AI Tools

If an AI model is the engine, then AI tools are everything around it that makes that engine actually usable: editors, command-line tools, libraries, databases and runtimes that translate a raw language model into real work. A model on its own answers a single request. The tools decide how you put it to work — inside your code editor, on the command line, in your own application, or as a model you run yourself on your own hardware.

What you work with directly

Closest to daily work are the AI IDEs — code editors with built-in AI such as Cursor, Windsurf or the Copilot extensions in VS Code. They know your whole project context and write, explain or refactor code right inside the editor. If you prefer to live in the terminal, you reach for a coding CLI: AI assistants as a command-line tool like Claude Code, Codex CLI or Gemini CLI, which work through entire tasks across multiple files autonomously.

If you want to run a model on your own hardware rather than from the cloud, tools for local LLMs come into play — Ollama, LM Studio or llama.cpp download open-weight models and run them offline and privacy-friendly on your own machine.

What you build your own AI applications with

As soon as you build AI into your own product, you need infrastructure. Hugging Face is the central platform here: a huge repository for models and datasets plus libraries like transformers. Vector databases (such as Pinecone, Qdrant or pgvector) store embeddings and enable semantic search — the foundation for RAG, meaning answers based on your own documents. The individual LLM calls are chained together via pipeline frameworks like LangChain or LlamaIndex.

If the AI should not just answer but act on its own, agent frameworks for multi-agent systems help out. And anyone who wants to assemble workflows without much code uses workflow automation — no-/low-code tools like n8n, Make or Zapier that connect AI steps with the rest of your systems.

What you’ll find on this page

Below you’ll see a topic world around AI tools: current news, blog articles with practice and background, lexicon articles for deeper dives and a glossary of the most important terms. Use the topic filters above to jump straight to a sub-topic — for example AI IDE, coding CLI, local LLM, vector databases or agent frameworks.