Ollama

You need an AI assistant that runs on your own machine. The cloud options are good, but they are not yours. You want something that actually does things — reads your screen, controls your smart home, answers questions in Finnish or English, and never sends your data to a server you don’t control. The local LLM trend is about as practical as it sounds. It works on mid-range hardware if you pick the right model size. A sub-3B parameter model fits comfortably on consumer gear and handles everyday tasks without needing a dedicated GPU or a rack of servers. The trade-off versus cloud options is that the response times are slower, but your parents’ conversations do not become training data for a future model owned by a third party. ...

I wanted a personal AI assistant. Not a chatbot. Not a copilot. Something that actually does things — controls my desktop, reads my screen, talks to my smart home, answers questions in Finnish or English, and never, ever sends my data to a server I don’t control. The cloud options are good. They’re not mine. So I started building one. This post is the architecture document for that project. It’s a work in progress — the docker-compose is ready, the Ollama instance is running, and the first agent is next. I’m writing this because the design decisions are interesting, and because someone else might want to build the same thing. ...

Once you understand what AI tools actually do with your parents’ chat logs, the natural next question is: what should they use instead? The honest answer is that there is no single “best” privacy-first AI — there are three good options, each suited to a different household. The right choice depends almost entirely on what hardware your parents already own, how technical you are willing to get, and what they actually want to do with AI in the first place. ...