ondevice ai

Run private, fast, scriptable AI on Linux with local LLMs: this guide explains why (privacy, predictable cost, low latency, hackable), shows how to install Ollama (simple) or llama.cpp (max control with CPU/GPU), fetch quantized models, call them from Bash or a local REST API, automate real tasks (logs, commits, translation), tune for speed/quality, fix common issues, and explore next steps like RAG and long contexts.

Local AI isn’t inherently safe. This guide hardens Linux on‑device models with practical, cross‑distro steps: verify downloads (checksums/GPG), sandbox file access (firejail/bubblewrap), block egress by default (sandbox flags or nftables + dedicated user), use rootless Podman with read‑only mounts and dropped caps, and sanitize/encrypt data—yielding safe‑by‑default local workflows.

Guide for Linux engineers to run local open-source LLMs with Ollama for private, reproducible, offline Bash-first workflows: install via curl, pick models (llama3.1:8b, qwen2.5-coder:7b, mixtral:8x7b, deepseek-coder, phi-3), wire into shell with an llm wrapper, benchmark via API/jq, apply to logs, awk/sed, systemd and Ansible, use system prompts/aliases, pin models—start small and automate a daily pain.

A Linux-first guide to local AI that’s private, fast, and cost-predictable: set up Ollama for instant LLMs, compile llama.cpp for lean performance (CPU/GPU), add whisper.cpp for offline speech-to-text, and script a grep-powered RAG-lite workflow—all from Bash—plus a sizing/performance checklist and real-world uses like log triage, PR summaries, and offline field notes.

Guide to running AI models locally on Linux—no cloud required—for privacy, low latency, cost control, and reliability. Covers setup, hardware tips, and quantized models; quick start with Ollama, power-user builds with llama.cpp (CPU/GPU, GGUF, HTTP server), offline speech-to-text with whisper.cpp, CLI/API scripting examples, and tuning advice to keep everything fast and fully under your control.