How I detect breakout YouTube videos with a median, a square root and a 10,000-unit quota

A developer built Seedflows, a tool that monitors YouTube channels in a niche daily and flags breakout uploads by comparing each video's views against a channel baseline taken as the median of long-form videos aged two weeks to five months. Expected views scale with age using the heuristic baseline * min(1, sqrt(max(age_days, 1) / 14)), and a score of 2x or more marks a breakout, with repeated near-identical titles across two to three channels within weeks flagged as a format spreading through the niche. The system runs on Next.js, Postgres with Drizzle, a Node worker and Stripe billing, constrained by the YouTube Data API's 10,000-unit daily quota.

By the time a video shows up in my recommendations, ten other channels have already made their version. So I built a small tool that checks the channels in a niche every morning and flags uploads that do much better than that channel normally does. Here's how it works, and what the YouTube API quota taught me. There's a live, read-only demo of one real niche: https://watch.seedflows.ai/demo https://watch.seedflows.ai/demo For each channel I take its long-form videos that are between 2 weeks and 5 months old and use the median of their views. One viral hit wrecks an average; the median ignores it. Shorts get their own baseline, because they live in a different world. A 3-day-old video hasn't had time to collect its views. So the expectation grows with age: expected = baseline min 1, sqrt max age days, 1 / 14 On day 1 a video "should" have about 27% of the baseline, on day 4 about 53%, from day 14 on 100%. It's a heuristic I picked by looking at data, not a fitted model. Better ideas are welcome. score = views / expected 2x or more is a breakout, 1.3x is "above average". Every check stores the view count, so the next check shows views per day: a lucky first day and a video YouTube keeps recommending look very different. After each check, all titles in the niche are compared with each other. When near-identical titles show up on 2–3 channels within a few weeks, that's a format spreading through the niche. In my experience that's the most useful signal of all. The YouTube Data API gives a project 10,000 units a day, and that's the real constraint: Next.js, Postgres with Drizzle, a small Node worker for the daily checks, Stripe for billing. The demo shows one real niche, read-only: https://watch.seedflows.ai/demo https://watch.seedflows.ai/demo If you want your own niches, it's at https://watch.seedflows.ai https://watch.seedflows.ai . I'd love feedback on the age curve in particular. How would you model expected views by age?