# 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.

How to Conduct Deep Research Using Claude's Dynamic Workflows

The article "How to Use Claude's Dynamic Workflows for Deep Research" discusses overcoming the pitfalls of technical research, where both humans and AI can get overwhelmed by information, leading to vague conclusions. It introduces Claude Code's new "Dynamic Workflows" feature, which automatically designs and executes task-specific workflows before starting a task, unlike simpler "planning modes." This approach incorporates validation, result convergence, and adversarial verification from the outset. The core of Dynamic Workflows is six predefined scheduling patterns that address how to decompose tasks and synthesize results: 1. **Classify-and-Act (Routing):** An agent classifies the task and routes it to the most suitable specialist agent for execution. It's precise and efficient but struggles with ambiguous tasks. 2. **Fan-out & Merge:** The task is split into parallel, independent subtasks whose results are later merged. It's fast and isolates contexts but is more expensive and challenging to synthesize. 3. **Adversarial Verification:** Multiple "challenger" agents critique a worker agent's conclusion, requiring majority approval. This counters confirmation bias and self-assessment errors but relies on verifiable facts. 4. **Generate & Filter:** Multiple agents generate many candidate solutions, which are then filtered against a rubric to output only the best. It fosters diversity but depends heavily on the filter's quality. 5. **Tournament:** Multiple agents compete on the same task, with pairwise comparisons eliminating contestants over rounds to select the best. This offers stable relative judgment but is complex. 6. **Loop:** An agent iteratively attempts a task, learning from errors and adjusting until a stop condition is met. It handles tasks with unknown scope but risks infinite loops without proper design. The author compares their own custom deep-research system, which involved multi-agent analysis and deduplication but lacked goal-oriented convergence, to Claude's built-in workflow. The official workflow adds critical layers: initial problem decomposition, credibility assessment of sources, cross-agent voting to delete weak conclusions (not just averaging), and output tightly focused on the user's original goals and actionable recommendations. This structurally addresses common AI issues like goal drift, premature stopping, context pollution, and output bias. In summary, Dynamic Workflows represent a shift from smarter single conversations to a structured research process, compressing what used to require many dialogues into 3-4 interactions, albeit at higher token cost. The author notes remaining challenges for their specific domain (blockchain research): the need for fact-based verification over official documentation, depth in truly novel interdisciplinary thinking, the practical validation of proposed solutions, and tailoring information density to the audience.

marsbit06/09 03:07

How to Conduct Deep Research Using Claude's Dynamic Workflows

marsbit06/09 03:07

Founder of Baixing.com: My Fourteen Claude Code Usage Experiences

Founder of Baixing.com Shares 14 Personal Tips for Using Claude Code The author outlines his personal, non-universal strategies for maximizing Claude Code. Key points include: focusing deeply on one primary tool (Claude Code) rather than constant comparison; mastering essential shortcuts for the editor and command line; utilizing voice input like HoldSpeak; starting projects with a structured PROJECT.md file; defaulting to Claude agents for most tasks; and leveraging integrations with GitHub and Cloudflare for build, deployment, and infrastructure. He emphasizes a clear separation between human and machine work: manually maintain a core CLAUDE.md file, and understand AI-generated content by asking the AI, not reading its raw code. Efficient communication involves dragging files (screenshots, audio, documents) directly into the interface. For knowledge management, he recommends a centralized, Git-synced memory system based on ~/.claude/CLAUDE.md to ensure permanence and avoid scattered project memories. Other practices include writing and continuously refining "Skills," using the expensive but reliable ultracode for complex dynamic workflows, and employing Git documentation as handoff points between agents. The overarching philosophy is to treat Claude Code like a horse (or a person) with its own pathfinding abilities—setting goals and boundaries rather than micromanaging every turn.

