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Introducing MiniMax H3, our next-gen open-weight multimodal video model, built for general intelligence beyond single-task generation. H3 understands text, images, video, and audio together, interpreting motion, sound, emotion, and cinematography as one unified creative language, delivering generation with fine-grained precision and cinematic quality. → Native Multimodal Understanding & Generation...

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97,618 Aufrufe • vor 6 Tagen •via X (Twitter)

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MiniMax H3 (Hailuo-03) is now available on fal. - Open-weight multimodal video model with native stereo audio on every generation. - Combines up to 9 images, 3 video clips, and 3 audio clips as references - Holds subjects, motion, and audio consistent - Weights will be released soon.
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MiniMax H3 (Hailuo-03) is now available on fal. - Open-weight multimodal video model with native stereo audio on every generation. - Combines up to 9 images, 3 video clips, and 3 audio clips as references - Holds subjects, motion, and audio consistent - Weights will be released soon.

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Vidu Q3 is now available on Akool Experience next-level AI video creation with Vidu Q3 — a powerful multi-modal model that seamlessly blends high-fidelity visuals with perfectly synchronized audio. Powered by Vidu AI, Q3 shines in: Deep narrative understanding Mastery of complex cinematic language Lifelike, human-feeling motion and emotion If storytelling and cinematic quality matter to you, Vidu Q3 is built for it. Try it now on Akool.

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Dreamina Seedance 2.0 is Officially here! Dreamina Seedance 2.0, ByteDance’s AI-powered creative platform, lets creators easily transform ideas into high-quality videos. You can now edit videos like images, using up to 4 reference modalities (video, image, audio, and text) with precise control over visual effects, camera movements, and more. Why is this revolutionary? - 🔥 One-Prompt Video Editing: Edit videos seamlessly as easily as editing images. - 🎬 Multimodal Creativity: Combine images, video, audio, and text—up to 12 files at once. - 🎥 Remake Viral Content: Create high quality, professional-level videos with intelligent AI. - 🔧 Full Creative Control: Keep consistency in shots, typography, camera flow, and more. This isn’t just another video editing tool, it’s a one-stop AI workspace for all your creative needs. Ready to level up your content? Explore Dreamina Seedance 2.0 and start creating today. 🔗 #dreamina #seedance2 #seedream5 #dreaminatutorial #ai #aitools #aidesign #ecommercedesign #digitalmarketing #startupbusiness

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This AI just turned me into a film director… No editing skills. No timeline headaches. Just one prompt. This is Seedance 2.0 🎬 You can literally combine: → Text → Images → Videos → Audio And it understands everything. Even crazier? You can control it like this: Image → character Video → camera movement audio1 → music/voice It doesn’t just generate clips… It builds full cinematic scenes with: → Consistent characters → Smooth transitions → Realistic motion → Built-in lip sync Basically… From a single prompt → you get a multi-shot story. Not AI video. AI filmmaking. Go try it before everyone catches on 👇
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This AI just turned me into a film director… No editing skills. No timeline headaches. Just one prompt. This is Seedance 2.0 🎬 You can literally combine: → Text → Images → Videos → Audio And it understands everything. Even crazier? You can control it like this: Image → character Video → camera movement audio1 → music/voice It doesn’t just generate clips… It builds full cinematic scenes with: → Consistent characters → Smooth transitions → Realistic motion → Built-in lip sync Basically… From a single prompt → you get a multi-shot story. Not AI video. AI filmmaking. Go try it before everyone catches on 👇

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Introducing Muse Image and Muse Video, the first media generation models developed by Meta Superintelligence Labs. Muse Image is our most advanced image generation model yet. It follows instructions faithfully, edits with precision, composes from multiple references, and draws on Instagram for social context. It also brings agentic tool use capabilities to image generation and integrates with Muse Spark. You can try Muse Image in the Meta AI app and web, as well as in Instagram Stories and WhatsApp – starting in limited countries with more locations on the way. Today we’re also previewing Muse Video, which is built upon the same pretraining base as Muse Image to deliver exceptional visual fidelity with native audio support. Learn more about both models:

AI at Meta

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currently experimenting with WAN 2.6 I2V on GMI Cloud in this test, I’m comparing two audio workflows and honestly both perform really well. one scene uses audio generated directly from the prompt, while the other uses manually uploaded audio taken from the film 300. visually, both deliver strong motion and solid performance. however, the version with audio coming straight from the prompt feels slightly more refined, camera movement is smoother, transitions flow more naturally and the sync between voice, facial motion, and pacing feels more cohesive. lip sync, especially for Chinese dialogue also comes across a bit cleaner. you can choose single shot for a clean, focused moment or multi shot if you want more cinematic transitions, even when working from just one reference image. one important note: always turn on prompt extension. it makes a noticeable difference in how well the model understands motion, transitions and overall scene flow. both audio approaches are totally usable, but if you’re building dialogue driven or cinematic scenes, starting with audio from the prompt gives WAN 2.6 a bit more context to work with. I’ll be pushing this further with more dynamic camera movement and transitions next. more experiments coming soon ✨ Wan
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currently experimenting with WAN 2.6 I2V on GMI Cloud in this test, I’m comparing two audio workflows and honestly both perform really well. one scene uses audio generated directly from the prompt, while the other uses manually uploaded audio taken from the film 300. visually, both deliver strong motion and solid performance. however, the version with audio coming straight from the prompt feels slightly more refined, camera movement is smoother, transitions flow more naturally and the sync between voice, facial motion, and pacing feels more cohesive. lip sync, especially for Chinese dialogue also comes across a bit cleaner. you can choose single shot for a clean, focused moment or multi shot if you want more cinematic transitions, even when working from just one reference image. one important note: always turn on prompt extension. it makes a noticeable difference in how well the model understands motion, transitions and overall scene flow. both audio approaches are totally usable, but if you’re building dialogue driven or cinematic scenes, starting with audio from the prompt gives WAN 2.6 a bit more context to work with. I’ll be pushing this further with more dynamic camera movement and transitions next. more experiments coming soon ✨ Wan

DStudioproject

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🇨🇳 Another great Chinese Model, OmniHuman-1.5 from ByteDance Turns 1 image plus a voice track into expressive avatar video by pairing a System 1 and System 2 inspired planner with a Diffusion Transformer, Produces coherent motion for over 1 minute with moving camera and multi character scenes. Most avatar models move to the beat of the audio but miss meaning, so gestures feel generic and emotions feel shallow. The fix here is a Multimodal LLM planner that listens to the speech and drafts a structured plan describing intent, emotions, beats, and high level actions, which gives the motion engine clear semantic targets instead of only rhythm. The motion engine is a Multimodal Diffusion Transformer that fuses the plan with audio, the single reference image, and optional text prompts, then synthesizes continuous body, face, and head motion that matches both words and tone. A key trick is a Pseudo Last Frame, a synthetic target that summarizes the next expected state, which stabilizes fusion across modalities and keeps motion consistent over long spans. From just 1 image and speech, the system outputs speaking avatars with synchronized lips, context aware gestures, and continuous camera movement, and it also supports multi character interactions without manual choreography. Reported results show strong lip sync accuracy, high video quality, natural motion, and close match to text prompts, and the same setup works on nonhuman characters too.

