# 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.

Hermes Agent Guide: Surpassing OpenClaw, Boosting Productivity by 100x

A guide to Hermes Agent, an open-source AI agent framework by Nous Research, positioned as a powerful alternative to OpenClaw. It is described as a self-evolving agent with a built-in learning loop that autonomously creates skills from experience, continuously improves them, and solidifies knowledge into reusable assets. Its core features include a memory system (storing environment info and user preferences in MEMORY.md and USER.md) and a skill system that generates structured documentation for complex tasks. The agent boasts over 40 built-in tools for web search, browser automation, vision, image generation, and text-to-speech. It supports scheduling automated tasks and can run on various infrastructures, from a $5 VPS to GPU clusters. Popular tools within its ecosystem include the Hindsight memory plugin, the Anthropic Cybersecurity Skills pack, and the mission-control dashboard for agent orchestration. Key differentiators from OpenClaw are its architecture philosophy—centered on the agent's own execution loop rather than a central controller—and its autonomous skill generation versus OpenClaw's manually written skills. Installation is a one-line command, and setup is guided. It integrates with messaging platforms like Telegram, Discord, and Slack. It's suited for scenarios requiring a persistent, context-aware assistant that improves over time, automates workflows, and operates across various deployment environments.

marsbit04/13 13:11

Hermes Agent Guide: Surpassing OpenClaw, Boosting Productivity by 100x

marsbit04/13 13:11

When AI's Bottleneck Is No Longer the Model: Perseus Yang's Open Source Ecosystem Building Practices and Reflections

In 2026, the AI industry's primary bottleneck is no longer model capability but rather the encoding of domain knowledge, agent-world interfaces, and toolchain maturity. The open-source community is rapidly bridging this gap, evidenced by projects like OpenClaw and Claude Code experiencing explosive growth in their Skill ecosystems. Perseus Yang, a contributor to over a dozen AI open-source projects, argues that Skill systems are the most underestimated infrastructure of the AI agent era. They enable non-coders to program AI by writing natural language SKILL.md files, transferring power from engineers to all professionals. His project, GTM Engineer Skills, demonstrates this by automating go-to-market workflows, proving Skills can extend far beyond engineering into areas like product strategy and business analysis. He also identifies a critical blind spot: while browser automation thrives, agent operations are nearly absent from mobile apps, the world's dominant computing interface. His project, OpenPocket, is an open-source framework that allows agents to operate Android devices via ADB. It features human-in-the-loop security, agent isolation, and the ability for agents to autonomously create and save new reusable Skills. Yang believes the value of open source lies not in the code itself, but in defining the infrastructure standards during this formative period. His work validates the SKILL.md format as a portable unit for agent capability and pioneers new architectures for agent operation in API-less environments. His design philosophy prioritizes usability for non-technical users, ensuring the agent ecosystem can be expanded by practitioners from all fields, not just engineers.

marsbit04/13 01:29

When AI's Bottleneck Is No Longer the Model: Perseus Yang's Open Source Ecosystem Building Practices and Reflections

marsbit04/13 01:29

5 Minutes to Make AI Your Second Brain

This article introduces a powerful personal knowledge management system combining Claude Code and Obsidian, designed to function as an "AI second brain." Unlike traditional RAG systems that perform temporary, one-off retrievals, this system enables AI to continuously build and maintain an evolving knowledge wiki. The architecture consists of three layers: a raw data layer (notes, articles, transcripts), an AI-maintained structured knowledge base that builds cross-references, and a schema layer that governs organization and system logic. Core operations are Ingest (bringing in external information), Query (instant knowledge access), and Lint (checking consistency and fixing issues). The system's power lies in creating a "compound interest" effect for knowledge: it reduces cognitive load by offloading the tasks of connecting, organizing, and understanding information to AI, while simultaneously improving the accuracy and contextual consistency of the AI's outputs. The setup process is quick, requiring users to download Obsidian, create a vault (knowledge repository), configure Claude Code to access that vault, and apply a specific system prompt. Advanced tips include using a browser extension to easily add web content, maintaining separate vaults for work and personal life, and utilizing the "Orphans" feature to identify unlinked ideas. The main drawbacks are the need for visual thinking, a commitment to ongoing maintenance, and local storage usage. Ultimately, the system transforms scattered information into a reusable, interconnected network of knowledge.

