Video wird geladen...

Video konnte nicht geladen werden

Zur Startseite

📖SEEDANCE 2.0 HAS TWO MODES. MOST PEOPLE ONLY KNOW ONE. Most creators are leaving 80% of its potential untapped. The first mode is what everyone uses. Type a prompt, get a video, move on. The second mode is what produces the outputs that make people stop scrolling and ask...

22,722 Aufrufe • vor 3 Monaten •via X (Twitter)

8 Kommentare

Profilbild von trungbep
trungbepvor 3 Monaten

Actually, I don't see much difference. In fact, normal seeding is even better than seeding using JSON.

Profilbild von Zentrix⌚️
Zentrix⌚️vor 3 Monaten

The difference is not very large, but it is often noticeable. Here too, you need to do several generations to get the desired result.

Profilbild von Daniel Mata
Daniel Matavor 3 Monaten

🤦🏻

Profilbild von Solomon Omolabi
Solomon Omolabivor 3 Monaten

Good prompts are like good instructions. The clearer you are, the better the result. A few words can change everything.

Profilbild von Zentrix⌚️
Zentrix⌚️vor 3 Monaten

That's right. Agents are like little children, they need more clues.

Profilbild von FILMFIRE
FILMFIREvor 3 Monaten

This is pure gold!

Profilbild von Zentrix⌚️
Zentrix⌚️vor 3 Monaten

Glad you liked it. Use it)

Profilbild von babuszek
babuszekvor 3 Monaten

Lmao you know fuck all about ai then, normal prompt are being changed to json just before generation you dumb head

Ähnliche Videos

📖THE JSON PROMPT TRICK THAT TURNS SEEDANCE 2.0 INTO A FILM STUDIO Everyone uses the same tool. Almost nobody uses it the right way. The difference isn't talent. It's structure. A normal prompt tells the model what to show. A JSON prompt tells it how to think — what the camera is doing, where the light is coming from, how fast the scene breathes, what emotional weight the color carries. Look at the pink ceremonial elephant rising from the water at golden hour. Normal prompt gives you an impressive creature. JSON gives you a frame that feels like it was pulled from a $40M fantasy film — the texture on the skin, the mist sitting low on the water, the way the warm light wraps around the gold ornaments. Same concept. One prompt format separates the two outputs completely. The Aztec jaguar in the jungle — normal prompt renders the creature. JSON builds the world around it. The glowing sun symbol on its chest, the waterfall in the background catching green light, the low camera angle that makes it feel genuinely threatening. Structure is the skill. The tool is just executing what you give it. • Step 1 — Describe your scene in one sentence, exactly what you see in your head • Step 2 — Open Claude and ask it to convert your sentence into a JSON prompt with these fields: subject, setting, camera angle, light source, texture detail, emotional tone, motion type, color palette • Step 3 — Paste the JSON directly into Seedance 2.0 and generate three variations • Step 4 — Take the strongest clip into CapCut or Premiere Pro, add ambient sound, and export The output gap between these two approaches is not small. It's the difference between content that gets scrolled past and content that gets saved 📥A different AI opportunity lands tomorrow. This one surprised me too. 🔖The full guide is waiting below. The creators who find it first win.

Zentrix⌚️

22,088 Aufrufe • vor 3 Monaten

📖THE SEEDANCE 2.0 PROMPT FORMAT THAT NOBODY IS USING BUT EVERYONE SHOULD Most people use Seedance 2.0 wrong. They type a normal prompt and wonder why the result looks generic. The difference between these two visuals isn't a better idea — it's a better prompt format. Normal prompt gives you a good result. JSON prompt gives you a cinematic one. Here's why it works: A normal prompt describes what you want. A JSON prompt tells Seedance exactly how to build the scene — camera movement, lighting angle, atmosphere, texture, depth, timing. You're not asking for a video. You're engineering one. The crystal dolphin jumping above an ancient courtyard with floating jellyfish — same concept, two completely different outputs. The JSON version has golden hour, dramatic clouds, volumetric light, and a sense of scale the normal prompt never reached. The glowing lotus fountain — same story. Normal prompt gives you a clean render. JSON gives you a full cinematic frame with layered depth, warm lantern light, and a composition that looks like it belongs in a feature film. The workflow that produces this: • Step 1 — Write your scene idea in plain language first •Step 2 — Ask Claude to convert it into a structured JSON prompt with fields for: subject, environment, camera, lighting, mood, motion, color grade • Step 3 — Paste the JSON into Seedance 2.0 • Step 4 — Take the best clip into Premiere Pro for final color correction and sound design The tools are free or cheap. The knowledge of how to prompt is what separates the results. This is the gap most creators never close. 📥 BACK TOMORROW. There will be new life hacks 🔖 The full guide is pinned below. Save it before you need it.

