### **You**

Marketing / rich materials such as
Slide decks presentation videos documents and effectively all that can be made via gsuite/office 365 + miro, clipchamp/imovie and audacity/garageband is lacking in agents currently 

We would like to close that vap
Elicit hevaiyl

---

### **ChatGPT**

Marketing / rich materials such as
Slide decks presentation videos documents and effectively all that can be made via gsuite/office 365 + miro, clipchamp/imovie and audacity/garageband is lacking in agents currently 

We would like to close that vap
Elicit hevaiyl

---

### **ChatGPT**

Marketing is one slice. Artifacts of all types are a concern

---

### **ChatGPT**

Fully headless solution is wanted.

---

### **ChatGPT**

Good. That constraint changes everything.
If you want fully headless, then forget “integrating with GSuite / Office / Miro / Clipchamp.” That keeps you trapped inside their UI-first models and API limitations.
What you actually want is this:

Agents that compile artifacts, not “edit SaaS documents.”

No browser.
No add-ins.
No UI automation.
No human tool in the loop.
Just: spec → deterministic build → artifact.

The Real Problem
Current agents:

Write text.

Maybe call an API.

Maybe fill a template.

Stop.

They do not:

Own layout engines.

Own timeline engines.

Own audio graphs.

Own document pagination.

Own brand systems.

Own export pipelines.

They rely on:

Google Slides to do layout.

PowerPoint to do chart rendering.

Clipchamp to do timeline editing.

GarageBand to do mixing.

That’s the gap.

Headless Solution: Treat Artifacts Like Code
You need an Artifact Compiler System.
Every artifact type becomes:

\`\`\`
CodeStructured Spec (IR)        ↓Compiler        ↓Renderer        ↓Validator        ↓Export Matrix
\`\`\`

No SaaS dependency.

Core Architecture
1. Universal Artifact IR Layer
Define canonical internal specs:

\`\`\`
CodeSlideSpecDocSpecSpreadsheetSpecTimelineSpecAudioSpecBoardSpecDiagramSpec
\`\`\`

Each one:

Pure JSON

Deterministic

Versionable

Diffable

Serializable

Example (SlideSpec fragment):

\`\`\`
JSON{  "slides": [    {      "layout": "two-column",      "elements": [        {          "type": "heading",          "text": "Market Overview",          "styleToken": "heading-xl"        },        {          "type": "chart",          "chartType": "bar",          "dataRef": "dataset.market\_share"        }      ]    }  ]}
\`\`\`

The agent never “edits slides.”
It edits SlideSpec.

2. Rendering Engines (Headless Only)
Slides
Use:

Open XML SDK

python-pptx

PptxGenJS

Direct OOXML manipulation

No PowerPoint runtime required.
Compile SlideSpec → PPTX.
Also:

Generate thumbnails via headless renderer

Export PDF

Documents
Use:

Markdown → AST → HTML → PDF

Pandoc

WeasyPrint

Direct DOCX generation via OpenXML

Compile DocSpec → DOCX / PDF / HTML.
Deterministic pagination rules.

Spreadsheets
Define SpreadsheetSpec:

\`\`\`
JSON{  "sheets": [    {      "name": "Revenue",      "cells": {        "A1": "Month",        "A2": "=SUM(B2:B12)"      }    }  ]}
\`\`\`

Compile → XLSX via:

openpyxl

ExcelJS

OpenXML

Video
This is the real shift.
You do NOT “integrate Clipchamp.”
You build:
TimelineSpec → FFmpeg / MLT → MP4
Example:

\`\`\`
JSON{  "tracks": [    {      "type": "video",      "clips": [        {          "asset": "intro.mp4",          "start": 0,          "duration": 5        }      ]    },    {      "type": "audio",      "asset": "voiceover.wav"    }  ]}
\`\`\`

Compiler generates FFmpeg filtergraph.
Deterministic output.
No NLE required.

Audio
AudioSpec → DSP pipeline.
Use:

FFmpeg filters

SoX

Librosa

Custom DSP nodes

Implement:

Loudness normalization

Noise reduction

EQ

Fade automation

Multi-track mixdown

Export WAV + MP3.
No GarageBand.