Rohan Paul

63,859 Aufrufe • vor 11 Monaten

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Kling 3.0 for AI UGC videos is absolutely insane 🤯 I spent 25,000+ credits in Kling perfecting the ultimate prompting framework to get the best AI UGC outputs possible. And the 3.0 update just made everything even better. Perfect for DTC brands and agencies who want high-quality AI UGC without paying $500/video for actual UGC. Here's what Kling 3.0 unlocks for AI UGCL → "AI Director" system that understands full scripts and auto-schedules camera angles (shot/reverse shot) in one generation → 3 to 15 second cinematic clips with full temporal coherence → Improved character and element locking so your subject stays consistent across shots and angles → Native 4K output for both video and stills — actually usable for professional ad creative And best of all: perfect character consistency ACROSS different shots. What this means for AI UGC: - Multi-shot storytelling in a single generation cycle - Longer clips that actually hold together - Consistent characters across your entire ad - Output quality that's ready for paid media This is the closest AI video has gotten to replacing a real shoot for performance creative. I recorded a full breakdown of the prompting framework I built after burning through 25,000 credits. Want access to all the prompts I use to create AI UGC with Kling? > Like this post > Comment "KLING" And I'll send it over (must be following so I can DM)

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25,478 Aufrufe • vor 6 Monaten

Big moment for text-to-speech. Qwen just open-sourced a text-to-speech model that lets you clone voices, design new ones, and control speech using natural language. Let me explain what I mean: You can literally tell it "speak in a cheerful tone with slight nervousness," and it actually does that. No complex audio engineering needed. What makes this special: - 3-second voice cloning - Covers 10 languages: English, German, French, and more - Latency as low as 97ms for real-time applications - Supports both streaming and non-streaming generation The model comes in two sizes (0.6B and 1.7B parameters), so you can pick based on your hardware and quality needs. Three modes to work with: 1. Custom Voice: Use pre-built premium voices with instruction-based style control 2. Voice Design: Describe the voice you want in plain English (or Chinese), and the model creates it 3. Voice Clone: Provide a 3-second reference audio and clone that voice The best part? It integrates with vLLM for production deployment and has a simple Python package you can pip install. I've shared a link to the GitHub repo in the next tweet.
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Big moment for text-to-speech. Qwen just open-sourced a text-to-speech model that lets you clone voices, design new ones, and control speech using natural language. Let me explain what I mean: You can literally tell it "speak in a cheerful tone with slight nervousness," and it actually does that. No complex audio engineering needed. What makes this special: - 3-second voice cloning - Covers 10 languages: English, German, French, and more - Latency as low as 97ms for real-time applications - Supports both streaming and non-streaming generation The model comes in two sizes (0.6B and 1.7B parameters), so you can pick based on your hardware and quality needs. Three modes to work with: 1. Custom Voice: Use pre-built premium voices with instruction-based style control 2. Voice Design: Describe the voice you want in plain English (or Chinese), and the model creates it 3. Voice Clone: Provide a 3-second reference audio and clone that voice The best part? It integrates with vLLM for production deployment and has a simple Python package you can pip install. I've shared a link to the GitHub repo in the next tweet.

Akshay 🚀

31,249 Aufrufe • vor 6 Monaten

I’ve used all the recent GenAI video models extensively & here’s my 2¢: 🎬 Runway Gen3 Alpha - best image quality & motion for text-to-video & embedded words. Great at prompt travel changes over the course of 10 sec. And I’m super bullish on how gen3 will evolve, hopefully adopting the features listed below. Kling - best quality for image-to-video with prompt control, like eating food. Great clip extension that accounts for character (ie walking stride) & camera movement (speed & angle), rather than just using final frame. But it’s limited availability & Chinese native language is limiting. Used for Spider-Man video below (via Midjourney). LumaLabs - best for keyframe start & end control (it can not be overstated how important this is. other services should add it ASAP!) and their high dynamic action movements are really fun. Luma was used in my viral Multiverse of Memes video. PikaLabs - they haven’t gotten as much attention as others lately. But they did update their video model a few weeks ago and it looks great. Also, they are notable for their unique & AWESOME features, like video in-painting & out-painting. My perfect AI video platform would have the following features: 1) Gen3’s quality, prompt control & text embedding. 2) KLing’s image-to-video quality, prompt control & clip extension quality. 3) Luma’s multi-keyframe control & dynamic movement ability. 4) Pika’s inpainting & outpainting ability. And a video-to-video (aka next-gen Runway gen1) could be a game changer, too. It’s an exciting time to be alive 🫶 Who will get there first? 🔉🔉
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I’ve used all the recent GenAI video models extensively & here’s my 2¢: 🎬 Runway Gen3 Alpha - best image quality & motion for text-to-video & embedded words. Great at prompt travel changes over the course of 10 sec. And I’m super bullish on how gen3 will evolve, hopefully adopting the features listed below. Kling - best quality for image-to-video with prompt control, like eating food. Great clip extension that accounts for character (ie walking stride) & camera movement (speed & angle), rather than just using final frame. But it’s limited availability & Chinese native language is limiting. Used for Spider-Man video below (via Midjourney). LumaLabs - best for keyframe start & end control (it can not be overstated how important this is. other services should add it ASAP!) and their high dynamic action movements are really fun. Luma was used in my viral Multiverse of Memes video. PikaLabs - they haven’t gotten as much attention as others lately. But they did update their video model a few weeks ago and it looks great. Also, they are notable for their unique & AWESOME features, like video in-painting & out-painting. My perfect AI video platform would have the following features: 1) Gen3’s quality, prompt control & text embedding. 2) KLing’s image-to-video quality, prompt control & clip extension quality. 3) Luma’s multi-keyframe control & dynamic movement ability. 4) Pika’s inpainting & outpainting ability. And a video-to-video (aka next-gen Runway gen1) could be a game changer, too. It’s an exciting time to be alive 🫶 Who will get there first? 🔉🔉

Blaine Brown

26,535 Aufrufe • vor 2 Jahren

Google dropped a new AI paper called LUMIERE. It's remarkably flexible, supporting video inpainting, image-to-video, AND stylized video generation tasks. Say hello to “space-time diffusion” for video generation! Now what the heck does that mean exactly?! 🌐⏳ → TL;DR it utilizes a “Space-Time UNet” architecture that generates the full duration of the video in one pass, rather than generating distant keyframes and interpolating between them like prior works. Because the computation is done in this “compressed space-time representation” to generate the full clip at once, it's far more temporally consistent. → Another benefit of generating the full video at once is that you can “direct” the video generation, making it easier to hand off to other models/tasks without having to stitch together partial solutions. You can condition generations on additional inputs, meaning you get the full stack of AI video capabilities – from video inpainting to image-to-video and beyond. → New SOTA for AI video generation? User study results in the paper suggest human evaluators preferred Lumiere over Runway Gen-2, Pika Labs, and Stable Video Diffusion in terms of quality, text alignment AND motion. But as always, we need to get hands-on with this tech when Google *actually* decides to ship it. → Could this end up inside YouTube? Y’all know i’m obsessed with blending reality and imagination – so it’s the video inpainting tech I'm most excited about. I really hope this model finds its way into YouTube's Generative AI efforts, and based on their prior announcements and the list of acknowledgments in the paper I think it might! 🤞🏽 Links: 🔗Paper: 🔗Project:
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Google dropped a new AI paper called LUMIERE. It's remarkably flexible, supporting video inpainting, image-to-video, AND stylized video generation tasks. Say hello to “space-time diffusion” for video generation! Now what the heck does that mean exactly?! 🌐⏳ → TL;DR it utilizes a “Space-Time UNet” architecture that generates the full duration of the video in one pass, rather than generating distant keyframes and interpolating between them like prior works. Because the computation is done in this “compressed space-time representation” to generate the full clip at once, it's far more temporally consistent. → Another benefit of generating the full video at once is that you can “direct” the video generation, making it easier to hand off to other models/tasks without having to stitch together partial solutions. You can condition generations on additional inputs, meaning you get the full stack of AI video capabilities – from video inpainting to image-to-video and beyond. → New SOTA for AI video generation? User study results in the paper suggest human evaluators preferred Lumiere over Runway Gen-2, Pika Labs, and Stable Video Diffusion in terms of quality, text alignment AND motion. But as always, we need to get hands-on with this tech when Google *actually* decides to ship it. → Could this end up inside YouTube? Y’all know i’m obsessed with blending reality and imagination – so it’s the video inpainting tech I'm most excited about. I really hope this model finds its way into YouTube's Generative AI efforts, and based on their prior announcements and the list of acknowledgments in the paper I think it might! 🤞🏽 Links: 🔗Paper: 🔗Project:

