# Пов'язані статті щодо Agents

Центр новин HTX надає останні статті та поглиблений аналіз на тему "Agents", що охоплює ринкові тренди, оновлення проєктів, технологічні розробки та регуляторну політику в криптоіндустрії.

Behind the Public Apology, Jesse Pollak Sets the Tone for Base's Next Phase of Development

Base founder Jesse Pollak issues a public apology and refocuses the platform's strategy after admitting the failure of its previous emphasis on on-chain social and creator coins. In a significant post, Pollak concedes his strategic misjudgment and announces a shift back to core chain infrastructure, relinquishing leadership of the Base App to Coinbase. The new direction centers on three key areas: Trading (encompassing all assets), Payments (focused on stablecoins), and Agents—the cross-cutting layer Pollak believes will accelerate the other two. He positions Base to capitalize on its early lead in the on-chain agent payment ecosystem, citing developments like the x402 protocol and projects like Virtuals Protocol that grew on Base first. The analysis raises three critical questions for Base's future in agent-driven commerce: defining its target stage (assisted vs. fully autonomous agents), establishing a defensible value capture mechanism beyond low fees, and detailing its specific ecosystem support strategy to convert early momentum into long-term dominance. Pollak's candid admission and strategic pivot set the stage for Base's concentrated push into trading, payments, and the emerging agent economy.

Foresight News07/17 12:02

Behind the Public Apology, Jesse Pollak Sets the Tone for Base's Next Phase of Development

Foresight News07/17 12:02

Blockchain Capital Partner: AI is Rewriting the Fundamental Unit of Labor

AI is fundamentally rewriting the basic unit of labor, moving beyond fears of job displacement to a deeper structural shift. Historically, companies form when internal coordination costs are lower than market transaction costs. Past revolutions—industrial, internet, gig economy—each reduced these costs, shifting work outside traditional firms. Now, two forces converge: programmable labor (AI agents that scale via compute, not headcount) and programmable money (stablecoins enabling instant, micro-transactions without intermediaries). Together, they enable a radical unbundling of work into discrete tasks, priced and settled between machines at high speed, without the need for a corporate container. Early examples like Meta paying creators in USDC and AWS's AgentCore signal large tech firms preparing for this. Products like Poncho give AI agents wallets to pay for APIs or data per-use, enabling granular microtransactions previously impossible with credit cards. This doesn't eliminate companies or humans. Instead, the human role evolves into that of an orchestrator—defining objectives, quality standards, and intelligently re-bundling the outputs of AI agents into valuable wholes. The future firm may be less a container for labor and more an intelligent layer atop a global, programmable marketplace of tasks.

marsbit07/07 03:17

Blockchain Capital Partner: AI is Rewriting the Fundamental Unit of Labor

marsbit07/07 03:17

Karpathy's Latest Outburst: A Single Sentence That Silenced the Entire Agent Developer Community

Andrej Karpathy, a core researcher at Anthropic, recently critiqued the current AI agent development frenzy. He argues that the biggest mistake is forcing agents to perform tasks without first thoroughly understanding the underlying large language models. Drawing from his 2016 "World of Bits" project at OpenAI—an early attempt at web-based agents that ultimately failed due to premature technology—he emphasizes that foundational model work is crucial. Karpathy offers three key pieces of advice: First, focus on getting the base models right before pushing agents. Second, recognize that creating a demo is easy, but building a real product takes a decade, akin to the journeys of autonomous driving and VR. Third, the product is the core capability, not the agent shell; a robust foundation will naturally enable advanced agents. He also suggests looking to neuroscience for inspiration, comparing agent components to brain structures like the hippocampus and thalamus. Despite his caution, Karpathy concludes that independent developers and startups, not large labs like OpenAI, are at the forefront of agent innovation. This is because the agent field is new, with no entity having a five-year head start, leveling the playing field for agile experimenters. His core message is not to abandon agent work, but to build it on a solid, deeply understood foundation.

marsbit07/06 02:33

Karpathy's Latest Outburst: A Single Sentence That Silenced the Entire Agent Developer Community

marsbit07/06 02:33

Claude Code Announces Major Next-Gen Upgrade: You Chat, and the Backend Gets the Job Done

Claude Code's next major upgrade will make its sub-agents run in the background by default, transforming the tool from a conversational interface into a multi-threaded workflow engine. Users can chat with Claude while sub-agents handle tasks like code refactoring, running tests, and opening PRs in parallel. Creator Boris Cherny, who hasn't written a line of code in eight months and sometimes manages thousands of AI agents, states that users are no longer just prompters; "another Claude writes the prompts." This evolution builds on recent features like Routines for scheduled tasks and Dynamic Workflows for complex, multi-agent jobs. The change signifies a shift where engineers focus on strategy and decision-making rather than writing code. Anthropic's internal data suggests Claude Code triples engineering output, creating a bottleneck for product managers who can't generate enough work. Spotify's case study reveals that 73% of its pull requests are AI-assisted, with executives using the tool to build prototypes directly from natural language descriptions. The core takeaway: the scarcest resource is no longer the ability to write code, but the ability to decide what code to write.

marsbit06/30 09:33

Claude Code Announces Major Next-Gen Upgrade: You Chat, and the Backend Gets the Job Done

marsbit06/30 09:33

AI Sweeps the Globe, So Why Is Crypto + AI Facing Gloom?

