Free Cursor Alternative: VS Code + AI Extensions Setup Guide

Key Points:

• Cursor is a fork of VS Code with added AI features
• Cursor’s free version has limitations, and the Pro version costs $20/month
• VS Code is open-source, highly extensible, and completely free
• Using VS Code with specific extensions provides similar functionality to Cursor

My Take:

While Cursor doesn’t violate VS Code’s license agreement, it’s worth noting that you can recreate much of its functionality without the monthly subscription cost. For hobbyists or developers working on personal projects, this free alternative is particularly valuable.

Key Points:

• VS Code – The base code editor
• Ollama – For running AI models locally on your machine
• Continue.dev extension – For code completion and editing features
• Client extension – For multi-file editing and advanced AI reasoning

My Take:

This setup actually provides better privacy than Cursor since all processing can happen locally. For developers concerned about their intellectual property or working on sensitive projects, this alone makes the setup worth considering.

Key Points:

• Download VS Code from code.visualstudio.com
• Install Ollama from ollama.com
• Ollama allows you to download state-of-the-art open-source models locally
• Local models require sufficient storage space on your computer
• If you have limited memory or slow internet, Open Router API can be used as an alternative

My Take:

Running AI models locally does require some system resources, so if you’re working on an older machine with limited RAM or storage, you might want to consider the Open Router option mentioned later in this guide.

Key Points:

• Open VS Code and click on the Extensions tab on the left sidebar
• Search for “Continue.dev” and install it
• Search for “Client” and install it as well
• Continue.dev enables auto code completion and multi-line edits
• Client allows AI to reason and handle multiple files editing better

# Commands to install AI models with Ollama
ollama pull deepseek-coder-1.3b-instruct
ollama pull qwen:1.5b-chat
    

My Take:

The combination of these two extensions creates a powerful synergy. Continue.dev handles most of the code-level interactions, while Client provides the multi-file project-level capabilities that make Cursor so useful.

Key Points:

• Open the Continue extension and click the cube icon to configure models
• For chat mode, use DeepSeek R1 with 7 billion parameters
• For code completion, use Qwen 2.5 Coder with 1.5 billion parameters
• Download models using terminal commands: ollama pull model-name
• Configure Continue.dev to use the downloaded models by specifying name, provider, and model
• Set the appropriate role for each model in the configuration

// Example Continue.dev configuration
{
  "name": "DeepSeek R1",
  "provider": "ollama",
  "model": "deepseek-coder-1.3b-instruct"
}

// For Qwen model with role
{
  "name": "Qwen 2.5 Coder",
  "provider": "ollama",
  "model": "qwen:1.5b-chat",
  "role": "coder"
}
    

My Take:

Using smaller models for code completion is actually a smart approach. The tutorial mentions that larger models don’t significantly improve autocompletion performance, so the 1.5B parameter model is plenty sufficient while being much lighter on system resources.

Key Points:

• Test code completion by creating a simple Python function
• Press Tab to accept code suggestions
• Test Client’s ability to create a complete project (e.g., a Flappy Bird game)
• Client can create necessary files, write code, and even install dependencies
• Continue.dev provides keyboard shortcuts for highlighting code and getting suggestions
• Use Control+I (or Command+I on Mac) as an alternative to Cursor’s “/” feature

My Take:

The Flappy Bird example demonstrates the real power of this setup. Being able to generate an entire working game from a single prompt is impressive and shows that this free alternative can truly compete with Cursor’s paid features.

Key Points:

• Open Router is a platform providing access to multiple AI models
• Sign up for an account and create an API key
• Configure Client to use Open Router as the provider
• Select a powerful free model like DeepSeek R1 (67B parameters)
• Free models may have longer request times when many people are using them

// Client configuration for Open Router
{
  "provider": "openrouter",
  "apiKey": "your-api-key-here",
  "model": "deepseek-coder/deepseek-coder-33b-instruct"
}
    

My Take:

Open Router offers a nice balance between local processing and cloud-based models. The free tier might be a bit slower during peak usage times, but for many developers, this trade-off is worth it to avoid downloading multi-gigabyte models.