AI Tools

Ubuntu 26.04 has become one of the mainstream platforms for AI development and deployment. This section systematically covers how to install, configure, and use various AI tools on Ubuntu, helping you quickly build an efficient AI working environment.

Section Overview

AI Editors & IDEs

Modern AI editors deeply integrate large language models into the code editing workflow, providing intelligent completion, code generation, refactoring suggestions, and more. This section covers:

• Cursor — AI-native editor based on VS Code, supporting multi-model conversations and code generation
• Windsurf (Codeium) — Free AI code completion tool and editor
• Zed — High-performance editor with built-in AI assistance
• VS Code + AI Extensions — Enhance VS Code with Copilot, Continue, Cline, and other extensions

Read the AI Editors & IDEs Guide —>

AI Coding Assistants (CLI)

Command-line AI coding assistants let you interact with AI models directly in the terminal to write, debug, and refactor code. This section covers:

• Claude Code — Anthropic’s official CLI coding assistant
• Aider — Open-source terminal AI pair programming tool
• GitHub Copilot CLI — GitHub’s official command-line AI assistant
• Gemini CLI — Google’s terminal AI tool
• OpenCode — Open-source terminal AI coding assistant

Read the AI Coding Assistants (CLI) Guide —>

Local LLMs

Run large language models locally to protect data privacy, reduce API costs, and enjoy offline inference. This section covers:

• Ollama — The most popular local model runtime framework
• LM Studio — Graphical local model management and chat tool
• llama.cpp — High-performance C++ inference engine
• Jan — Open-source local AI chat client
• Open WebUI — Web interface for local model interaction
• Model comparison — Comparison of popular models including Llama 3, Qwen, DeepSeek, Mistral, and more

Read the Local LLMs Guide —>

AI Image & Speech

AI can do more than process text — it can generate images and process speech. This section covers:

• Stable Diffusion WebUI (AUTOMATIC1111) — The most popular AI image generation tool
• ComfyUI — Node-based advanced image generation workflows
• OpenAI Whisper — Powerful speech-to-text model

Read the AI Image & Speech Guide —>

AI Platforms & Automation

Platform tools for building AI applications and automation workflows. This section covers:

• Dify — Open-source LLM application development platform
• n8n — Visual workflow automation tool with AI node support
• LangChain / LangGraph — Development framework for building LLM applications

Read the AI Platforms & Automation Guide —>

OpenClaw

OpenClaw is a local-first personal AI assistant that runs on your own devices, talks to you through messaging channels like WhatsApp, Telegram, and Slack, and can call tools and run automated tasks.

• Local-first — The gateway daemon runs on your own machine
• Multi-channel — Chat with the assistant inside the apps you already use
• Tools & automation — Built-in tool execution, cron jobs, and webhooks

Read the OpenClaw Guide —>

GPU Setup

AI workloads typically require GPU acceleration. This section covers:

• NVIDIA driver installation — Automatic installation with ubuntu-drivers
• CUDA Toolkit — GPU compute platform installation and configuration
• cuDNN — Deep learning acceleration library
• GPU verification — Confirming your GPU environment works correctly
• ROCm (AMD GPU) — AI compute support for AMD graphics cards
• Common troubleshooting — Solutions for driver conflicts, version mismatches, and more

Read the GPU Setup Guide —>

Getting Started

If you’re new to AI tools, we recommend reading in this order:

1. First configure your GPU environment (if you have an NVIDIA GPU)
2. Install Ollama to run local models and experience local AI
3. Choose an AI editor to boost your coding productivity
4. Try CLI coding assistants to use AI in the terminal

System Requirements