Boards / Whiteboards
Define spatial IR:

\`\`\`
JSON{  "nodes": [    { "id": "1", "type": "sticky", "x": 100, "y": 200 }  ],  "edges": [    { "from": "1", "to": "2" }  ]}
\`\`\`

Render to:

SVG

HTML canvas

PDF

Optional exporter to Miro via API — but not required.

What “Fully Headless” Really Means
It means:

Agents own the data model.

Agents own the compilation.

SaaS platforms become export targets — not editing surfaces.

Artifacts are reproducible builds.

Required Subsystems
1. Artifact Build Orchestrator
Like a CI system:

\`\`\`
CodeArtifactRequest    ↓Planner    ↓Spec Generator    ↓Compiler    ↓Validation    ↓Export Targets
\`\`\`

Each stage:

Deterministic

Logged

Versioned

2. Brand Token Engine
Central token store:

\`\`\`
Codecolor.primaryfont.headingspacing.lgradius.sm
\`\`\`

Compile tokens into:

PPTX themes

CSS

PDF styles

Video overlays

Audio intro/outro metadata

No drift allowed.

3. Validation Layer
Before export:

Layout overflow detection

Font fallback detection

Color contrast checks

Accessibility enforcement

Caption completeness

Loudness threshold checks

Broken reference detection

Agents must pass validation before shipping.

4. Asset Gateway
All assets:

Stored in object storage

Hashed

Versioned

Signed URLs for export

Cached proxies generated

No direct SaaS linking.

Why This Is Superior
Compared to API-based integration:
API Editing ModelHeadless Compiler ModelRate limitsInfinite local opsAPI gapsFull controlExpiring previewsDeterministic renderUI dependencyPure computeLayout surprisesEngine-defined rulesVendor lock-inPortable artifacts

The Strategic Insight
You don’t want an “AI that uses Google Slides.”
You want:

A document/video/audio compiler that happens to output Google Slides-compatible files.

That’s the inversion.

Minimal Viable Stack
If building this today:
Backend:

Python or Go orchestration

JSON IR schemas

OpenXML + python-pptx

Pandoc + WeasyPrint

FFmpeg + MLT

SoX / Librosa

openpyxl / ExcelJS

Object storage (S3-compatible)

Postgres for manifests

Frontend:

Chat UI + artifact preview pane

Live diff view (spec vs previous spec)

Thumbnail viewer

Timeline scrub preview

No SaaS editing required.

The Real Work
The hard problems are not LLM-related:

Designing IRs cleanly.

Building compilers that don’t degrade fidelity.

Building layout engines for slides.

Handling timeline math correctly.

Validation + brand linting.

Making preview loops fast enough.

LLMs are the easy part.

If You Want To Close The Gap Properly
You build:

\`\`\`
CodeAgent → Artifact DSL → Deterministic Compilers → Multi-target Export
\`\`\`

Not:

\`\`\`
CodeAgent → Google Slides API
\`\`\`

If you want next step, we can:

Design a unified Artifact DSL.

Design a Slide layout engine.

Design a TimelineSpec schema.

Define a full artifact build DAG.

Produce a production-ready architecture doc.

Tell me which direction you want to go first.

---

### **ChatGPT**

For editing  and creating audio and video clips? E.g. From the slides

---

### **ChatGPT**

Yes — slide → video/audio is totally doable headlessly. The trick is: you don’t “edit in Clipchamp/iMovie,” you compile a timeline from slide assets + rules.
Here are the clean headless patterns (from simplest to “real production”).

A. Slide-to-video: the canonical headless pipeline
1) Render slides to images (PNG)
You need a deterministic slide renderer.
Best practical headless options:

LibreOffice headless: PPTX → PDF → PNG (very common in CI)

Chromium headless: HTML slides → PNG (if you generate HTML decks)

Custom: render via your own layout engine (harder, but ultimate control)

Example (LibreOffice PPTX → PDF):

\`\`\`
Bashsoffice --headless --convert-to pdf --outdir out deck.pptx
\`\`\`

Then PDF → PNG frames:

\`\`\`
Bashmagick -density 200 out/deck.pdf out/slide-%03d.png# or poppler:pdftoppm -png -r 200 out/deck.pdf out/slide
\`\`\`

Now you have slide-000.png, slide-001.png, etc.

2) Generate timing (per slide) + transitions
You can do:

fixed seconds/slide, OR

derive timing from speaker notes / script length (better), OR

align to narration timestamps (best).

