I built TIMING as a transparent way to look at public attention trends. For any Wikipedia topic, it pulls the last 90 days of daily pageviews from the Wikimedia Analytics API, works on log(1+views), computes a 7-day rolling average and a linear regression, and flags rupture points using a z-score at 2σ — after subtracting the expected false-positive rate from pure noise (~4.6% at that threshold). It outputs two normalized scenarios (continuity vs rupture), and every sentence in the output is a template filled from the actual computed numbers — no LLM involved.

Could be useful for OSINT/monitoring work tracking public attention around a topic, though worth being clear: this models attention, not real-world events. Free, no account: https://timing.london.li/