# 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.

Meeting the Founder of OpenClaw at a Hackathon: What Else Can These Lobsters' Do?

In March 2026, Imperial College London hosted the UK AI Agent Hackathon 2026, centered around the OpenClaw framework. The event attracted over 1,200 participants and 5,000 online viewers. OpenClaw creator Peter Steinberger attended personally. Notable projects included: - AgroMind: An AI system using satellite and weather data to predict and automatically hedge agricultural supply chain risks. - ClawBio: An open repository for reproducible bioinformatics tools, enabling local and private analysis. - BioSentinel: An automated pipeline that identifies pathogens and designs therapeutic molecules using public health data. - London Neural System: A real-time urban monitoring system that integrates traffic, air quality, and infrastructure data. - Highstreet AI: A multi-agent tool for small businesses to automate orders and inventory management. - AlphaMind AI: An agent that helps retail investors analyze and rebalance portfolios using institutional-grade logic. Steinberger’s participation was nearly derailed by a last-minute visa issue. His humble, hands-on approach resonated with attendees. Although Steinberger is skeptical of crypto, several Web3-oriented projects emerged around agent sovereignty, automated trading, and public governance. However, security remains a major concern for OpenClaw in financial and blockchain applications.

Odaily星球日报03/20 05:30

Meeting the Founder of OpenClaw at a Hackathon: What Else Can These Lobsters' Do?

Odaily星球日报03/20 05:30

Crypto.com cuts 12% workforce as CEO warns AI shift is ‘do or die’

Crypto.com has cut roughly 12% of its workforce as part of a strategic shift toward enterprise-wide AI integration, with CEO Kris Marszalek warning that companies slow to adopt AI risk failure. The layoffs target roles incompatible with an AI-focused direction, following the company's acquisition of AI.com for $70 million. This reflects a broader industry trend where crypto firms are combining cost-cutting with AI-driven efficiency, moving beyond purely market-driven layoffs to structural resets. Similarly, Algorand Foundation reduced its workforce by 25%, citing market pressures, though its cuts appear more reactive than strategic. The trend indicates that crypto layoffs are increasingly tied to technological realignment, with AI integration becoming a key competitive factor.

ambcrypto03/19 19:01

Crypto.com cuts 12% workforce as CEO warns AI shift is ‘do or die’

ambcrypto03/19 19:01

Top 11 Telegram trading bots of March 2026

Telegram trading bots have become essential tools for crypto traders, offering speed, automation, and convenience directly within the messaging app. These bots help users execute trades quickly, manage portfolios, and automate strategies like sniping new tokens, copy trading, and setting limit orders. The top bots include BONKbot and GMGN for Solana-focused trading; BullX Bot and Maestro for multi-chain support; BananaGunBot and TrojanBot for security and speed; and beginner-friendly options like TradeSanta. While each bot has unique strengths—such as security features, low fees, or user-friendly interfaces—traders should research carefully to match a bot’s capabilities with their individual strategies and risk tolerance.

ambcrypto03/18 12:46

Top 11 Telegram trading bots of March 2026

ambcrypto03/18 12:46

You've Been Training Google's AI for Free for 15 Years, Completely Unaware

For 15 years, Google has leveraged reCAPTCHA to harness free human labor to train its AI, unbeknownst to users. Initially created to digitize books by having users transcribe distorted text, the system evolved under Google's ownership. With reCAPTCHA v2, users were tasked with identifying objects like traffic lights and crosswalks in images from Google Street View. This provided massive, free training data for Google's computer vision models, directly benefiting products like Google Maps and the autonomous vehicle company Waymo, valued at $45 billion. At its peak, 200 million reCAPTCHAs were solved daily, amounting to 500,000 hours of free human labor—worth an estimated $5 million per day at minimum wage. This data-labeling operation, embedded as a mandatory gateway to essential websites, was unparalleled in scale and cost-efficiency. The latest version, reCAPTCHA v3, invisibly analyzes user behavior to verify humanity, further feeding AI systems. The profound irony is that users spent years proving they were human by performing tasks AI couldn't do, thereby training the very systems that now make their contributions obsolete. Google never asked for consent, paid for this labor, or disclosed its purpose, turning the entire internet-using population into unwitting, unpaid trainers for its commercial AI empire.

marsbit03/18 09:32

You've Been Training Google's AI for Free for 15 Years, Completely Unaware

marsbit03/18 09:32

HashKey Accelerates AI Strategy Implementation: From Organizational Efficiency to New-Generation Digital Financial Infrastructure

