The era of relying exclusively on paid, cloud-hosted AI coding assistants is ending. While services like GitHub Copilot and Cursor are powerful, they come with two significant downsides: monthly subscription costs and the inherent privacy risk of sending proprietary codebase data to third-party servers. For Principal Engineers and privacy-conscious developers, the solution lies in Local Inference . By running high-performance open-weight models like DeepSeek R1 on your own hardware, you gain total data sovereignty and zero latency networking, all without a credit card. This guide details the exact technical implementation of a local AI stack using Ollama , DeepSeek R1 , and VS Code . The Architecture: Why Local Inference Matters Before executing the setup, it is vital to understand the architectural shift. Cloud-based assistants operate via REST API calls. Every time you trigger a completion, your IDE packages the current file and cursor context, encrypts ...
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