LLM Video Pipeline
Local LLM-friendly video editing pipeline — transcribes footage, LLM authors an EDL, renders final video with cuts, overlays, color grading, subtitles. Zero cloud APIs.
The Manual Video Editing Tax
Traditional video editing is manual — watch footage, decide cuts, create timeline, render. For AI-generated narration videos (product launches, explainers), the pattern is formulaic: given source footage and a script, the computer should edit automatically. Yet every video still requires a human to scrub through clips, place cuts, sync voiceover, add overlays, and adjust color — a repetitive, hours-long process that resists automation because creative tools lack programmable decision-making.
The core insight: video editing for structured content is a compilation problem, not a creative one. A product launch has a known arc — hook, problem, solution, features, CTA. The source footage is deterministic (screen recordings, talking head, b-roll). The script is written in advance. The only missing piece is an automated director that can watch the footage, understand what each clip contains, and decide how to assemble them.
Existing tools — Premiere Pro, DaVinci Resolve, Final Cut — provide scripting (AppleScript, Python bindings) but no autonomous decision layer. Cloud AI services (Runway ML, Descript) offer AI-assisted editing but require uploading content to external servers, incur per-minute costs, and cannot operate offline or with sensitive material.
The goal: an LLM authoring a video the way a compiler generates code. You write the DESIGN.md, SCRIPT.md, STORYBOARD.md in natural language, and the pipeline transcribes the footage, has the LLM analyze narrative structure, write an Edit Decision List (EDL), and renders the final video — all locally, with zero cloud APIs.
LLM-as-Director Pipeline
The pipeline is a sequence of stages: Capture raw footage (screen recording, webcam), Transcribe via faster-whisper, Analyze with a local LLM to understand narrative structure, Author an EDL (Edit Decision List) describing every cut, transition, overlay, and color grade, Render compositions through HyperFrames, and Assemble the final MP4 via FFmpeg. The LLM acts as the director: it views transcripts, understands the narrative arc, decides what footage to use, where to cut, and what overlays to apply — all without a human touching a timeline.
{
"tracks": [
{"src": "intro.mp4", "cut": "0:12-0:45", "overlay": "title.png"},
{"src": "demo.mp4", "cut": "1:05-2:30", "transition": "crossfade"},
{"src": "outro.mp4", "cut": "0:00-0:20", "color_grade": "warm"}
]
}
# HyperFrames renders the EDL
renderer = HyperFrames(width=1920, height=1080, fps=30)
renderer.compose_from_edl("output.edl")
renderer.render("final.mp4")Capture → Transcribe → LLM → Render Pipeline
HyperFrames Pipeline Architecture
Languages, AI & Rendering
Key Design Records
Development Journey
Results & Industry Alignment
- Complete LLM-driven video pipeline — from raw footage capture to final rendered MP4, zero manual timeline editing
- End-to-end from product website capture to final 20s render — tested across two different product launches
- Industry alignment: LLM-as-compiler (emerging pattern) — the LLM authors an EDL the way a compiler generates code from source, treating DESIGN.md as source and video as compiled output
- Industry alignment: AI-assisted creative (Runway ML, Descript) — but fully local, no cloud dependency, no per-minute AI costs, no data leaving the machine
- DESIGN.md → SCRIPT.md → STORYBOARD.md → HyperFrames workflow establishes a repeatable template for AI-generated video production