This guide consolidates Boris Cherny’s internal practices and official best‑practice documentation into ten actionable techniques that help you manage Claude Code’s context window, plan work, automate repetitive tasks, run parallel sessions, and use prompts effectively for faster, higher‑quality software development.
ZeroClaw, a Rust‑based open‑source AI assistant, delivers a 3.4 MB binary with sub‑10 ms startup, 7.8 MB RAM usage, sandbox security, plug‑in architecture and full OpenAI‑compatible model support, offering a lightweight, fast, and secure alternative to OpenClaw for edge and server deployments.
Claude Code forgets all prior context each new session because large language models only see the current window, but the open‑source claude‑mem plugin records project actions, compresses them into semantic summaries, and injects the relevant history back into Claude, dramatically reducing re‑explanation overhead.
With Ollama 0.17 you can start the powerful OpenClaw AI assistant using a single command, automatically install the software, choose cloud or local models, enable web‑search, connect to multiple messaging platforms, and keep all data private on your own machine.
This guide walks through two practical approaches—Skill and Prompt—to configure OpenClaw for automated RSS aggregation, AI‑driven filtering, summarization, and channel delivery, comparing their setup steps, code examples, advantages, limitations, and when to choose each method.
This step‑by‑step guide shows how to deploy OpenClaw on an Alibaba Cloud ECS instance, replace its default model with GLM‑4.7, configure a Feishu bot, and verify a continuously running AI assistant that can handle code, web browsing, and system tasks.
The article walks through how the author refactored the Go‑based AI assistant GoClaw by leveraging Claude Code's Agent Teams feature, detailing the motivation, architecture comparison, configuration steps, team role assignments, and practical tmux integration to achieve a more modular and reliable agent module.
This step‑by‑step tutorial shows how to install the Webman PHP framework, integrate the Neuron AI Agent, configure a custom Alibaba Cloud Baileian model provider, build an AssistantAgent, and expose a Server‑Sent Events controller for real‑time AI chat responses.
This article provides an in‑depth technical analysis of GoClaw, a Go‑based personal AI assistant that mirrors OpenClaw's design while introducing a more robust agent loop, reflective reasoning, extensible skill system, sophisticated error handling, and multi‑provider failover, complete with code excerpts, architecture diagrams, and performance trade‑offs.
This guide shows how to deploy OpenCode Server as a backend and connect it to Telegram and Discord bots, enabling on‑the‑go AI‑powered code assistance with multi‑model support, detailed setup steps, code examples, and a comparison of the two platforms.
The 2026.1 EAP 3 release of IntelliJ IDEA introduces several practical updates, including moving deleted files to the system recycle bin, deeper AI assistant integration with Roots and improved commands, automatic SQL dialect detection, enhanced Spring debugging, built‑in Git Worktree support, UI refinements, and numerous bug fixes across Java, Kotlin, Docker, and HTTP client features.
WebClaw provides a web‑based client for the open‑source OpenClaw AI programming assistant, offering browser access via WebSocket, token or password authentication, and MIT‑licensed source code, with simple installation steps and a gateway that supports OpenAI‑compatible APIs.
The article presents ten actionable techniques—from running parallel Claude sessions and planning tasks to using subagents, data analysis, and a learning partner—to help developers turn Claude into an efficient, versatile assistant across coding, debugging, and knowledge work.
This article shares a hands‑on walkthrough of using OpenClaw as a personal AI assistant on macOS, covering file search, knowledge‑base syncing, calendar automation, unified chatbot access, task scheduling, and screen‑capture skills, while highlighting model choices and future integration ideas.
The article explains how to create personal AI profile files (soul.md) and project memory logs (memory.md) to eliminate repetitive introductions, improve code suggestions, and speed up debugging for Vue + Vite developers, with concrete before‑after examples, step‑by‑step configuration for Claude Desktop and OpenCode, and measurable productivity gains.
OpenClaw attracted massive attention but is hard to deploy, insecure, and plugin‑heavy, while SkyBot offers a zero‑setup cloud AI assistant with sandboxed security, integrated skills, and step‑by‑step demos such as PDF‑to‑PPT generation and automated daily AI news digests.
This article walks through configuring OpenClaw as a personal AI infrastructure, covering session isolation, multi‑agent and Feishu bot setups, exposing a standard OpenAI‑compatible API, and automating tasks with custom skills and scheduled jobs.
Deploying OpenClaw on an internal Ubuntu 22.04 server exposed a prompt‑injection flaw that let colleagues trigger shutdown and data‑deletion commands, prompting a step‑by‑step hardening process that includes removing default bindings, editing gateway settings, and defining strict security prompt templates.
