Can AI replace Wholana?

Build me a personal TikTok outlier tracker to replace Wholana. Requirements:

- A nightly Node script (node-cron) that pulls recent videos for up to 25 handles listed in
  handles.txt, using a TikTok scraper actor on Apify, token in .env. Do not scrape TikTok
  directly, you will be blocked inside a day.
- Store videos in SQLite via better-sqlite3: handle, video id, url, caption, posted date,
  views, likes, comments, shares, date first seen. Upsert on video id so a re-scrape
  updates metrics instead of duplicating rows.
- Per creator, keep a rolling median of views over their last 30 videos and score each
  video as views divided by that median. 3x or higher is a breakout. Skip creators under
  10 videos, the median is noise below that.
- Label each breakout with one LLM call (Anthropic or OpenAI, key in .env): caption plus
  the first 15 seconds of subtitles from yt-dlp, returning one hook type from a fixed list
  of 12 in hooks.json. Fixed list, not free text, or nothing groups.
- A page on localhost:3000 (Express, server-rendered HTML, Chart.js): last 7 days of
  breakouts sorted by score, filterable by handle, each row showing score, views, hook
  type, and a link, plus a per-creator sparkline of views over time.
- A save button per row that writes the video into a swipe collection and appends it to
  swipe.md, so my picks survive the database.
- Localhost only. No accounts, no telemetry, everything on my machine except the Apify and
  LLM calls.
- Out of scope: search across creators I am not already tracking, and a shared craft
  taxonomy. Do not build auth, multi-user workspaces, or hosting config.
- README: Apify token and actor id, the cron entry, and the cost per 1,000 videos scraped.
  The scraper bill, not the code, is what makes people quit this build.