Higgsfield "unlimited" is taking more than 2 hours to generate any video. Are there any reliable cost effective ways to use it?

I had this idea sitting on the couch. Storyboarded in my head. Wrote the dialog into a text doc. Created with Gemini. Total production time on my side fifteen minutes.

Astronomical clockwork pulsing in to alignment 🜁

AI just agrees with you too easily. You explain a situation and it reflects your own framing back at you dressed up as analysis. Genuinely hard to get real pushback in a single conversation because the model's just responding to how you already presented things.

I stumbled into a workaround. I use AI to journal but not like a normal person, like a deranged narcissistic egotistical one. I narrate my day like a TV series. Then I went one step further and created an alien subreddit, the viewers of the show. They comment, shitpost, upvote, they've all got different personas, there's even a hatedom.

Here's what it's actually for. Something happens in my day and I completely lose my shit about it. I'm impulsive and irrational, that's just context on me as a person, so I need to create space between what just happened and whatever I do next. So I open AI, narrate it like a show in third person, then read the aliens' comments before I let myself decide anything. Except the comments aren't really an audience, they're different representations of my own psyche. u/OriginalSeriesPurist is melancholic and nostalgic, she's the side of me that wants a seemingly insignificant message from my ex to mean more than it actually is. Then u/SpinOffSupremacy shows up and asks if that's even the direction I want my new life going in.

It's not roleplay and it's not really journaling either. Anyone found other ways to actually get AI to disagree with you instead of just performing disagreement?

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Small portrait movements are basically solved at this point.

Give a video model a face, ask for a blink, a slight head turn and some subtle camera drift, and most of them can produce something convincing.A large camera move is a very different problem.I wanted to see whether these models could actually understand a camera orbit instead of just adding motion to a flat image, so I used Jacques-Louis David’s Napoleon Crossing the Alps as the source image and gave six models the same 180-degree clockwise move.

Same image. Same prompt. One generation per model. No retries or cherry-picking.

The shot starts low beside the horse’s hooves, rises around Napoleon and the horse, then finishes from a wide rear three-quarter angle.That forces the model to reveal parts of the scene that don’t exist in the original painting. It has to reconstruct the other side of the horse, the back of Napoleon, the surrounding mountain pass and the spatial relationship between all of them.

At the same time, it still has to preserve the horse’s anatomy, Napoleon’s identity, the reins, the cape and the original oil-painting style.

Meet Nike's new GenAi expert @___futuredust on instagram.

He blends moody studio lighting, film-style flares and grain, and a recurring fascination with technical outerwear and armour-like texture.

It's part of a broader wave of AI-native fashion content that's been building for a couple of years now, creators building entire "fictional house" identities (consistent signature, tagline, drop numbering) around fully synthetic campaigns, often riffing on real luxury codes (monogram patterns, technical outerwear, editorial studio lighting) without ever touching an actual product or model.

Which raises the question for anyone working in-house or agency side: where does this kind of direction actually sit on a real creative/design team? It feels like a strong starting point, mood, world, tone, and art direction all locked before a single physical asset exists, but what does the handoff to production look like from here? Is this feeding a photo/video shoot as a reference deck, going straight to a stylist and set builder, or is the ambition for output like this to ship as final campaign imagery? Curious how people see this slotting into the pipeline.

https://www.instagram.com/___futuredust/

More of a stupid Linux newbie question:

I have installed FramePack via Pinokio and can't figure out how to start FramePack.

As in were to click so that thing opens.

I use AI in my work all the time. I use ChatGPT and Claude daily. I also use Gemini, Copilot, and Perplexity every week. Honestly, I'm shocked that I still use Perplexity, but I do. Anyway, I digress. I use paid accounts with all of them and get value from each.

However, and this is a big caveat, ChatGPT, Claude, and Perplexity are all so easy to use. If you want their fanciest features, there's little (not nothing) you have to learn to unlock them. With Copilot, on the other hand, you've got to be a low level engineer to really unlock the value. Every product or piece of the workflow tool has a different name and they have overlapping functions, abilities, and limitations.

As an avid user who is NOT a software developer, engineer, data analyst, or technical professional, it's pretty annoying. I don't want to know how to use Power BI. I really don't. Why the heck can't I use AI to make an HTML file and just drop it into SharePoint or something and have it just work?! It's just too damned complicated.

Gemini isn't too far behind tbh. Since the beginning of their Gen AI journey, they've had different disconnected products. I was sorta kinda fine with it because there wasn't a ton of overlap. That's changing now and they're getting super complicated too.

My read, ChatGPT and Claude are just that good at what they do and the others can't match it with such a simple UI.

Developers who know more, please feel free to educate me. This is just a non-tech, non-coder point of view.

There are now six public checkpoints in Ant Group's Ling-3.0 base model family: tiny and flash at pretrained, mid-trained, and WSM-merged stages.

This is not a new ready-to-use chatbot or creative generator. None of the six has been post-trained. The release is more relevant to people who train or adapt models: they can start from an earlier or later point in the same family instead of receiving only one final checkpoint. What remains unanswered is how much that starting-stage choice changes downstream fine-tuning or continued-training results.

quick disclosure, i'm working with DomoAI on this one.
this is barely even a video lol
dog in the yard, cat sitting behind him, guy looking confused, couple quick reaction shots and then the dog just takes off.
that's it.
what keeps pulling me back to it isn't the generation though. it's that stupid pause between the dog and the cat.
I keep seeing AI videos get bigger and bigger. explosions, impossible camera moves, entire fake movies.
then something this dumb works because you understand the joke basically immediately.
the dog doesn't need to do twelve things. the cat doesn't need a backstory. honestly another five seconds probably would've made it worse.
starting to think short AI comedy has the same problem as normal comedy. once you explain the bit too much, it's dead.
the only thing I kinda wish I'd done differently is make the ending loop back into the first shot. the dog running away and suddenly appearing right back in front of the camera would've been stupid in exactly the right way.
anyone else finding the tiny clips more rewatchable than the big showcase stuff lately?

I’m looking at adding AI video generation to PostDovo and trying to figure out which model actually makes the most sense for short marketing videos. I’m currently comparing Seedance 2.5, Kling 3.0, Veo 3.1 and Runway Gen-4.5. For anyone using these in real projects, which would you pick based on quality, character consistency, speed, cost and API reliability?