Bilawal Sidhu

44,822 Aufrufe • vor 2 Jahren

Big win for open-source LLMs! DeepSeek V4 Pro holds the top open-weights score on SWE-bench Verified, in the GPT-5.5 range. GLM 5.2 leads the open-weight intelligence index and sits near the closed frontier on long-horizon coding. But this leaderboard number is a weak proxy for real performance. It comes from one task set, run through one harness, served at one precision. The same weights can even score differently across providers, since many hosts quantize activations to fp8 and drift the model off its reference weights. Real performance is determined based on whether a model can read a repo, make coordinated edits across files, run the tests, and recover when one breaks. By that measure, the top open models hold up, but only inside the right harness. The teams that actually put DeepSeek V4 into production pipelines as a frontier substitute got there through the harness they built around the model, not by picking a stronger model. If you want to see this in practice, Cline (64k+ stars) has actually built that harness around open models, tuned so they run at production quality. And it's tuned so that these LLMs can run at production quality, with plan and act modes, checkpoints, and terminal feedback. ClinePass is the new access layer on top of it. It runs a curated set of those models inside Cline, narrowed to the ones tested for coding-agent use, with 2 to 5x the standard rate limits and no separate provider accounts, keys, or billing to track. The video below shows the setup, and I worked with the team to put this together. It runs alongside custom keys and local models as well, not in place of them.
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Big win for open-source LLMs! DeepSeek V4 Pro holds the top open-weights score on SWE-bench Verified, in the GPT-5.5 range. GLM 5.2 leads the open-weight intelligence index and sits near the closed frontier on long-horizon coding. But this leaderboard number is a weak proxy for real performance. It comes from one task set, run through one harness, served at one precision. The same weights can even score differently across providers, since many hosts quantize activations to fp8 and drift the model off its reference weights. Real performance is determined based on whether a model can read a repo, make coordinated edits across files, run the tests, and recover when one breaks. By that measure, the top open models hold up, but only inside the right harness. The teams that actually put DeepSeek V4 into production pipelines as a frontier substitute got there through the harness they built around the model, not by picking a stronger model. If you want to see this in practice, Cline (64k+ stars) has actually built that harness around open models, tuned so they run at production quality. And it's tuned so that these LLMs can run at production quality, with plan and act modes, checkpoints, and terminal feedback. ClinePass is the new access layer on top of it. It runs a curated set of those models inside Cline, narrowed to the ones tested for coding-agent use, with 2 to 5x the standard rate limits and no separate provider accounts, keys, or billing to track. The video below shows the setup, and I worked with the team to put this together. It runs alongside custom keys and local models as well, not in place of them.

Avi Chawla

44,124 Aufrufe • vor 1 Monat

This Kling 3.0 UGC workflow is absolutely insane 🤯 It generates hyper-realistic UGC-style ad videos from a single text prompt — talking heads, unboxings, testimonials, tutorials — in one shot. Perfect for DTC brands and agencies who need to scale UGC without paying $500 per UGC video. Here's the problem: You're either paying creators who take weeks to deliver, or you're stitching together janky 5-second AI clips that look obviously fake. Kling 3.0 solves it: → Write a prompt with character, scene, and dialogue → Get a 10-15 second UGC video with realistic facial expressions → Native voice control — tone, pacing, emotion per line → Multi-shot scenes in one generation (hook → demo → CTA) → Paste negative rules to kill AI artifacts instantly No creator fees. No stitching clips. No uncanny valley. What you get: → 6 copy-paste prompt templates for every UGC format →Voice & dialogue cheat sheet with tone keywords → Multi-shot ad structure (hook → problem → solution → CTA) → Image-to-video workflow for branded product shots → A "where it fails" section so you don't waste credits → ChatGPT template to convert any rough idea into a Kling prompt I built a full prompting guide for UGC ads on TikTok and Meta. Want the guide? > Like this post > Comment "KLING" And I'll send it over (must be following so I can DM)
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This Kling 3.0 UGC workflow is absolutely insane 🤯 It generates hyper-realistic UGC-style ad videos from a single text prompt — talking heads, unboxings, testimonials, tutorials — in one shot. Perfect for DTC brands and agencies who need to scale UGC without paying $500 per UGC video. Here's the problem: You're either paying creators who take weeks to deliver, or you're stitching together janky 5-second AI clips that look obviously fake. Kling 3.0 solves it: → Write a prompt with character, scene, and dialogue → Get a 10-15 second UGC video with realistic facial expressions → Native voice control — tone, pacing, emotion per line → Multi-shot scenes in one generation (hook → demo → CTA) → Paste negative rules to kill AI artifacts instantly No creator fees. No stitching clips. No uncanny valley. What you get: → 6 copy-paste prompt templates for every UGC format →Voice & dialogue cheat sheet with tone keywords → Multi-shot ad structure (hook → problem → solution → CTA) → Image-to-video workflow for branded product shots → A "where it fails" section so you don't waste credits → ChatGPT template to convert any rough idea into a Kling prompt I built a full prompting guide for UGC ads on TikTok and Meta. Want the guide? > Like this post > Comment "KLING" And I'll send it over (must be following so I can DM)

