Artículos Relacionados con AI Agents

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Yongche Founder Zhou Hang: Cryptocurrency, Finally Its Time Has Come

Cryptocurrency, after years of being perceived primarily as a speculative asset or a complex technological curiosity, is finally finding its true purpose: not as money for humans, but as money for machines. While cryptocurrencies like Bitcoin and Ethereum failed to become practical daily payment tools for people due to volatility, poor user experience, and regulatory complexities, they are perfectly suited for the emerging Agent-to-Agent (A2A) economy. In this new paradigm, AI Agents autonomously transact with each other—purchasing data, API calls, or computational resources—using crypto wallets and smart contracts. They require no customer support, handle micro-payments efficiently, and operate across borders without traditional banking hurdles. Protocols like x402 are reviving the long-dormant "402 Payment Required" HTTP status code, enabling machines to negotiate and pay for services seamlessly within milliseconds. We are witnessing a fundamental shift: machines will use cryptocurrency as a high-frequency, efficient medium of exchange in the background, while humans continue to use traditional fiat currency for daily life. Cryptocurrency isn’t replacing the bank account; it’s becoming the silent, foundational layer of a new machine-driven economy.

marsbitAyer 07:10

Yongche Founder Zhou Hang: Cryptocurrency, Finally Its Time Has Come

marsbitAyer 07:10

Rhythm X Zhihu Hong Kong Event Skills Recruitment, Sign Up Now for a Chance to Showcase On-Site

Six months ago, "how to write good prompts" was the hottest topic in group chats. Now, that question is clearly outdated. It has been replaced by Skills. The shift was largely triggered by the emergence of OpenClaw, which brought the concept of AI agents into the mainstream. Unlike a smart search engine that answers questions in isolated interactions, an agent can plan, remember, and complete entire tasks autonomously, creating the novel feeling that it is genuinely working for you. This has led to the rise of Skills—specialized capabilities that equip agents to handle specific domains efficiently. Without Skills, an agent is like a smart but untrained newcomer; with them, it can execute complex, precision-sensitive workflows without constant guidance. Popular Skills currently spreading within communities focus on areas like workflow automation, domain-specific rule injection (e.g., for law, finance, or medicine), personalization, and even financial operations such as identifying arbitrage opportunities on Polymarket or executing quantitative trading strategies. This shifts the门槛 from requiring programming and financial expertise to simply installing a Skill. The underlying change is that people are starting to view agents as long-term collaborators, not just disposable tools. Now, with vibe coding, turning an idea into a functional Skill no longer requires a technical team, code, or infrastructure—it can be done over a weekend. The gap between a good idea and a working product has dramatically narrowed.

marsbit04/03 09:18

Rhythm X Zhihu Hong Kong Event Skills Recruitment, Sign Up Now for a Chance to Showcase On-Site

marsbit04/03 09:18

AI Agents Are About to Take Market Share from Visa

Artificial intelligence agents are poised to disrupt Visa's business model by bypassing the traditional credit card interchange fee structure. Unlike humans, AI agents are purely rational: they don't accumulate rewards, seek fraud protection, or desire premium cards. Their sole objective is to complete transactions at the lowest cost, fastest speed, and with minimal fees. This shift threatens the 2-3% interchange fees that underpin Visa’s $500 billion valuation, as these fees essentially tax human irrationality—something agents lack. Recent developments, such as the launch of Tempo (a high-volume stablecoin settlement blockchain), the Machine Payment Protocol (enabling autonomous micro-payments), and Visa’s own command-line payment tool for AI, indicate a rapid move toward agent-driven commerce. While current transaction volumes remain small, infrastructure is being built to support machine-to-machine payments that avoid card networks. Major players like Stripe, Mastercard, and Circle are investing heavily in this space. Visa network’s distribution advantage relies on human behavior—consumer trust and merchant acceptance—a cycle that doesn’t apply to agents. They optimize for efficiency, not brand loyalty. Although widespread consumer adoption is still emerging, the infrastructure for agent-commerce is advancing quickly, starting with micro-payments for AI services. The fundamental challenge is that interchange fees are a tax on human psychology, and agents are purely rational actors.

marsbit03/31 11:14

AI Agents Are About to Take Market Share from Visa

marsbit03/31 11:14

Existing AI Agents Are All Pleasing Humans, None Truly Know How to 'Survive'

