AI That Respects Your Privacy: The Case for Local LLM Inference

As cloud-based activity trackers raise privacy concerns, this project demonstrates that sophisticated AI inference can run entirely on users' machines without sacrificing functionality. By cleverly packaging a 1.2GB language model with dynamic DLL loading, Focus Stream shows how to build privacy-preserving AI applications that weigh under 70MB in the installer. This approach reflects a growing industry shift toward edge computing and local-first architecture, giving users control over their data while reducing dependence on centralized cloud services.