Discover ten practical techniques—from parallel Git worktrees and plan‑mode workflows to custom skills, sub‑agents, and advanced prompt engineering—that help you get the most out of Claude Code for coding, debugging, data analysis, and continuous learning.
NanoClaw trims the bloated OpenClaw codebase to just 500 lines, runs inside an Apple container for security, adds Docker support for Linux, and discusses the trade‑offs between sandbox restrictions and AI assistant capabilities while warning of its fleeting popularity.
The author, a designer who prefers self‑hosted tools, walks through installing OpenClaw on a home NAS using PVE and Ubuntu, configures password‑less sudo, sets up Discord as a communication channel, troubleshoots model settings, and demonstrates the assistant’s basic capabilities.
Clawdbot, an open‑source AI assistant, uses local Markdown files and a SQLite‑based vector index to provide a transparent, searchable, and long‑term memory that separates temporary context from durable storage, enabling autonomous task handling across sessions.
This article walks through installing the open‑source OpenClaw AI assistant on a MacBook (or cloud server), configuring it with the MiniMax model and Feishu, enabling 24‑hour remote control, local memory, data sovereignty, and thousands of ready‑made skills, while highlighting setup commands, security considerations, and cost factors.
The article debunks the claim that Moltbot has made Mac mini a hot‑selling item by examining the tool’s requirements, API pricing, hardware alternatives, cloud‑hosted options, and the marketing tactics that inflate the narrative, ultimately showing that API costs, not the hardware, are the real barrier.
Google’s integration of Gemini into Chrome brings AI‑driven browsing, on‑the‑fly summarization, translation, image editing, and automated tasks, and this article walks through the required US‑based account setup, the open‑source enable‑script, and hands‑on experiences across four real‑world scenarios for designers.
This article analyzes the ten most popular Claude Skills on GitHub, detailing their core capabilities, ideal user groups, installation methods, and how they can boost AI‑assistant productivity up to four times for developers, teams, and designers.
This guide walks you through installing the open‑source Moltbot (formerly Clawdbot), configuring it with the MiniMax M2.1 large‑language model, and connecting it to messaging platforms such as Telegram, WhatsApp, and Feishu, enabling a continuously running AI assistant that can handle emails, code edits, and complex tasks.
This guide walks you through installing the Moltbot AI assistant on Alibaba Cloud's lightweight application server, configuring Baidu's Bailei API key, opening required ports, generating access tokens, and accessing the chat interface, plus a quick overview of the pre‑installed Shadow Cloud Computer image.
This article explores the rapid rise of the open‑source ClawdBot AI assistant, explains its four core advantages, outlines security risks with mitigation strategies, and provides a detailed step‑by‑step guide for deploying ClawdBot using MiniMax 2.1 on a dedicated machine.
Clawdbot is an open‑source personal AI assistant that runs on your own machine and can be controlled via chat apps, offering email handling, automation, and code generation while requiring careful security design to prevent dangerous actions and data loss.
This article provides an in‑depth evaluation of the AI agent tool Clawdbot, highlighting its seamless integration, strong execution, and automation capabilities while exposing steep configuration hurdles, security concerns, and unpredictable behavior, and offers recommendations for both technical enthusiasts and everyday users.
Clawdbot is an open‑source, locally‑run AI assistant that integrates with over 14 chat platforms—including WhatsApp, Telegram, Discord, and iMessage—allowing you to manage email, calendars, browsing, and system commands directly from your favorite messaging apps, with multi‑model support, skill extensions, and robust security controls.
ClawdBot is a 24‑hour AI assistant that can clean your inbox, schedule meetings, analyze code, and execute voice‑controlled tasks; the guide explains its architecture, two deployment options (local or AWS), low cost, security pairing, quick tests, advanced features, and real‑world use cases.
This guide walks through why you might run a large language model locally—privacy, zero latency, and no token costs—then details hardware requirements, installs Ollama, pulls the appropriate DeepSeek‑R1 model, tests it with a coding prompt, and optionally adds a web UI via Docker.
This article compiles Ado Kukic's 31‑day Claude Code tip series, detailing essential commands, shortcuts, session management, productivity tools, browser integration, and advanced capabilities that help developers streamline AI‑assisted coding, improve context handling, and boost overall efficiency.
The open‑source Claude‑Cowork project, built by DevAgentForge, recreates Anthropic’s Claude Cowork as a desktop GUI wrapper for Claude Code, adding real‑time streaming, syntax‑highlighted code, session management via SQLite, and cross‑platform Electron support, while remaining compatible with Claude Code’s configuration and allowing any Anthropic‑compatible model to be used.