Mike Futia

36,144 Aufrufe • vor 5 Monaten

🚨 JUST IN: THIS FREE TOOL JUST REPLACED FOUR AI IMAGE AND VIDEO SUBSCRIPTIONS AT ONCE. Midjourney. Krea. Higgsfield. Openart. One repo. 200+ models. Zero dollars a month. Here is what it actually does. It is a full image and video studio that runs in your browser or as a desktop app. Text to image, image to image, text to video, image to video, lip sync, cinema mode with real camera controls. All of it. 4,500 people already starred this. What you get for free: → 50+ image models including Flux, Midjourney v7, Ideogram, GPT-4o, Seedream → 60+ video models including Kling, Sora, Veo, Runway, Wan, Hailuo → lip sync studio with 9 dedicated models. upload a portrait and audio and it talks → cinema studio with real camera controls. lens, focal length, aperture, film stock → feed up to 14 reference images into one generation → self-hosted. your data never leaves your machine The crazy part is there is also a hosted version that needs zero setup. Just open the link and start generating. Now the math. Midjourney Standard: $30/month Krea AI Pro: $30/month Higgsfield Plus: $49/month Openart AI: $15/month That is $124 a month. $1,488 a year. This repo does everything all four do. With more models than any of them. For free. Forever. No subscription. No vendor lock-in. MIT licensed. Download it in one click on Mac or Windows. Someone should have told me about this sooner. I feel like an idiot. ( save this )
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🚨 JUST IN: THIS FREE TOOL JUST REPLACED FOUR AI IMAGE AND VIDEO SUBSCRIPTIONS AT ONCE. Midjourney. Krea. Higgsfield. Openart. One repo. 200+ models. Zero dollars a month. Here is what it actually does. It is a full image and video studio that runs in your browser or as a desktop app. Text to image, image to image, text to video, image to video, lip sync, cinema mode with real camera controls. All of it. 4,500 people already starred this. What you get for free: → 50+ image models including Flux, Midjourney v7, Ideogram, GPT-4o, Seedream → 60+ video models including Kling, Sora, Veo, Runway, Wan, Hailuo → lip sync studio with 9 dedicated models. upload a portrait and audio and it talks → cinema studio with real camera controls. lens, focal length, aperture, film stock → feed up to 14 reference images into one generation → self-hosted. your data never leaves your machine The crazy part is there is also a hosted version that needs zero setup. Just open the link and start generating. Now the math. Midjourney Standard: $30/month Krea AI Pro: $30/month Higgsfield Plus: $49/month Openart AI: $15/month That is $124 a month. $1,488 a year. This repo does everything all four do. With more models than any of them. For free. Forever. No subscription. No vendor lock-in. MIT licensed. Download it in one click on Mac or Windows. Someone should have told me about this sooner. I feel like an idiot. ( save this )

Kanika

14,760 Aufrufe • vor 3 Monaten

I just ran Gemma 4 31B on @CerebrasSystems at 1,800+ tokens/sec and it's multimodal. For context: that's 35x faster than a typical GPU endpoint, and the first token (reasoning included) lands in 1.5 seconds. This isn't a benchmark slide, I recorded the inference live. Prompt I used: "Create a simulation of an iPhone. Include at least one working dummy note taking app, a functional notification pulldown, high quality graphics, single HTML file, any libs via CDN." - Generation time: 3 seconds. - Notes app worked. - Notification panel worked. - Rendered first try. This is what wafer-scale inference unlocks, not just "faster," but a different category of product. When generation is this fast, you stop waiting and start iterating in real time. Why this matters: Gemma 4 31B is Google DeepMind's flagship open weight model, Apache 2.0 licensed, dense (not MoE), and built for efficiency over raw parameter count. It scores close to Claude Haiku 4.5 on the Artificial Analysis Intelligence Index (30 vs 29) but runs ~18x faster on Cerebras. It's also the first multimodal model on Cerebras's platform, meaning you can now feed it screenshots, documents, charts, and UI states at wafer scale speed. # Applications I'm most excited about: - Screenshot → Insight: Drop in a dashboard or document screenshot, get structured findings back instantly. no waiting, no batching. - Live UI generation: Full interactive interfaces (like my iPhone sim) generated and rendered in under 2 seconds. - Screenshot -> Patch: Feed it a broken UI + console error, get a minimal code fix and verification steps back. - Computer use & agentic loops: See -> reason -> act - verify, fast enough to keep a human in the loop instead of waiting on the model. - Long context summarization: Full research reports condensed into decision ready summaries you can read and requery in one sitting. The bigger unlock isn't the speed number itself, it's that agentic and multimodal loops (see -> reason -> output -> tool call -> verify -> retry) finally run in real time instead of feeling sluggish. As Logan Kilpatrick (Logan Kilpatrick) put it: "If every model was doing 2,000 tokens per second, you wouldn't build the same product and just have it be faster, you'd build different products." Gemma 4 31B is live now on Cerebras Inference Cloud in public preview. If you're building multimodal, agentic, or real time apps, this is worth testing today. What would you build with such insane inference throughput?
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I just ran Gemma 4 31B on @CerebrasSystems at 1,800+ tokens/sec and it's multimodal. For context: that's 35x faster than a typical GPU endpoint, and the first token (reasoning included) lands in 1.5 seconds. This isn't a benchmark slide, I recorded the inference live. Prompt I used: "Create a simulation of an iPhone. Include at least one working dummy note taking app, a functional notification pulldown, high quality graphics, single HTML file, any libs via CDN." - Generation time: 3 seconds. - Notes app worked. - Notification panel worked. - Rendered first try. This is what wafer-scale inference unlocks, not just "faster," but a different category of product. When generation is this fast, you stop waiting and start iterating in real time. Why this matters: Gemma 4 31B is Google DeepMind's flagship open weight model, Apache 2.0 licensed, dense (not MoE), and built for efficiency over raw parameter count. It scores close to Claude Haiku 4.5 on the Artificial Analysis Intelligence Index (30 vs 29) but runs ~18x faster on Cerebras. It's also the first multimodal model on Cerebras's platform, meaning you can now feed it screenshots, documents, charts, and UI states at wafer scale speed. # Applications I'm most excited about: - Screenshot → Insight: Drop in a dashboard or document screenshot, get structured findings back instantly. no waiting, no batching. - Live UI generation: Full interactive interfaces (like my iPhone sim) generated and rendered in under 2 seconds. - Screenshot -> Patch: Feed it a broken UI + console error, get a minimal code fix and verification steps back. - Computer use & agentic loops: See -> reason -> act - verify, fast enough to keep a human in the loop instead of waiting on the model. - Long context summarization: Full research reports condensed into decision ready summaries you can read and requery in one sitting. The bigger unlock isn't the speed number itself, it's that agentic and multimodal loops (see -> reason -> output -> tool call -> verify -> retry) finally run in real time instead of feeling sluggish. As Logan Kilpatrick (Logan Kilpatrick) put it: "If every model was doing 2,000 tokens per second, you wouldn't build the same product and just have it be faster, you'd build different products." Gemma 4 31B is live now on Cerebras Inference Cloud in public preview. If you're building multimodal, agentic, or real time apps, this is worth testing today. What would you build with such insane inference throughput?