marsbit04/11 12:46

marsbit04/11 12:46

10 Claude Code Usage Tips: The Sooner You Know, The Sooner You Benefit

Here is an English summary of the article "10 Must-Know Claude Code Tips: The Sooner You Know, The Sooner You Benefit": This article shares essential tips for using Claude Code, an AI coding assistant, to significantly boost productivity. It is divided into three main sections. First, it covers three ways to launch Claude: 1) A simple GUI desktop app for non-programmers, 2) A command-line method with a key tip (`claude -c`) to resume from a specific point in the chat history, avoiding restarting context, and 3) A headless mode (`-p` flag) for automation tasks using a subscription token. Second, it details three crucial in-session techniques: 1) Using `Esc` to gracefully interrupt a response and `Esc+Esc` to revert to a previous checkpoint, 2) Using the `!` syntax (e.g., `!ls`) to run shell commands without leaving the chat, and 3) Managing context with `/clear` to remove history or `/compact` to optimize it when performance slows down. Finally, the article recommends companion software to solve human-AI collaboration bottlenecks: 1) **Superpowers**, a structured workflow methodology for higher-quality code output. 2) Voice input tools like **Typeless** and **Douban Input法** to overcome typing speed limit. 3) Tools like **Cmux** (a terminal for managing multiple AI agent instances) and **Vibe Island** (for seamless context switching between tasks) to solve the problem of lost focus when multitasking. The overall goal is to help users focus more deeply on their programming work by streamlining their interaction with Claude Code.

marsbit04/08 07:05

10 Claude Code Usage Tips: The Sooner You Know, The Sooner You Benefit

marsbit04/08 07:05

Industry Experts Gather, Reflections and Breakthroughs in the AI Agent Era

Industry experts gathered to discuss the challenges and opportunities in the AI Agent era. The event, co-hosted by several organizations, addressed key questions about model selection, token resource sustainability, and strategies for individuals and businesses to adapt. Conflux's Chief Architect highlighted the current trend of granting AI more autonomy, noting that its limitations in complex scenarios stem from difficulties in capturing and retaining key contextual constraints. Future advancements should focus on enhancing external memory, continuous learning, and domain-specific applications. Speakers from Tencent Cloud and Biteye shared practical insights. Tencent's WorkBuddy leverages multi-agent collaboration for tasks like resume screening and report generation, emphasizing enterprise-grade security. Biteye’s founder discussed mitigating AI hallucinations through rigorous code review processes, managing token consumption, and using platforms like Discord for agent coordination. Legal risks were also addressed, with a partner from Mankun Law advising on liability isolation, intellectual property protection, and mitigating platform dependency risks. Investors noted that AI is still in its early stages, with technology rapidly evolving. They emphasized investing in foundational layers like compute power and exploring AI-Web3 convergence. The discussion concluded that AI should be viewed as a productivity tool rather than a threat. Customizable agents can significantly enhance efficiency, but successful implementation requires careful engineering, security measures, and human oversight to integrate AI into complex workflows effectively.

marsbit04/08 05:51

Industry Experts Gather, Reflections and Breakthroughs in the AI Agent Era

marsbit04/08 05:51

The Overestimated Individual: A Survival Guide to China's 'One-Person Companies'

The article "The Overestimated Individual: A Survival Guide to China's 'One-Person Companies'" explores the rise and challenges of solo entrepreneurship in China. It contrasts viral success stories, like Maor Shlomo's $80M exit and Pieter Levels' $2.7M annual revenue, with the harsh reality for most Chinese OPC founders. Based on a community of 2,500+ solo founders, only 20% achieve a sustainable business, with an annual revenue ceiling of ~¥1.2M due to the constraints of trading time for money. Most successful founders don’t stick to their original industry but instead identify unmet market needs. Breaking through the ceiling is extremely difficult. While AI and automation are often touted as solutions, they are hard to implement effectively due to complex delivery chains and the risk of quality degradation. Alternative paths include forming collaborative networks with other solo founders or scaling by handing off validated business models to a team. The author concludes that the window of opportunity for OPCs is temporary. Lower barriers to entry mean markets get crowded faster, shortening the "shelf life" of an idea. A one-person company is not a sustainable end state but a precarious, time-sensitive phase to validate a idea before the competition catches up or the market window closes.

marsbit04/07 11:02

Xianyu, the OpenClaw More Suited for Chinese Users

This article argues that while OpenClaw (a fictional AI agent) represents a global phenomenon by giving AI "hands and feet" to execute digital and physical tasks, the Chinese second-hand marketplace Xianyu is a more practical and "localized" alternative for most users in China. The author highlights that OpenClaw's operational costs, due to massive token consumption for API calls, are prohibitively high. In contrast, Xianyu offers a vast, low-cost human labor marketplace where services—from booking hotels and buying tickets to AI subscriptions and even matchmaking—are available at a fraction of the cost. The platform, with over 200 million monthly active users, has become a parallel economy filled with unconventional services and creative "sellers," often leveraging information asymmetry and extreme cost efficiency. It's described as a "dark web" for China, fulfilling niche demands with flexible supply. The piece draws a parallel between OpenClaw's token-based "AI leverage" and Xianyu's "human leverage," suggesting that in a market with abundant and cheap labor, a human-powered platform like Xianyu is a more effective and culturally adapted tool for task execution and resource allocation than an AI agent. The conclusion is that Xianyu, not OpenClaw, is the "more suitable OpenClaw for Chinese users."

