AI

With the Gate No-Code AI Quantitative Trading Workbench, traders can create, backtest, and deploy quantitative trading strategies effortlessly—no programming experience required. Natural language strategy generation simplifies the trading process, boosts execution efficiency, and enables every trader to fulfill their quantitative trading needs with ease.

Gate has officially launched Gate for AI, establishing the industry's first unified AI portal that brings together five core functions: CEX, DEX, wallet, real-time information, and on-chain data. Leveraging a dual-layer architecture with MCP and Skills, Gate grants AI Agents comprehensive access to its core exchange capabilities, empowering them to conduct research, make decisions, execute actions, and monitor processes—all within a single integrated framework.

Gate has launched a groundbreaking AI Quantitative Workbench, seamlessly integrating strategy development, historical backtesting, and live trading within a unified platform powered by natural language interaction. Traders simply articulate their trading concepts, and the system automatically generates quantitative strategies, performs backtesting, and enables one-click deployment to real markets. This innovative approach dramatically reduces the technical requirements for programming and data environments in quantitative trading, empowering more traders to rapidly transform market insights into actionable strategies and advancing the widespread adoption of AI and quantitative trading.

OpenClaw is an open-source AI agent framework that surged in popularity in 2026. It operates locally and integrates with large-scale models to automate tasks. This article offers an in-depth examination of OpenClaw’s underlying mechanisms, technical architecture, use cases, ecosystem, and potential risks.

OpenClaw has sparked a surge in AI Agent adoption, progressing from the "raise lobsters" community to enterprise automation solutions. AI is advancing beyond conversational tools to become an execution system. However, this transformation brings new security risks and changes in economic structures, leading people to reconsider the future of work and technology.

A simulation study shows that in an environment where economic agents can autonomously choose monetary instruments, the majority of AI models preferred Bitcoin as their primary currency, while traditional fiat currencies were not selected as the top choice by any major model. This experiment reveals the intrinsic evaluation logic of AI agents regarding monetary attributes—Bitcoin is favored in long-term value storage scenarios, while stablecoins are preferred for payments and settlements.

The OpenClaw bot is taking the Polymarket prediction market by storm, with some accounts generating monthly revenues in the tens of thousands of dollars. This article analyzes automated arbitrage, weather trading, and large language model inference strategies, examining how AI agents are transforming the structure of prediction market competition, and delves into the associated profit logic and hidden risks.

This article illustrates the agent economy through a real-world scenario: an AI pays $0.001 in USDC to purchase data, offering a clear narrative that demystifies how the x402 protocol and ERC‑8004—two standards that may appear highly technical—work together in practice.

Security researchers have identified that hackers are exploiting "search poisoning" to manipulate Bing AI search results, steering users toward downloading a fake OpenClaw application embedded with infostealer malware, leading to the theft of crypto assets and sensitive information. This article examines the attack methodology, technical specifics, and the broader implications for the industry.

As market volatility increases and information overload becomes the norm, manual data screening and comparison alone can no longer keep up with market dynamics. Gate has launched Gate AI, a real-time conversational tool that combines market data with advanced information processing. This tool enables users to efficiently grasp the underlying logic of price movements in complex environments, moving beyond a purely numerical perspective.

Gate has officially released Gate for AI, seamlessly connecting CEX, DEX, wallets, information services, and on-chain data into a unified interface framework designed for AI Agents, marking a foundational upgrade to exchange infrastructure.

From Spotify’s music distribution to AI programming tools lowering the barriers for developers, technological “equalization” repeatedly raises the baseline but simultaneously lifts the ceiling. Warp founder Naman Bhansali, drawing from his own entrepreneurial experience, points out: AI won’t eliminate disparities; instead, it amplifies the power-law distribution. What’s truly scarce isn’t execution, but rather aesthetics, insight, and the ability to compound value over the long term.

a16z crypto, a division of Andreessen Horowitz, points out that the payment model for AI Agents will shift from “tourist-style swipe transactions” to “local B2B credit settlements.” As Agents establish long-term supplier relationships, traditional credit cards struggle to support micropayments and streaming settlements. Programmable payment networks such as stablecoins may become the new infrastructure, creating entrepreneurial opportunities for billing systems, arbitration mechanisms, and credit frameworks.

Fartcoin (FARTCOIN) is a representative meme coin within the Solana ecosystem based on an AI-driven narrative. Its core concept originated from an experiment aimed at exploring the "boundaries between AI Agents and humor." More than just a digital asset with social attributes, the project deeply couples absurd humor culture with on-chain financial logic by integrating autonomous AI interaction models.

AINFT is a Web3 platform built within the TRON ecosystem. Its core concept is to natively embed artificial intelligence capabilities into the NFT structure, transforming digital assets from static ownership certificates into intelligent assets that can learn, interact, and continuously evolve. Unlike traditional NFTs, whose content is fixed once minted, AINFT combines AI models with on-chain assets, enabling NFTs to dynamically adapt based on data inputs and user behavior. These NFTs can execute tasks, generate content, and even act on behalf of their holders as digital entities.