# Сопутствующие статьи по теме Automation

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

A Person Who Can't Code Single-Handedly Handled All of Anthropic's Growth Marketing for Ten Months

Anthropic, a company valued at $380 billion, had a single non-technical growth marketer, Austin Lau, managing all performance marketing channels—paid search, paid social, app store optimization, email marketing, and SEO—for nearly ten months. Despite having no coding experience, Lau used Claude Code to automate key workflows. He built a Figma plugin to generate hundreds of creative ad variants in seconds instead of 30 minutes, and created a Google Ads automation tool that drafts compliant ad copy and exports it for upload. He also integrated Claude with Meta’s Ads API to analyze campaign performance in real-time and built a memory system to inform future A/B tests. As a result, ad creation time dropped from 2 hours to 15 minutes, and creative output increased tenfold. Anthropic’s internal case study highlights how AI is erasing the barrier between technical and non-technical roles, enabling individuals to achieve output comparable to full teams. The key to scaling efficiency lies in identifying repetitive tasks, breaking them into sub-tasks handled by specialized AI agents, and designing systematic workflows—not in having advanced technical skills.

marsbit03/12 09:45

A Person Who Can't Code Single-Handedly Handled All of Anthropic's Growth Marketing for Ten Months

marsbit03/12 09:45

2026 is Not the Year of AI, But the Starting Point of a Great Reshuffle of Human Professions

The author, an AI entrepreneur and investor, argues that 2026 will not be the "Year of AI" but rather the starting point of a massive reshuffling of human professions. He states that the current pace of AI advancement, driven by a small number of researchers at companies like OpenAI and Anthropic, is exponential and will soon impact nearly all white-collar industries, not in a decade but within 1-5 years. He provides a personal account of how the latest models (e.g., GPT-5.3 Codex, Opus 4.6) can now autonomously complete complex tasks, such as writing flawless code for an entire software application and testing it, with human-level judgment and decision-making. The author emphasizes that public perception lags far behind reality, as those using free, outdated models are unaware of the capabilities of current paid versions. Key points include: AI is now involved in its own development, creating a feedback loop that accelerates progress ("intelligence explosion"); it will replace cognitive work across law, finance, medicine, and more; and the common belief that AI cannot replicate human judgment, creativity, or empathy is becoming uncertain. The author advises readers to act now by: 1) Seriously using top-tier AI tools in their daily work, 2) Gaining a competitive advantage in their careers by mastering AI before others, 3) Strengthening their financial resilience, 4) Focusing on skills AI cannot easily replace (e.g., building trust, in-person work), 5) Rethinking education for children to emphasize creativity and AI collaboration, and 6) Pursuing personal dreams with AI's help. He concludes that this is a pivotal moment for civilization, posing both immense opportunities (e.g., curing diseases) and existential risks (e.g., uncontrollable AI, weaponization). The future is already here for the tech industry and is imminent for everyone else. Success belongs to those who embrace this reality with curiosity and urgency.

marsbit03/12 00:43

2026 is Not the Year of AI, But the Starting Point of a Great Reshuffle of Human Professions

marsbit03/12 00:43

VCs Have Bet $200 Billion That AI Will Disrupt Everything, But Are They Ready for AI to Disrupt Them?

Venture capitalists invested over $200 billion in AI last year, but the industry now faces a dual disruption from the very technology it champions. The article highlights ADIN, an AI-powered platform that automates investment analysis, using specialized agents to evaluate pitch decks, conduct due diligence, and suggest valuations in about an hour—a task that typically takes human analysts days or weeks. While some VCs, like Marc Andreessen, argue that venture investing is an irreplaceable "art" reliant on human intuition and relationships, others acknowledge AI's potential to improve efficiency in sourcing deals and administrative tasks. A more existential threat emerges as AI drastically lowers startup costs, enabling companies to achieve scale with minimal funding, potentially making traditional large VC investments unnecessary. Examples like Midjourney, a highly profitable AI company with a small team, illustrate this shift. The industry may contract, leaving VCs competing for fewer deals and fundamentally challenging their role. The central question remains: as AI transforms entrepreneurship, will VCs adapt, or become obsolete?

marsbit03/11 09:33

VCs Have Bet $200 Billion That AI Will Disrupt Everything, But Are They Ready for AI to Disrupt Them?

marsbit03/11 09:33

Sequoia Capital: The Next Trillion-Dollar Company Doesn't Sell Software, It Sells Outcomes

