I built a DeepSeek R1 powered VS Code extension…

YouTube Video

This YouTube video demonstrates how to build a VS Code extension that integrates the open-source AI model DeepSeek R1. Key points include:

1. DeepSeek R1 and Local Execution: The video highlights DeepSeek R1 as a low-cost, open-source alternative to paid AI models like ChatGPT. It emphasizes running DeepSeek locally using Olama to avoid sending data to China. Olama supports various DeepSeek model sizes, trading speed for accuracy.

2. VS Code Extension Development: The tutorial walks through creating a basic VS Code extension using TypeScript. It covers:

• Generating a project using the official VS Code template.
• Utilizing the VS Code API to access and manipulate the editor.
• Registering commands within the extension and the package.json.
• Debugging the extension.

3. Integrating DeepSeek: The core of the video shows how to integrate the locally running DeepSeek model (via Olama’s REST API and JavaScript SDK) into the VS Code extension. This involves:

• Creating a web view panel within VS Code to display the chat interface.
• Handling user prompts and sending them to Olama.
• Streaming the AI’s response back to the web view using postMessage.
• Using VS Code’s es6-string-html extension for syntax highlighting within the HTML string.

4. Testing and Results: The video concludes by demonstrating the functional extension, showing how to test it and interact with DeepSeek R1 directly within the VS Code editor. The example uses the 7B model, highlighting its speed and functionality.

In short: The video provides a practical, step-by-step guide to building a custom VS Code extension powered by a locally-running open-source AI model, offering a cost-effective and privacy-conscious alternative to commercial AI services.