# Automation Related Articles

HTX News Center provides the latest articles and in-depth analysis on "Automation", covering market trends, project updates, tech developments, and regulatory policies in the crypto industry.

No Coding Required: Build Your First AI Agent in 2 Days (Complete Tutorial)

A No-Code Guide to Building Your First AI Agent in a Weekend This article presents a weekend, zero-code tutorial for beginners to build a functional AI Agent using tools like Claude. It clarifies the core difference between a chatbot, which responds to queries, and an Agent, which autonomously plans and executes multi-step tasks using tools to deliver a final result. The process is broken into four stages over two days: 1. **Saturday Morning: Understanding Agents.** Learn that an Agent requires a clear Goal, a Plan, necessary Tools, and an execution Loop. Identify a simple, multi-step task from your own work/life as your first project. 2. **Saturday Afternoon: Building with Claude.** Create a one-page "Agent Blueprint" answering: the Goal, sequential Steps, required Tools, the desired Output format, and error-handling rules. Implement this blueprint in Claude (Desktop Cowork or web Projects) and run the Agent for the first time. 3. **Sunday Morning: Debugging & Optimization.** Review the initial (often 60-70% accurate) output. Identify flaws, trace them back to vague instructions in your blueprint, and refine it with more specific criteria and error handling. Iterate this run-review-refine cycle 3-4 times to reach ~90% reliability. 4. **Sunday Afternoon: Expansion.** Apply the learned workflow to quickly build a second, different Agent (e.g., for research, content repurposing, or meeting prep), experiencing the compounding efficiency gains. The core skill is not writing a perfect blueprint initially, but rapidly iterating based on output. By the end of the weekend, you'll have built two usable Agents, moving beyond just chatting with AI to automating multi-step workflows, fundamentally changing how you approach repetitive tasks.

marsbit05/16 15:19

No Coding Required: Build Your First AI Agent in 2 Days (Complete Tutorial)

marsbit05/16 15:19

Revealed: The "Super Small Shop" with Daily Revenue Over Ten Thousand? How NewOne Robot is Reconstructing the Coffee and Tea Beverage Business

"New One Coffee," China's first robot-operated coffee and tea beverage shop, opened in Beijing, showcasing a highly automated "one person, one robot, one shop" model. This concept, powered by the fully integrated 'Robot in store' system, aims for ultimate staff efficiency, requiring only one employee per location. The store combines coffee, tea, and bakery items to drive all-day sales, overcoming the typical morning-only or afternoon-only traffic peaks of traditional cafes or tea shops. The business model focuses on super-compact stores (around 10 square meters) to minimize rental costs. Its robotic system, capable of handling numerous SKUs, uses big data for rapid, data-driven product development and selection. Furthermore, the automation frees up staff to focus on customer relationship and private community building. Targeting rapid national expansion, New One is offering a special rapid ROI partnership policy for its first 20 partners. This policy significantly lowers the initial investment barrier by waiving various traditional franchise fees, promising a lighter asset model and faster break-even period, supported by claimed high daily revenue potential.

marsbit05/16 02:45

Revealed: The "Super Small Shop" with Daily Revenue Over Ten Thousand? How NewOne Robot is Reconstructing the Coffee and Tea Beverage Business

marsbit05/16 02:45

YC Partner Reveals: Building an AI-Native Company from Scratch

"YC Partner Reveals: Building an AI-Native Company from Scratch" YC partner Diana Hu argues that true AI-native companies operate 1000x faster than incumbents, not by using AI for mere efficiency, but by making it the company's core operating system. This requires a fundamental shift: companies must become "queryable" to AI, with all workflows and communications generating data for AI to learn from, creating a "closed-loop" system for continuous optimization. For example, an AI agent with access to tickets, code, meetings, and customer feedback can analyze past performance and autonomously plan future engineering cycles, dramatically increasing output. In product development, the new paradigm is the "AI software factory": humans write specifications and tests, while AI agents generate the code. This transparent, data-driven model renders traditional middle management obsolete. Future AI-native companies will consist of three roles: Independent Contributors (who build/operate with AI), Directly Responsible Individuals (who own outcomes), and the AI Founder who leads by example. The critical shift is maximizing token usage over headcount. A small, AI-augmented team can outperform large traditional teams. Startups have a key advantage: they can design their entire culture and systems around AI from day one, unburdened by legacy processes. The core takeaway: Founders must personally experience AI's transformative power. The future belongs to those who embed AI into their company's DNA from the start.

