This article explains Bilibili's large‑scale big‑data platform architecture, the huge volume of offline and real‑time tasks it handles, common failure and slowdown causes, and why the company is exploring AI‑driven assistants to help engineers troubleshoot these issues efficiently.
This article explains how Xiaomi’s XiaoAI assistant integrates large language models for intent distribution, vertical intent understanding, and response generation, detailing the architecture, challenges such as knowledge requirements and sub‑200 ms latency, and the shift from prompt engineering to model fine‑tuning that boosted user retention by 10% and query satisfaction by 8%.
This article explains why the chaotic nature of modern operations—spanning mixed‑technology stacks, cross‑domain tasks, and legacy‑new architecture battles—creates value, outlines a fair post‑mortem process, and introduces practical tools and AI agents such as LinuxMirrors, kubectl‑ai, Zread AI, and Lerwee that help turn disorder into reliable, automated workflows.
This article explains how Bilibili leverages a large‑language‑model‑driven assistant to diagnose and resolve failures and slowdowns in its massive big‑data platform, detailing the platform’s five‑layer architecture, common task issues, and the need for intelligent troubleshooting tools.
The article explains the concept of AI‑native applications, distinguishes them from AI‑plugin extensions, outlines their core principles such as model‑first design, data flywheel, event‑driven agents, multimodal semantics, continuous learning, and provides a seven‑step practical guide with code examples for building an AI‑native app.
This guide walks you through installing the Ollama local large‑language‑model runtime on Windows, deploying a Gemma2 model, then setting up ShellGPT on Linux to interact with the local LLM, covering configuration, basic commands, and advanced usage examples.
A first‑batch tester spends two days with Meituan’s AI assistant XiaoMe, detailing its restaurant recommendation and ordering flow, pinpointing mismatched venue selections, coupon‑saving quirks, unsupported tasks like ride‑hailing, and evaluating the underlying LongCat‑Flash‑Chat model’s speed and personalization.
A Chinese courier hub handling 70,000 parcels daily struggled with low service scores, but after deploying a data‑driven "Site Manager Intelligent Assistant" that pushes responsibility, metrics, and corrective actions to managers, its KPIs surged from a 30‑point rating to over 80, dramatically improving efficiency and customer satisfaction.
This article explains how to create a RAG‑based intelligent QA system that fetches the latest documentation (e.g., PlantUML) before querying Gemini, detailing knowledge‑base creation, embedding, vector store management, LangChain integration, and deployment tips.
This article walks through retrieving ten years of Youdao Cloud Notes, selecting a RAG implementation (self‑built or using Coco AI), configuring cookies, loading the notes locally, and integrating a large language model to enable full‑text search and intelligent question‑answering.
This guide details how to transform static semantic documentation into an interactive AI assistant using Coze, covering workflow tool selection, knowledge‑base optimization, step‑by‑step workflow construction, UI design, testing strategies, and future optimization directions.
This guide explores Cursor AI's billing mechanisms, tool integrations, conversation management strategies, rule configurations, and practical pitfalls, offering developers a comprehensive methodology to maximize productivity, avoid common errors, and leverage advanced features like MAX mode and multi‑dialog workflows.
This guide explains the challenges of traditional operations in the mobile era, introduces the Alibaba Cloud OOS AI Assistant for natural‑language, cross‑device incident response, and provides a step‑by‑step tutorial on configuring DingTalk bots, ChatOps integration, and best‑practice usage.
Gemini CLI, a Google‑released command‑line AI workflow tool, brings Gemini models into the terminal, enabling natural‑language coding, debugging, content creation, research and task management, with features like a 1 million‑token context window, generous free quota, and full open‑source availability under Apache 2.0.
This guide shows how to create a fully local Retrieval‑Augmented Generation (RAG) system using n8n, Docker, Ollama and the free Qwen3 embedding model, enabling secure, up‑to‑date AI assistants that answer enterprise questions without exposing any proprietary data.
After upgrading to Coco AI 5.0+, the author reports faster response times and higher answer accuracy, explains the two assistant modes, details the limited Easysearch support versus Elasticsearch, and provides step‑by‑step troubleshooting for mapping‑related and model‑configuration errors.
