
What happened
Using a roughly 45-second Snipping Tool recording of an article draft, the writer fed the MP4 to Codex, which split it into images and returned a summary; the tool chain was FFmpeg/ffprobe, Python, OpenCV and Pillow.
Why it matters
Codex cannot read video directly but can recognize images, so video-to-stills conversion appears to lower the effort of describing on-screen clicks and menus to an AI agent.
WHO IT HITSThis mainly lands on non-engineers who want to hand off repetitive screen tasks to an AI agent — office staff and writers who currently explain 'click here, open this menu' in chat — though the demo covers summarization only.
Summaries like this, in your inbox every morning.
Behind the demo is a simple workaround: Codex has no native video understanding, but it does recognize images. So the author inserted a conversion step between video and model, cutting the recording into stills with OpenCV at set intervals and assembling them with Pillow, while FFmpeg/ffprobe handled length, resolution and audio checks. In the walkthrough, the writer recorded their own screen while drafting this very article with Snipping Tool, saved it as MP4, and asked Codex to describe the clip; the model produced a timeline noting the intro text and later edits under a 'Codex' heading. The recording only covers the note-taking, not a moment where Codex answers on camera.
The author used GPT-6.1 Sol and frames the technique as a first step, suggesting the same captured content could later support requests to build automation scripts or generate procedure manuals. That remains a suggestion rather than something demonstrated in the article.
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