marsbit05/15 01:12

Global SaaS Crisis, AaaS Becomes Mainstream, Claude is Undermining SME Software

Claude's new "Claude for Small Business" product integrates directly with tools like QuickBooks, PayPal, and Google Workspace to automate core business workflows. Aimed at small businesses, it handles tasks such as payroll, month-end closing, invoicing, and marketing campaigns through simple conversational commands. The service, included with a Claude subscription, offers pre-built automations for 15 high-frequency scenarios and operates with user-approval steps for security. This move represents a shift from traditional SaaS toward AI Agent-as-a-Service (AaaS), targeting the underserved small business market with a low-cost, intuitive alternative to complex, multi-software stacks. The release follows Anthropic's strategy of industry-specific AI integration, signaling potential disruption for single-function SaaS companies as AI becomes the primary operational interface.

marsbit05/15 00:34

Global SaaS Crisis, AaaS Becomes Mainstream, Claude is Undermining SME Software

marsbit05/15 00:34

MuleRun CTO: The Moat of Agents Lies in Data Density and User Memory

In a speech titled "Handing AI's Keys to the On-Chain Controllers," MuleRun CTO Shu Junliang discussed the evolution and security of AI Agents in finance and Web3. He outlined six dimensions for a complete AI assistant: dialogue, data input, agent capability, execution environment, user memory, and continuous learning. MuleRun's product integrates these through features like multi-platform IM bots, real-time multi-asset data, smart model routing, cloud sandboxes, persistent user profiles, and a shared knowledge network. Shu emphasized that while AI Agents are advancing from assisting to autonomously executing decisions—potentially enabling individuals to operate like small funds—safety remains paramount. He detailed MuleRun's security measures, including local key handling, isolated sandboxes, full audit trails, and strict permission controls. However, he acknowledged persistent risks like data exposure, model hallucinations, prompt injection, and the "black box" nature of AI decisions, advising manual confirmation for financial operations. He identified key trends: the shift from human-led to Agent-led on-chain interactions requiring infrastructure redesign; the erosion of information advantages replaced by competition in execution speed and strategy; and the balancing effect of Agents, which democratize access but ultimately advantage those with superior judgment. Shu concluded that an Agent's true moat lies in data density and accumulated user memory, not easily replicable technology, and that while Agents will reshape finance and Web3, human oversight over critical decisions must remain.

marsbit05/14 08:50

MuleRun CTO: The Moat of Agents Lies in Data Density and User Memory

marsbit05/14 08:50

Research Debunks AI Layoff Myth: 80% of Companies Cut Jobs, None Made Money From It

Research Debunks AI Layoff Myth: 80% of Firms That Cut Jobs Saw No Financial Gain A Gartner survey of 350 large enterprises (revenue >$10B) found 80% that deployed AI/automation conducted layoffs, but found no positive correlation between the scale of layoffs and financial return. Companies that cut more jobs did not outperform those that cut fewer. The highest returns were achieved by "human-amplified" businesses using AI to augment employee productivity rather than replace them. Meanwhile, nearly 50,000 U.S. jobs were cut due to AI in the first four months of 2026, with tech sector layoffs hitting a three-year high. The report suggests that current AI-driven layoffs may often be small-scale experiments or "AI washing"—using AI as a pretext for cuts driven by other economic pressures. Short-term challenges are significant, with over 40% of AI agent projects predicted to be cancelled by 2027 due to cost and value issues. However, Gartner forecasts AI will become a net job creator by 2028-2029, creating new roles that AI cannot fulfill. Despite high project failure rates, AI software spending is projected to grow rapidly, from $86.4B in 2025 to $376.3B in 2027.

marsbit05/13 05:01

Research Debunks AI Layoff Myth: 80% of Companies Cut Jobs, None Made Money From It

marsbit05/13 05:01

How to Automate Any Workflow with Claude Skills (Complete Tutorial)

This is a comprehensive guide to mastering Claude Skills, a feature for creating permanent, reusable instruction sets that automate specific workflows. Unlike simple saved prompts, Skills function like trained employees, delivering consistent, high-quality outputs by defining the entire task process, standards, error handling, and output format. The guide is structured in four phases: **Phase 1: Installation (5 minutes).** Skills are folders containing a `SKILL.md` file. The user is instructed to find a relevant Skill online, install it, test it on a real task, and compare its performance to one-off prompts. **Phase 2: Building Your First Custom Skill.** Start by rigorously defining the Skill's purpose, trigger phrases, and providing a concrete example of perfect output. The `SKILL.md` file has two parts: a YAML frontmatter with a specific name/description/triggers, and a detailed, step-by-step workflow written in natural language with examples and quality standards. **Phase 3: Testing & Optimization for Production.** Test the Skill in three scenarios: 1) a standard, common task; 2) edge cases with missing or conflicting data; and 3) a pressure test with maximum complexity. Any failure indicates a needed instruction. Implement a weekly optimization cycle to continuously refine the Skill based on real usage. **Phase 4: Building a Complete Skill Library.** The goal is to create a team of Skills for all repetitive tasks. Examples are given for industries like real estate, marketing, finance, consulting, and e-commerce. The user should list their tasks, prioritize them, and build one new Skill per week, maintaining a master document to track their library. The conclusion emphasizes the compounding time savings: ten Skills saving 30 minutes each per week reclaims over 260 hours (6.5 work weeks) per year, fundamentally transforming one's work system.

