Four lines in.
A social video out.

Research social hooks and editing patterns

I loaded social-platform, hook-writing, LinkedIn, FFmpeg, video-editing, and Remotion guidance. The useful rules were: lead with the viewer’s problem, keep the hook above “see more,” preserve real product proof, re-encode frame-accurate cuts, and make every overlay earn its place.

Remotion was considered, but a custom Pillow/NumPy renderer plus FFmpeg was faster and more deterministic in the available environment.

Open the preview and locate the replay artifact

A browser/CDP harness opened the supplied preview. I inspected the page and API responses, found the rrweb recording, and isolated the replay from surrounding admin UI. Nothing from the tokenized URL was put into the output.

open_preview(TOKENIZED_URL)
status = read_test_status()        # processed
generation = read_generation_status() # completed
run = read_run_status()            # passed
recording = download_rrweb_artifact()

Capture the replay as editable video

The isolated rrweb player was recorded at 1920×1080, 30 fps. The resulting replay-raw.mp4 was 23.37 seconds, H.264, and silent. This gave FFmpeg and the Python renderer a conventional media source rather than a browser event log.

ffprobe -v error \
  -show_entries stream=codec_name,width,height,r_frame_rate \
  -show_entries format=duration,size \
  -of json replay-raw.mp4
# 1920×1080 · 30/1 fps · 23.366667 s · H.264

Set a privacy boundary before creative work

I made an explicit denylist: preview token, test UUIDs, source host/IP, credentials, signer emails, signing links, raw generated code, connection details, and any traceable source identifier. The social edit uses blurred or tightly cropped product proof and replaces sensitive detail with safe editorial labels.

Translate the run into four editorial lines

The final prompt card is a compact narrative device:

• Log in and open the template
• Create a two-signer request
• Complete both signatures
• Verify the final document

These lines summarize the demonstrated run for a social viewer. They are not presented as a verbatim export of the private test specification.

Create a clean product plate

The raw 16:9 replay was trimmed to the useful 20.73-second window and converted into a clean 1016×572 product plate. That plate was decoded to 622 JPEG frames so the renderer could choose, crop, scale, and animate exact moments.

ffmpeg -i clean-demo.mp4 \
  -q:v 2 demo-frames/frame-%05d.jpg
# 622 frames available to the compositor

Write the visual compositor in Python

render_premium_v2.py is the main program: 509 lines of Pillow/NumPy composition. It creates texture, typography, brand header, prompt card, browser chrome, progress labels, focused camera crops, yellow wipes, success graphics, and the end card.

def render_frame(t):
    if t < 3.30:  frame = opening_scene(t)
    elif t < 3.82: frame = wipe_transition(...)
    elif t < 7.25: frame = prompt_scene(t)
    elif t < 7.82: frame = wipe_transition(...)
    elif t < 23.35: frame = demo_scene(t)
    elif t < 24.05: frame = wipe_transition(...)
    else: frame = end_scene(t)

Render small, then upscale cleanly

Each of the 825 frames was composed at 720×1280 for speed, piped directly to FFmpeg, and upscaled with Lanczos to 1080×1920. H.264 High profile, yuv420p, and +faststart keep the file compatible and web-friendly.

ffmpeg -f rawvideo -pix_fmt rgb24 -s 720x1280 -r 30 -i - \
  -vf scale=1080:1920:flags=lanczos \
  -c:v libx264 -preset medium -crf 17 -pix_fmt yuv420p \
  -movflags +faststart premium-v2-silent.mp4

Add voice, music, and interaction cues

The narration was generated through the configured Hermes text-to-speech tool. generate_sound_bed.py synthesized an original 48 kHz stereo music/SFX bed with NumPy: harmonic pulses, a restrained bass pattern, UI clicks, transition sweeps, and a success chord. FFmpeg mixed the tracks and normalized the result.

After every deployment, someone still has to click through the product
and hope nothing broke. With Diffie, describe the workflow in four lines
of plain English. Diffie turns it into a real browser test, runs the journey
end-to-end, and checks the actual outcome. Here, both signers complete the
document, and the final state passes. Four lines in, a real test out.

Audit the export instead of trusting the render

The final file was fully decoded to catch corrupt frames, probed for codecs and dimensions, measured with EBU R128, sampled into contact sheets, checked at transition boundaries, and scanned across 55 frames for exact matches to sensitive source identifiers.

ffmpeg -v error -i diffie-premium-social-v2.mp4 -f null -
ffprobe -v error -show_streams -show_format -of json \
  diffie-premium-social-v2.mp4
ffmpeg -i diffie-premium-social-v2.mp4 \
  -filter_complex ebur128=peak=true -f null -
# Full decode: PASS · Integrated loudness: −16.4 LUFS