Continue.dev Setup Guide: Free GitHub Copilot Alternative That Works in VS Code and JetBrains
Learn how to configure Continue.dev with local LLMs to build a completely free, private alternative to GitHub Copilot inside your IDE.
GitHub Copilot is the industry standard, but it costs $10/month and requires uploading your codebase to a remote cloud server. For privacy-conscious developers or those looking to cut subscription fees, Continue.dev is the ultimate open-source alternative. In this tutorial, we will set up Continue inside VS Code and connect it to a local model running on our own hardware.
What is Continue.dev?
Continue is a highly flexible, open-source IDE extension that plugs directly into VS Code and JetBrains editors. Instead of locking you into a single proprietary AI model, Continue lets you swap backend models instantly. You can connect it to local Ollama endpoints, self-hosted API engines, or cloud models like Anthropic Claude and OpenAI GPT-4.
Figure 1: High-fidelity conceptual render analyzing Continue.dev Setup Guide: Free GitHub Copilot Alternative That Works in VS Code and JetBrains.
Connecting Continue to Local Ollama
After installing the Continue extension in VS Code, click the gear icon to open the configuration file (config.json). You can define a local model configuration block pointing to your Ollama service: {"model": "gemma4:12b", "provider": "ollama"}. Now you can highlight code in your editor, hit Ctrl+I, and ask the AI to refactor or write tests completely locally.
Electromagnetic Wave Propagation & CSI Physics
To fully grasp how wireless sensing works, we must investigate the mathematical principles of modern radio frequency (RF) propagation. Traditional signals like RSSI only provide the average overall power of a received wireless packet. Conversely, Channel State Information (CSI) extracts complex vectors mapping individual Orthogonal Frequency-Division Multiplexing (OFDM) subcarrier channels. In a standard 20 MHz or 40 MHz WiFi spectrum, the signal is split into 56 to 114 separate subcarrier channels. For each subcarrier, the CSI packet header records the exact Amplitude (signal attenuation) and Phase (fractional cycle shift).
Human bodies are comprised of more than 60% water, making them highly conductive dielectric objects in the path of 2.4 GHz and 5.8 GHz frequencies. As waves travel between the transmitter and receiver, they bounce off walls, obstacles, and humans in a phenomenon known as Multipath Propagation. The physical displacement of a human body perturbs this multipath beam network, creating constructive and destructive interference waves. For a comprehensive overview of how these physical shifts are visualized in real-time, try our Interactive 3D WiFi Radar Demo.
Selecting and Configuring ESP32 Microcontrollers
Implementing a spatial WiFi radar does not require industrial SDR (Software Defined Radio) equipment. The RuView project operates entirely on standard, inexpensive microcontrollers. For high-fidelity telemetry, we highly recommend the ESP32-S3 DevKit. The S3 series features dual XTensa LX7 cores with custom vector instruction extensions that provide hardware acceleration for raw signal matrices.
A typical DIY radar setup consists of a transmitter (Tx) emitting beacon packets and a receiver (Rx) listening on the same WiFi channel. During selection, look for boards featuring an external IPEX antenna connector instead of a standard PCB trace antenna, as high-gain external antennas heavily minimize noise. For a full list of certified microcontrollers and specific command line flashing commands, read our extensive ESP32 WiFi Radar Guide.
Figure 2: Technological block diagram demonstrating Selecting and Configuring ESP32 Microcontrollers.
Privacy Preserving Spatial Sensing & Surveillance Alternatives
As ambient computing spreads, security systems raise massive privacy concerns. Cameras record actual visual images, creating permanent files that are vulnerable to hacks. Passive WiFi sensing is **100% privacy-preserving**. It captures no optical features, faces, or bodies — only numeric signal amplitude vectors.
The data is entirely ephemeral: processed locally and instantly discarded. It is impossible to reconstruct a face from a CSI matrix. This makes WiFi sensing ideal for bedrooms, bathrooms, and private offices. For a comprehensive introduction to camera-free spatial computing, explore our starter overview What is RuView? Complete Beginner Guide.
FAQ
Can Continue suggest code completions as I type?
Yes. Continue supports tab-completions. We recommend using a very fast, smaller model like Qwen 3.6 3B for completions to prevent keystroke latency.
Is my codebase safe with Continue?
Yes. When configured with local models, all code context stays entirely on your physical machine, making it fully compliant with strict corporate privacy policies.