AIHOT is a self-hosted engine for running your own AI news site: it watches the sources you choose, scores every story twice, and publishes only the coverage worth reading. Point it at RSS feeds, web list pages, JSON APIs, X accounts, WeChat public accounts, or content pushed in by scripts, polled on schedules you control. After deduplication and pre-screening, every story faces two independent scoring passes under the same rubric, so one generous run cannot wave a weak story through. Coverage of the same story clusters into one event across the last two weeks, so forty outlets on one launch read as a single developing story. Hotness is measured per event: each source counts once per 48 hours and its weight halves daily, so repeat posting cannot inflate the ranking. Digests compile on schedule: a daily edition at 08:00, a weekly review on Mondays, and a monthly rollup on the first, assembled by rules without model calls. Readers browse topic pages by company, research direction, and content form, search titles, summaries, and full text, and keep local favorites with export and import. From the admin backend you can test sources, correct scores by hand, calibrate selection against labeled samples, and swap the model behind each pipeline step. Output flows through RSS, a public API, MCP, agent Markdown, and llms.txt, so humans and AI agents can subscribe. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.