🔥Introduction:

Artificial Intelligence is transforming how developers write code. Tools like GitHub Copilot and ChatGPT have made coding faster — but they come with cost, internet dependency, and privacy concerns.

👉 What if you could run AI directly on your laptop, inside VS Code, without any API key or subscription?

In this guide, you’ll learn how to build a free, offline AI coding assistant using:

• Ollama (local AI runtime)
• DeepSeek models (coding + reasoning)
• VS Code + Continue extension

🚀 What You Will Build

By the end of this tutorial, you will have:

• ✅ AI assistant inside VS Code
• ✅ No API or internet required
• ✅ Multiple AI models working together
• ✅ Full control over your data

🧰 Prerequisites

Before starting, ensure you have:

• Windows / Mac / Linux system
• Minimum 16GB RAM recommended
• VS Code installed
• Basic understanding of development (.NET preferred)

⚙️ Step 1: Install Ollama (Local AI Engine)

Ollama allows you to run large language models locally with a simple command.

Installation:

1. Visit: https://ollama.com
2. Download and install
3. Restart your system

Verify Installation:

ollama --version

👉 If version appears → installation successful

📦 Step 2: Install AI Models

Now install the required models:

ollama pull deepseek-coder:6.7b
ollama pull deepseek-r1:8b
ollama pull llama3:8b

🧠 Model Roles Explained

👉 Using multiple models gives better results than using just one.

🧪 Step 3: Test AI Locally

Run:

ollama run deepseek-coder

Try prompt:

Create a .NET Web API controller for product CRUD

👉 You will see full code generated locally.

🧩 Step 4: Install VS Code Extension

1. Open VS Code
2. Go to Extensions
3. Install Continue

This extension connects your local AI models to VS Code.

⚙️ Step 5: Configure Models in Continue

Open config/settings:

Ctrl + Shift + P → Continue: Open Config

Paste this:

name: Local Config
version: 1.0.0
schema: v1

models:
  - name: DeepSeek Coder
    provider: ollama
    model: deepseek-coder:6.7b

  - name: DeepSeek R1
    provider: ollama
    model: deepseek-r1:8b

  - name: Llama
    provider: ollama
    model: llama3:8b

Save and reload VS Code.

🤖 Step 6: Use AI in VS Code

Open Continue panel and start using AI.

🧪 Example 1: Generate Code

Create a .NET Web API controller for Product CRUD operations

🧠 Example 2: Improve Architecture

Switch to DeepSeek-R1:

How can I improve scalability and security of this API?

🔍 Example 3: Code Review

Switch to Llama:

Review this code and suggest improvements

🔥 Multi-Model AI Workflow (Advanced)

Instead of using one AI, use a team-based workflow:

1. R1 → Plan architecture
2. Coder → Generate code
3. Llama → Review and optimize

👉 This approach improves quality significantly.

⚠️ Limitations of Local AI

While powerful, local AI has some limitations:

• ❌ No full automation (agent mode limited)
• ❌ Slightly slower than cloud models
• ❌ Limited context handling

👉 Still, it is highly useful for everyday development.

💡 Best Practices

• Use smaller models (7B–8B) for speed
• Avoid large unnecessary context
• Provide clear and structured prompts
• Use file-specific context instead of full project

🎯 Who Should Use This Setup?

This setup is ideal for:

• .NET developers
• Students learning AI
• Developers who want free tools
• Privacy-conscious engineers

🏆 Benefits of Local AI

• 💸 Zero cost
• 🔒 Full privacy
• ⚡ Works offline
• 🧠 Better understanding of AI workflows

🔎 Common Questions

❓ Can I use AI in VS Code for free?

Yes, using Ollama and local models, you can run AI without any cost.

❓ Is this better than GitHub Copilot?

It depends:

• Copilot → faster, cloud-based
• Local AI → free, private, offline

❓ Do I need internet after setup?

No. Once models are downloaded, everything works offline.

🚀 Future of Local AI

Local AI is evolving rapidly. Soon, we will have:

• Better agent workflows
• Faster models
• Full automation locally

👉 Learning this today gives you a strong advantage.

🏁 Conclusion

You don’t need expensive tools to be productive.

With this setup, you can:

• Run AI locally
• Write better code
• Learn faster

👉 And most importantly — stay independent of cloud tools