Build Your Own “Private Copilot” in 10 Minutes: Ollama, Continue, and DeepSeek-V3

2026-04-29 · Source: Artificial Intelligence in Plain English - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, short

Summary

This article details a 10-minute setup to create a "private Copilot" for developers, replacing cloud-based AI coding assistants like GitHub Copilot with a local, offline alternative. The architecture involves three components: Ollama as the local inference engine, a quantized DeepSeek model (specifically deepseek-coder-v2) as the language model "brain," and the Continue extension for VS Code as the interface. The process includes installing Ollama, pulling the optimized DeepSeek model, installing the Continue VS Code extension, and configuring Continue to route requests to the local Ollama instance. This setup aims to eliminate monthly subscription costs, ensure code privacy by preventing data transmission to third-party servers, and remove internet dependency and associated latency.

Key takeaway

For AI Engineers or Software Engineers seeking to reduce cloud dependency and enhance code privacy, implementing a local AI coding assistant is a practical step. You can eliminate monthly "cloud tax" payments like the $20/month for GitHub Copilot and ensure proprietary code remains on your machine. This setup provides zero-latency assistance and allows for work on projects with strict NDAs, offering greater control over your development environment.

Key insights

Developers can build a private, offline AI coding assistant using local inference engines and optimized models.

Principles

• Own tools, don't rent them.
• Prioritize privacy and offline capability.
• Hardware can support local AI.

Method

Install Ollama, pull a quantized DeepSeek model (e.g., deepseek-coder-v2), install the Continue VS Code extension, then configure Continue to use the local Ollama API endpoint.

In practice

• Use `ollama run deepseek-coder-v2` to pull the model.
• Configure `apiBase` to "http://127.0.0.1:11434" in Continue.
• Set `allowAnonymousTelemetry` to `false` for privacy.

Topics

• Ollama
• DeepSeek-Coder
• Continue VS Code Extension
• Local LLM Deployment
• AI Coding Assistant

Best for: Software Engineer, AI Engineer, MLOps Engineer

Related on AIssential

• I Replaced GitHub Copilot With a Local Setup. Here’s What Nobody Tells You — Local AI coding assistants using Gemma 4 and Ollama offer a private, cost-effective, and controllable alternative to cloud-based tools.
• Running AI Models Locally with Ollama Completely Changed My AI Journey — Ollama simplifies running advanced AI models locally, enhancing privacy, speed, and experimentation without cloud dependency.
• OpenJarvis: a local-first personal AI is now available to run with Ollama — OpenJarvis provides a local-first, open-source framework for personal AI agents, prioritizing efficiency and user control over cloud reliance.
• Microsoft Mulls Using DeepSeek for Copilot Cowork — Microsoft considers DeepSeek V4 or other open-source models for Copilot Cowork to reduce AI assistant operational costs.
• Build Your Own Cursor This Weekend. Yes, the One SpaceX Just Paid $60 Billion For. — A frontier coding assistant can be built by integrating open-source components and open-weight models, utilizing a two-model architecture.
• How Relay Network Adopted AI Coding Securely and Built the Foundation for Agentic Development — Embedding security "secure at inception" into AI-assisted development prevents vulnerabilities and accelerates remediation.

Counsel's verdict on this

AIssential's Counsel cites this article in its editorial verdict on the decision it informs:

• Build our own vertical copilot — or buy from a category vendor? — Open-weight models reach frontier capability parity within 3 to 6 months, but usage-based billing can increase copilot costs by 10x to 100x, risking massive budget overruns.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence in Plain English - Medium.