Research / Field Results
Testing Short-Form Video Automation at Scale
A working short-form production system for organizing premade clips, generating overlays, rendering videos, and managing publishing, with peak results from Instagram and TikTok.
Published on
Instagram Peak
14 million views and more than 1,000 followers gained with this method.
TikTok Peak
More than 10 million views and more than 4,000 followers gained with this method.
The Workflow
A library of premade clips, automated production steps, and a repeatable publishing process with measured audience results.
Why I Built It
I created Short-Form-Video-Automation to answer a practical question. How much of running TikTok and Instagram accounts could be automated when the raw material was already available as premade short-form clips?
The resulting workflow organizes clips, creates and manages overlay text, matches text to source footage, renders vertical videos, and prepares them for publishing. It turns an existing media library into a repeatable production process.
How the System Works
The project uses a local Flask dashboard and SQLite database to manage clips, overlays, matches, renders, and schedules. It can import clip metadata from CSV files, generate overlay ideas with a local Ollama model, and rank clip and text combinations with deterministic matching.
FFmpeg turns the selected combinations into 1080 by 1920 videos with text overlays. The current source-available version can publish finished reels to Instagram through Cloudflare R2 and the Instagram Graph API. TikTok publishing remains manual, but the clip selection, text workflow, matching, and rendering can still be handled locally before upload.
Peak Results
At the peak of this method, the Instagram account reached 14 million views and gained more than 1,000 followers. The TikTok account reached more than 10 million views and gained more than 4,000 followers.
Those results showed me that a large part of short-form production can be systemized when the source clips are already prepared. The strongest advantage was consistency. Instead of rebuilding each post by hand, the workflow made it easier to reuse the media library, track what had been used, and keep finished videos moving.
What I Learned
Automation works best on the repetitive parts of the process. Organizing files, generating options, matching assets, rendering videos, and tracking schedules are all good candidates. Human judgment still matters for choosing the source material, setting the direction, reviewing the output, and understanding what an audience actually responds to.
This project connected software engineering with practical media operations. The workflow handled repetitive production work while content selection, review, and publishing decisions remained part of the operating process.