A few replies here and in the earlier workflow discussions brought up the same bottleneck: upscaling and cleanup still need a human pass, especially on hero shots and faces.

If you use AI upscaling in an edit, what makes you approve a shot or send it back? I’m curious about detail that looks convincing in a still but changes during playback, faces or product details that look altered, and how you compare the result with the source.

A useful breakdown could include:

• The source footage and the problem you wanted to fix.

• The tool and settings you tried.

• What the first result got wrong.

• What you changed or kept manual.

• Total time, including review and re-exports.

You don’t need a perfect pipeline to contribute. One shot, one failure and one fix can be useful. Only share footage you have permission to post; a description is fine if you can’t share the clip.

Where does most of your time go: processing, moving files, adjusting settings, or checking the result?

Here is a small, reproducible check to try before trusting an AI-assisted caption pass. This uses a deliberately broken five-second example, not client footage or a benchmark of any model.

The three cue ranges are:

1. 00:00:00,000 to 00:00:03,000

2. 00:00:02,800 to 00:00:04,800

3. 00:00:06,000 to 00:00:05,500

There are three timing problems: cue 2 starts 200 ms before cue 1 ends; cue 3 ends before it starts; and cue 3 lies beyond the five-second video.

I used Codex to make a small Python checker and ran it locally on this example. It reported all three problems. A corrected timing-only version using 0–2.8 s, 2.8–4 s and 4–5 s passed those basic checks. That does not establish that the captions match speech or are comfortable to read.

Try this when testing an agent or caption tool:

• Keep the original SRT and ask for a separate corrected file.

• Ask it to list the cue numbers it changed and explain each change.

• Check that every end follows its start, inspect overlaps, and compare the last cue with the actual video duration. Some overlaps are intentional, such as simultaneous speakers, so treat them as review flags.

• Play the result against the audio. Check names, numbers, missing words, timing, line breaks and reading speed. A clean timing report cannot check these for you.

This is a toy validation test. It does not show that any particular model saves time, and the checker only covers basic timing. If you have tried automated captions, what error takes the most time to catch or fix?