beBetterStudio

The problem I thought I had

Publishing short-form content by hand does not scale. Pick an image, pick a line, lay out the text, render, upload to four networks, write four captions, schedule. Twenty minutes per post, every day, forever.

So I built the machine. It worked. And then the account started dying anyway — which turned out to be the actually interesting problem.

The system

A web app plus an automation layer. The app holds a bank of images and a bank of phrases; each is analysed and vectorised so that pairing an image to a line is a similarity query, not a judgement call I have to make at 11pm.

From there: the composition renders through FFmpeg into a 9:16 video, an audio track is picked by matching mood and energy tags, and the piece goes into a queue with its own publishing cadence. n8n drains that queue and posts to YouTube Shorts, Instagram Reels, Facebook Reels and TikTok — with the platform-specific dance each one demands, including Facebook’s three-phase upload and tokens that expire and have to rotate themselves.

There is also a carousel mode: a script is drafted, slides are generated in series with the cover first as a style reference for the rest, and the whole set publishes natively.

1. 01

   Banks

   Images and phrases are analysed and vectorised on the way in.

2. 02

   Pairing

   Matching an image to a line is a similarity query, not a decision I make at 11pm.

3. 03

   Render

   FFmpeg composes the 9:16 video with the text burned into the frame.

4. 04

   Audio

   A track is picked by matching mood and energy tags against the phrase.

5. 05

   Queue

   The piece waits its turn under a cadence I control, not a posting spree.

6. 06

   Publish

   n8n drains the queue and handles the specific dance each platform demands — including Facebook’s three-phase upload and tokens that rotate themselves.

   • YouTube Shorts
   • Instagram Reels
   • Facebook Reels
   • TikTok
7. 07

   Measure

   Daily insight snapshots are joined back to the recipe that produced each post.

When the numbers started falling

The pipeline was producing. The account was not.

	April	May	June	July
Median reach	3,840	2,310	1,138	385

The easy explanation is the algorithm, and it is the explanation I wanted. Two other numbers said otherwise:

• Skip rate rose from 43.7% to 52.8%
• Watch time dropped from 6.4s to 5.0s

Neither of those depends on how many people the platform shows you to. They depend on what happens once someone is already watching. The reach was falling because the content had gotten worse, and I could not see it because I had no idea which decisions produced which results.

The missing link

Every post has a recipe: which phrase, which image, which mood, which format, what time. That recipe lived in my database. The performance lived in the platform’s API. Nothing connected them.

Building the link for new posts was easy. The problem was the 40 posts published before any of this existed — the ones covering the entire decline. The plan assumed they were lost.

They were not. The videos have the phrase burned into the frame, which means the thumbnail literally contains it. The caption does not — the caption is a rewrite, which is exactly why matching by lexical overlap kept landing at 25–50% even for posts that did correspond.

So I read the thumbnails with vision and compared phrase against phrase. Of 40 orphan posts, 39 were recovered. One remains. For contrast, the embedding pass I tried first recovered exactly one.

The rule that made the reconciliation trustworthy was refusing to guess: four passes, strongest signal to weakest, and when confidence was not there the post stayed unlinked. A wrong link poisons every conclusion downstream and leaves no trace. An orphan is visible and fixable.

What the data said

With performance joined to recipe, one variable moved almost exactly in step with the decline. Phrases attributed to a named author:

April 17% → May 32% → June 91% → July 83%

I watched the ten extremes — five best, five worst. The ones that traveled name somebody recognisable and land on a turn. The flat ones are quoted philosophers in quotation marks.

That is a hypothesis with evidence behind it, not a conclusion. It is now a controlled test: ten new pieces written in the account’s own voice, tagged as a distinct cohort, interleaved with quotes — and measured on skip rate and watch time, not on reach, because reach is the number that lies.

Where it stands

Running. Publishing on schedule across four networks, with its own analytics panel built on the platform APIs, collecting daily snapshots.

The part worth taking away is not the renderer. It is that a content pipeline without measurement is a machine for producing more of whatever you were already doing wrong — faster.