Sequoia Capital partner Julien Bek argues that the next trillion-dollar company will not sell software tools, but will instead sell outcomes directly. For every dollar spent on software, companies spend six dollars on services. As AI drives the cost of "doing" toward zero, the real opportunity lies not in Copilots (assistive tools) but in Autopilots (fully automated work delivery). The key distinction is between "intelligence" (rule-based tasks like coding or data translation) and "judgement" (tasks requiring experience and intuition). AI is increasingly capable of autonomous intelligence work, leaving judgement to humans. While Copilots sell tools to professionals, Autopilots sell the final result to the end customer. The optimal strategy is to target outsourced, intelligence-intensive tasks first. Outsourcing indicates a company is already comfortable with external party handling the work, has a dedicated budget, and buys results. Replacing an outsourced contract is a vendor change; replacing internal staff is a reorganization. The article maps high-opportunity verticals by their intelligence/judgement mix and outsourcing prevalence. Major opportunities include: - Insurance brokering ($140-200B): Highly standardized,智力-intensive. - Accounting & Auditing ($50-80B outsourced in US): Facing a structural labor shortage. - Medical billing ($50-80B outsourced): Rules-based medical coding. - Claims adjusting ($50-80B): Often outsourced to third-party administrators. - Tax preparation ($30-35B): High智力-work, with regulatory moats. - Legal transactional work ($20-25B): Contract drafting, NDAs. - IT Managed Services ($100B+): Routine, repetitive tasks across many SMEs. - Procurement ($200B+): Automating neglected tail-spend supplier management. - Recruitment ($200B+): Target high-volume, low-judgement role matching. - Management Consulting ($300-400B): Harder to automate due to high judgement component. The conclusion is that while 2025's fastest-growing AI companies were Copilots, 2026 will see a shift toward Autopilots. Pure Autopilot companies have a window to capture vast service budgets by delivering work directly, unlike incumbents who may hesitate to automate their own customers' jobs.

marsbit03/11 04:46

Sequoia Capital: The Next Trillion-Dollar Company Doesn't Sell Software, It Sells Outcomes

marsbit03/11 04:46

Making Money While Laying Off: Where Did Silicon Valley's 170,000 Workers Go?

A significant wave of layoffs is sweeping through the U.S. tech industry, with over 170,000 jobs cut in 2025—surpassing levels seen during both the 2008 financial crisis and the 2020 pandemic. Unlike previous downturns driven by external economic shocks, the current restructuring is characterized by profitable companies proactively reducing headcount despite record revenues. The trend accelerated in early 2026, with more than 30,000 additional layoffs in the first six weeks alone. Major firms like Amazon, Block, Autodesk, and Salesforce announced significant cuts, often citing strategic shifts rather than financial distress. While AI and automation are frequently cited as causes, data shows that only about 28.5% of layoffs are directly attributable to AI adoption. The primary driver appears to be a correction after years of over-hiring during the low-interest, high-growth pandemic era. Companies are now prioritizing efficiency, smaller teams, and AI-integrated workflows in what analysts term a "structural reset"—meaning many eliminated roles may not return. The shift is creating a polarized job market: high demand for AI-specialized talent contrasts with shrinking opportunities in generalist roles like product operations and traditional engineering. Economists warn that continued tech sector contraction could slow U.S. GDP growth to near-recession levels. However, some data suggests the rate of layoffs may be moderating compared to 2024. Ultimately, the industry is undergoing a fundamental reorganization centered on redefining the role of human labor in an AI-driven ecosystem—a transition with no clear endpoint.

比推03/10 13:44

Making Money While Laying Off: Where Did Silicon Valley's 170,000 Workers Go?

比推03/10 13:44

BingX Launches the BingX AI Skills Hub, Enabling More Powerful Trading with OpenClaw

BingX, a leading cryptocurrency exchange and Web3-AI company, has launched the BingX AI Skills Hub, a new infrastructure layer designed to transform user interaction with crypto markets through artificial intelligence and OpenClaw. The hub introduces AI-native ways to trade via various AI agents, lowering the entry barrier and improving ease of use. It includes 15 skill modules covering perpetual futures, spot trading, and account management, enabling users to query data, view trends, execute trades, and manage accounts using natural language. The hub supports multi-skill workflows, allowing AI assistants to combine multiple capabilities into automated trading processes. This initiative is part of BingX's broader $300M vision to develop the first truly AI-native crypto exchange.

TheNewsCrypto03/10 11:40

BingX Launches the BingX AI Skills Hub, Enabling More Powerful Trading with OpenClaw

