local inference

A practical guide to running private, fast AI on Linux the Bash way: why local models matter (privacy, latency, cost, hackability), how to prep your system, pick Ollama or llama.cpp, tune performance (threads, cgroups, NUMA, quantization), secure workloads (firejail, localhost), and automate with systemd and a clipboard summarizer—plus distro commands, real examples, and next steps to integrate AI into daily workflows.

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.

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.