Create Your Own Offline AI Coding Assistant with Local Cursor: No Cloud, Full Control

Build a Local AI Coding Agent (No Cloud Needed) Most coding assistants today rely on cloud services to perform their tasks, even for something as simple as reading files or running shell commands. This can be a significant drawback if you value privacy, operate in secure environments, or prefer to maintain full control over your data. In this guide, we'll show you how to build Local Cursor — a terminal-based AI coding assistant that operates entirely offline using open-source models. What is Local Cursor? Local Cursor is a versatile AI coding agent designed to run on your local machine. It doesn't make any API calls or depend on cloud services, ensuring that your code and data remain private and secure. The agent can assist you in various tasks, such as: Code Completion: Suggesting code snippets and completing lines or functions. Error Detection: Identifying and highlighting syntax errors. File Management: Reading and writing files directly on your system. Shell Commands: Executing terminal commands. Core Components of Local Cursor Local Cursor consists of three main components: CLI Interface Built using click, a lightweight Python library that simplifies the process of defining commands and options. This component allows you to interact with the AI agent directly through your terminal, making it seamless and user-friendly. Ollama Runtime Uses qwen3:32b, a fast and powerful open-source reasoning model. Ollama is the runtime environment that enables qwen3:32b to run fully offline on your machine, ensuring high performance and complete privacy. Building Local Cursor To get started with building Local Cursor, follow these steps: Install Dependencies Ensure you have Python installed on your machine. You can download it from the official Python website if you don't have it. Install the click library and any other necessary dependencies using pip: bash pip install click Set Up the CLI Interface Create a Python script named local_cursor.py. Import the click library and define the commands and options you want to support. For example: ```python import click @click.group() def local_cursor(): pass @local_cursor.command() @click.argument('filename') def read_file(filename): with open(filename, 'r') as file: print(file.read()) @local_cursor.command() @click.argument('command') def run_command(command): import subprocess result = subprocess.run(command, shell=True, capture_output=True, text=True) print(result.stdout) if name == 'main': local_cursor() ``` Integrate the Ollama Runtime Download and set up the Ollama runtime and the qwen3:32b model. Use the model to perform tasks like code completion and error detection. For instance, you can define a function to handle code suggestions: ```python from ollama.runtime import Qwen3 model = Qwen3() @local_cursor.command() @click.argument('code_snippet') def suggest_code(code_snippet): suggestion = model.suggest_completion(code_snippet) print(suggestion) ``` Test and Refine Test the CLI interface and the AI functionalities to ensure they work as expected. Make adjustments as needed to improve performance and usability. Advantages of Local Cursor Privacy: Since all operations run locally, your code and data are never sent to external servers. Security: Ideal for working in secure environments where internet access is restricted or prohibited. Control: You have full control over the AI's actions and the data it accesses. Speed: Local execution can often be faster than cloud-based solutions due to reduced latency. Conclusion By building Local Cursor, you gain a powerful, secure, and user-friendly AI coding assistant that runs entirely on your local machine. This setup is particularly beneficial for developers who value privacy and security or need to work in environments with limited internet access. With the tools and components outlined here, you can start enhancing your coding workflow right away.