Privacy

You can build a private AI search engine modeled on Perplexity . You combine SearXNG with a local language model running through Ollama . Here is the stack. SearXNG pulls results from many search engines at once. A Python scraper fetches and cleans the actual page content. The LLM then turns everything into a cited answer with inline references like [1], [2]. No API keys, no telemetry, no query logging to third-party AI services. A machine with 12 GB VRAM runs the whole pipeline, and most queries come back in 5-15 seconds.

Every DNS query your devices make tells a story. When your home network sends those queries to Google (8.8.8.8), Cloudflare (1.1.1.1), or your ISP’s resolver, that provider builds a record of every domain every device visits. Your phone, your laptop, your smart TV, your thermostat: all of it. You can fix this. Run Pi-hole as a DNS sinkhole to block ads and trackers across the whole network. Then pair it with Unbound , a local recursive resolver, so your queries go straight to the DNS root servers instead of a third-party middleman.

Placing IoT devices on a dedicated VLAN with firewall rules that block all traffic to your main network - except specific connections to your Home Assistant server - prevents a compromised smart bulb or camera from becoming a pivot point into your personal computers and NAS. This setup works with consumer-grade managed switches and either UniFi or OpenWrt routers, and takes about an hour to configure properly.

The core idea is straightforward: instead of trusting every device on your network, you divide the network into isolated segments and only allow the traffic you explicitly approve. Your smart plugs, cameras, and voice assistants get their own network segment where they can reach the internet and your home automation server, but nothing else. If one of them gets compromised, the attacker is stuck in a sandbox with no path to your laptop or file server.

Home Assistant AI voice control with a local LLM as the brain is practical in 2026. No Amazon, no Google, no cloud. The Assist pipeline already handles the plumbing: wake word, speech-to-text, a conversation agent, and text-to-speech, all on your own hardware. Setting that up is the easy part. The hard part is picking a local model that calls Home Assistant’s tools without guessing. The loop also has to be fast, or it will never feel like a real assistant. This guide covers both: the 2026 stack, the models the community actually trusts, and the latency budget that makes it work.

Most blogs reach for Disqus on day one. It takes about five minutes to set up. What you don’t see at sign-up is the deal you’re making. Disqus is free because it monetizes your readers. Every person who loads your comment section gets tracked, profiled, and served ads. They never agreed to it. That’s just the business model behind the embed script you pasted into your template.

Remark42 changes the equation. It is a self-hosted, open-source comment engine built in Go. It ships as a single Docker image. It collects only the data needed to run a comment section, and nothing more. This guide walks through the whole setup. You’ll deploy Remark42 behind Nginx with HTTPS, wire it into a Hugo site, set up moderation, and keep your data safe with automated backups.

You can automate your Gmail inbox on your own machine. The Gmail API feeds messages into a private Python script. A local LLM then handles summaries, sorting, and draft replies. You get the smart inbox features that tools like Google’s Gemini sidebar or Microsoft Copilot for Outlook offer. None of your email content ever leaves your computer.

This guide walks through the full build. You’ll set up the Gmail API with minimal OAuth scopes. You’ll fetch and parse raw email data, then mask any PII with Microsoft Presidio before the model sees it. You’ll build a daily summarizer that ranks mail by urgency. You’ll also build a smart draft writer that learns from your sent mail, and you’ll wire the whole pipeline up with cron. By the end, you’ll have a working local email agent that runs on any mid-range Linux or macOS box with Ollama installed.