TheNewsCrypto03/10 11:40

From Understanding Skill to Learning How to Build Crypto Research Skill

This article explores the evolution and application of Agent Skill, a modular framework introduced by Anthropic in late 2025, which has become a foundational design pattern in the AI Agent ecosystem. Initially a tool to improve Claude's performance on specific tasks, it evolved into an open standard due to high developer adoption. Agent Skill functions like a "dynamic instruction manual" that AI can reference to perform tasks consistently without repetitive user prompting. It is built using a `skill.md` file containing metadata (name and description) and detailed instructions. The system operates through an on-demand loading workflow: the AI first scans lightweight skill metadata, matches the user's intent, then loads only the relevant skill's full instructions, optimizing token usage. Two advanced mechanisms enhance its functionality: - **Reference**: Conditionally loads external documents (e.g., a finance handbook) only when triggered by specific keywords, avoiding unnecessary context consumption. - **Script**: Executes external code (e.g., a Python script) without reading its content, enabling actions like file uploads with zero token cost. The article contrasts Agent Skill with Model Context Protocol (MCP), noting that MCP connects AI to data sources, while Skill defines how to process that data. For advanced use cases like crypto research, combining both is recommended: MCP fetches real-time data (e.g., blockchain info, news APIs), while Skill structures the analysis and output format. A practical example demonstrates building a crypto research agent using an `opennews-mcp` server. The Skill automates workflows like due diligence on new tokens (pulling Twitter data, news sentiment, KOL tracking) and real-time event monitoring (e.g., ZK-proof breakthroughs) to generate structured reports or trading alerts. This combination creates a powerful, automated research system tailored for Web3 analytics.

marsbit03/10 10:41

From Understanding Skill to Learning How to Build Crypto Research Skill

marsbit03/10 10:41

The One-Person Company: The Path to Million-Dollar Revenue

Nat Eliason, a writer and entrepreneur, is building a one-person company named Felix with the goal of generating $1 million in revenue using AI agents as his sole employees. Leveraging the OpenClaw framework, Felix has rapidly progressed, achieving nearly $200,000 in revenue in just a few weeks. The venture began when a post about OpenClaw went viral, leading to the creation of a $Felix token. Eliason tasked his AI agent, the "CEO" of this zero-human company, with generating revenue. Felix started by autonomously building a website and selling a $29 OpenClaw setup guide, generating $41,000. It then identified market needs and expanded into two main businesses: Claw Mart, a marketplace for AI skills (generating ~$14,000), and Clawcommerce, a service building custom AI agents for enterprises. The system uses sub-agents for tasks like support and sales, with Discord as its operational hub. Operating costs are minimal at ~$1,500 monthly. A key development is Felix beginning to "hire" a human for affiliate distribution, signaling a shift from replacing humans to employing them. Challenges include AI unpredictability, memory management, and market education. Despite this, Eliason is optimistic. Future plans include optimizing existing services, exploring blockchain integration, and scaling further. He believes this model represents a new era of AI-driven commercialization and a significant wealth creation opportunity.

比推03/10 07:32

One-Person Company: The Path to Million-Dollar Revenue

Nat Eliason, a writer and entrepreneur, is pioneering the concept of a "one-person company" named Felix, aiming to generate $1 million in revenue using AI agents as his sole employees. Built on the OpenClaw framework, Felix operates autonomously with minimal human intervention. Felix began by selling a $29 OpenClaw setup guide, earning $41,000 initially. It then expanded into Claw Mart, a marketplace for AI tools where it collects a 10% fee, and Clawcommerce, a service offering custom AI agents for businesses at $2,000 upfront plus $500 monthly. Sub-agents like Iris (support) and Remy (sales) handle specific tasks, with Felix acting as the central coordinator. Within weeks, Felix reached nearly $200,000 in total revenue—$100,570 via Stripe and $94,974 in ETH—achieving about 20% of its $1 million goal. Operating costs are low at around $1,500 per month, primarily for AI model subscriptions. Challenges include AI unpredictability, memory management issues, and market education. Despite risks, Nat remains focused on sustainable growth, avoiding meme-driven hype. Future plans include scaling Clawcommerce, exploring blockchain integration, and leveraging community support. Felix demonstrates how AI agents can not only replace but also "hire" humans, signaling a shift toward AI-augmented solo entrepreneurship and new wealth creation opportunities.

marsbit03/10 02:35

One-Person Company: The Path to Million-Dollar Revenue

marsbit03/10 02:35

A Monthly Salary of 20,000 RMB, But Can't Afford to Keep a 'Lobster'?

The article "Can a 20,000 Yuan Monthly Salary Not Afford a 'Lobster'?" discusses the viral trend and misconceptions surrounding OpenClaw, an open-source personal AI agent framework. It highlights two contrasting views: one praising its potential as a virtual team member, and another criticizing the hype and privacy concerns. Key points include: 1. OpenClaw experiences vary widely based on deployment methods (local hardware, cloud servers, personal PCs, or vendor-hosted services), affecting functionality and cost. 2. High permissions granted to OpenClaw pose significant security risks, including data breaches and malicious attacks. 3. Performance depends heavily on the underlying AI model (e.g., GPT, Claude) and its associated costs, with token consumption potentially leading to high expenses. 4. OpenClaw is still an immature, technically complex tool requiring substantial time, financial investment, and expertise to use effectively. 5. Users are advised to assess their actual needs, technical capability, and risk tolerance before adoption, as the AI serves as an "amplifier" rather than a standalone solution. The conclusion emphasizes cautious experimentation and independent thinking amidst the hype.

比推03/09 17:12

A Monthly Salary of 20,000 RMB, But Can't Afford to Keep a 'Lobster'?