链捕手06/08 12:54

Founder of Baixing.com: My Fourteen Claude Code Usage Experiences

链捕手06/08 12:54

It Took Me a Year to See the Bitter Truth About Agent Payments

After a year building infrastructure for the Agent economy, engaging with major players like Stripe, Visa, and Coinbase, the author shares a sobering analysis of the current state of Agent payments. The core finding is a stark lack of genuine, immediate demand across most envisioned use cases. The article breaks down four key market segments: 1. **Agent-to-Merchant (Consumer Shopping):** For most product categories (e.g., clothing, electronics), conversational AI shopping is a step backwards from visual e-commerce interfaces. While agents excel at understanding needs, they can't replace side-by-side product comparison. Real merchant interest is defensive "Agent Engine Optimization," not driven by current customer demand. Potential exists for high-frequency, low-decision purchases (like food delivery) or navigating complex store UIs, but these require massive B2C distribution channels dominated by giants like Amazon. 2. **Agent-to-API (Developer Services):** Developers already have subscriptions and billing relationships for APIs (compute, data). Prepaid balances solve micro-payment issues for low transaction volumes. A deeper structural problem is that major SaaS vendors' business models rely on enterprise contracts, resisting granular pay-per-call pricing. While protocols like MPP and x402 serve the long tail of niche services, this market is small and developers are historically low-willingness-to-pay. 3. **Agent-to-Agent:** This remains largely theoretical with minimal transaction volume. While it represents a long-term bet on a fundamentally new transaction infrastructure (sub-second, micro-penny to million-dollar, multi-party settlements), it does not constitute a present market. 4. **Agent-to-Finance:** This is the only category with existing, paying demand. Integrating AI into financial workflows (trading, portfolio management) is a natural evolution and enables new capabilities like autonomous rebalancing. However, competition favors established, regulated institutions. The "real problem" is not moving money between agents, but the broader challenge of **coordination**—orchestrating work between agents and humans, verifying outcomes, and settling results. Payment is just one component of settlement, which is itself part of coordination. Companies that solve the coordination layer will subsume payment, not the other way around. While well-funded incumbents build defensively for a long-term future, startups must find where the market is today—which, for the author's team, lies outside these four categories in an area of real, growing, and underserved activity.

marsbit06/06 10:19

It Took Me a Year to See the Bitter Truth About Agent Payments

marsbit06/06 10:19

Codex Goal Mode Usage Guide: How to Make AI Continuously Pursue a Specific Objective

"Codex Goal Mode: How to Make AI Work Continuously Toward a Specific Goal" OpenAI's Codex "goal mode" (/goal) transforms the AI from a reactive code assistant into a proactive execution agent capable of working autonomously for hours or even days to achieve a defined objective. To maximize its effectiveness, follow these key principles: 1. **Define Clear, Verifiable Exit Criteria:** The goal prompt should be a concise, measurable success condition, not a lengthy specification. Use quantifiable metrics like "reduce build time by 30%" or "achieve 100% test parity." 2. **Provide Initial Guidance and Tools:** Direct Codex toward likely problem areas and specify available tools (e.g., browsers, testing environments) to prevent it from exploring unproductive paths. 3. **Enable Progress Measurement:** Equip Codex with ways to track advancement, such as creating comparison tools for visual tasks or evaluation sets, ensuring it can gauge its own progress. 4. **Use a Realistic Execution Environment:** For tasks like performance optimization, provide access to environments that closely mimic production (e.g., similar configs, databases) to yield valid results. 5. **Be Cautious with Visual Goals:** Avoid vague "pixel-perfect" instructions. Instead, supplement visual references with functional checklists or design system specifications to prevent Codex from obsessing over minor details. 6. **Implement Progress Tracking:** For long-running tasks, have Codex commit code to draft PRs, update progress documents, or send Slack updates to maintain visibility into its work. 7. **Review and Consolidate Results:** Once the goal is met, instruct Codex to review its work, clean up ineffective experimental code, and reflect on what strategies succeeded or failed. Ultimately, using goal mode shifts the developer's role from writing prompts to managing a persistent engineering agent—defining objectives, establishing metrics, configuring environments, and conducting final reviews.

marsbit06/06 08:11

Codex Goal Mode Usage Guide: How to Make AI Continuously Pursue a Specific Objective

marsbit06/06 08:11

Huawei Cloud Rejects Token Price War, Zhou Yuefeng Seeks a New Winning Formula for AI Cloud

