I’ve been experimenting a lot lately with AI Employees inside
@higgsfield #Supercomputer, and these four videos are all outputs from one of the Employees I’m currently building and training.
The idea behind this one is pretty simple: give it a brand, product, or website, and let it handle much more of the advertising workflow itself - research the brand, understand its visual DNA, look at the competitive space, choose a direction, build the concept and pre-production, and then move into actual video generation.
What I find especially interesting is that this isn’t really about making AI “press one button and magically create an ad.”
It’s much closer to building your own production logic.
You can teach an Employee how you want it to research, how much freedom it should have, which models and workflows to use, what makes a concept good enough to move forward, how strict it should be about product consistency, when it should create storyboards first, how it should approach camera language, and even how it should QC the final result.
And then you keep refining it.
Some of these workflows can easily take 10–20 minutes from the initial task to finished generations, especially when
#research and pre-production are involved. But the interesting part is that I don’t really have to sit there and manually execute every stage anymore.
And because Supercomputer can run multiple tasks at the same time, the equation changes even more.
Instead of spending those 10–20 minutes producing one direction manually, I can launch several different tasks or creative directions in parallel and come back to a much larger pool of material in roughly the same window of time.
That’s probably the part I’m enjoying the most right now: not just saving individual clicks, but multiplying how many ideas I can actually explore.
The Employee itself is still very much a work in
#progress. I’m constantly changing instructions, testing where it needs more
#control and where it actually performs better with more creative freedom. Some decisions still need human judgment, and there are plenty of things I want to improve.
But these four
#videos came out of that process - different directions, different executions, all produced through the same evolving system.
And the
#results are already making me very curious about where this way of working goes next.
AI Employees become much more interesting when you stop treating them like assistants and start treating them like
#workflows you can teach, test, and continuously improve.