The article argues that current AI agents are not truly autonomous because they are primarily trained to please humans rather than to perform specialized tasks or survive in real-world environments. Foundation models undergo pre-training (learning from vast data) and post-training, including Reinforcement Learning from Human Feedback (RLHF), which optimizes for human preference and approval, not task-specific excellence. The author shares an example from a hedge fund where a general-purpose model failed to predict stock returns from news articles until it was specifically fine-tuned using proprietary data to minimize prediction error. This demonstrates that without specialized training, general models lack domain expertise. The piece contends that achieving world-class performance in areas like trading or autonomous survival requires fine-tuning models with specialized data to rewire their objectives—shifting from “preference fitness” to “agent fitness.” Merely providing rules or documents is insufficient. The future of effective agents lies in targeted training on proprietary datasets and iterative improvement based on performance telemetry. The author introduces the OpenForager Foundation, an open-source initiative to develop autonomous agents that learn survival strategies through evolutionary pressure, fine-tuning, and continuous data collection, aiming to advance truly autonomous AI.

marsbit03/30 04:37

Existing AI Agents Are All Pleasing Humans, None Truly Know How to 'Survive'

marsbit03/30 04:37

Don't Hand Over Your Right to Think: A Psychological Defense Guide for Web3 People

"Don't Outsource Your Thinking: A Psychological Defense Guide for the Web3 Generation" explores the anxiety and FOMO (Fear Of Missing Out) driven by the rapid emergence of AI tools like OpenClaw in Web3 communities. The author argues that many adopt these technologies not for practical utility, but to avoid decision-making fatigue and gain social validation, leading to loss of autonomy and increased anxiety. Key psychological factors include decision fatigue, herd mentality, and the fear of being left behind. The article suggests strategies to counter this: imposing a 24–48 hour cooling-off period before adopting new tools, seeking out critical perspectives to break echo chambers, using AI as a collaborative partner rather than a replacement, and focusing on human skills like empathy and trust-building that AI cannot replicate. The core message is to maintain independent judgment, prioritize mental well-being over trends, and reaffirm one’s core identity amid technological hype.

比推03/24 08:33

Don't Hand Over Your Right to Think: A Psychological Defense Guide for Web3 People

比推03/24 08:33

Dragonfly Partner: Most Agents Will Not Conduct Autonomous Transactions, How Will Crypto Payments Win?

Dragonfly partner Robbie Petersen argues that the prevailing narrative about AI agents driving massive adoption of crypto payments is flawed. He contends that most agents—whether enterprise or consumer-facing—will not engage in autonomous transactions. Enterprise agents, which will constitute the majority of agent deployments, are an evolution of SaaS and will operate within closed organizational structures. They automate internal tasks (e.g., sales, accounting, legal review) without spending autonomously. Costs for API calls or data are abstracted into bulk, pre-negotiated invoices from platform providers, not paid per transaction. Consumer agents will act more as research assistants than independent economic actors. While they will excel at coordination and discovery (e.g., finding travel options), humans will retain final decision-making and payment authorization for all but the most repetitive purchases due to the qualitative, situational nature of consumer choice. Petersen identifies a narrow third category where crypto could win: permissionless, bottom-up agents (e.g., those inspired by OpenClaw) that operate truly autonomously and require high-frequency, granular payments. For these, blockchain's key advantage is not just technical efficiency but its open, permissionless nature, allowing experimental development without regulatory hurdles. However, he concludes that the larger bottleneck to a full autonomous agent economy is not payment infrastructure but human-centric legal, regulatory, and social frameworks.

marsbit03/24 05:02

Dragonfly Partner: Most Agents Will Not Conduct Autonomous Transactions, How Will Crypto Payments Win?

marsbit03/24 05:02

Visa Spent $1.8 Billion Just to Avoid Being Left Behind by AI

Visa's $1.8 billion acquisition of stablecoin infrastructure firm BVNK is a defensive move against the rise of AI agents disrupting its core business model. Visa’s empire is built on interchange fees (2-3% per transaction), which fund rewards, fraud protection, and status-driven perks that appeal to human psychology. However, AI agents are purely rational: they seek the cheapest, fastest payment path, avoid fees, and have no use for rewards or brand loyalty. A recent report warning that AI agents could bypass traditional card networks by 2027 using stablecoins and decentralized payment protocols (like x402 and Tempo) caused significant stock drops for Visa, Mastercard, and Amex. While current transaction volumes on new infrastructures remain small, projects like Visa CLI, Circle’s Nanopayments, and Stripe’s blockchain initiatives are laying the groundwork for machine-to-machine commerce. Visa isn’t idle—it’s participating in new protocols and launching crypto tools—but its historical advantage (consumer trust and merchant acceptance) relies on human behavior. In an AI-driven economy, that advantage vanishes. The disruption begins with micro-payments for API calls and AI services, but may eventually extend to consumer spending. Visa’s massive investment signals it knows the threat is real.