This article introduces Eino, an open‑source Golang framework for large‑model AI applications, explains its core capabilities, walks through creating a simple AI assistant with message templates and chat model integration, and demonstrates how to extend the system with tools and a modular architecture for future expansion.
The article details how Youzan’s Store Smart Assistant was built on the Feishu Aily platform, describing why Aily was chosen, the three‑stage development process, deep system integration, practical tips for knowledge‑base management and model stability, and the resulting efficiency gains such as handling 80% of routine queries.
IntelliJ IDEA 2025.1 introduces comprehensive Java 24 support, makes Kotlin K2 the default mode, delivers a major AI assistant upgrade with free unlimited code completion, and adds numerous productivity enhancements such as improved debugging, UI redesign, containerfile support, and tighter Gradle integration.
This article walks through the motivation, core concepts of Retrieval‑Augmented Generation, and a step‑by‑step guide to constructing a pet‑hospital AI assistant on Alibaba Cloud using LLMs, vector databases, and automated pipelines, complete with code examples and practical tips.
This guide outlines practical project‑management practices and tool configurations—such as using Cursor rules, Notepad, milestone documents, and regular code‑base re‑indexing—to maximize the effectiveness of the Cursor AI assistant in large‑scale software projects.
WeChat’s latest update introduces an enhanced floating‑window system, a built‑in job‑search function, a new star‑mark feature for quick access, and an AI assistant, all aimed at improving multitasking, productivity, and user interaction across iOS, Android, and HarmonyOS platforms.
This case study demonstrates how the AI assistant ChatDBA identifies and resolves a tenant data‑synchronization failure in an OceanBase primary‑backup cluster, detailing four interactive troubleshooting rounds, the final SQL fix, and a comparative analysis with the DeepSeek‑R1 model.
This tutorial walks you through installing Ollama on Windows, configuring and running a local large language model, then setting up ShellGPT on Linux to communicate with Ollama, including configuration files, command examples, and REPL usage, while omitting unrelated promotional content.
This article explains how to integrate Gitee's Model Control Protocol (MCP) with Java using LangChain4j, covering download, Maven setup, configuration, both stdio and SSE transport modes, sample code, output examples, and a comparison of the two modes to create an AI‑powered repository assistant.
The article examines Harvey’s rapid rise to a $3 billion valuation, its AI‑driven legal products, strategic positioning as an “AI assistant” with scenario‑specific solutions, market‑entry tactics, and broader insights on selecting high‑value, AI‑friendly use cases for legal AI.
This guide shows how to quickly integrate Alibaba Cloud's AI Search platform and the DeepSeek‑R1 large model with Elasticsearch to create a smart AI Assistant that automates cluster diagnostics, query generation, and visual analytics for operations tasks.
This guide walks you through using Alibaba Cloud's PAI‑RAG platform to deploy a DeepSeek large‑language‑model assistant that combines real‑time web search with an enterprise knowledge‑base, covering deployment, network‑search configuration, testing, and advanced enterprise features.
JetBrains has updated its AI Assistant to run local large language models for enhanced privacy, added support for Anthropic's Claude 3.5 series and OpenAI's o1, o1‑mini, and o3‑mini models, and highlighted faster, cost‑effective inference for coding and scientific tasks.
ChatGPT’s new scheduled tasks feature lets Plus or Pro users automate actions without coding, and this guide explains prerequisites, enabling notifications, creating effective and advanced tasks, managing, editing, and optimizing them, plus tips for practical use and staying updated.
This guide explains how to set up DeepSeek in the popular Java IDE IntelliJ IDEA by installing and configuring the open‑source Continue plugin and the CodeGPT plugin, covering installation steps, activation, API key acquisition, and basic usage examples.
This tutorial explains how to prepare the environment, install the CodeGPT plugin, configure DeepSeek with an API key, set up chat and inference models in IntelliJ IDEA, and use the AI assistant for code generation, completion, explanation, and token usage monitoring, all illustrated with Java examples.
This guide explains why DeepSeek is a valuable AI assistant, outlines the challenges of high demand, and provides step‑by‑step instructions for creating, configuring, testing, and publishing a DeepSeek AI assistant within the DingTalk platform to ensure stable access.
This step‑by‑step tutorial shows beginners how to install Ollama, deploy the DeepSeek large language model locally, and configure the Chatbox AI client to create a functional AI assistant on Windows, macOS, Linux, or mobile devices within ten minutes.