Alok

12,962 Aufrufe • vor 1 Monat

AI Is Moving Beyond “Generating Videos” — Toward “Generating Worlds” Over the past two years, AI video models have advanced at an astonishing pace. From Runway and Pika to Sora and Veo, AI-generated videos have become increasingly realistic and more consistent with the physical laws of the real world. Many people believe the next objective is simply to generate videos that are longer, sharper, and more lifelike. But if we take a step back, we can see that the real transformation is not happening in video itself. It is happening in world models. What Is a World Model? In 1943, psychologist Kenneth Craik proposed an idea that would influence artificial intelligence research for decades. He argued that the human brain does not merely react to the outside world. Instead, it maintains an internal model of how the world works. Because we have this internal model, we can predict the outcome of an action before we actually take it. Before crossing a road, we estimate whether a car will pass by. Before catching a ball, we predict its trajectory. These abilities come from continuously simulating the world in our minds, rather than relying entirely on trial and error. This idea later became known by a more formal term: World Model. A world model does not describe a single image or a fixed video clip. It is an internal representation capable of continuously simulating the rules and dynamics of the real world. Why Is AI Research Turning Toward World Models? Because predicting “what comes next” is becoming increasingly central to how AI systems work. Language models predict the next token. Image models predict the next step in the denoising process. Video models predict the next frame. A world model, however, attempts to predict something broader: What should the world look like in the next moment? In 2018, David Ha and Jürgen Schmidhuber proposed in their paper World Models that an intelligent agent could first learn a model of the world, and then use that internal model to plan its actions. The Dreamer series later demonstrated that many complex tasks could be learned by training agents inside an “imagined world.” At the same time, the development of video models such as Sora and Veo led researchers to another realization: A model capable of continuously generating video has already learned, at least implicitly, many of the rules governing the real world. As a result, these two research directions have gradually begun to converge. But Video Is Not Yet a World This is where the distinction is often misunderstood. For a world model to support meaningful real-time interaction, it must solve several critical problems. Most video models today are essentially answering one question: What should the next frame look like? A true world model needs to answer much more: What happens if I take one step forward? If I walk behind a building and then return, will the building still be there? If I suddenly change the camera angle, will the entire space remain consistent? If I enter a command such as: “Summon a dragon.” Will the world respond immediately? In other words, a world model must do more than generate content. It must understand space. It must understand time. It must understand causality. And it must understand interaction. Moving from watching to participating is where the real difficulty of world models begins. World Models Are Entering the Interactive Era One of the latest attempts in this direction is Alaya World, recently open-sourced by Alaya World, or Alaya Lab. Instead of generating a fixed video clip, it generates a world that users can explore in real time. Users can begin with text, an image, or a video, enter the generated scene, move freely through it, and introduce new prompts at any moment during generation. The world responds immediately. According to the publicly released information, Alaya World provides: Real-time streaming generation at 720p and 24 FPS Stable continuous exploration for more than one minute The ability to switch prompts and trigger skills or events during generation Model weights and inference code released under the Apache 2.0 License Training code and datasets planned for future release What makes these capabilities important is not simply the technical specifications. It is that the generated “world” can now support continuous interaction. The official demo shows that users can genuinely control, transform, and explore the generated environment. AI Is Evolving From a Tool Into an Environment Over the past few years, most discussions around AI have focused on content generation. Generating text. Generating images. Generating videos. But world models raise a fundamentally different question: Can AI generate an environment that people can inhabit, explore, and continuously evolve? If the answer is yes, the impact will extend far beyond video generation. Game development, robotics training, embodied intelligence, digital twins, virtual production, and many other fields could be transformed by the development of world models. World models are still at a very early stage. Yet from Craik’s proposal of an internal mental model more than eighty years ago to the emergence of today’s interactive world-generation systems, a clear evolutionary path is beginning to take shape. Perhaps what AI is ultimately learning has never been limited to images, videos, or language. Perhaps it is learning the world itself. References GitHub: Technical Report:
0:10

AI Is Moving Beyond “Generating Videos” — Toward “Generating Worlds” Over the past two years, AI video models have advanced at an astonishing pace. From Runway and Pika to Sora and Veo, AI-generated videos have become increasingly realistic and more consistent with the physical laws of the real world. Many people believe the next objective is simply to generate videos that are longer, sharper, and more lifelike. But if we take a step back, we can see that the real transformation is not happening in video itself. It is happening in world models. What Is a World Model? In 1943, psychologist Kenneth Craik proposed an idea that would influence artificial intelligence research for decades. He argued that the human brain does not merely react to the outside world. Instead, it maintains an internal model of how the world works. Because we have this internal model, we can predict the outcome of an action before we actually take it. Before crossing a road, we estimate whether a car will pass by. Before catching a ball, we predict its trajectory. These abilities come from continuously simulating the world in our minds, rather than relying entirely on trial and error. This idea later became known by a more formal term: World Model. A world model does not describe a single image or a fixed video clip. It is an internal representation capable of continuously simulating the rules and dynamics of the real world. Why Is AI Research Turning Toward World Models? Because predicting “what comes next” is becoming increasingly central to how AI systems work. Language models predict the next token. Image models predict the next step in the denoising process. Video models predict the next frame. A world model, however, attempts to predict something broader: What should the world look like in the next moment? In 2018, David Ha and Jürgen Schmidhuber proposed in their paper World Models that an intelligent agent could first learn a model of the world, and then use that internal model to plan its actions. The Dreamer series later demonstrated that many complex tasks could be learned by training agents inside an “imagined world.” At the same time, the development of video models such as Sora and Veo led researchers to another realization: A model capable of continuously generating video has already learned, at least implicitly, many of the rules governing the real world. As a result, these two research directions have gradually begun to converge. But Video Is Not Yet a World This is where the distinction is often misunderstood. For a world model to support meaningful real-time interaction, it must solve several critical problems. Most video models today are essentially answering one question: What should the next frame look like? A true world model needs to answer much more: What happens if I take one step forward? If I walk behind a building and then return, will the building still be there? If I suddenly change the camera angle, will the entire space remain consistent? If I enter a command such as: “Summon a dragon.” Will the world respond immediately? In other words, a world model must do more than generate content. It must understand space. It must understand time. It must understand causality. And it must understand interaction. Moving from watching to participating is where the real difficulty of world models begins. World Models Are Entering the Interactive Era One of the latest attempts in this direction is Alaya World, recently open-sourced by Alaya World, or Alaya Lab. Instead of generating a fixed video clip, it generates a world that users can explore in real time. Users can begin with text, an image, or a video, enter the generated scene, move freely through it, and introduce new prompts at any moment during generation. The world responds immediately. According to the publicly released information, Alaya World provides: Real-time streaming generation at 720p and 24 FPS Stable continuous exploration for more than one minute The ability to switch prompts and trigger skills or events during generation Model weights and inference code released under the Apache 2.0 License Training code and datasets planned for future release What makes these capabilities important is not simply the technical specifications. It is that the generated “world” can now support continuous interaction. The official demo shows that users can genuinely control, transform, and explore the generated environment. AI Is Evolving From a Tool Into an Environment Over the past few years, most discussions around AI have focused on content generation. Generating text. Generating images. Generating videos. But world models raise a fundamentally different question: Can AI generate an environment that people can inhabit, explore, and continuously evolve? If the answer is yes, the impact will extend far beyond video generation. Game development, robotics training, embodied intelligence, digital twins, virtual production, and many other fields could be transformed by the development of world models. World models are still at a very early stage. Yet from Craik’s proposal of an internal mental model more than eighty years ago to the emergence of today’s interactive world-generation systems, a clear evolutionary path is beginning to take shape. Perhaps what AI is ultimately learning has never been limited to images, videos, or language. Perhaps it is learning the world itself. References GitHub: Technical Report:

雪踏乌云

112,114 Aufrufe • vor 22 Tagen

YOMIRGO #Product #Update YOMIRGO AI-HUB OFFICIALLY LAUNCH ---A Structural Upgrade from a Single-Product Model to an AI Agent Ecosystem Platform In its first phase, 11 AI projects have been integrated, spanning high-value sectors including finance, scientific research, enterprise services, development tools, and experiential AI. ➡️AI-Hub: This is not merely a feature expansion — it represents a critical structural upgrade from a single-product architecture to a multi-vertical AI Agent aggregation and capitalization platform. This milestone marks the initial structural formation of the YOMIRGO ecosystem. 1. Structural Distinction Between Agent Matrix Lab and AI-Hub To avoid positioning ambiguity, we formally clarify the structural division between the two: 🔘 Agent Matrix Lab — Internal AI Production & Incubation Platform Agent Matrix Lab serves as YOMIRGO’s proprietary AI development and internal incubation platform, responsible for: • R&D and testing of in-house AI products • Incubation of native AI Agents • Technical architecture experimentation and runtime validation • Testing of AI Agent models, memory systems, and runtime orchestration It functions as the production workshop and experimental engine of YOMIRGO’s “AI Super Factory.” 🔘 AI-Hub — External AI Agent Aggregation & Ecosystem Layer AI-Hub is a market-facing AI Agent aggregation and showcase platform, responsible for: • Curation and onboarding of high-quality AI projects • Cross-vertical structured ecosystem layout • Rating and classification systems • Traffic distribution and ecosystem collaboration entry points AI-Hub is not an internal incubation unit, but a standardized aggregation framework at the ecosystem level. 2. Integrated Project Structure (First Batch) ✅1. Finance & Prediction 🔹Cointoken AI — AI Agent-powered quantitative trading engine 🔹VVAI — AI-driven real-time Web3 intelligence and decision system 🔹AlphaQuant — Global financial market forecasting engine 🔹NextGoals — AI-powered global sports prediction agent This vertical forms the real-time information, trading, and predictive decision infrastructure for Web3-native users. ✅2. Science 🔹Charmen AI — Large-model-based pet acoustic recognition technology 🔹Encore Health — AI-driven health forecasting and longevity management system for high-net-worth individuals 🔹Reproducibility AI — AI expert system for financial engineering validation and academic reproducibility This sector focuses on research-grade AI capabilities, collaborating with universities and research institutions to drive real-world scientific deployment. ✅3. Business 🔹GlobalSales — B2B automated lead-generation AI Agent 🔹ResearchBot — Business intelligence and deep due diligence AI Agent This vertical targets the enterprise market, delivering scalable and commercially viable AI productivity tools. ✅4. Coding 🔹CodeMatrix — Full-stack development assistant Providing AI-driven development infrastructure and low-barrier building capabilities to global users. ✅5. Interesting 🔹Fortunetell AI — AI-powered symbolic analysis and interactive insight system Exploring the application boundaries of AI within experiential and interactive scenarios. 3. YOMIRGO Four-Layer Structural Framework YOMIRGO has now established a clearly defined four-layer structure: ▶️Layer 1: Agent Matrix Lab — Internal Production & Incubation ▶️Layer 2: AI-Hub — Ecosystem Aggregation & Rating ▶️Layer 3: LaunchPad — Capitalization Pathway ▶️Layer 4: Market — Circulation & Value Realization Together forming a complete industrial pipeline: Incubation → Validation → Aggregation → Rating → Capitalization → Market Circulation This is the structural model behind YOMIRGO’s defined “AI Super Factory.” 4. Strategic Significance The launch of AI-Hub signifies: • YOMIRGO has established standardized AI Agent aggregation capabilities • A cross-vertical ecosystem structure is now in place • Internal incubation and external aggregation mechanisms are structurally separated • The AI Agent industrial flywheel has begun operating YOMIRGO is no longer merely an AI product platform, but a structured AI Agent industrial system integrating production, aggregation, capitalization, and circulation. 5. Next Phase • Continue expanding high-utility AI Agents with real-world application value • Optimize AI-Hub’s scoring, rating, and filtering mechanisms • Strengthen synergy with LaunchPad and Market • Enable AI Agents to complete value realization within the ecosystem The first 11 projects are only the beginning. AI-Hub is designed to become a continuously expanding AI Agent gateway — not a static product showcase. Further structural expansion is underway.🔥
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YOMIRGO #Product #Update YOMIRGO AI-HUB OFFICIALLY LAUNCH ---A Structural Upgrade from a Single-Product Model to an AI Agent Ecosystem Platform In its first phase, 11 AI projects have been integrated, spanning high-value sectors including finance, scientific research, enterprise services, development tools, and experiential AI. ➡️AI-Hub: This is not merely a feature expansion — it represents a critical structural upgrade from a single-product architecture to a multi-vertical AI Agent aggregation and capitalization platform. This milestone marks the initial structural formation of the YOMIRGO ecosystem. 1. Structural Distinction Between Agent Matrix Lab and AI-Hub To avoid positioning ambiguity, we formally clarify the structural division between the two: 🔘 Agent Matrix Lab — Internal AI Production & Incubation Platform Agent Matrix Lab serves as YOMIRGO’s proprietary AI development and internal incubation platform, responsible for: • R&D and testing of in-house AI products • Incubation of native AI Agents • Technical architecture experimentation and runtime validation • Testing of AI Agent models, memory systems, and runtime orchestration It functions as the production workshop and experimental engine of YOMIRGO’s “AI Super Factory.” 🔘 AI-Hub — External AI Agent Aggregation & Ecosystem Layer AI-Hub is a market-facing AI Agent aggregation and showcase platform, responsible for: • Curation and onboarding of high-quality AI projects • Cross-vertical structured ecosystem layout • Rating and classification systems • Traffic distribution and ecosystem collaboration entry points AI-Hub is not an internal incubation unit, but a standardized aggregation framework at the ecosystem level. 2. Integrated Project Structure (First Batch) ✅1. Finance & Prediction 🔹Cointoken AI — AI Agent-powered quantitative trading engine 🔹VVAI — AI-driven real-time Web3 intelligence and decision system 🔹AlphaQuant — Global financial market forecasting engine 🔹NextGoals — AI-powered global sports prediction agent This vertical forms the real-time information, trading, and predictive decision infrastructure for Web3-native users. ✅2. Science 🔹Charmen AI — Large-model-based pet acoustic recognition technology 🔹Encore Health — AI-driven health forecasting and longevity management system for high-net-worth individuals 🔹Reproducibility AI — AI expert system for financial engineering validation and academic reproducibility This sector focuses on research-grade AI capabilities, collaborating with universities and research institutions to drive real-world scientific deployment. ✅3. Business 🔹GlobalSales — B2B automated lead-generation AI Agent 🔹ResearchBot — Business intelligence and deep due diligence AI Agent This vertical targets the enterprise market, delivering scalable and commercially viable AI productivity tools. ✅4. Coding 🔹CodeMatrix — Full-stack development assistant Providing AI-driven development infrastructure and low-barrier building capabilities to global users. ✅5. Interesting 🔹Fortunetell AI — AI-powered symbolic analysis and interactive insight system Exploring the application boundaries of AI within experiential and interactive scenarios. 3. YOMIRGO Four-Layer Structural Framework YOMIRGO has now established a clearly defined four-layer structure: ▶️Layer 1: Agent Matrix Lab — Internal Production & Incubation ▶️Layer 2: AI-Hub — Ecosystem Aggregation & Rating ▶️Layer 3: LaunchPad — Capitalization Pathway ▶️Layer 4: Market — Circulation & Value Realization Together forming a complete industrial pipeline: Incubation → Validation → Aggregation → Rating → Capitalization → Market Circulation This is the structural model behind YOMIRGO’s defined “AI Super Factory.” 4. Strategic Significance The launch of AI-Hub signifies: • YOMIRGO has established standardized AI Agent aggregation capabilities • A cross-vertical ecosystem structure is now in place • Internal incubation and external aggregation mechanisms are structurally separated • The AI Agent industrial flywheel has begun operating YOMIRGO is no longer merely an AI product platform, but a structured AI Agent industrial system integrating production, aggregation, capitalization, and circulation. 5. Next Phase • Continue expanding high-utility AI Agents with real-world application value • Optimize AI-Hub’s scoring, rating, and filtering mechanisms • Strengthen synergy with LaunchPad and Market • Enable AI Agents to complete value realization within the ecosystem The first 11 projects are only the beginning. AI-Hub is designed to become a continuously expanding AI Agent gateway — not a static product showcase. Further structural expansion is underway.🔥