At this point you should have something like:

\`\`\`
JSON[  {"img":"slide-000.png","dur":5.0},  {"img":"slide-001.png","dur":7.5},  {"img":"slide-002.png","dur":6.0}]
\`\`\`

3) Compile to video with FFmpeg
Simple “static slides” video:

\`\`\`
Bashffmpeg -y \\  -loop 1 -t 5.0 -i slide-000.png \\  -loop 1 -t 7.5 -i slide-001.png \\  -loop 1 -t 6.0 -i slide-002.png \\  -filter\_complex "[0:v][1:v][2:v]concat=n=3:v=1:a=0,format=yuv420p[v]" \\  -map "[v]" out/video.mp4
\`\`\`

Add crossfades (example pattern; scales better when generated by a compiler):

generate a filtergraph that chains xfade between segments.

this is exactly where a TimelineSpec → FFmpeg filtergraph compiler pays off.

Add subtle motion (“Ken Burns”) per slide:

\`\`\`
Bashffmpeg -y -loop 1 -t 6 -i slide-000.png \\  -vf "zoompan=z='min(zoom+0.0008,1.08)':d=180:s=1920x1080:fps=30,format=yuv420p" \\  out/slide0.mp4
\`\`\`

Then concatenate those per-slide clips.

B. Narration + audio editing (headless)
1) Get narration text
Use speaker notes, or auto-generate from slide content.
2) Produce voiceover
Options:

cloud TTS (fast + consistent)

local TTS (privacy; quality depends)

Outputs: narration.wav
3) Mix narration + music + sfx + ducking
You can do this fully in FFmpeg.
Example: background music ducked under voice + loudness normalize:

\`\`\`
Bashffmpeg -y \\  -i narration.wav -i music.wav \\  -filter\_complex "\\  [1:a]volume=0.25[a\_music]; \\  [a\_music][0:a]sidechaincompress=threshold=0.02:ratio=8:attack=5:release=200[a\_duck]; \\  [0:a][a\_duck]amix=inputs=2:duration=first:dropout\_transition=2[a\_mix]; \\  [a\_mix]loudnorm=I=-16:TP=-1.5:LRA=11[a\_out]" \\  -map "[a\_out]" out/final\_audio.wav
\`\`\`

That’s “basic DAW” capability without a DAW.

C. “From the slides” as semantic edits (not just stitching images)
If you want real editing (cutting to a specific bullet, highlighting regions, animating builds):
You need slide element coordinates.
Two ways:

If you own the deck generation (recommended):

your SlideSpec already knows each element bbox (x,y,w,h)

then you can render overlays: highlight boxes, pan/zoom to regions, animate bullet reveals

If you start from PPTX/Slides:

parse PPTX OOXML for element geometry (doable, annoying)

or use the Slides API to read element transforms (not headless-only in the “no SaaS” sense, but still non-UI)

Once you have bboxes, you can generate per-slide overlay instructions:

\`\`\`
JSON{  "at": 12.4,  "type": "highlight",  "bbox": [320, 210, 1280, 140],  "style": "yellowGlow"}
\`\`\`

…and compile those to FFmpeg overlays (drawbox, alpha masks, blur, etc.).

D. What you should build (minimal “artifact compiler” for media)
TimelineSpec (video)

tracks: video, overlays, captions

clips: slide frames, b-roll, screen recordings

transitions

camera moves: pan/zoom to bbox

export settings

AudioSpec

narration track

bgm track

ducking rules

loudness targets

fades

Then:

Renderer: TimelineSpec → FFmpeg/MLT

Validator: duration matches narration, no black frames, captions cover time, loudness meets target

E. Practical recommendation
If you want a fast MVP:

PPTX → PNG (LibreOffice headless)

speaker notes → narration (TTS)

timing = narration length per slide (word-count heuristic)

FFmpeg: ken-burns + crossfades + audio mix + captions

If you want “production-grade”:

adopt TimelineSpec (or OpenTimelineIO) and compile to FFmpeg/MLT.

If you tell me which input you’re assuming:

PPTX, Google Slides, or your own SlideSpec
…I’ll give you the exact “compiler contract” + a concrete timeline schema and the FFmpeg generation strategy that won’t turn into a spaghetti filtergraph.

---

### **ChatGPT**

Pipelines w nanobanana and veo for img/vid gen?

---