The article "AI Sweeps the Globe, But Why Is Crypto + AI So Bleak?" analyzes the disconnect between the booming AI industry and the struggling crypto+AI sector. It argues the issue is not flawed logic but severe demand-supply mismatch across four key sub-sectors. Decentralized compute and storage projects offer theoretical benefits like cost savings and data sovereignty but lack a decisive technical edge over entrenched cloud providers (AWS, GCP). Enterprises are unwilling to risk migration for unproven infrastructure that can't guarantee the performance and reliability needed for critical AI workloads. ZKML and privacy solutions address important issues like model verification but solve non-urgent, long-term concerns for most businesses currently focused on core performance and ROI. Demand here is likely to be regulation-driven (e.g., EU AI Act) rather than organic. AI agent infrastructure is developing foundational tech for a future multi-agent economy. However, the current market phase is dominated by internal process automation within single companies, making this technology premature. AI agent payments is highlighted as the only sub-sector where blockchain competes on a level playing field with traditional finance, as neither has adequately solved the challenges of machine-to-machine micropayments and real-time settlement. Overall, crypto+AI projects are building for future needs (data ownership, decentralization, transparency) that don't align with the industry's immediate priorities (performance, cost, stability). The absence of a flagship, large-scale use case further hinders mainstream adoption and capital inflow. The path forward requires either adapting to current market demands or patiently building the foundational infrastructure for the next phase of AI.

marsbit06/29 06:45

AI Sweeps the Globe, So Why Is Crypto + AI Facing Gloom?

marsbit06/29 06:45

AI is Sweeping the Globe, So Why is Crypto + AI in a Slump?

AI Booms, But Crypto + AI Remains Sluggish: A Demand-Side Analysis Despite the AI industry's explosive growth and massive investment, the convergence of blockchain and AI (Crypto + AI) has seen limited traction. The core issue is a severe supply-demand mismatch, not a flawed premise. Analyzing four key sub-sectors reveals specific gaps: 1. **Decentralized Compute/Storage:** Offer logical benefits like data sovereignty and cost savings but lack a decisive technical advantage over entrenched cloud giants (AWS, GCP). Enterprises prioritize performance and stability and are unwilling to bear the switching risk and uncertainty of decentralized networks. 2. **Model Verification/Privacy (e.g., ZKML):** Address important long-term issues like auditability and data privacy, but these are not urgent operational pain points for most businesses today. Widespread demand will likely follow regulatory mandates (like the EU AI Act), not precede them. 3. **AI Agent Infrastructure:** Projects are building infrastructure for a future of autonomous, interacting agents. However, the current market focus is on internal process automation within corporate firewalls. The technology is ahead of market readiness. 4. **AI Agent Payments:** This is the only sub-sector where blockchain is on a level playing field with traditional finance. Both are trying to solve the unsolved problem of real-time, micro-transactions for machines, making it the most immediately competitive area. The overarching problem is that the AI industry invests heavily in solutions that solve immediate bottlenecks (e.g., faster memory, more power). Most Crypto + AI solutions target secondary, longer-term concerns (decentralization, transparency) and often come with performance trade-offs. The lack of a flagship, large-scale commercial success case further hinders mainstream capital inflow. The path forward requires either aligning more closely with the current industry's performance demands or patiently building the foundational infrastructure for the next phase of AI.

Foresight News06/29 06:15

AI is Sweeping the Globe, So Why is Crypto + AI in a Slump?

Foresight News06/29 06:15

Interview with MicroStrategy CEO: Beyond the 32 BTC Selling Stir, 6 Trillion AI Agents are the Ultimate Endgame for Bitcoin

Interview with Strategy CEO: Beyond the 32 BTC Sale, 6 Trillion AI Agents are Bitcoin's Ultimate Endgame Strategy CEO Phong Le discusses the recent sale of 32 BTC, clarifying it was a minor, strategic move to demonstrate operational liquidity and internal process robustness to creditors and rating agencies, not a reaction to market fears. He emphasizes Strategy's disciplined, data-driven decision-making framework involving its board and complex financial modeling, distancing the company from centralized "black box" operations seen elsewhere in crypto. Le outlines the company's resilience and long-term focus, citing the "doing nothing" strategy during the 2022 bear market as a testament to its conviction in Bitcoin's underlying value proposition for global sovereignty and freedom. He reveals that generative AI was instrumental in developing their Stretch (STRC) preferred stock product, cutting development time from years to months. The most visionary part of the discussion centers on Agentic AI. Le envisions a future with 6 trillion autonomous AI agents conducting commerce, particularly in off-world environments like Mars, which would naturally adopt decentralized crypto rails and seek yield-bearing assets like Bitcoin as a core store of value. Finally, Le addresses the STRC product, expressing confidence it will return to its $100 par value through reserve replenishment and the initiation of dividend payments, and dismisses concerns about competition with stablecoins. He concludes by affirming Strategy's philosophy of expanding Bitcoin access through all available means, from self-custody to ETFs, to onboard the next wave of users.

