Local AI Model for Coding Assistant · April 26

Build a Coding Assistant With a Local AI Model — Your Code Stays Private

Sending your code to GitHub Copilot or ChatGPT sends it to external servers. A coding assistant powered by a local AI model through Docker Model Runner keeps your code completely private — processing everything on your own machine with no cloud AI dependency.

Sunday, April 26 9am to 1pm EDT

4 Hours Hands-on coding

Live Online Interactive

Workshop Details

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Date and Time

Sunday, April 26, 2026

9:00am to 1:00pm EDT

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Duration

4 Hours · Hands-on

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Format

Live Online · Interactive

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Includes

Certificate of Completion

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Privacy

100% Local · No Cloud Required

By Packt Publishing · Refunds up to 10 days before

About This Workshop

Why a Local AI Model Is Better Than Cloud AI for Coding Assistance

Cloud coding AI services send your code — including proprietary business logic, API keys, and confidential algorithms — to external servers. A local AI model for coding assistance processes everything on your own hardware, giving you capable coding help without IP exposure.

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What is OpenClaw?

OpenClaw is the open-source personal AI assistant that went viral in early 2026 with 200K+ GitHub stars. It runs on your own devices and connects to WhatsApp, Telegram, Slack and more. No subscription. No data leaving your machine.

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What is Docker Model Runner?

Docker Model Runner is Docker's native feature for running large language models locally on your machine. It gives you an OpenAI-compatible API that OpenClaw uses as its AI brain — complete data privacy, no cloud costs.

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Why Combine OpenClaw and Docker?

OpenClaw gives you the assistant interface and messaging integrations. Docker Model Runner gives you the AI brain running privately on your machine. Together they create a production grade private AI assistant you fully own.

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Why Attend as a Live Workshop?

Setting this up from scattered documentation takes days of debugging. This live workshop gives you a complete guided build in 4 hours with a live instructor answering your questions. Packt has delivered 108 workshops worldwide.

Workshop Curriculum

How to Build a Coding Assistant With a Local AI Model

Six modules. Four hours. One working private AI assistant by the time you finish.

How OpenClaw Works

Understand the Gateway, channels, and skills architecture. Set up and configure OpenClaw locally from scratch.

Docker Model Runner Setup

Run and manage local LLMs using Docker Model Runner. Pull models, configure memory, and understand the OpenAI-compatible API.

Security and Privacy

Configure DM pairing, allowlists, sandbox mode, and proper access controls for your local AI deployment.

Connect to WhatsApp or Telegram

Deploy your AI assistant to real messaging platforms without sending data to any third party cloud service.

Scalable Architecture

Design an extensible assistant architecture. Add skills, configure personality, and set up proactive automation.

Production Deployment

Deploy your OpenClaw and Docker setup to a VPS for always-on availability running 24 hours a day.

What You Walk Away With

By the End of This Workshop You Will Have

Concrete working deliverables — not just theory.

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A fully functional local AI assistant running on your machine

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Docker Model Runner configured with your chosen LLM model

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OpenClaw connected to WhatsApp or Telegram

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Security and privacy configuration you can trust

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A reusable architecture for future AI assistant projects

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Certificate of completion from Packt Publishing

Your Instructor

Learn Local AI Coding Assistant Development From a Docker Captain

Rami Krispin builds and uses local AI coding tools in his own development work.

Rami Krispin

Workshop Instructor · April 26, 2026

Rami is a Senior Manager of Data Science and Engineering, Docker Captain, and LinkedIn Learning Instructor with deep expertise in building and deploying production AI systems. He guides you step by step from a blank terminal to a fully deployed private AI assistant — answering your questions live throughout the 4-hour session.

Frequently Asked Questions

Common Questions About Local AI Models for Coding Assistants

Common questions about the workshop, what to expect, and how to prepare.

Which local AI models work best for coding assistance? +

Models with strong code training work best for coding assistance. Llama 3 8B, Mistral 7B Instruct, and DeepSeek Coder variants available through Docker Model Runner all perform well for coding tasks. The instructor evaluates code assistance quality for different models during the workshop.

Can a local AI model provide the same coding assistance as GitHub Copilot? +

For conversational coding assistance — code review, explaining code, suggesting refactors, answering technical questions — local AI models are very capable. For inline autocomplete integrated into your IDE, Copilot has a workflow advantage. The workshop builds a conversational coding assistant that handles most coding assistance tasks effectively.

Is my code truly private with a local AI model coding assistant? +

Yes. When you paste code into your local AI coding assistant, the AI processing happens through Docker Model Runner on your own machine. Your code never travels to any external AI service. Only WhatsApp or Telegram message delivery uses external network connections.

What coding languages does a local AI model coding assistant support? +

Modern open weight models have been trained on code in dozens of programming languages. Python, JavaScript, TypeScript, Java, Go, Rust, SQL, Bash, and many others are well supported.

Can I use my local AI coding assistant to review pull requests? +

Yes. Paste your diff or code changes into the chat and ask your local AI coding assistant to review them. Because the AI processes everything locally you can safely share proprietary code and business logic without data privacy concerns.

How do I optimise my local AI model for coding assistance tasks? +

The instructor covers model selection for coding use cases, appropriate system prompt configuration for a coding assistant persona, and how to interact with your local AI model most effectively for different types of coding tasks.