Local AI Models Are Assiting Software Development in VS Code

AI is no longer just a futuristic add-on for software development, it is rapidly becoming a core part of the developer workflow. The latest evolution? Local AI models that run directly on your own device, promising a transformative shift in how code is written, tested, and maintained.

The Advantages of Running AI Locally

Unlike traditional, cloud-based AI services, local models process data right on your machine. This means developers get immediate feedback and maintain full control over their intellectual property. No more waiting for network calls or sharing sensitive code with external servers. Local AI models deliver speed, privacy, and customization.

• Rapid Feedback: Instant code suggestions and bug detection help maintain productivity.
• Enhanced Privacy: All code remains on your computer, reducing security concerns.
• Tailored AI: Developers can adapt models to fit specific language, project, or style needs.

Real-World Impact: AI in Everyday Coding

Modern editors and integrated development environments (IDEs) now feature AI assistants that do more than autocomplete code. These tools offer:

• Smart, context-aware code completions
• On-the-fly bug detection and suggestions
• Automated test and documentation generation
• Natural language explanations for complex code blocks

Thanks to advances in consumer hardware and efficient model design, these powerful AI features are accessible even on standard laptops and desktops, not just high-end workstations.

Overcoming Local AI Challenges

Despite the benefits, local AI models come with their own set of challenges. Resource consumption tops the list, as complex models can demand significant processing power. Developers also need to keep models secure and compatible with existing tools, while ensuring performance doesn't drain battery life or slow down other tasks.

• Performance Tuning: Right-sizing models for your device is essential.
• Security Considerations: Local models must be safeguarded from tampering.
• Cross-Platform Support: Seamless integration across languages and systems remains a hurdle.

Supported and Recommended Models

Foundry Local offers a variety of models optimized for code-related tasks that can be run locally on your hardware. For example:

• Qwen models are multilingual models that support code generation and natural language tasks.
• Microsoft Phi models are small, efficient models designed for reasoning, code generation, and natural language understanding.
• OpenAI GPT models – Offers advanced capabilities and broad compatibility.

Open Source and the Rise of Local AI Ecosystems

Open-source projects and leading tech companies are driving innovation in local AI. Models like Llama, Code Llama, and Phi are being optimized for personal devices, while new tools make it easier to integrate AI directly into daily development routines. These advances are lowering the barrier to entry, enabling teams of all sizes to leverage AI more effectively.

How to Set Up the Foundry Local Integration for GitHub Copilot in VS Code

1. Install Visual Studio Code (VS Code) on your computer.
   
   
2. Add the GitHub Copilot extension from the VS Code Marketplace.
   
   
3. Add the AI Toolkit extension from the VS Code Marketplace.
   
   
4. Open GitHub Copilot chat, use the model picker to add a model, and select “Foundry Local via AI Toolkit” as the provider.
   
   
5. AI Toolkit will prompt you to download the models to your local machine if they have not been downloaded.

Looking Ahead: The Developer’s New AI Toolkit

Local AI-assisted development is poised to become a staple in every coder’s toolkit. By empowering developers with speed, privacy, and adaptability, these tools are setting a new standard for software engineering. As technology continues to evolve, expect local AI models to play an even bigger role in shaping the future of coding.

Source: Original blog on AI-Assisted Development Powered by Local Models