YOMIRGO

23,685 Aufrufe • vor 5 Monaten

Would you dare chase justice while swinging thousands of feet above traffic below? Seedance 2 prompt on BudgetPixel AI Create a 15-second ultra-realistic cinematic high-altitude tether-swinging action sequence in strict 16:9 landscape, native 4K, 24fps. Use one seamless continuous drone follow shot with no cuts, no teleporting, and no time skips. The motion must feel physically continuous, dynamic, thrilling, and always readable. REFERENCE: image1 = main heroine reference. Use image1 as the strict identity reference for the heroine’s face, facial proportions, hairstyle, hair color, body proportions, age impression, outfit, shoes, accessories, styling, and overall recognizable appearance. Preserve her identity consistently throughout the whole video. Keep her as an original urban tether-swinging action heroine. Do not redesign her into a branded superhero character. Do not add franchise logos, copyrighted chest emblems, or recognizable third-party superhero symbols. CORE CONCEPT: This is an original urban tether-swinging action short. The heroine moves through the city using thin wrist-launched fiber lines, momentum, wall-running, rooftop movement, and real parkour body mechanics. She travels at high altitude between tall buildings, then lands on a rooftop, defeats one villain, and ends with a powerful shout. HOOK: The first second must be an instant scroll-stopping hook. Start with the heroine already falling backward off the edge of a very tall skyscraper. For a brief moment, it looks like she may actually fall. Then she instantly fires one thin tether line upward, it catches, and her body snaps into a huge high-altitude swing between buildings. STYLE: Photorealistic live-action realism. Premium cinematic action quality. Bright daytime Los Angeles atmosphere with realistic haze, realistic motion blur, realistic fabric movement, realistic body weight, real inertia, and practical environmental interaction. The sequence should feel like a premium action movie shot, not animation, not a game cutscene, and not a cartoon. CAMERA: One uninterrupted drone follow shot only. No cuts. No resets. No jumpy edits. No impossible viewpoint teleporting. The drone camera must stay wide enough to show both the heroine and the environment together. It may tilt, roll, arc, climb, and dive with the motion, but it must always feel like one real flying camera tracking her. Keep the framing intense and fast, but always readable. ENVIRONMENT: Bright daytime in a dense modern city inspired by Los Angeles and Hollywood. Show: - tall glass and concrete high-rises - rooftop edges - billboards and signage - palm trees far below where visible - busy roads and traffic far beneath - bright haze and sunny atmosphere - believable large-scale urban depth The action must happen mainly high above the street between tall buildings, not low near the ground for most of the video. VILLAIN RULE: Only one villain appears in the entire video. The villain is one adult male enemy only. He wears a fitted black suit, black shirt, and black shoes. No mask, no armor, no fantasy costume. He appears only in the rooftop combat section. Do not generate multiple enemies. Do not generate background enemies. Do not clone or duplicate the villain. ACTION RULES: The heroine’s movement must feel hand-and-foot driven, not magical floating. She must visibly: - fire thin tether lines from her hands - swing with real tension and momentum - push off building surfaces - run along walls with clear foot placement - absorb landings with bent knees - sprint briefly on a rooftop - fight one villain using fast practical action - finish in control Her body mechanics must stay realistic: - core engaged during swings - arms extended or flexed according to line tension - knees bend on landing and push-off - visible transfer of momentum between swing, wall-run, leap, landing, and combat Do not make her hover weightlessly. Do not make the tether line act like magic. Do not make her float in place unnaturally. EMOTIONAL ARC: - opening: shock and immediate control - mid-swing: intense focus - rooftop approach: rising confidence - rooftop fight: sharp aggression and urgency - ending: victorious adrenaline and fearless release AUDIO: No music. Effects and ambience only: - rushing wind - tether firing and tension snaps - air pass-by - foot impacts on walls and rooftop surfaces - city ambience far below - distant traffic and horns - fabric movement - breathing - one short rooftop fight impact sequence - one powerful final shout from the heroine TIMELINE: 0:00–0:01 Start from black into a shocking rooftop-edge fall. The heroine is already dropping backward off a skyscraper. For a fraction of a second it feels dangerous and uncontrolled. She immediately flicks her wrist and fires one thin tether line upward. It catches instantly. The drone yanks back and reveals the start of a huge swing. 0:01–0:04 The heroine swings at high altitude between tall buildings. The city is far below. Her body forms a long aerodynamic arc, one arm holding tension through the line, legs trailing cleanly behind. The drone follows wide and slightly rolled, emphasizing height, speed, and scale. 0:04–0:06 At the swing’s forward rise, she releases the line and redirects toward a nearby glass-and-concrete building. She plants onto the wall and runs across it diagonally with 4 to 5 clear steps. Her feet hit the wall with visible force. Her jaw is set and focused. The drone stays close but wide enough to keep the city depth visible. 0:06–0:08 She pushes explosively off the wall, fires a new tether line, and swings again through a narrower corridor between tall buildings. The movement should feel faster and more controlled now. She threads cleanly through the urban gap and angles toward a rooftop landing zone ahead. 0:08–0:09.5 She releases the line and lands hard but controlled on a rooftop. Knees bend deeply to absorb impact. She rolls into a short forward recovery step, then rises immediately into a sprint across the rooftop surface. 0:09.5–0:12 One villain in a black suit steps in to stop her. Keep only this single enemy. The heroine engages him in a short, sharp rooftop fight. She avoids his first attack with a quick slip, grabs or redirects his arm, drives one fast body shot or elbow, then uses his off-balance momentum to throw or slam him down onto the rooftop. The fight must feel quick, practical, and decisive. Real impact reactions. No slow choreography. No extra enemies. 0:12–0:13.5 The villain is down and no longer a threat. The heroine steps past him and moves to the rooftop edge. Wind moves her hair and outfit. She looks outward over the city with intense adrenaline and triumph. 0:13.5–0:15 At the rooftop edge, she turns slightly toward the open skyline, lifts her chest, and shouts one powerful final line: “가자!” She immediately launches forward off the rooftop edge into another leap just as the clip ends. End on the feeling that the action is continuing beyond the cut. IMPORTANT RULES: - one continuous drone follow shot only - no cuts - no teleporting - no cloning - no multiple villains - only one black-suited villain - no giant web canopy - use only thin functional tether lines - no franchise logos - no copyrighted chest symbols - preserve the uploaded identity consistently - action must stay realistic and momentum-driven - rooftop fight must be short, sharp, and readable - final shout must be “가자!” NEGATIVE: no cartoon, no anime, no game-engine look, no fake CGI stiffness, no floating, no weightless hovering, no random disconnected acrobatics, no city-wide web canopy, no superhero logo, no copyrighted spider emblem, no extra enemies, no masked villain, no armored villain, no cloned villain, no empty city, no dark night setting, no rain, no slow motion, no blurred identity, no outfit drift, no face drift, no extra limbs, no broken anatomy, no unrealistic hand deformation, no collision with buildings during swings, no messy unreadable fight.
