Turn editing feedback into a reusable skill
Ad creator Alex Cooper gave Claude Code raw footage and a script, then received a first cut of a Facebook ad after about 20 minutes. He says the result had jolty sections, but he could request changes in plain English instead of opening a video timeline.
The more useful move came next: Cooper converted recurring feedback about pacing, b-roll placement, and other editing choices into a reusable skill. His team also records explanations of creative decisions, turns the transcripts into context, and reviews several agent-produced edits in batches. It is a practitioner’s workflow, not an independent test of Claude’s editing quality.
Make saved videos searchable overnight
Developer Daniel Nwaneri built a local night shift for saved tech videos. Whisper transcribes speech, while Gemma creates searchable notes or reads four frames when a video is silent. The system runs on a CPU-only laptop and pauses when the machine switches to battery power.
The checks are unusually concrete: quoted text must appear word for word in the transcript, and every tool name and number must exist in the source material. The run produced 56 notes from 72 short videos, with eight speech-based notes refused when their quotes failed verification. Nwaneri manually checked ten notes and found nine correct on the first pass.
Also
- What AI search means for publishers The Verge traces declining referral traffic, publisher lawsuits, new source-link treatments, and Google’s experiments with licensing and payments.
- Build a small AI work sample Swati Gupta offers a no-code exercise that records the task, safe input, prompt, reviewed output, limitation, and next improvement.
- Batch-generate images, then review the misses Shotfill draws and checks images locally, but its creator found only 64 percent agreement between Gemma’s verdicts and his labels.
- Crop a section from the middle of an image Simon Willison’s Seam Cutter makes a horizontal or vertical cut, then lets you slide the two sides together.
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