At the 2026 Huawei Cloud INSPIRE Creator Conference, CEO Zhou Yuefeng outlined Huawei Cloud's distinct strategy in the competitive AI cloud market. Instead of engaging in price wars based on token volume or Maas revenue—a common focus for rivals like Alibaba Cloud and ByteDance's Volcano Engine—Huawei Cloud is shifting the competition towards real-world productivity gains. Zhou highlighted three core differentiators: a fully domestic computing stack (Ascend, Kunpeng), a focus on government and enterprise clients rather than consumer internet, and a deep commitment to open-source ecosystems. To this end, Huawei Cloud launched a suite of new products under the "Agentic Infra" paradigm, including the AICS Lingqu computing cluster, AMS memory storage, and the ModelArts Next platform. These aim to solve enterprise challenges in deploying AI agents, such as latency, memory, scheduling, and security. The strategy further involves creating specialized industry zones ("AI Dream Factories") for sectors like healthcare and embodied intelligence. For example, a smart medical zone developed with Shanghai Ruijin Hospital aims to democratize expert-level diagnostic capabilities. In essence, Huawei Cloud is positioning itself not as a commodity token provider, but as the foundational infrastructure for industrial AI, leveraging its domestic supply chain and hybrid cloud solutions to serve sectors where productivity, not just scale, is the ultimate measure of value.

marsbit06/06 05:47

Huawei Cloud Rejects Token Price War, Zhou Yuefeng Seeks a New Winning Formula for AI Cloud

marsbit06/06 05:47

From Banning Doubao to Embracing Honor: Why Did WeChat Suddenly 'Change Its Face'?

The article explores the sudden shift in WeChat's strategy towards AI assistants from mobile phone manufacturers, transitioning from strict opposition to active collaboration. For over a year, WeChat fiercely resisted attempts by phone AI assistants (like ByteDance's Doubao in late 2025) to control its features via GUI automation ("simulated clicking"), citing security and data control concerns. This stance created a significant barrier for system-level AI integration. Now, Tencent has initiated A2A (Agent-to-Agent) partnerships with major phone brands like Honor, Xiaomi, OPPO, and vivo. This model allows a phone's system AI (e.g., Honor's YOYO) to parse a user's voice command and send a structured request directly to WeChat's own internal AI agent via secure APIs. WeChat then executes the action (e.g., sending a message) and returns the result. The article attributes Tencent's "change of face" to strategic pressure. While leading in social app usage, Tencent trails rivals like ByteDance and Alibaba in standalone AI app popularity. WeChat, with its vast mini-program ecosystem, is Tencent's key asset for an AI comeback. The upcoming WeChat AI agent aims to handle tasks like booking and payments within the app. However, phone system assistants remain the primary AI entry point for most users. The A2A collaboration allows Tencent to extend WeChat's AI reach to this crucial system layer while maintaining control over its core functions and data. For phone manufacturers, embracing A2A is a pragmatic move. The GUI route proved unviable due to WeChat's blocks. A2A offers a compliant path to integrate a vital service, enhancing their AI assistants' usefulness. It allows them to focus on developing their own AI ecosystems for other services while cooperating on WeChat access. The collaboration is framed as a mutual, strategic necessity: Tencent gains a distribution channel, and manufacturers gain a key functionality. The partnership relies on a "dual authorization" mechanism for security, requiring both user and app consent for each action. While questions about long-term data privacy practices remain, experts note A2A is more secure and compliant than GUI automation. Ultimately, this cooperation is seen as a tentative, calculated truce. Tencent's long-term goal is to make WeChat an AI-powered "service OS." Phone manufacturers aim to make their system AI the central user interface. Their paths may converge or clash in the future, but for now, the A2A deal represents the opening chapter in the battle for the AI-era user入口, driven by necessity and strategic calculus on both sides.

marsbit06/06 01:48

From Banning Doubao to Embracing Honor: Why Did WeChat Suddenly 'Change Its Face'?

marsbit06/06 01:48

Near Returns to the AI Stage: Transformation into a Public Chain Due to 'Payroll Difficulties,' Agent and Privacy Emerge as New Growth Narratives

NEAR Returns to AI Origins: From Payroll Struggles to Blockchain, Now Focusing on AI Agents and Privacy NEAR Protocol's journey began not with grand blockchain ambitions, but from a practical hurdle: its AI startup founders, including Transformer paper co-author Illia Polosukhin, couldn't efficiently pay international developers in 2017. This led them to pivot and build a high-performance, scalable blockchain. After years navigating various crypto narratives like sharding and cross-chain interoperability, NEAR is now leveraging its AI roots to re-enter the AI arena. A key driver is its "NEAR Intents" layer, which abstracts complex cross-chain transactions. Users simply state their goal (e.g., swap BTC for ETH), and a solver network finds the optimal route. This system has processed over $20B in cross-chain volume, generating significant fee revenue. A major growth area is private transactions via "Confidential Intents/Swaps," which hide trade details until settlement to protect against MEV and front-running. Remarkably, private swaps recently accounted for over 40% of NEAR's transaction volume, highlighting strong demand but also potential regulatory scrutiny. With its AI-founder pedigree, NEAR is positioning itself at the intersection of blockchain, AI agents, and privacy, aiming to become infrastructure for the emerging agent economy while navigating the challenges of its rapid adoption.

