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天工超能AI

AI learning

Powered by the AI technology of the Tiangong large model, it provides an intelligent, efficient, and fast search experience. Tiangong AI Search can not only find materials, look up information, search for answers, and search for files, but also performs AI-powered intelligent aggregation of massive search results, answering your questions more systematically, improving your efficiency in understanding information, and serving as your best AI partner for study, work, and life.

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宝玉的分享

AI learning

"Baoyu's Sharing" is a personal tech blog whose content mainly revolves around large language models, Prompt Engineering, software engineering, engineering management, and frontend, among other areas, sharing observations and practical retrospectives in the form of articles on AI coding agents, AI-native development workflows, Agent architecture, and team management. The blog lists multiple articles on topics such as Claude Code, Codex, AI Agent, Vibe Coding, AI products, and organizational change, making it suitable for Chinese readers who want to understand developments in AI programming and AI engineering practice. The material does not provide specific information such as fees, pricing, or platform support.

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触手AI-AI图片生成工具产品-免费AI智能绘画生成在线平台

AI learning

Supports text-to-AI image generation; supports image-to-image; supports ControlNet conditional image generation, upload reference feature images and features, and create based on the features; supports inpainting's magic smudge, allowing local modifications, and supports self-trained AI painting models; supports using stacked AI drawing models on top of base style models; supports bookmarking various art styles, IP, scenes, characters, and design models in the model plaza.

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A Visual Guide to Mixture of Experts (MoE)

AI learning

This is a visual guide written by Maarten Grootendorst that uses more than 50 illustrations to explain the Mixture of Experts (MoE) technique in large language models (LLMs) in an accessible way. Starting from the feedforward neural network (FFNN) in the traditional Transformer, the article introduces the basic concepts of sparse models and MoE, and focuses on analyzing MoE's two core components: Experts and the Router. The content covers what experts actually learn (they lean toward syntax rather than specific domains), MoE's architectural design, routing mechanisms (such as KeepTopK, Token Choice, Top-1/Top-k routing), load balancing and auxiliary loss, expert capacity and token overflow, and other key issues. The article also introduces the Switch Transformer, which simplifies MoE training, and explores the application of MoE in vision models (such as V-MoE and Soft-MoE). The guide is suitable for readers who want to understand the principles of MoE and grasp the core ideas without complex mathematical derivations.

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