So far you've been learning about GenAI tools for development, but there are different ways to integrate them into your workflow.

There are two main approaches: cloud-based AI services and local AI models. Each has distinct advantages and limitations for software development.

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Have you ever wondered why some AI coding tools work offline while others need internet connection?

Cloud-based AI uses a three-step process: your code context is sent to remote API → AI processes on powerful servers → response sent back to your IDE.

This approach leverages massive models with extensive training but requires internet connectivity and API calls.

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Why might sending your code to external servers concern some developers?

Local AI models take a different approach - they run directly on your machine without sending code externally.

Think of it like having a coding assistant sitting right next to you: your code stays on your computer, the AI processes locally, and responds immediately.

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Which sounds more secure - sending your proprietary code to external servers or keeping everything local?

The biggest advantage of local models is zero latency and privacy. Cloud-based APIs add delays: network request time + server processing + network response time.

Local models can provide instant suggestions because there's no network round-trip. Some local coding assistants respond in under 100 milliseconds.

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How important is instant feedback when you're in the flow of coding?

Local models also preserve complete code context and company confidentiality that gets exposed when using cloud APIs.

Cloud services often have usage logging and data retention policies, while local models keep your intellectual property entirely on your infrastructure.

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