ondevice machine learning

Hands-on guide to local-first AI that's private, fast, and bash-friendly. Learn why on-device models win on privacy, cost, and latency, then follow step-by-step installs for Debian/Ubuntu, Fedora/RHEL, and openSUSE plus scripts for three case studies: RAG-lite doc Q&A (ripgrep+Ollama), offline transcription (whisper.cpp), and log triage with a tiny LLM, plus tips and upgrade paths (embeddings, GPU, systemd).

Build powerful, private, offline AI tools on Linux without cloud or APIs. This hands-on guide gives cross-distro setup and four Bash-friendly projects: a llama.cpp terminal copilot, whisper.cpp dictation, semantic "grep" with embeddings, and LLM-powered log summaries. Includes copy-paste commands, performance/security tips, and next steps to scale models or add GPU.

Guide to building a fully offline AI automation stack on Linux: install local LLMs (Ollama or llama.cpp) and whisper.cpp STT, then connect them with Bash to turn voice requests into safe, allowlisted shell commands, with JSON validation and optional execution; includes setup for scheduling via systemd/cron, performance and security tips, troubleshooting, and real-world use cases—all private, fast, and internet-free.