AI

Wafer-Scale Engine (WSE) is a wafer-level AI computing chip architecture developed by Cerebras. By designing the entire semiconductor wafer as a single ultra-large-scale AI accelerator, it surpasses the limitations of traditional chips that depend on interconnecting multiple processors to scale hashrate. WSE delivers a new computational paradigm for training and inference of large AI models through massive integration of compute cores, high-speed on-chip communication, and an optimized dataflow architecture.

BitMine Immersion (BMNR) is a company specializing in Bitcoin mining infrastructure, immersion cooling technology, and high-performance computing. Its core strategy centers on advancing digital asset infrastructure to next-generation computing platforms by optimizing hash rate efficiency and developing AI data center applications.

Meme projects rely heavily on community participation, cultural narratives, and continuous content creation. Unlike traditional applications that grow through product features alone, Meme projects often gain attention through user-generated content, social discussions, and viral distribution.

The 2U2 token is the native token designed for the 2U2.ai ecosystem. It connects AI Meme creation, platform services, user incentives, and community participation through a token-based economic model.

The growth of Meme communities has created a new demand for faster content creation, stronger community engagement, and more efficient ways to distribute viral narratives. As AI-generated content becomes increasingly common, Web3 projects are exploring new tools that connect creation, social interaction, and user participation.

Fully Homomorphic Encryption (FHE) is a privacy-preserving computation technology that allows AI systems to perform calculations directly on encrypted data. Its key capability enables AI model training, inference, and data analysis without revealing the underlying data, while ensuring that results remain consistent with those from plaintext computations. By leveraging ciphertext-based computation, FHE empowers AI systems to utilize data value and simultaneously mitigates the risk of sensitive information exposure.

This article examines Cecuro’s role in the Web3 security ecosystem, analyzes how AI supports smart contract security audits and enhances traditional manual auditing processes, and explores the interplay between AI Agents, multi-factor authentication, automated analysis, and the future evolution of Web3 security.

This article presents Cecuro’s AI Agent smart contract auditing framework, detailing how the platform leverages multiple AI Agents to collaboratively analyze code, identify vulnerabilities, and produce security reports. It further explores the distinctions between AI-driven automated audits and traditional manual security audits, highlighting their value in the Web3 security landscape.

AEON is a crypto payment infrastructure designed for AI agents and real-world commerce. Through its node network, cross-chain settlement protocol, and AEON Pay, it connects users, autonomous agents, wallets, and merchants to support scan-to-pay transactions, multichain payments, agent-to-agent settlements, and AI-driven real-world purchases.

To trade AEON on Gate, users need a Gate account and sufficient USDT, then enter the AEON/USDT spot market and place either a market order or a limit order. Once the order is filled, the purchased AEON appears in the account balance and can be held, sold, or transferred on-chain when deposits and withdrawals are available on a supported network.

The AEON tokenomics model is designed around cross-chain payments and AI agent commerce. It connects the AEON token to AEON Pay, cross-chain settlement, and the node network through payment gas, validator staking, protocol governance, fee-funded buybacks, and liquidity incentives. AEON has a fixed total supply of 1,000,000,000 tokens allocated across ecosystem development, the team, the foundation, early investors, marketing, liquidity, and airdrops.

As the blockchain ecosystem evolves rapidly, smart contract security has emerged as a crucial concern in the Web3 landscape. Cecuro, a smart contract auditing platform powered by AI Agent technology, recently achieved a 91.45% vulnerability detection rate in the EVMBench benchmark test introduced by OpenAI and Paradigm.

AI stock trading is the use of machine learning, natural language processing, statistical models, and automated software to process stock market data and support investment research, signal generation, portfolio management, or order execution. For retail investors and traders exploring AI-powered stock trading tools, that can mean anything from screening stocks and identifying market trends to analyzing news and earnings reports, managing position sizes, and executing strategies under predefined conditions.

AI stock trading bots are not inherently safe or unsafe. Their actual risk depends on model quality, data sources, execution permissions, risk controls, and the level of human oversight. A bot can improve data processing and execution efficiency, but it can also turn flawed signals, software errors, or poorly designed strategies into real orders at high speed.

To use AI stock trading tools on Gate effectively, start by matching the tool to the job: use the stock portfolio bot for rule-based portfolio management, use Gate AI for market research and platform guidance, and use Gate Skills Hub to build or extend AI workflows for analysis, signal generation, or trade execution when supported by the specific Skill and account permissions. For sophisticated retail traders and investors managing diversified stock portfolios, as well as developers or advanced users building AI agent workflows on Gate.com, the key is not to treat these products as one interchangeable “AI stock trading bot.”