraging volatility. The true potential lies in AI agent economy's ability to generate massive, 24/7 consumption that far surpasses human limits. This creates a new battlefield for crypto—not by providing utility to AI, but by creating speculative assets (Crypto Tokens) that capture the value and FOMO generated by the AI boom (AI Tokens). The ultimate goal should be converting the immense economic activity of AI agents into liquidity for crypto assets. The conclusion states that while Circle's vision of agents using stablecoins offers a story of infinite users to the market, crypto's real strength is its position as a financial laboratory on the frontier, thriving on ambiguity and speculation. The future of the convergence depends on crypto creating volatility and wealth effects from the stable foundation of agent-driven consumption, ultimately completing the cycle from AI Token back to Crypto Token.

marsbit03/30 07:38

The Time of Machines: When Agents Consume Stablecoins

marsbit03/30 07:38

Tiger Research: What AI Services Do Crypto Companies Offer?

This Tiger Research report examines the growing trend of cryptocurrency companies integrating AI services, driven by a fear of missing out (FOMO). Unlike previous cycles, established and profitable firms like Coinbase and Binance are leading this charge, moving AI from theory to practical necessity. Key areas of AI adoption include: - **Research:** Projects like Surf are building crypto-native AI tools that aggregate fragmented on-chain and social data, providing more accurate answers than general AI models. - **Trading:** Exchanges are deploying AI to let users execute trades via natural language commands, lowering the barrier for non-developers and automating strategies. The goal is user retention in an increasingly competitive landscape. - **Security/Audit:** Firms like CertiK use AI to enhance smart contract audits by automating initial code scans and enabling post-audit, real-time monitoring, thus addressing previous security blind spots. - **Payment Infrastructure:** Protocols are emerging to enable AI agents to make autonomous payments (e.g., for APIs or services) using on-chain wallets and stablecoins. Circle’s proposed Gateway-x402 integration is a notable example, though this field is still nascent. The push is fueled by rapid AI advancements (e.g., MCP, OpenClaw) and competitive anxiety. However, the report cautions that while adoption is accelerating, the gap between offering a feature and its actual, trusted use remains significant. The motivation is strategic positioning for an AI-driven future, not just marketing.

marsbit03/30 06:41

Tiger Research: What AI Services Do Crypto Companies Offer?

marsbit03/30 06:41

ARO Network Raises $5M in Strategic Round to Build “The Agentic Edge”

ARO Network, a pioneer of "The Agentic Edge" for the autonomous AI era, has raised $5 million in a strategic funding round co-led by NoLimit Holdings and an undisclosed leading Asian data center operator. The funding supports ARO's vision to make AI agents more personalized, privacy-preserving, and accessible. The network decentralizes AI by placing agents directly in users' homes, utilizing local resources to create personal digital extensions. This approach aims to overcome limitations of centralized cloud systems. ARO has over 1.18 million active nodes and has launched Testnet Sprint 2, offering a simplified user experience. CEO Randy emphasized ARO's mission to put autonomous, secure AI directly into users' hands, ensuring private, uncensored operation with low latency. The partnership with a major Asian data center will provide enterprise-level infrastructure across APAC.

TheNewsCrypto03/30 03:15

ARO Network Raises $5M in Strategic Round to Build “The Agentic Edge”

TheNewsCrypto03/30 03:15

Slow Down: The Answer in the Age of Agents

In the era of generative AI, the software industry is shifting from amazement to efficiency anxiety. However, as coding agents are increasingly used in production, issues like amplified errors, uncontrolled complexity, and reduced system reliability emerge. The author argues that agents lack human-like learning from mistakes and, without proper bottlenecks and feedback, minor issues quickly escalate. Their limited perspective and low recall in complex codebases further worsen structural chaos. The core problem isn’t the technology but the premature surrender of human judgment and control driven by anxiety. Instead of fully outsourcing work to agents, the author advocates for a balanced approach: assign agents localized, well-defined tasks while retaining human oversight for system design, quality assurance, and critical decisions. Slowing down becomes a strength—it ensures understanding, enables informed trade-offs, and maintains control. Ultimately, what’s scarce in the AI age isn’t faster code generation but the judgment to manage complexity and the discipline to choose quality over speed.

marsbit03/29 15:35

Slow Down: The Answer in the Age of Agents

marsbit03/29 15:35

OpenAI Bets on 'Robot Army': 23-Year-Old Prodigy Wins Favor from Sam Altman

While OpenAI adjusts its video strategy, Sam Altman is setting his sights on the more ambitious field of "multi-agent systems." According to The Wall Street Journal, OpenAI has secretly invested in Isara, an AI startup founded by 23-year-old researchers Eddie Zhang and Henry Gasztowtt. Despite being established only in June last year in San Francisco, Isara has already recruited over a dozen top researchers from Google, Meta, and OpenAI itself, forming a highly skilled technical team. Isara’s core vision is to develop a system that enables thousands of AI agents to collaborate efficiently. While individual AI assistants are powerful, they often struggle with large-scale industrial challenges such as biotech R&D or complex financial modeling. Isara aims to solve this by creating a framework where diverse AI agents can communicate, align goals, share data, and tackle interconnected problems—functioning like a coordinated "robot army." This multi-agent approach is seen as a critical step toward Artificial General Intelligence (AGI). OpenAI’s endorsement signals industry recognition of distributed intelligence. In biopharma, the system could simulate thousands of protein-folding pathways, with specialized agents identifying patterns. In finance, it could perform real-time stress tests using global market data. Led by young innovators, this shift suggests the next breakthrough in AI lies not in building larger models, but in enabling smarter collective intelligence.