比推03/20 17:55

Visa Spent $1.8 Billion Just to Avoid Being Left Behind by AI

比推03/20 17:55

Three Years Later: How Has AI Evolved from a 'Chat Tool'?

Three years ago, AI was primarily seen as a novel tool for chatting, image generation, and entertainment—products like ChatGPT, Midjourney, and Character.AI were used more for demonstration than daily reliance. The evolution occurred in two major phases. First, AI became embedded into established applications like CapCut, Canva, and Notion, transforming from a feature into core infrastructure. Platforms diverged: ChatGPT aimed to become a super-app entry point for consumer internet use, while Claude evolved into a professional operating system for knowledge work, creating sticky platform flywheels through integration into calendars, email, and workflows. The true breakthrough emerged recently as AI shifted from generating content to executing tasks autonomously. AI agents like OpenClaw now decompose goals, retrieve information, process data, and deliver results without human intervention. Simultaneously, "Vibe Coding" tools (e.g., Cursor, Replit) enable AI to build entire software products based on human-defined objectives. This progression toward autonomous action is naturally aligning AI with Web3. Blockchain offers machine-native interfaces, programmable assets, and 24/7 operational capability, allowing AI to execute and settle transactions trustlessly without human intermediaries. Together, AI and Web3 are forming the foundational stack for the next internet—where AI acts, and Web3 enables seamless, auditable machine-to-machine coordination and commerce.

marsbit03/20 03:00

Three Years Later: How Has AI Evolved from a 'Chat Tool'?

marsbit03/20 03:00

Crypto xAI Weekly Roundup: TAO Surges 40% in a Week, Decentralized Computing Trains First Competitive-Grade Large Model

This week in crypto xAI was dominated by Bittensor subnet Templar's historic achievement: the decentralized pre-training of Covenant-72B, a 720-billion-parameter LLM, reportedly rivaling LLaMA-2-70B. Trained on 1.1 trillion tokens using consumer-grade hardware within Bittensor’s permissionless network, this milestone shifts the narrative from proving subnet viability to delivering outputs competitive with centralized alternatives. TAO surged 40% in response. The broader DeAI market cap rose 12% to $16.2B, with FET (+61%) and RENDER (+31%) also posting significant gains. The robotics sector grew 6% to $811M, led by PEAQ’s 48% rise. Nansen DEX flow data showed net inflows into sUSDAI, WTAO, and VIRTUAL, indicating smart money accumulation. x402 transaction analysis revealed a key trend: while transaction counts fell, the total transaction value spiked, signaling a shift toward higher-value, legitimate commercial use cases over low-value spam. AWS further validated the space by releasing a full technical reference architecture for x402, marking its transition from concept to infrastructure.

marsbit03/18 07:40

Crypto xAI Weekly Roundup: TAO Surges 40% in a Week, Decentralized Computing Trains First Competitive-Grade Large Model

marsbit03/18 07:40

Still Not Enough? Prediction Market Giants Set Sights on Payments and AI

Prediction market giants Kalshi and Polymarket are aggressively expanding their user base and influence, moving beyond traditional betting sector. During the recent Oscars, the two platforms saw over $100 million in bets, a significant jump from the previous year. Kalshi is focusing on payment integration, partnering with Block’s Cash App to simplify funding and tap into its 59 million monthly active users. This move aims to attract new users from an established financial platform. Polymarket, on the other hand, is targeting AI agents as potential users. After resuming U.S. operations, it has processed $750 million in nominal trading volume. It now supports AI Agent trading through integrations like Alchemy’s AgentCard and its own CLI tool, enabling 24/7 automated trading. This positions Polymarket at the forefront of AI-driven prediction markets. A Paradigm survey notes that 36% of U.S. voters have used prediction markets, with stronger adoption among younger and non-white demographics. Both platforms are exploring new growth avenues—Kalshi through payment accessibility and Polymarket through AI automation—as they compete for a larger share of the evolving market.

marsbit03/16 13:46

Still Not Enough? Prediction Market Giants Set Sights on Payments and AI

marsbit03/16 13:46

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