02. Continue.dev Installation

Chapter 2 of 18 · 20 min

KEY INSIGHT

Continue is a VS Code and JetBrains extension that brings local AI coding capabilities directly into your development environment with a declarative configuration file. Continue requires two components: the editor extension and a local model server. The extension handles UI, context management, and communication with the model backend. The backend runs the actual inferenceΓÇötypically through LM Studio, Ollama, or a custom API endpoint. Install the VS Code extension by opening VS Code, pressing `Ctrl+Shift+X` (or `Cmd+Shift+X` on macOS), and searching for "Continue". Click Install on the Continue extension by Continue Dev. The extension appears in the sidebar after installation. Alternatively, for JetBrains IDEs, open Settings > Plugins, search for "Continue", and install the JetBrains-compatible version. After installation, create the configuration file at `~/.continue/config.json`. This file controls model selection, context providers, and UI behavior. ```json { "models": [ { "title": "Codestral", "provider": "openai", "model": "codestral", "api_base": "http://localhost:1234/v1" } ], "tabAutocompleteModel": { "title": "Starcoder", "provider": "openai", "model": "bigcode/starcoder", "api_base": "http://localhost:1234/v1" } } ``` The `api_base` points to your local server. The configuration above uses port 1234, which is LM Studio's default. If using Ollama, the base would be `http://localhost:11434/v1`. Test the installation by opening any Python or JavaScript file in VS Code. Type a function signature like: ```python def calculate_fibonacci(n: int) -> list[int]: ``` After a brief pause, you should see autocomplete suggestions appearing. If suggestions don't appear, check the Continue status bar at the bottom of VS CodeΓÇöit shows the current model and connection status. Red indicators mean the backend isn't reachable. Common installation failures include the backend not running, firewall blocking localhost connections, and incorrect `api_base` URLs. Verify the backend with: ```bash curl http://localhost:1234/v1/models ``` This should return a JSON list of available models. If you receive a connection refused error, start your model server (covered in Chapter 3). The sidebar UI provides access to chat, context management, and extension settings. The chat panel sits on the right, context files appear in the main workspace, and a configuration button opens the `config.json` editor. Keyboard shortcuts include `Ctrl+L` to open chat and `Ctrl+K` to invoke autocomplete manually.

EXERCISE

Install Continue, start your preferred model server, and verify autocomplete triggers in a new Python file. Document any errors you encounter and their resolution for future reference.