private ai

A practical guide to running private, offline AI on Linux with Ollama: install on Debian/Ubuntu, Fedora, or openSUSE; pull and chat with models like llama3; integrate via Bash pipelines; use the local HTTP API for generation and embeddings; safely expose on LAN; customize with a Modelfile; manage GPUs and storage; and apply it to log triage, coding help, documentation, and local RAG—your AI, on your machine.

Hands-on guide to private AI on Linux without cloud, accounts, or telemetry: covers benefits (control/compliance, predictable cost, low latency, reproducibility, open ecosystem) and step-by-step setups for Ollama (easy local LLMs), llama.cpp (fine control), a 15-min RAG with Chroma + Sentence-Transformers, and offline Vosk STT, plus security, tuning, reproducibility, licensing tips, real-world uses, and next steps.

A Bash-first, Linux-focused guide to private AI with local LLMs: install Ollama, pull 7B–8B quantized models, and script against a localhost REST API to summarize logs, classify, redact PII, and review patches. Includes model sizing, CPU/GPU tips, privacy hardening, automation/version pinning, an optional llama.cpp path, real-world pipelines, troubleshooting, and next steps to keep data on-device, offline, and cost-controlled.

Hands-on guide to building fast, private AI workflows on Linux: install core deps; run local LLMs via Ollama or compile llama.cpp; add a minimalist RAG with Chroma and sentence-transformers to query your PDFs; transcribe audio offline with whisper.cpp; lock down networking, pin versions, and automate via systemd—delivering data sovereignty, predictable cost, low latency, and reproducible setups.