This guide walks you through installing Ollama, downloading the DeepSeek model, and configuring the Chatbox AI client so you can run a powerful local AI assistant on Windows, macOS, or Linux within minutes.
This tutorial explains why a personal knowledge base is essential, then walks you through installing Cherry Studio, configuring DeepSeek chat and embedding models, creating and populating the knowledge base, and finally using an AI assistant to retrieve information efficiently.
This guide shows how to create a zero‑code AI chatbot using Alibaba Cloud Function Compute and the Bailei large‑model platform, configure API keys, deploy a sample site, embed the assistant, and enhance it with a private knowledge base for accurate product support.
The article analyses the rapid growth of Retrieval‑Augmented Generation (RAG) in enterprises, explains why self‑built RAG solutions often collapse under cost and maintenance pressures, and demonstrates how Tencent LeXiang AI Assistant addresses these issues through a robust knowledge‑management core, extensive industry experience, scalable resources, and advanced multimodal capabilities.
The article outlines a step‑by‑step methodology for creating Retrieval‑Augmented Generation and custom Agent applications with LangChain, illustrated by an AI assistant for activity components that evolves from a rapid Dify prototype to a LangChain‑based RAG system and finally a hand‑crafted ReAct‑style agent, detailing LCEL chain composition, vector‑search integration, model performance trade‑offs, and a unified routing layer.
This guide explains how to install the JetBrains AI Assistant plugin, outlines its code‑generation and refactoring features, and provides a practical workaround for mainland China users by adjusting account region, configuring system and IDE proxies, and starting AI‑driven conversations.
This article details Huolala's exploration of large‑language‑model (LLM) based AI assistants across multiple business scenarios, describing their architecture, implementation challenges, prompt engineering techniques, and the progressive stages from professional assistants to multi‑agent systems that drive efficiency and innovation in logistics.
This article details the design and implementation of an AI-driven assistant for Lynkco car online marketing, covering its capabilities, workflow architecture, prompt engineering, intent recognition, backend operations, and report analysis to enhance sales conversion and user experience.
The IntelliJ IDEA 2024.3 EAP preview introduces an experimental embedded AI assistant that transforms natural‑language prompts into code changes, improves Java constant‑condition analysis to better handle aliasing, adds spell‑check during indexing, displays branch names in the Welcome screen, and offers a new workspace feature for managing multiple projects.
This article presents a comprehensive technical overview of how large language models are integrated into Xiaomi's Xiao AI assistant, detailing the architecture for intent routing, domain‑specific intent understanding, function‑calling mechanisms, fine‑tuning strategies, performance gains, and future research directions.
This article shares a recent graduate's three‑year journey at JD, offering practical advice for newcomers, detailing a large‑scale system redesign for a merchant conference, outlining promotion‑season preparation, and describing the development of an AI assistant while emphasizing continuous learning and professional mindset.
This article outlines practical methods for evaluating generative AI assistants, covering cost‑benefit metrics, existing evaluation models, three‑dimensional experience criteria, common assessment techniques, and two detailed business case studies that illustrate design decisions and implementation strategies.
Tencent developers recommend a curated set of VS Code extensions—including Code Runner for multi‑language execution, the AI‑powered Tencent Cloud Code Assistant, Settings Sync, LeetCode integration, rainbow‑style indentation and CSV highlighting, TODO tracking tools, and the Material Theme with icons—to boost coding efficiency and productivity.
This article examines the essential UX design components of generative AI assistants—from core functions, command guidance, and smart agents to input methods, conversation bubbles, generation feedback, and voice call interactions—offering practical principles and real‑world case studies to help designers create intuitive, trustworthy AI experiences.
This article presents Xiaomi's work on LLM‑based Agent technology, covering its perception‑thinking‑action pipeline, technical framework, prompt management, executor and API platform, workflow, optimization strategies, evaluation metrics, and future directions for AI assistants.
The article reviews the evolution of large‑model technology, details Kuaishou’s self‑developed “Kuaishou Yi” model architecture, data pipeline, evaluation benchmarks, and explains how the AI XiaoKuai assistant achieves multimodal, personable interactions while also announcing related recruitment opportunities.