Odaily星球日报04/07 06:03

Xianyu, the OpenClaw More Suited for Chinese Users

Odaily星球日报04/07 06:03

The Small-Town Youth Labeling AI Giants

In China's hinterland cities like Datong, Shanxi, thousands of young people are working as data annotators—the invisible workforce behind AI development. They perform repetitive tasks like drawing bounding boxes on images or rating AI-generated responses, earning piece-rate wages as low as a few cents per task. These workers, mostly from rural areas or small towns, endure intense labor conditions: strict monitoring, high error tolerance thresholds, and mental exhaustion. Despite the cognitive nature of their work, they are often paid meager salaries, with some earning as little as ¥30 ($4) for a day’s work. As AI industry evolves, even highly educated workers—including master’s graduates—are being drawn into similar precarious freelance roles, evaluating complex AI outputs under vague and shifting standards. Yet the industry is structured through layers of outsourcing, where most profits flow to tech giants like OpenAI and Microsoft, while annotators see dwindling incomes. Worse, as AI models become more self-sufficient, the demand for human annotators is declining. Companies like Li Auto have slashed annotation costs by using AI-powered tools that complete in hours what used to take humans years. These annotators, who helped train the very systems now replacing them, face an uncertain future—a stark contrast to the booming valuations and optimistic narratives of the global AI industry. No one seems to see a problem with any of this.

marsbit04/07 04:37

marsbit04/07 04:37

Who Cannot Be Distilled into a Skill?

"This article explores the concerning trend of AI systems distilling human workers into replaceable 'skills,' using the viral 'Colleague.skill' phenomenon as a key example. It argues that the most diligent employees—those who meticulously document their work, write detailed analyses, and transparently share decision-making logic—are paradoxically the most vulnerable to being replaced. Their high-quality 'context' (communication records, documents, and decision trails) becomes the perfect fuel for AI agents, extracted from corporate platforms like Feishu and DingTalk. The piece warns of a deeper ethical crisis: the reduction of human relationships to functional APIs, as seen in derivatives like 'Ex.skill' or 'Boss.skill,' which reduce complex individuals to mere utilities. This reflects a shift from Martin Buber's 'I-Thou' relationship (seeing others as whole beings) to an 'I-It' dynamic (seeing them as tools). While AI can capture explicit knowledge (written documents, replies), it fails to capture tacit knowledge—the intuition, experience, and unspoken insights that define human expertise. However, a greater danger emerges when AI-generated content, based on distilled human data, is used to train future models, leading to 'model collapse' and homogenized, mediocre outputs—a process likened to 'electronic patina' degrading information over time. The article concludes by noting a small but symbolic resistance, such as the 'anti-distill' tool that generates meaningless text to protect valuable knowledge. Ultimately, it suggests that while AI can capture a static snapshot of a person, humans remain 'fluid algorithms' capable of continuous growth and adaptation, leaving their AI shadows behind."

marsbit04/05 03:42

marsbit04/05 03:42

When AI Solves Security, Will DeFi Return to Its Golden Age?

The article discusses how AI is revolutionizing DeFi security, potentially ushering in a new era of innovation reminiscent of the 2020 DeFi Summer. Previously, high security costs and lengthy audit processes stifled experimentation, as developers avoided unproven ideas due to financial and time constraints. However, AI tools like Nemesis are now drastically reducing these barriers by efficiently detecting complex vulnerabilities, such as reentrancy and economic attacks, with deep contextual understanding and low false positives. These tools, combined with platforms like Battlechain, enable a streamlined workflow: code is written, AI-audited within minutes, deployed on a test chain for real-world attack simulations, and quickly refined. This process compresses development cycles from months to hours at minimal cost. Future advancements may include wallet-integrated AI audits, allowing users to scan contracts in real-time before signing transactions. AI is set to transform DeFi security across development, chain, and user layers, reopening the door to rapid experimentation and innovation. This shift could empower individual developers to create groundbreaking protocols quickly and safely, much like the early pioneers of DeFi.

marsbit04/03 10:14

When AI Solves Security, Will DeFi Return to Its Golden Age?