This article walks you through the entire process of creating Claude Code plugins—from understanding why plugins are needed, setting up the directory structure, writing commands, agents, skills, and hooks, to testing, debugging, advanced features, and publishing—providing concrete code examples, configuration files, and best‑practice recommendations.
Anthropic’s new Claude Cowork feature shifts AI from a passive question‑answer tool to an autonomous coworker that can set goals, break down tasks, edit files, and deliver results, with current macOS‑only preview, subscription requirements, and notable security considerations.
This guide walks through installing Claude Code, an Anthropic AI programming assistant, covering prerequisites, npm installation, authentication, optional proxy setup for Unix and Windows, MCP server configuration, common commands, and troubleshooting tips.
This article details the end‑to‑end design of an enterprise AI office assistant, covering the three‑layer framework of intent planning, context engineering, and data self‑iteration, the key pain points of intent understanding, knowledge integration, and quality control, and practical architectural and implementation solutions for scalable deployment.
This tutorial explains Cursor AI’s Plan mode, a structured task‑management workflow that lets the AI analyze a codebase, create a detailed execution plan, track tasks via a todo list, and safely apply complex code changes with minimal risk.
The article traces how ByteDance’s limited‑release Doubao phone assistant reuses SmartisanOS’s deep‑customized Android code, revealing three layers of inherited assets, the system‑level AI‑assistant architecture, security concerns, and the broader implications for the future of mobile operating systems.
The article evaluates Doubao’s AI phone by testing everyday scenarios, highlighting its screen‑recognition‑driven automation, high latency, privacy risks, and comparing its performance and usability against Honor’s YOYO assistant and other AI‑enabled smartphones.
This article details how an automotive AI assistant migrated from keyword matching to a Milvus‑based vector retrieval system, overcoming semantic gaps, scaling to millions of daily queries, optimizing indexing, introducing multi‑vector and sparse‑vector search, and building a real‑time RAG pipeline with Flink.
This guide explains how to combine Obsidian’s local markdown vault and bi‑directional linking with Google Antigravity’s AI‑driven code analysis to create, organize, and continuously update a personal knowledge base, covering concepts like Zettelkasten, PARA, markdown syntax, and practical workflows for developers.
Yearning is an open‑source, self‑hosted MySQL audit platform that combines AI‑assisted SQL optimization, multi‑level approval workflows, automatic syntax checking, privacy protection, and flexible deployment options to help developers and DBAs secure and manage database operations.
This article maps the rapid evolution of open‑source large language models in 2025, explains the underlying architectural breakthroughs such as MLA, MoE, and NSA, compares dozens of models—including DeepSeek‑V3, OLMo2, Gemma3, Llama4, Qwen3, and Kimi‑K2—and highlights the emergence of powerful AI assistants like Dola, providing developers with a concise technical roadmap.
This article explains Bilibili's large‑scale data platform architecture, the common offline‑task failures and slowdowns users encounter, and how a large language model‑driven intelligent assistant is being built to automatically diagnose and resolve these engineering problems.
This article introduces four cutting‑edge open‑source AI projects—Glass, CraftGPT, AI Trend Publish, and Klavis—detailing how they read screens and audio, run language models inside Minecraft, auto‑generate AI‑focused articles, and integrate dozens of external tools for powerful, automated assistance.
This article explains how Xiaomi’s Xiao AI assistant leverages large language models for intent routing, domain‑specific intent understanding, and response generation, detailing the system architecture, challenges such as knowledge requirements and latency constraints, and the shift from prompt engineering to model fine‑tuning.
This guide evaluates Codex against Claude Code, walks through installing the Codex CLI on Windows, macOS and Linux, explains API‑Key and ChatGPT Plus authentication, details advanced MCP server setups, and provides a full set of configuration files and commands for turning Codex into a true AI programming partner.
This article explains how large language models are integrated into Xiaomi’s Xiao AI assistant, covering intent distribution, domain‑specific intent understanding, response generation, architectural design, challenges such as knowledge requirements and latency, and the shift from prompt engineering to model fine‑tuning.
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.
Amazon Finance built an AI assistant that combines Amazon Bedrock, Claude 3 Sonnet, and Amazon Kendra to let analysts query financial data in natural language, achieving a 30% reduction in search time, an 80% increase in accuracy, and high precision and recall across data‑discovery and document‑search tasks.
This article walks through installing JDGenie's Docker image, fixing configuration and Dockerfile issues, and evaluating its code‑analysis and animation‑generation capabilities against other open‑source agents, highlighting both its strengths and current limitations.
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.