OpenAI’s open‑source openai‑assistants‑quickstart project shows how to integrate the Assistants API into a Next.js app, offering streaming chat, code‑interpreter, file‑search, and function‑calling tools, and provides step‑by‑step setup instructions so developers can quickly build and customize AI assistants.
GitLab introduces its Duo Chat AI assistant for streamlined development while also previewing the upcoming jQuery 4.0 release and promoting JavaScript "Naked Day," offering developers insights into new tools, security practices, and upcoming web standards.
This guide introduces CozeIDE, explains its main features such as code editing, dependency management, AI programming assistant, testing and publishing, and walks you through two complete plugin development examples—including a stock‑price lookup plugin—showing how to create, test, and release plugins using Node.js or Python runtimes.
IntelliJ IDEA 2023.3 introduces a production‑ready AI Assistant, full Java 21 support with virtual threads and pattern‑matching switch, a Run‑to‑Cursor debugging feature, a floating toolbar with quick edit actions, out‑of‑the‑box Kubernetes tooling, and numerous UI enhancements for a smoother developer experience.
Google’s latest Android Studio release, Iguana, brings AI‑driven crash reporting, GitLab integration, enhanced Compose tools, and policy alerts, while the forthcoming Jellyfish preview adds a global Studio Bot and Gemini AI templates, expanding developer productivity across 180 countries.
This article explains how to create a custom GPT assistant using OpenAI’s no‑code builder, configure multimodal features, and publish it on the upcoming Custom GPT Store to share or monetize the assistant, with step‑by‑step instructions and target user scenarios.
The IntelliJ IDEA 2023.3 update introduces the fully launched AI Assistant, complete Java 21 support, enhanced debugging tools, a floating toolbar, out‑of‑the‑box Kubernetes integration, numerous UI improvements, faster Gradle and Maven imports, and a host of new framework and technology features.
This guide walks through setting up Azure OpenAI (GPT‑4) as a connector for Elastic Observability’s AI Assistant, covering prerequisites, Azure resource creation, connector configuration, URL formatting, and practical examples of log analysis and chat‑based troubleshooting.
This guide walks you through why GPT‑4 outperforms GPT‑3.5, shows step‑by‑step how to create a Zabbix expert consultant using the new GPTs feature, and explains advanced configuration, knowledge‑base feeding, testing, and future possibilities for AI‑enhanced monitoring.
The article explains prompt engineering techniques, supervised fine‑tuning of large language models, and their practical deployment in the Mobile Tmall AI shopping assistant, detailing ChatGPT’s generation steps, Transformer architecture, prompt clarity, delimiters, role‑play, few‑shot and chain‑of‑thought prompting, SFT versus pre‑training, LoRA adapters, data collection, Qwen‑14B training configuration, SDK‑based inference, and comprehensive evaluation.
This article explains the design and implementation of the Codebase AI Assistant in the Chocolate Factory framework, covering its problem‑solving DSL, retrieval‑augmented generation pipeline, indexing and querying stages, prompt strategies, and code‑splitting rules that together enable efficient semantic code search.
JetBrains has rolled out an AI Assistant across its entire IDE suite, offering chat, code analysis, and documentation generation powered by OpenAI, while highlighting its deeper integration compared to GitHub Copilot, current limitations, privacy concerns, and early developer feedback.
JetBrains has launched a new AI Assistant feature that is integrated into all its IDEs and .NET tools, offering chat, code understanding, documentation generation, naming suggestions, and automatic commit‑message creation to boost developer productivity.
This article introduces AutoGPT, an experimental open‑source AI agent powered by GPT‑4 that can autonomously achieve user‑defined goals, showcases real‑world examples such as website creation and market research, and provides step‑by‑step setup instructions including repository cloning, environment configuration, package installation, Docker usage, and execution.
This article details the development journey of OPPO's conversational AI assistant XiaoBu, covering its historical background, product features, four years of technical evolution, challenges in skill building and user understanding, anti‑failure strategies, and future directions in multimodal AI assistants.
This article reviews the evolution of voice assistants, compares commercial and open‑source solutions, explains why Rasa was chosen for a city‑governance AI assistant, and details its architecture, data processing, NLU/Core workflow, code implementation, and practical demonstration.
