# 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.

Kimi, Zhipu, Douban Gather at an Encryption Hackathon: What Did AI Developers Build on Monad?

Monad's "Rebel in Paradise AI" Hackathon, held on March 21, 2026, brought together leading LLM providers like Kimi, Zhipu AI, and Doubao to explore AI agent development on its high-performance parallel EVM blockchain. The event focused on three key areas: Agent Payments, Smart Markets, and Application Innovation, with over $40,000 in prizes and resources. Winning projects included OpenAlice (Grand Prize), a locally run trading agent with transparent workflows; Orbit AI (NVIDIA Special Award), a decentralized AI cloud using satellite GPU clusters; and Kimi-swarm, an open-source multi-agent IDE. Other notable winners were Libra, a Git-like system for machine-written code, and Anime AI Studio, a one-stop anime short film generation agent. The hackathon highlights Monad's strategic push into AI, leveraging its high throughput (10,000+ TPS), low latency, and low-cost infrastructure to support autonomous agent economies. Monad's existing initiatives, like the AI Blueprint program and x402 payment guides, further position it as a key infrastructure for AI and DeFi integration, enabling agents to execute transactions, settle payments, and operate as independent economic entities on-chain.

marsbit03/26 02:15

Kimi, Zhipu, Douban Gather at an Encryption Hackathon: What Did AI Developers Build on Monad?

marsbit03/26 02:15

OpenAI Exposes 'Polaris' Project, the '2028 Great Unemployment' May Really Be Coming

OpenAI has disclosed its "North Star" project, aiming to build a fully automated multi-agent AI research system by 2028. This goal, announced by Chief Scientist Jakub Pachocki, includes launching an "autonomous AI research intern" capable of handling specific research tasks by September 2024. The project represents a major strategic shift, consolidating resources and products like ChatGPT and Codex into a unified super-app. The initiative reflects a broader industry trend, with competitors like Anthropic also advancing AI agent integration into workflows, such as through Claude Code on platforms like Discord. However, Pachocki acknowledges significant challenges in safety and controllability, noting that full control over LLMs remains unresolved. Economically, the project could be highly lucrative, with projections suggesting AI agents—including a $20,000/month "research agent"—generating $29 billion annually by 2029. The core implication is profound: if AI can autonomously conduct research, the pace of scientific and AI advancement could accelerate beyond human limitations, potentially reshaping labor markets and research paradigms.

marsbit03/24 02:46

OpenAI Exposes 'Polaris' Project, the '2028 Great Unemployment' May Really Be Coming

marsbit03/24 02:46

From OpenClaw to the History of the Web: When AI Gains Sovereignty, What Remains for Humanity?

From Web1 to Web4: A History of Power and Ownership in the Digital Age This article examines the evolution of the web not as a series of technical upgrades, but as a fundamental shift in power—specifically, who owns data, controls wealth, and wields productive force. **Web1 (Read-Only):** Characterized by one-way communication. Platforms like Yahoo owned all content and users were merely passive consumers, or "traffic," with no digital assets. **Web2 (Read-Write):** Users became content creators, but platforms like Facebook and TikTok established a "panoptic dictatorship." They harvested user data to create immense value, but users retained only usage rights, not ownership, of their digital assets and social presence. **Web3 (Read-Write-Own):** A movement to reclaim digital rights through cryptography and decentralization. It enables true digital ownership (e.g., via private keys) and trustless systems (e.g., DAOs, smart contracts). However, it remains a wild frontier with significant legal and security challenges, lacking a capable "workforce" to realize its full potential. **Web4 (Agent Economy):** The convergence of AI Agents and Crypto. AI Agents (autonomous, task-completing AIs) use Crypto as their native currency for machine-to-machine transactions. This shifts power from humans to algorithms, creating independent AI economic actors. This raises critical legal questions, such as liability for AI errors. The future could lead to two extremes: a utopia of liberated human creativity or a dystopia of extreme inequality if AI power is monopolized by a few. **Survival Guide for Web4:** * **Work:** Become a director and risk-manager for AI, not an executor. * **Invest:** Focus on projects with genuine utility, not hype-driven "air tokens." * **Risk Management:** Prioritize robust legal and compliance frameworks for AI operations. The conclusion emphasizes that understanding the transfer of power and assets is key to navigating the future, urging innovation within the boundaries of regulation.

marsbit03/23 13:32

From OpenClaw to the History of the Web: When AI Gains Sovereignty, What Remains for Humanity?

marsbit03/23 13:32

10 Cold Takes on WeChat's Integration of OpenClaw

WeChat's recent integration of OpenClaw as a plugin has sparked discussions, but its immediate impact may be overestimated. The feature allows users to connect their existing OpenClaw agents to WeChat via a simple setup, but it comes with limitations: no group chat support, no streaming output, and no multi-agent connections. The move is seen as a necessary, though delayed, step rather than a groundbreaking change. WeChat’s approach is elegant—acting as a platform rather than building its own agent, thus supporting various OpenClaw-compatible models. However, the integration doesn’t lower the barrier to effectively using AI agents; it only simplifies access. The real challenge remains in configuring and leveraging agents meaningfully. Long-term, this signals WeChat’s shift from connecting people to also connecting humans and AIs, paving the way for future native AI-agent integrations within WeChat’s ecosystem. But for now, it’s a cautious, incremental update—typical of WeChat’s product philosophy.