marsbit06/05 12:51

Near Returns to the AI Stage: Transformation into a Public Chain Due to 'Payroll Difficulties,' Agent and Privacy Emerge as New Growth Narratives

marsbit06/05 12:51

The Merger of Codex and ChatGPT Marks the Beginning of a Major Reshuffle in Programming Tools

OpenAI is shifting its strategic focus from ChatGPT to Codex, merging them along with the browser tool Atlas into a unified desktop super-app. This move signals an internal belief that Codex, originally a programming tool, represents the next evolution of AI more than conversational models like ChatGPT. Over the past year, Codex's weekly active users have surged past 5 million. The key distinction is that while ChatGPT answers questions, Codex executes tasks. Enterprises increasingly value this ability to get work done over simply receiving advice. Consequently, Codex is attracting professionals beyond developers, including analysts, bankers, marketers, and product managers. OpenAI's reorganization and increased investment in Codex stem from recognizing that the future of AI competition lies in execution capabilities, not just conversation. The company is launching role-specific plugins (e.g., for data analysis, sales, design) to transform Codex into a broad knowledge work platform that automates and redefines white-collar workflows. Beyond being a tool, Codex reflects OpenAI's ambition to redefine software. New features like "Sites"—which generates interactive websites from documents—and collaborative "Annotations" aim to create a paradigm where the AI understands the goal and handles the tools and steps, functioning more like a digital colleague than traditional software. The ultimate goal is a unified experience where the user cares only about the completed task.

marsbit06/04 11:32

The Merger of Codex and ChatGPT Marks the Beginning of a Major Reshuffle in Programming Tools

marsbit06/04 11:32

ByteDance Adopts Arm CPUs, Jensen Huang: So Sad I Didn't Buy Arm

**Summary:** At Computex 2026, Arm CEO Rene Haas announced that ByteDance and Oracle have adopted Arm's self-designed Arm AGI data center CPU. The company expects significant revenue growth from this product, projecting $20 billion in demand for the 2027/2028 fiscal years. Haas noted that restricting AI-capable CPUs from the US to China is nearly impossible due to their widespread applications. Arm's stock has surged dramatically this year, notably rising 16% after NVIDIA's Arm-based Vera CPU and RTX Spark announcements. A highlight was the informal, humorous on-stage conversation between Haas and NVIDIA CEO Jensen Huang. Huang joked about NVIDIA's failed attempt to acquire Arm and playfully lamented selling his Arm shares. Both executives showed a clear sense of camaraderie and shared regret over the missed merger. Key technical topics were discussed: 1. **AI PC Design:** Huang explained NVIDIA's RTX Spark superchip (with a 20-core Arm CPU) is designed for future AI agents that will autonomously run and use tools on PCs, blending local and cloud processing. 2. **Agent vs. OS:** Huang emphasized the operating system remains crucial, as AI agents rely on its APIs and tools to function. 3. **Growth Constraints:** He identified the shift to "useful AI" that generates profitable tokens as a primary driver for immense, almost limitless, computational demand. Haas outlined Arm's strategy across PC and data centers. For PCs, Arm collaborates with partners like NVIDIA and MediaTek, offering its compute subsystem (CSS) for custom SoCs. In data centers, its Arm AGI CPU (built on TSMC's 3nm process) has gained major partners including OpenAI, Meta, and now ByteDance and Oracle. Arm presented a multi-year roadmap for its in-house CPU line. The article concludes that while GPUs dominated the AI training race, the explosion of AI agents is shifting significant focus to CPUs for inference, state management, and tool orchestration. The industry is trending towards vertical integration, with companies like cloud providers designing chips and chip/IP firms offering full solutions, all competing to deliver more efficient computing per watt.

marsbit06/04 04:51

ByteDance Adopts Arm CPUs, Jensen Huang: So Sad I Didn't Buy Arm

marsbit06/04 04:51

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