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Yes—but “without a video editor” does not mean without production work. In a first-person account published October 1, 2026, Hamdi LAADHARI says he used Claude Opus 5.5 in Cowork to create a 55-second LinkedIn video about moving from software engineering toward AI engineering. His reported workflow involved code, browser automation, video encoding, audio tools, repeated reviews and his own creative decisions. It produced nine versions and 666 rendered frames, according to the author; this is a personal case study, not an independently reproduced test. Read LAADHARI’s account.
What the AI-assisted video involved
LAADHARI’s brief was a personal-branding video for LinkedIn explaining a career transition. He says the assistant first asked about the video’s purpose, format, tone, length and language. The final result, as he describes it, was 55 seconds long at 1080 × 1350, using a cut-paper stop-motion look, music, sound effects and narration.
The author’s central distinction is between avoiding a conventional editing timeline and avoiding production effort. He supplied the brief and feedback, chose the story and tone, reviewed visuals and sound, and corrected details. As he put it, “The story is mine to tell.” The reported counts—nine versions and 666 frames—describe this project alone; they are not a general estimate of how long another video will take.
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How the code-based workflow worked
Draw each frame, then render it
Rather than assembling footage on a video-editing timeline, the described workflow used JavaScript to draw images on a canvas. A deterministic draw(frameNumber) function meant a specific frame could be rendered again, while small changes in position and rotation from frame to frame gave the artwork a handmade stop-motion quality.
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Playwright rendered the frames in a browser, and ffmpeg encoded them into an MP4. A browser-based player let the author inspect scenes, while contact sheets—a grid of frame previews—helped reveal visual issues across the sequence. The author also says he used official logo files he supplied.
Build sound as a separate part of the production
Animation events generated timestamped cues for sound effects. Python generated the music, and a connector supplied narration through ElevenLabs. The author says he retimed scenes to fit the voiceover and lowered the music beneath speech. These were connected but distinct stages: drawing and rendering visuals did not by itself create the finished video’s sound mix.
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Review, revise and check the story
The author’s version log moves from an initial silent draft to a clearer career-transition story and character, then to a stronger focus on generative AI and the addition of sound. Later revisions refined the character and supplied logos, fixed a visual glitch and changed props, added and adjusted an end card with a QR code, and retimed scenes to narration. This sequence shows why “nine versions” is more informative as a record of decisions than as a simple measure of prompting.
What still required human judgment
LAADHARI says the assistant could check loudness and cue timing but could not listen to the final result. He listened and judged it himself: “It can’t hear.” That gap matters for anyone planning a similar project. Automated checks can help with measurable properties, but they do not replace listening for whether narration is clear, music is distracting or the overall timing feels right.
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The author also reports practical constraints: his sandbox could not access Google Fonts or download the final ElevenLabs audio, and his narration account rejected three of four attempted takes. He says the assistant flagged a mismatch between names in the video and his CV. These are details of his session, not independently verified claims about how those services generally behave.
His takeaway that “Clarifying questions are a feature” captures a useful part of the process: a clear brief and answers about audience, format, tone and length help shape the work before production begins. They do not settle the story for you. The author still had to decide what the career change meant and whether the finished narrative represented him accurately.
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How this differs from prompt-led video tools
The code-and-render approach described here is not the same workflow as asking a service to generate a clip from a prompt or animate a still image. The practical differences below are workflow distinctions, not results from a controlled comparison.
| Workflow | What the documentation describes | Revision and constraints |
|---|---|---|
| Custom canvas renderer | In LAADHARI’s account, JavaScript drew frames, Playwright rendered them and ffmpeg encoded the MP4. | Changes to code and repeatable frame rendering offered a way to refine individual visuals; audio and review were separate steps in this reported project. |
| Google Vids AI clips | Google’s help page describes generating clips and animating an uploaded JPG or PNG into an eight-second clip, with landscape and portrait options. | The page recommends a clear, detailed motion prompt. It states that users in the European Economic Area, United Kingdom, Switzerland, Illinois and Texas cannot upload and edit videos with that feature, though they may edit generated videos in their generation history. Availability and limits can change. |
| Google Flow | Google’s Flow documentation describes video creation from prompts, ingredients, and start or end frames. | Google advises specifying subject, action, setting, lighting and style; feature availability may differ by region. This is not a like-for-like comparison with a custom frame renderer. |
| Runway generative video | Runway’s getting-started guide covers text-to-video and image-to-video generation and iterative adjustment. Its image-to-video prompting guide advises focusing prompts on motion and camera movement. | The documentation is model-version dependent. It does not establish that this workflow matches the control, result or effort of LAADHARI’s approach. |
These options answer different needs. A custom renderer is built around code and repeatable frames; prompt-led generation or image animation uses a service’s documented creation workflow. The choice depends on the desired control, revision method, audio and assembly needs, and which features are available to you—not on an established claim that one method is universally better.
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What to take from the experiment
- AI can assist with a career-change video without a conventional video-editing timeline, but the reported process still involved several tools and substantial human direction.
- In this case, image rendering, MP4 encoding, music, effects and narration were separate connected tasks.
- Deterministic frame rendering made a particular image repeatable in the described setup, while review helped catch visual and story details.
- People still need to assess the final audio and decide whether the career narrative is accurate and appropriate to share.
The evidence for the project’s output and process comes from LAADHARI’s own account; the video was not independently inspected or reproduced here. Treat its figures and session constraints as the author’s report, not as a benchmark for AI video production.
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