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I don’t think AI editing is about removing the timeline. I think it’s about making the timeline more useful. With GPT-6 Astra, I can generate a custom .cube LUT from a specific creative direction, import it into CapCut PC, then adjust the intensity until it actually works with my... show more
48,988 просмотров • 9 часов назад •via X (Twitter)
Комментарии: 22

This could make experimenting with different visual identities much faster.

The feedback loop is probably the most important part here.

I like how the workflow keeps the creative process iterative instead of linear.

This could be especially useful when working with brand guidelines and repeatable styles.

AI doing the repetitive parts while humans make the creative calls makes perfect sense.

Editable outputs are much more useful than finished outputs when you care about creative control.

This is a much smarter way to think about AI video editing.

That workflow of using Astra for the heavy lifting before refining in CapCut is fascinating. We actually went deeper on Astra's video editing impact here:

This is a much better definition of AI productivity in video.

The LUT plus SRT combination alone could speed up a lot of editing workflows.

This is the kind of workflow that could turn hours of repetitive work into minutes.

Another user says the $100 Pro plan still has weekly Astra quota left.

Instead of asking AI to make the whole thing, give me assets I can build with.

Try it today :

The refinement loop is what makes this more than just another AI generation workflow.

I like the distinction between AI generation and human refinement.

The custom LUT generation alone opens up some interesting possibilities for creators.

Prompting for editable assets is something more creators should experiment with.

The real value here is reducing the friction between an idea and an editable project.

This workflow feels much closer to how creators actually work.

Having Astra prepare the assets and CapCut PC handle the creative refinement feels very natural.

AI should make experimentation cheaper, and this workflow does exactly that.
