How to Build a Free and Private AI Coding Assistant with Open Source Tools

As AI transforms software development, many developers and tech leaders are asking: Is it possible to build a private AI coding assistant for free, without relying on commercial tools like GitHub Copilot or OpenAI?

Yes—it is.

In this guide, you'll learn how to create your own AI coding assistant using open-source tools, without vendor lock-in, and with complete control over your source code. Whether you're building enterprise applications or indie projects, this DIY approach can save time, boost productivity, and protect your IP.

✅ What Is an AI Coding Assistant?

AI coding assistants are tools powered by large language models (LLMs) that help developers write, refactor, and understand code. They operate in three core modes:

• Chat Mode – Ask questions, get explanations, and generate code through a chatbot interface.
• Inline Mode – Insert code directly where your cursor is based on a short prompt.
• Autocomplete Mode – Predict the next few lines of code as you type.

Tools like GitHub Copilot, Replit, and Cursor offer these features, but they’re tied to proprietary ecosystems. In this guide, you’ll build your assistant—free and fully private.

🚀 Why Build Your Coding Assistant?

Here are a few key reasons:

• Data privacy – Your source code never leaves your system.
• No subscriptions – Completely free to use, no per-seat fees.
• Full control – Tailor the assistant to match your team’s language, framework, and codebase.
• No vendor lock-in – Choose your models and tools.

🧩 What You'll Need

To build your assistant, you’ll need:

1. An IDE plugin – We’ll use Continue.dev, which supports VS Code and JetBrains IDEs.
2. An open-source LLM – You can either host a model on a GPU server using vLLM, or run it locally on your machine using Ollama.

🧠 Choose Your Deployment Strategy

🔧 Option 1: Use vLLM on a Server (GPU Required)

If your company has a GPU server or you use services like RunPod or Vast.ai, this is the ideal option.

Steps:

bash

pip install vllm huggingface_hub[cli]

vllm serve Qwen/Qwen2.5-Coder-14B-Instruct --port 8081        

✅ Recommended for teams with access to 24GB–36GB VRAM.

💻 Option 2: Use Ollama Locally (No GPU Required)

Perfect for solo developers or laptop users.

Install:

bash

curl -fsSL https://ollama.com/install.sh | sh        

Run a model:

bash

ollama run qwen2.5-coder:0.5b        

⚡ Great for privacy, portability, and cost-saving.

🔌 Integrate Continue.dev into your IDE

For VS Code:

• Open the Extensions tab → Search “Continue” → Click Install.

For JetBrains IDEs (PyCharm, IntelliJ, etc.):

• Go to Plugins → Search “Continue” → Install and Restart.

Once installed, a Continue icon will appear in your IDE sidebar.

⚙️ Configure the Plugin

Open the config.json in Continue’s settings and use one of the following configurations:

✅ vLLM Configuration:

json

{

  "allowAnonymousTelemetry": false,

  "models": [{

    "title": "My Assistant",

    "provider": "openai",

    "model": "Qwen/Qwen2.5-Coder-14B-Instruct",

    "apiBase": "http://YOUR_SERVER_IP:8081/v1"

  }],

  "tabAutocompleteModel": {

    "title": "My Assistant",

    "provider": "openai",

    "model": "Qwen/Qwen2.5-Coder-14B-Instruct",

    "apiBase": "http://YOUR_SERVER_IP:8081/v1"

  }

}        

✅ Ollama Configuration:

json

{

  "allowAnonymousTelemetry": false,

  "models": [{

    "title": "My Assistant",

    "provider": "ollama",

    "model": "qwen2.5-coder:0.5b"

  }],

  "tabAutocompleteModel": {

    "title": "My Assistant",

    "provider": "ollama",

    "model": "qwen2.5-coder:0.5b"

  }

}        

💡 Real-World Use Examples

🧠 Chat Mode

Ask your assistant:

perl        

Why use logging instead of print statements in production?

And it will generate a concise, contextual explanation.

✍️ Inline Mode

Prompt:

python        

Replace all print statements with logger.info

The assistant will update your code in place.

⚡ Autocomplete Mode

Start typing, and suggestions appear as ghost text. Hit Tab to accept. It’s fast, predictive, and great for repetitive code patterns.

🔗 Advanced Features: Context and Commands

Use @context providers to feed additional data like:

• @git diff → Summarise changes and generate commit messages
• @file utils.py → Ask for explanations or refactors

Create Slash Commands in config.json:

json
"slashCommands": [{

  "name": "commit",

  "description": "Generate a commit message from code changes"

}]

Use: /commit → Instant commit message!
        

Use in IDE:

sql

/commit → Instant commit message!        

🧠 Pro Tips for Customization

• Fine-tune prompts based on your coding standards.
• Modify autocomplete behaviour per language.
• Analyze usage data from:

bash

~/.continue/        

• Add new context providers (e.g., Jira, Notion, GitHub).

✅ Final Thoughts

With just a few tools and simple configurations, you can build a private, powerful AI coding assistant that improves developer productivity, protects your codebase, and reduces reliance on external vendors.

This is more than a productivity hack—it’s an essential step toward AI-augmented software development on your terms.

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