Timeline algorithms should be useful for people, not for companies. Their quality should not be evaluated in terms of how much more time people spend on a platform, but rather in terms of how well they serve their users’ purposes. Objectives might differ, from delving deeper into a topic to connecting with like-minded communities, solving a problem or just passing time until the bus arrives. How these objectives are reached might differ too, e.g. while respecting instances’ bandwidth, one’s own as well as others’ privacy, algorithm trustworthiness and software licenses. This talk introduces an approach to personal, local timeline algorithms that people can either run out-of-the-box or customize. The approach relies on a stack which makes use of Mastodon.py to get recent timeline data, llamafile to calculate post embeddings locally, and marimo to provide a UI that runs in one’s own browser. Using this stack, we will show how to perform search, clustering, and recommendation of posts from the fediverse without any of them leaving your computer.