marsbit03/23 01:10

10 Cold Takes on WeChat's Integration of OpenClaw

marsbit03/23 01:10

From Tencent and Circle: Looking at the Easy and Hard Questions of Investment

The article contrasts the investment prospects of Tencent and Circle in the AI era, framing the decision as a choice between "easy" and "hard" problems, inspired by Charlie Munger's philosophy. Tencent's stock has declined despite strong earnings, as the market shifted from fearing insufficient AI investment to worrying about excessive spending. The author argues this pessimism is overdone. WeChat's nascent AI agent, Yuanbao, is seen as a prototype for a future, more powerful system-native agent. Crucially, this agent would have system-level permissions to seamlessly interact with the massive Mini Program ecosystem (housing apps like Meituan, Didi, etc.), making it a practical, usable product for billions. The author believes the high-probability success of this inevitable development makes investing in Tencent an "easy" decision that the market is currently overlooking. Conversely, Circle's recent rise is fueled by the AI narrative, specifically the belief that AI agents will require blockchain-based stablecoins for settlement, with USDC as the leading compliant option. The author deconstructs this bullish thesis, identifying high uncertainties in its core assumptions: whether AI transactions will *necessarily* use stablecoins (vs. other protocols like Google's UCP), USDC's ability to maintain its lead against competitors like Tether or PayPal, and whether stablecoins even possess strong network effects in an agent-dominated world where cost and friction are paramount. The compounding uncertainty makes investing in Circle a "hard" problem, riskier than market sentiment suggests. In summary, the author posits that Tencent presents a clear, high-probability opportunity (easy), while Circle's future is built on a chain of speculative assumptions (hard).

marsbit03/21 11:20

From Tencent and Circle: Looking at the Easy and Hard Questions of Investment

marsbit03/21 11:20

Xiaomi and MiniMax Unleash Major Upgrades Simultaneously, Officially Kicking Off the Agent Pricing War

Chinese AI companies MiniMax and Xiaomi's MiMo have both launched major Agent-focused models, M2.7 and V2-Pro, respectively, within two days in March. Both models rank in the top tier globally on Agent benchmarks but are priced significantly lower than leading Western models—MiniMax at $1.2 per million tokens (1/21 of Claude Opus) and MiMo at $3 (1/8 of Claude Opus). The two represent divergent technical strategies. MiMo-V2-Pro adopts a scale-driven approach with over 1 trillion parameters and a hybrid attention mechanism optimized for long-context and multi-tool agent tasks. In contrast, MiniMax’s M2.7 uses a self-iterative optimization method, autonomously refining its architecture over 100+ cycles to improve performance without disclosing parameter count. Their release rhythms also differ: MiniMax iterates rapidly with four versions in five months, while Xiaomi releases fewer but more substantial upgrades. Notably, Xiaomi debuted V2-Pro anonymously on OpenRouter as "Hunter Alpha," topping the platform’s usage chart before revealing its identity—a first for a Chinese AI model gaining global developer traction through pure performance.

marsbit03/20 08:03

Xiaomi and MiniMax Unleash Major Upgrades Simultaneously, Officially Kicking Off the Agent Pricing War

marsbit03/20 08:03

Meeting the Founder of OpenClaw at a Hackathon: What Else Can These Lobsters' Do?

In March 2026, Imperial College London hosted the UK AI Agent Hackathon 2026, centered around the OpenClaw framework. The event attracted over 1,200 participants and 5,000 online viewers. OpenClaw creator Peter Steinberger attended personally. Notable projects included: - AgroMind: An AI system using satellite and weather data to predict and automatically hedge agricultural supply chain risks. - ClawBio: An open repository for reproducible bioinformatics tools, enabling local and private analysis. - BioSentinel: An automated pipeline that identifies pathogens and designs therapeutic molecules using public health data. - London Neural System: A real-time urban monitoring system that integrates traffic, air quality, and infrastructure data. - Highstreet AI: A multi-agent tool for small businesses to automate orders and inventory management. - AlphaMind AI: An agent that helps retail investors analyze and rebalance portfolios using institutional-grade logic. Steinberger’s participation was nearly derailed by a last-minute visa issue. His humble, hands-on approach resonated with attendees. Although Steinberger is skeptical of crypto, several Web3-oriented projects emerged around agent sovereignty, automated trading, and public governance. However, security remains a major concern for OpenClaw in financial and blockchain applications.

Odaily星球日报03/20 05:30

Meeting the Founder of OpenClaw at a Hackathon: What Else Can These Lobsters' Do?

Odaily星球日报03/20 05:30

Why Did Five Giants Jump In Within a Week to Open Bank Accounts for AI?

The article discusses a significant trend where five major companies—Stripe, Paradigm, Visa, Mastercard, and Coinbase—collectively launched initiatives within a week to enable AI systems to autonomously conduct financial transactions. Stripe and Paradigm introduced Tempo, a $5 billion blockchain project focused on machine-to-machine payments via its Machine Payments Protocol (MPP). Visa launched a command-line tool for AI agents to make credit card payments, while Coinbase upgraded its x402 protocol to support broader token payments. Mastercard acquired stablecoin firm BVNK for $1.8 billion to facilitate crypto-based transactions. World, co-founded by Sam Altman, released an identity verification toolkit for AI agents. The push stems from the growing capability of AI agents to perform complex tasks independently, creating a need for payment infrastructure that doesn’t require human intervention at every step. Traditional payment giants see an opportunity to leverage their existing networks, while crypto companies argue that blockchain-based systems offer a more seamless solution for non-human entities. Despite the high valuations and investments, current transaction volumes remain low (e.g., x402 recorded ~$65k in 24 hours). The situation parallels past infrastructure booms, where early investment outpaced immediate demand. The race to dominate AI payments is underway, but widespread adoption may take time.

比推03/19 13:15

Why Did Five Giants Jump In Within a Week to Open Bank Accounts for AI?