marsbit03/26 02:32

OpenAI Bets on 'Robot Army': 23-Year-Old Prodigy Wins Favor from Sam Altman

marsbit03/26 02:32

Only Two Ways to Survive in the Software Industry: AI Growth or High Profitability

The software industry faces a binary future: companies must either achieve >10% revenue growth through truly AI-native products or deliver >40% true operating margins (including stock-based compensation) within 12-18 months. The comfortable middle ground—modest growth with diluted equity—is unsustainable. Path 1 requires radical reorganization: identify top performers, restructure leadership, and allocate 50% of R&D to new AI products built with small, token-driven teams. Success depends on creating dynamic context layers, adopting usage-based pricing, and eliminating organizational barriers weekly. Path 2 demands extreme profitability through flattened hierarchies, minimized customization, price increases where defensible, and treating all equity as real expense. AI should drive engineer productivity gains, but this path involves significant layoffs and honest assessment of eroding competitive moats. Companies must choose decisively or face continued valuation pressure. The era of intermediate outcomes is over.

比推03/24 08:40

Only Two Ways to Survive in the Software Industry: AI Growth or High Profitability

比推03/24 08:40

The Software Industry Only Has Two Paths Left: Either 10% Growth with AI-Native Products or 40% Real Profit Margins

Software industry faces a binary future: companies must either achieve 10%+ revenue growth through truly new AI-native products or reach 40-50% real operating margins (including stock-based compensation) within 12-18 months. According to a16z's David George, the "comfortable middle ground" is disappearing. Public markets have repriced software valuations, forcing leaders to choose one path decisively. Path 1 requires radical restructuring to build AI-native offerings—not superficial additions—that drive meaningful growth. This involves reallocating 50% of R&D resources, adopting small agile teams, implementing usage-based pricing (tokens), and replacing executives who can't adapt. Path 2 demands extreme cost discipline: flattening hierarchies, minimizing customizations, raising prices strategically, and treating equity as real expense. AI should maximize engineer productivity, with token budgets per engineer seen as essential. Companies failing to commit will face valuation compression and dilution. Leaders must choose: hyper-growth through AI innovation or profitability through ruthless efficiency. No middle path exists.

marsbit03/24 07:18

The Software Industry Only Has Two Paths Left: Either 10% Growth with AI-Native Products or 40% Real Profit Margins

marsbit03/24 07:18

Karpathy Diagnosed with "AI Psychosis"! Not Eating or Sleeping, 16 Hours a Day Raising Lobsters

Andrej Karpathy recently revealed that he has developed what he calls "AI psychosis," an obsessive state where he spends up to 16 hours a day directing AI agents instead of writing code himself. In a podcast with Sarah Guo, he explained that his workflow has shifted from 80% hand-coding and 20% AI-assisted to the reverse, or even more extreme. He now manages multiple AI agents simultaneously, treating them as a team to execute tasks. Karpathy admitted that he’s become addicted to optimizing AI performance, constantly worrying about whether he’s using tokens efficiently or pushing the system to its limit. He highlighted the importance of an agent’s “personality,” noting that Claude Code feels more like a collaborative teammate compared to colder, more mechanical alternatives. He also shared practical applications, such as "Dobby," a Claude-based smart home agent that integrates and controls all his home devices through natural language, replacing six separate apps. In research, his "AutoResearch" project used AI to run 700 experiments, resulting in an 11% training speed improvement for an AI model—discovering optimizations he had missed as a human researcher. Despite the capabilities, Karpathy noted that AI agents still exhibit uneven performance—sometimes brilliant, other times childlike—due to limitations in reinforcement training. He predicts that 2026 will see a "slopacolypse," with AI generating vast amounts of mediocre content. His experience signals a broader shift: humans are becoming directors of AI systems rather than executors, navigating a new era of human-AI collaboration.

marsbit03/23 11:44

Karpathy Diagnosed with "AI Psychosis"! Not Eating or Sleeping, 16 Hours a Day Raising Lobsters

marsbit03/23 11:44

What AI Services Are Crypto Companies Offering?

Crypto companies across various sectors—exchanges, security firms, payment infrastructure, and research platforms—are rapidly integrating AI-driven services. Unlike previous hype cycles, this wave is led by established players like Coinbase, Binance, and Bitget, who are motivated by both genuine utility and fear of missing out (FOMO) as AI transitions from concept to operational necessity. Key applications include: - **Research tools** (e.g., Surf AI) that aggregate fragmented on-chain and market data to provide accurate crypto-specific insights. - **Trading automation**, where exchanges enable users to execute strategies via natural language commands, lowering barriers for non-developers but potentially reducing platform loyalty. - **Security and auditing** (e.g., CertiK), using AI to enhance code review efficiency and post-audit monitoring, addressing historical limitations. - **Payment infrastructure** (e.g., Circle’s proposals), exploring AI-agent-compatible stablecoin payments for the emerging autonomous economy, though this remains nascent. While AI adoption is driven by competitive pressure and the need to retain users, gaps remain between feature launches and real-world usage. The focus is on distinguishing value-adding integrations from superficial implementations. The entry of mature firms signals AI’s strategic importance, ensuring crypto remains relevant in the AI evolution.

marsbit03/23 10:41

What AI Services Are Crypto Companies Offering?