A fully automated AI pipeline that turns ideas into finished TikTok videos

Over the course of a month, a developer built a system that goes from raw idea to a finished, captioned vertical video with almost no manual work, wiring the whole flow together in n8n (Zapier works too). The first stage is ingestion: a scheduled job scrapes roughly 12 RSS sources (Twitter, Reddit, Hacker News, and several AI blogs) every few hours and stores the posts as markdown in S3, building up around 100 raw stories by the end of each day. The second stage is curation: once a day, a prompt reads that day's stories and picks the top 3 to 5 based on how likely they are to resonate (breakthrough, practical value, drama, or wow-factor), removes duplicates covering the same event, and outputs structured JSON.

One hard-won lesson was forcing the model to copy source URLs exactly rather than guessing, since guessed links break. The third stage is scripting: each story is processed one at a time, with a prompt generating 5 hooks per story, keeping the best 2, then drafting two roughly 55-second talking-head scripts.