marsbit05/12 09:45

How to Automate Any Workflow with Claude Skills (Complete Tutorial)

marsbit05/12 09:45

The AI Agent Era Accelerates Its Arrival: Questflow Defines a New Paradigm of Financial Intelligence with On-Chain AI Brokerage

The AI Agent era is accelerating, with the CB Insights AI 100 list highlighting global investment confidence. The focus has shifted from whether AI works to its speed of deployment and ability to manage complex workflows, with autonomous AI Agents driving this transformation. At the forefront is Questflow, a Singapore-based startup redefining financial intelligence through its on-chain AI brokerage. Unlike tools that merely provide data dashboards, Questflow deploys AI Agents that proactively scan markets, form judgments, and execute trades via a conversational interface—operating 24/7 without requiring manual confirmation for each decision. This embodies the new AI paradigm of agents capable of executing multi-step workflows autonomously. Questflow's mission is to democratize institutional-grade trading intelligence. Historically reserved for the ultra-wealthy, this capability is now accessible starting from just $1 through Questflow's "AI Clone + Copy Trade" model. The platform charges only a 1% execution fee, aligning its incentives directly with users and eliminating traditional management or performance fees. The timing is opportune, aligning with key trends identified by CB Insights: the scalable deployment of AI Agents, accelerated AI adoption in financial services, and the maturation of on-chain infrastructure. With robust liquidity on platforms like Hyperliquid and Polymarket, alongside advancements in AI reasoning and non-custodial wallet security, Questflow is positioned to merge the roles of broker, fund, and exchange into a single, accessible platform for millions.

链捕手05/11 13:19

The AI Agent Era Accelerates Its Arrival: Questflow Defines a New Paradigm of Financial Intelligence with On-Chain AI Brokerage

链捕手05/11 13:19

The Largest IPO in History Is Approaching, Surpassing SpaceX, 28 Years of AI Self-Iteration, Countdown to Intelligence Explosion

"Anthropic Nears Trillion-Dollar IPO, Fueled by Explosive Growth and 2028 'Intelligence Explosion' Warning Anthropic is considering a deal valuing the AI company near $1 trillion, potentially leading to one of the largest IPOs ever and surpassing SpaceX. Its revenue has skyrocketed, with Annual Recurring Revenue (ARR) reaching $45 billion in May 2026—a 500% increase in just five months. This vertical growth curve is attributed to its key products, Claude Code and Cowork, dominating AI coding and enterprise collaboration. Beyond commercial success, co-founder Jack Clark issued a pivotal warning in an interview: there is a greater than 50% chance that by the end of 2028, AI systems will achieve recursive self-improvement—the ability to autonomously build a 'better version' of themselves, initiating an 'intelligence explosion.' This prophecy underpins the company's astronomical valuation, as the market prices in the potential for transformative and disruptive AI. Further signaling its ambition, Anthropic formed a $1.5 billion joint venture with Goldman Sachs and Blackstone, aiming to disrupt traditional consulting firms like McKinsey by deploying Claude AI for complex strategic work. This move tests AI's capacity to replace high-level cognitive labor, a precursor to its predicted autonomous evolution. The narrative presents a dual future: unprecedented economic opportunity alongside significant risks like economic restructuring and security threats. Anthropic's meteoric rise and Clark's 2028 prediction frame the coming years as a countdown to a potential technological singularity."

marsbit05/11 07:08

The Largest IPO in History Is Approaching, Surpassing SpaceX, 28 Years of AI Self-Iteration, Countdown to Intelligence Explosion

marsbit05/11 07:08

Y Combinator CEO's AI Usage Guide: The Future Belongs to Those Who Build Compound Interest Systems

The article presents the vision of using AI not as a simple chatbot, but as a personalized operating system that creates compounding value. The author, Y Combinator CEO Garry Tan, details his system, built on open-source tools, which continuously structures all his inputs—meetings, books, emails—into a vast, interconnected "second brain." He describes concrete examples like "book mirroring," where a book's content is analyzed and mapped to his personal context, and automated meeting preparation that leverages accumulated knowledge. The core philosophy is "skillification": turning recurring tasks into reusable, self-improving "skills" that form the system's building blocks. A key insight is the "meta-skill" that creates new skills, ensuring continuous improvement. The architecture consists of a thin "harness" for routing, thick "skills" for specific tasks, and thick "data"—a 100,000-page knowledge base. The author argues the future belongs to individuals who build such personalized, compounding AI systems, not just those using centralized tools. He concludes by encouraging readers to start building their own systems using the open-sourced framework he provides.

marsbit05/11 06:05

Y Combinator CEO's AI Usage Guide: The Future Belongs to Those Who Build Compound Interest Systems