I Created a BIG GAME Commercial Using KLING 3.0 in 1 day!
Shows how rapid AI video models can replace traditional production pipelines, giving indie filmmakers the ability to deliver broadcast‑quality ads on tight schedules.
What this lesson covers
Thomas demonstrates how he created a full‑length Big Game commercial in a single day using the Cling 3.0 model from the Artlist AI Toolkit. He walks through frame generation, animation, and manual post‑production in Final Cut Pro while highlighting the $60 k challenge.
Key takeaways from the creator
AI-extracted notes, not independently verified product claims. Timestamp links let you check each point in the original video.
- 00:02 ↗
Cling 3.0 can generate realistic human performances and voice‑overs directly from a prompt.
- 01:03 ↗
The $60,000 challenge rules illustrate how AI‑generated ads can be monetized through brand contests.
- 02:08 ↗
Frames were first generated with Nano Banana Pro, then animated in Clean Tree, demonstrating a two‑step pipeline.
- 03:03 ↗
All generated clips were assembled in Final Cut Pro, showing that traditional NLEs still play a central role.
- 03:15 ↗
Manual addition of music and sound effects gives creators finer control despite AI audio tools.
Workflow outlined in the video
- Sign up for the Artlist AI Toolkit and access the Cling 3.0 model.
- Draft a concise prompt describing the commercial concept, including desired characters and voice‑over.
- Use a frame‑generation tool (e.g., Nano Banana Pro) to create keyframes, then animate them with a tool like Clean Tree.
- Import the generated clips into your NLE (Final Cut Pro, Premiere, etc.) and fine‑tune timing, transitions, and audio.
- Publish the ad, tag Artlist, and enter the $60 k Big Game challenge.
Before you use this workflow
These notes describe the source video at its publication date. Model access, pricing, connectors and interfaces may have changed. Check the original source and the provider’s current documentation before installing an add-on, connecting an account or spending credits.
ReelStack has not independently tested this workflow. Preview one representative shot and check motion, continuity and output quality before applying it to a full production. No result, cost saving or model capability is guaranteed.
How to interpret the numbers
2,778 views captured 24 October 2026. ReelStack recommendation score: 21/100. Formula: radar-v5. This is ReelStack’s calculation, not a YouTube rating or a measure of factual accuracy.
Show the calculation
- 65% performance: 0.32× current views vs the channel's recent median; same-age history is not yet available. View-evidence factor 74% (views / (views + 1,000))
- 25% freshness: 0/100 with a 14-day half-life
- Momentum pending: collecting daily snapshots; its 20% weight goes to observed performance, not free points
- 10% engagement: 93/100; likes + 4× comments, smoothed with a 500-view neutral prior