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KAMAL KISHOR
KAMAL KISHOR

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I Replaced Cursor with a Free Local AI Coding Assistant (And Saved $240/Year)

#ai #webdev #programming #productivity

Build a powerful AI coding assistant using Ollama, Continue.dev, Qwen3-Coder, and VS Code — completely free, private, and local.

The Problem

Like many developers, I rely heavily on AI while coding.

Whether it's:

  • Generating React components
  • Refactoring TypeScript code
  • Creating APIs
  • Writing unit tests
  • Understanding unfamiliar codebases

AI has become part of my daily workflow.

The downside?

Most popular AI coding assistants require a subscription.

Tool Monthly Cost
Cursor Pro $20
GitHub Copilot $10
Windsurf Pro $15

For a single developer, that's manageable.

For a team, it becomes expensive quickly.

So I started asking:

Can I build my own AI coding assistant that runs entirely on my machine and costs nothing?

The answer surprised me.

Yes.

And it's much better than I expected.

🏗 Final Architecture 


VS Code
    ↓
Continue.dev
    ↓
Ollama
    ↓
Qwen3-Coder
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What You'll Build

By the end of this tutorial you'll have an AI coding assistant capable of:

✅ Code Generation

✅ Refactoring

✅ Unit Test Creation

✅ Project-Aware Context

✅ Local Processing

✅ Offline Usage

✅ Complete Privacy

✅ $0 Monthly Cost

Why Local AI Is Exploding

A few years ago, local coding models were difficult to use and often produced poor results.

That's no longer true.

Modern models like:

  • Qwen3-Coder
  • DeepSeek-Coder
  • CodeLlama

are surprisingly capable.

For React, TypeScript, Node.js, and backend development, they handle most day-to-day tasks extremely well.

The biggest benefit?

Your code never leaves your machine.

Step 1: Install Ollama

Think of Ollama as Docker for AI models.

Download and install:

https://ollama.com

Verify installation:

ollama --version
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Step 2: Install Qwen3-Coder

Pull the model:

ollama pull qwen3-coder
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Alternative models:

ollama pull deepseek-coder-v2

ollama pull codellama
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Step 3: Test the Model

Launch:

ollama run qwen3-coder
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Try:

Create a reusable React hook for debouncing user input.
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If you receive generated TypeScript code, you're ready.

Step 4: Install Continue.dev

Install Continue.dev inside VS Code.

Continue acts as the bridge between your editor and the local model.

Features:

  • AI Chat
  • Code Generation
  • File Editing
  • Project Search
  • Context Awareness

Step 5: Connect Continue to Ollama

Open Continue configuration.

Add:

{
  "models": [
    {
      "title": "Qwen3-Coder",
      "provider": "ollama",
      "model": "qwen3-coder"
    }
  ]
}
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Restart VS Code.

Done.

You now have a local AI coding assistant.

🚀 Using It Like Cursor

Here are some prompts I use daily.

Generate Components 

Create a reusable React DataTable component using TypeScript and Tailwind CSS.
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Generate APIs 

Create an Express CRUD API using PostgreSQL and TypeScript.
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Refactor Code 

Convert this React class component to a functional component using hooks.
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Generate Tests 

Write Jest and React Testing Library tests with high coverage.
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Explain Code 

Explain this file and identify performance issues.
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🎬 Real Demo

Prompt:

Create a reusable React DataTable component with:

- TypeScript
- Tailwind CSS
- Sorting
- Filtering
- Pagination
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Generated:

✅ Full Component

✅ TypeScript Types

✅ Search Functionality

✅ Sorting

✅ Pagination

✅ Responsive UI

Generated locally in seconds.

📊 Cursor vs Local AI Setup

Feature Cursor Local Setup
Monthly Cost $20 $0
Offline Mode ❌ ✅
Privacy Cloud Local
React Development ⭐⭐⭐⭐⭐ ⭐⭐⭐⭐⭐
TypeScript ⭐⭐⭐⭐⭐ ⭐⭐⭐⭐⭐
API Development ⭐⭐⭐⭐⭐ ⭐⭐⭐⭐
Unit Testing ⭐⭐⭐⭐⭐ ⭐⭐⭐⭐
Custom Models Limited Unlimited

Real-World Results

After using this setup for over a month:

What Worked Well

✅ React

✅ Next.js

✅ TypeScript

✅ Node.js

✅ Express

✅ PostgreSQL

✅ Unit Testing

Where It Struggled

❌ Large repositories

❌ Complex architectural decisions

❌ Slightly slower responses than cloud models

Hardware Used 

CPU: Intel i7-13700H
RAM: 32GB
GPU: RTX 4060 (8GB)
OS: Windows 11
Model: Qwen3-Coder
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Minimum recommendation:

16GB RAM
Modern CPU
Optional GPU
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Common Mistakes

Using Tiny Models

Small models generate weaker code.

Use:

Qwen3-Coder
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whenever possible.

Ignoring Project Context

Bad Prompt:

Create a login page.
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Good Prompt:

Analyze my authentication architecture and generate a login page that follows existing patterns.
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The results are dramatically better.

Enterprise Benefits

Most developers focus on cost savings.

The bigger advantage is privacy.

Cloud Workflow 

Your Code
    ↓
External Servers
    ↓
AI Processing
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Local Workflow 

Your Code
    ↓
Your Machine
    ↓
AI Processing
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For many organizations, that difference matters.

Cost Breakdown

Item Cost
VS Code Free
Ollama Free
Qwen3-Coder Free
Continue.dev Free
Total $0

Annual savings compared to Cursor:

$240+
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For a team of 10 developers:

$2,400+
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Future Upgrades

Once you have this setup working, you can add:

Code Review Agent

Automatically reviews pull requests.

Test Generation Agent

Creates unit and integration tests.

Documentation Agent

Generates technical documentation.

Security Agent

Finds common vulnerabilities.

Bug Hunter Agent

Identifies performance bottlenecks.

Using frameworks like:

  • LangGraph
  • CrewAI

you can evolve this into a full AI software engineering team.

Final Verdict

Would I completely replace Cursor?

For React, TypeScript, Node.js, Next.js, and most full-stack development tasks:

Yes.

The combination of:

  • VS Code
  • Continue.dev
  • Ollama
  • Qwen3-Coder

provides a surprisingly capable AI coding assistant that:

✅ Runs Locally

✅ Protects Your Code

✅ Works Offline

✅ Costs Nothing

The gap between local AI and cloud AI is shrinking fast.

And for many developers, local AI is already good enough.

What About You?

Would you rather use:

Cursor Pro ($20/month)
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or

Local AI Setup ($0/month)
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Let me know your thoughts in the comments.

👇👇👇

AI #Programming #WebDev #React #TypeScript #NodeJS #Ollama #OpenSource #Productivity #MachineLearning #koolkamalkishor

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