Zentrix⌚️

136,911 Aufrufe • vor 3 Monaten

your agent reviewing its own work is not a check. it is a second opinion from the same source. this is the most common gap in agent systems and it hides in plain sight, because the step exists. there is a review. it just cannot do the thing you think it does. here is the mechanism. the model produced an output from a context. you then ask the same model, holding the same context, whether that output is correct. it answers fluently, because that is what it does. and the answer is drawn from the same distribution that produced the thing being judged. same weights, same window, same blind spots. if the reason the output is wrong is something the model does not know, the review does not know it either. if the reason is something the context does not contain, the review has the same context. the failure mode and the detector share a cause. > why it feels like it works because most of the time the output is fine, and the review says fine. agreement is not evidence of detection. a reviewer that says pass on everything agrees with reality most of the time too. what you actually want to measure is what happens on the cases that are wrong. that is the only place a check earns its name, and it is exactly the place where a self-review is weakest. there is research on this. Huang and colleagues at DeepMind showed at ICLR 2024 that intrinsic self-correction, revising without external grounding, does not reliably help and often makes things worse. > what to actually do move the check outside the model. a test that runs, a schema that validates, a file that exists or does not, an exit code from something you did not write. these are not smarter than the model. they are just not correlated with it, and that is the entire value. when the judgement genuinely needs a model, at minimum use a different family. same family means shared blind spots, and frontier judges measurably inflate scores for outputs that look like their own. and split the work by kind. anything objectively checkable goes to code. only the genuinely semantic calls go to a judge, and those get a rubric written as one line. a review inside the loop tells you the model is confident. a check outside it tells you whether the work is done. save this - then read the eval setup below

Hanako

14,325 Aufrufe • vor 1 Monat

Elon Musk was asked what happens to people when the machines no longer need them. He didn’t soften it. Musk: “There will be fewer and fewer jobs that a robot cannot do better. These are not things I wish would happen. They probably will.” Sit with that second sentence. He is not celebrating. He is not selling a vision. He is telling you what he believes is inevitable and admitting he wishes it weren’t. That is not optimism. That is a confession. Most people are still arguing over whether this is real. Whether it’s their job or someone else’s. Whether the timeline is years away or decades. Musk isn’t arguing. He resolved it. And it bothers him. Musk: “I think ultimately we will have to have some kind of universal basic income. I don’t think we’re going to have a choice.” Not a political position. Not a utopian proposal. A concession. We are building something so capable that human labor stops being a required input to the economy. The machine does not need rest. It does not need a salary. It does not call in sick. It does not ask for a raise. And it improves every single month. The jobs that feel safe right now are not safe because they are irreplaceable. They feel safe because the technology hasn’t fully arrived yet. It’s arriving. Musk: “How do people then have meaning? If there’s not a need for your labor, what’s the meaning? Do you feel useless?” He said that is the harder problem. Not the economics. Not the policy. Not how you fund UBI or make it hold. The harder problem is what happens to a person who built their entire identity around being needed. That is most people. You were trained from childhood to believe your value is what you produce. That your worth is what you earn. That rest is something you survive the week to reach, not something you deserve simply by existing. When the machine removes the need for your labor, that belief does not update. It breaks. The people least prepared for that moment are the ones who worked the hardest. The ones who took the most pride in being indispensable. The ones who made work the whole answer. Losing the job is survivable. Losing the reason to get up is not. That is what Musk is actually asking. Not how do we pay people. How do we build a world where people still feel like they matter when the economy no longer needs them. Nobody in power is seriously working on that answer. The machine didn’t wait.

Dustin

247,138 Aufrufe • vor 5 Monaten

What you're looking at started as something that most people drive past without a second thought. - Industrial - boring - overlooked And someone looked at it and saw something no one else was seeing. That's the whole game here. You don't have to invent something brand new to make serious money. You just have to see potential where everyone else sees limitations. You have to look at what already exists and imagine it just a little differently. So when you turn something familiar into something unexpected, it becomes shareable and memorable. It becomes the thing that people can't stop talking about because it's different in a world where everything feels the same. People will literally travel hours just to stay somewhere like this because different is the experience now. Nobody cares about another cookie cutter hotel room that they've seen a thousand times, but this gets filmed and posted and sent to friends with the caption "you have to see this place" And suddenly you're booked out months in advance because you gave something people can't get anywhere else. I love that the raw materials here aren't special, it's just space that was sitting there unused and cheap to acquire The success of this is found in the willingness to look at what everyone else ignored and ask "what could this become?" Instead of just accepting what currently is. That's where the opportunity is. There are thousands of: - shipping containers rotting in fields - old grain silos sitting empty - warehouses no one's using - barns that haven't been touched in decades And every single one of them could become something people would pay to experience if someone just had the vision to see it through. You don't need to build it from scratch. You just need to reimagine what's already sitting in front of you. Because the world is full of overlooked things waiting for someone to look at them differently. And I don't care if AI generated the video because the inspiration you get from it is still the same.

Chris Koerner

18,278 Aufrufe • vor 7 Monaten