HashKey Group is accelerating its AI strategy, transitioning from organizational efficiency to building next-generation digital financial infrastructure. The company has established a "Group Technology Steering Committee" to oversee the overall planning and implementation of AI and cutting-edge technologies. According to CTO Devin Zhang, the move marks a shift from fragmented, individual use of AI to a group-level systematic adoption aimed at upgrading organizational capabilities. Key priorities include improving internal operational efficiency—particularly in R&D and non-R&D functions like compliance and finance—and enhancing user experience through intent-driven interactions. Initial AI applications focus on high-repetition, measurable scenarios such as automated development pipelines, threat detection, risk management, and anti-money laundering analysis. Devin emphasized that a robust security framework is essential for financial institutions adopting AI, as agent-based systems require careful management of permissions, resource access, and accountability. HashKey is taking a compliant, risk-aware approach: prioritizing back-end and internal use cases first, while cautiously evaluating customer-facing innovations like automated trading. In the long term, HashKey envisions AI and blockchain converging, with AI agents gaining digital identities and payment capabilities, potentially making blockchain a key infrastructure for managing AI-driven economies. The company aims to boost efficiency near-term, strengthen mid-term technical foundations, and ultimately contribute to the evolution of digital financial infrastructure.

marsbit03/18 06:07

HashKey Accelerates AI Strategy Implementation: From Organizational Efficiency to New-Generation Digital Financial Infrastructure

marsbit03/18 06:07

Private Equity's "Detroit: Become Human" Moment: When AI Takes Over Alpha, What's Left for Human Fund Managers?

A recent report by Anthropic highlights that 94% of financial roles are susceptible to AI replacement, though only 28% have been automated so far. The private fund industry is rapidly adopting AI to enhance efficiency and reduce costs, especially in research, operations, and sales. AI researchers, such as tools like OpenClaw, offer advantages over human analysts: they work 24/7, require no salary or benefits, and avoid human limitations like bias or job-hopping. Quantitative funds, with strong computational infrastructure, are already deploying multi-agent systems that minimize human involvement. In contrast, subjective fund managers face pressure to adopt AI to stay competitive, as散户 investors also use AI to narrow information gaps. However, AI has limitations: it cannot fully grasp market sentiment, human irrationality, or emotional nuances in investing. If AI eventually replaces most fund managers and researchers, markets may become efficient, eliminating alpha opportunities. The future role of humans in asset management may lie in intuition, passion for investing, and handling uncertainty—areas where AI falls short. The industry must determine how humans and AI can collaborate effectively, rather than merely reacting to technological change.

marsbit03/18 02:54

Private Equity's "Detroit: Become Human" Moment: When AI Takes Over Alpha, What's Left for Human Fund Managers?

marsbit03/18 02:54

Stratechery Overturns the AI Bubble Theory: What Should We Use AI For?

Stratechery's Ben Thompson revises his stance on the AI bubble debate, arguing that current AI investment reflects structural growth driven by fundamental technological shifts, not speculation. He identifies three key transitions in LLM development: ChatGPT’s debut (making AI broadly usable but unreliable), OpenAI’s o1 (introducing reasoning and reliability), and the recent emergence of true Agent systems (e.g., Anthropic’s Opus 4.5 and OpenAI’s GPT-5.2-Codex). The critical innovation is the "agent harness"—a control layer that autonomously schedules models, uses tools, and verifies outcomes, reducing human intervention. This transforms AI from an assistive tool into an actionable infrastructure, enabling complex task execution. Agentic systems drive surging demand for compute, as they require repeated model calls and higher usage frequency. Thompson emphasizes that AI demand now depends not on user numbers, but on per-user agent utilization. Enterprises are adopting AI not only for efficiency gains but for structural workforce reduction, as agents amplify the impact of key employees while lowering coordination costs. He concludes that massive capital expenditures are justified by real demand, and profits will flow to integrated model-harness providers rather than commoditized standalone models.

marsbit03/17 03:57

Stratechery Overturns the AI Bubble Theory: What Should We Use AI For?

marsbit03/17 03:57

From 'Scientist' to 'Prophet': How Did OpenClaw Help Him Earn $100,000 in 10 Days?

This article features an interview with Kevin, a trader who used OpenClaw to turn $30,000 into over $100,000 in just 10 days on the prediction market platform Polymarket. Originally an ERP architect and later a developer of sports betting systems, Kevin entered the crypto space in 2018. He made his first significant profit during the 2023 "inscription summer" with ORDI. In summer 2025, he shifted to Polymarket, attracted by its improved liquidity and user experience. Initially, he used automated arbitrage strategies between betting odds, doubling his initial $100,000 investment. Since late February, he integrated OpenClaw into his strategy. While 60% of his funds still run automated arbitrage, 40% are now managed with OpenClaw-assisted subjective betting. OpenClaw helps by gathering and weighting factors like smart money movements, public sentiment, and team lineups—even uncovering new influencing factors. It also automates backtesting and execution. Kevin highlights Polymarket’s AI-friendly API as a key advantage. Currently, OpenClaw is experimenting mainly in sports betting with small automated bets (~$1,000). Kevin plans to develop and sell packaged OpenClaw "Skills" based on his strategy in the future.

比推03/16 13:13

From 'Scientist' to 'Prophet': How Did OpenClaw Help Him Earn $100,000 in 10 Days?