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Would you dare chase justice while swinging thousands of feet above traffic below? Seedance 2 prompt on BudgetPixel AI Create a 15-second ultra-realistic cinematic high-altitude tether-swinging action sequence in strict 16:9 landscape, native 4K, 24fps. Use one seamless continuous drone follow shot with no cuts, no teleporting, and no time skips. The motion must feel physically continuous, dynamic, thrilling, and always readable. REFERENCE: image1 = main heroine reference. Use image1 as the strict identity reference for the heroine’s face, facial proportions, hairstyle, hair color, body proportions, age impression, outfit, shoes, accessories, styling, and overall recognizable appearance. Preserve her identity consistently throughout the whole video. Keep her as an original urban tether-swinging action heroine. Do not redesign her into a branded superhero character. Do not add franchise logos, copyrighted chest emblems, or recognizable third-party superhero symbols. CORE CONCEPT: This is an original urban tether-swinging action short. The heroine moves through the city using thin wrist-launched fiber lines, momentum, wall-running, rooftop movement, and real parkour body mechanics. She travels at high altitude between tall buildings, then lands on a rooftop, defeats one villain, and ends with a powerful shout. HOOK: The first second must be an instant scroll-stopping hook. Start with the heroine already falling backward off the edge of a very tall skyscraper. For a brief moment, it looks like she may actually fall. Then she instantly fires one thin tether line upward, it catches, and her body snaps into a huge high-altitude swing between buildings. STYLE: Photorealistic live-action realism. Premium cinematic action quality. Bright daytime Los Angeles atmosphere with realistic haze, realistic motion blur, realistic fabric movement, realistic body weight, real inertia, and practical environmental interaction. The sequence should feel like a premium action movie shot, not animation, not a game cutscene, and not a cartoon. CAMERA: One uninterrupted drone follow shot only. No cuts. No resets. No jumpy edits. No impossible viewpoint teleporting. The drone camera must stay wide enough to show both the heroine and the environment together. It may tilt, roll, arc, climb, and dive with the motion, but it must always feel like one real flying camera tracking her. Keep the framing intense and fast, but always readable. ENVIRONMENT: Bright daytime in a dense modern city inspired by Los Angeles and Hollywood. Show: - tall glass and concrete high-rises - rooftop edges - billboards and signage - palm trees far below where visible - busy roads and traffic far beneath - bright haze and sunny atmosphere - believable large-scale urban depth The action must happen mainly high above the street between tall buildings, not low near the ground for most of the video. VILLAIN RULE: Only one villain appears in the entire video. The villain is one adult male enemy only. He wears a fitted black suit, black shirt, and black shoes. No mask, no armor, no fantasy costume. He appears only in the rooftop combat section. Do not generate multiple enemies. Do not generate background enemies. Do not clone or duplicate the villain. ACTION RULES: The heroine’s movement must feel hand-and-foot driven, not magical floating. She must visibly: - fire thin tether lines from her hands - swing with real tension and momentum - push off building surfaces - run along walls with clear foot placement - absorb landings with bent knees - sprint briefly on a rooftop - fight one villain using fast practical action - finish in control Her body mechanics must stay realistic: - core engaged during swings - arms extended or flexed according to line tension - knees bend on landing and push-off - visible transfer of momentum between swing, wall-run, leap, landing, and combat Do not make her hover weightlessly. Do not make the tether line act like magic. Do not make her float in place unnaturally. EMOTIONAL ARC: - opening: shock and immediate control - mid-swing: intense focus - rooftop approach: rising confidence - rooftop fight: sharp aggression and urgency - ending: victorious adrenaline and fearless release AUDIO: No music. Effects and ambience only: - rushing wind - tether firing and tension snaps - air pass-by - foot impacts on walls and rooftop surfaces - city ambience far below - distant traffic and horns - fabric movement - breathing - one short rooftop fight impact sequence - one powerful final shout from the heroine TIMELINE: 0:00–0:01 Start from black into a shocking rooftop-edge fall. The heroine is already dropping backward off a skyscraper. For a fraction of a second it feels dangerous and uncontrolled. She immediately flicks her wrist and fires one thin tether line upward. It catches instantly. The drone yanks back and reveals the start of a huge swing. 0:01–0:04 The heroine swings at high altitude between tall buildings. The city is far below. Her body forms a long aerodynamic arc, one arm holding tension through the line, legs trailing cleanly behind. The drone follows wide and slightly rolled, emphasizing height, speed, and scale. 0:04–0:06 At the swing’s forward rise, she releases the line and redirects toward a nearby glass-and-concrete building. She plants onto the wall and runs across it diagonally with 4 to 5 clear steps. Her feet hit the wall with visible force. Her jaw is set and focused. The drone stays close but wide enough to keep the city depth visible. 0:06–0:08 She pushes explosively off the wall, fires a new tether line, and swings again through a narrower corridor between tall buildings. The movement should feel faster and more controlled now. She threads cleanly through the urban gap and angles toward a rooftop landing zone ahead. 0:08–0:09.5 She releases the line and lands hard but controlled on a rooftop. Knees bend deeply to absorb impact. She rolls into a short forward recovery step, then rises immediately into a sprint across the rooftop surface. 0:09.5–0:12 One villain in a black suit steps in to stop her. Keep only this single enemy. The heroine engages him in a short, sharp rooftop fight. She avoids his first attack with a quick slip, grabs or redirects his arm, drives one fast body shot or elbow, then uses his off-balance momentum to throw or slam him down onto the rooftop. The fight must feel quick, practical, and decisive. Real impact reactions. No slow choreography. No extra enemies. 0:12–0:13.5 The villain is down and no longer a threat. The heroine steps past him and moves to the rooftop edge. Wind moves her hair and outfit. She looks outward over the city with intense adrenaline and triumph. 0:13.5–0:15 At the rooftop edge, she turns slightly toward the open skyline, lifts her chest, and shouts one powerful final line: “가자!” She immediately launches forward off the rooftop edge into another leap just as the clip ends. End on the feeling that the action is continuing beyond the cut. IMPORTANT RULES: - one continuous drone follow shot only - no cuts - no teleporting - no cloning - no multiple villains - only one black-suited villain - no giant web canopy - use only thin functional tether lines - no franchise logos - no copyrighted chest symbols - preserve the uploaded identity consistently - action must stay realistic and momentum-driven - rooftop fight must be short, sharp, and readable - final shout must be “가자!” NEGATIVE: no cartoon, no anime, no game-engine look, no fake CGI stiffness, no floating, no weightless hovering, no random disconnected acrobatics, no city-wide web canopy, no superhero logo, no copyrighted spider emblem, no extra enemies, no masked villain, no armored villain, no cloned villain, no empty city, no dark night setting, no rain, no slow motion, no blurred identity, no outfit drift, no face drift, no extra limbs, no broken anatomy, no unrealistic hand deformation, no collision with buildings during swings, no messy unreadable fight.

Sharon Riley

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