marsbit06/23 01:16

Interview with MicroStrategy CEO: Beyond the 32 BTC Selling Stir, 6 Trillion AI Agents are the Ultimate Endgame for Bitcoin

marsbit06/23 01:16

Interview with Strategy CEO: Beyond the 32 BTC Selling Controversy, 6 Trillion AI Agents Are Bitcoin's Ultimate Endgame

Interview with Strategy CEO Phong Le: The company's sale of 32 BTC sparked market reactions, but Le emphasizes their long-term focus on Bitcoin. He explains the sale was a small test to demonstrate liquidity to creditors and internal processes, not a reaction to "death spiral" fears. The decision-making process involves the board and rigorous financial modeling, moving away from a founder-centric model. Le highlights the strategic option of "doing nothing," holding their 845,000 BTC reserve, as a key lesson from the 2022 bear market. He expresses unwavering belief in Bitcoin's underlying value proposition for global financial sovereignty. Looking ahead, Le is most excited about AI's role. He reveals their AI-aided development of the STRC (Strategy Preferred Stock) product and envisions a future with trillions of AI agents conducting commerce on decentralized networks, with Bitcoin as the foundational store of value. Le advocates for inclusivity in Bitcoin adoption, welcoming all methods from self-custody to ETFs. Regarding STRC trading below its $100 face value, he attributes it to temporary reserve drawdowns for bond buybacks and expects it to stabilize with replenished reserves and the start of dividend payments. He also confirms the company did sell BTC before May 31st, as disclosed in their filings.

链捕手06/23 01:13

Interview with Strategy CEO: Beyond the 32 BTC Selling Controversy, 6 Trillion AI Agents Are Bitcoin's Ultimate Endgame

链捕手06/23 01:13

AI Agents Also Need 'Credit Checks': ERC-8126 is Filling the Gap in On-chain Trust

The article discusses ERC-8126, a proposed standard designed to address the lack of trust and verification for AI Agents operating on-chain. While ERC-8004 provides AI Agents with a basic on-chain identity (answering "Who are you?"), it does not guarantee trustworthiness. ERC-8126 aims to fill this gap by establishing a verification layer (answering "Are you reliable?"). It standardizes how independent verification providers can assess an agent's associated risks across five key areas: Token/Contract Verification (ETV), Media Content Verification (MCV), Solidity Code Verification (SCV), Web Application Verification (WAV), and Wallet Verification (WV). These providers generate a standardized risk score (0-100) and proofs based on their checks, without acting as a single authoritative certifier. This allows wallets, marketplaces, dApps, and other agents to consume these risk signals—for example, to display warnings, filter listings, or make interaction decisions. The standard also incorporates concepts like Private Data Verification (PDV) and Zero-Knowledge Proofs (ZKP) to allow verification without exposing sensitive underlying data. Positioned alongside ERC-8004 (Identity) and ERC-8183 (Commerce for agents), ERC-8126 represents a step toward building a verifiable and accountable infrastructure for the emerging on-chain AI Agent economy, shifting trust assessment from purely user-based judgment to standardized, consumable signals.

marsbit06/22 13:54

AI Agents Also Need 'Credit Checks': ERC-8126 is Filling the Gap in On-chain Trust

marsbit06/22 13:54

Chips, Open-Source Models, and $50 Trillion: Joe Tsai Reassesses Alibaba Once Again

Alibaba Executive Chairman Joe Tsai recently outlined the company's comprehensive AI strategy in a public discussion. He believes AI represents a massive opportunity, estimating its potential economic impact at up to $50 trillion, stemming from the automation of human intelligence and productivity. Tsai detailed Alibaba's four-layer investment approach across the AI stack: starting from the chip level, moving to cloud infrastructure (Alibaba Cloud), then the model layer with its open-source Qwen model, and finally applications within its vast digital ecosystem (e-commerce, logistics, etc.). The company avoids the energy layer due to China's efficient infrastructure. This broad strategy is designed to ensure Alibaba captures value regardless of where it ultimately concentrates in the AI value chain. He dismissed concerns about an AI investment bubble, pointing to the enormous $50 trillion opportunity. While acknowledging U.S. cloud giants' higher capital expenditure, he argued Chinese firms, including Alibaba (funded by its cash-generative e-commerce core), need to invest more in AI infrastructure. A key theme was technological sovereignty. Tsai positioned open-source models like Qwen as a solution for companies, especially in Europe, seeking independence from proprietary U.S. models and greater data privacy control. He contrasted this with the trend of U.S. giants keeping their models closed-source. Tsai highlighted Alibaba's collaborations with European manufacturers like Bosch and Siemens, using AI for design and quality control. He concluded with an optimistic vision of AI agents enhancing productivity, ultimately freeing up human time for leisure, family, and experiences like live entertainment.

marsbit06/22 07:51

Chips, Open-Source Models, and $50 Trillion: Joe Tsai Reassesses Alibaba Once Again

marsbit06/22 07:51

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