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Google Veo3 → I2V Prompting Guide. Playing with "human expression" Tips: + Guide the performance + Control facial expressions/gestures + It can handle finer details. + Don't overcomplicate the prompt BASE PROMPT STRUCTURE: [scene description], [expression/gesture description] (More exploration in thread 🧵) #google #veo3 #promptshare

Rory Flynn

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160,500 views • 1 year ago •via X (Twitter)

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The Making of a Coliseum Ship Battle. With Google Earth + Weavy + Veo3 (With help from Runway/Flux/etc) PROCESS: 01. Screenshot Location on GE 02. Rexture + add details 03. Composite elements 04. Diagram Shot 05. Generate + iterate Used a lot of tools in this one. (More details in thread) #veo3 #promptshare

The Making of a Coliseum Ship Battle. With Google Earth + Weavy + Veo3 (With help from Runway/Flux/etc) PROCESS: 01. Screenshot Location on GE 02. Rexture + add details 03. Composite elements 04. Diagram Shot 05. Generate + iterate Used a lot of tools in this one. (More details in thread) #veo3 #promptshare

Rory Flynn

64,886 views • 1 year ago

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)

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

36,409 views • 7 months ago

You can now create AI images directly from Google Slides. No need to spend hours searching for images for your presentations. And this feature is available for free. Here's how to activate it: 1. Go to labs .google .com 2. Scroll down to "Google Workspace". 3. Click on the "Learn more" button to access the waitlist. When it's activated, you'll see the button that appears in Google Slides as in the video. Click on it and enter your prompt: E.g.: "a cat in front of a raspberry pie". You can even choose different styles: photography, vector art, sketch, ... This will save a lot of time when creating slideshows! Don't hesitate to follow me to learn how to do more with AI.

You can now create AI images directly from Google Slides. No need to spend hours searching for images for your presentations. And this feature is available for free. Here's how to activate it: 1. Go to labs .google .com 2. Scroll down to "Google Workspace". 3. Click on the "Learn more" button to access the waitlist. When it's activated, you'll see the button that appears in Google Slides as in the video. Click on it and enter your prompt: E.g.: "a cat in front of a raspberry pie". You can even choose different styles: photography, vector art, sketch, ... This will save a lot of time when creating slideshows! Don't hesitate to follow me to learn how to do more with AI.

Paul Couvert

318,258 views • 3 years ago

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

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

97,086 views • 9 months ago

📖THE STEP MOST CREATORS SKIP IS WHY THEIR AI ANIMATION LOOKS INCONSISTENT Consistency across clips doesn't come from prompting — it comes from the reference image. The pipeline, step by step: ▪ Start with ChatGPT Image 2 — generate a full character design sheet first, not just a single frame. Multiple angles, expressions, and outfit variations in one image keeps the character consistent across every scene ▪ Build a storyboard inside ChatGPT Image 2 as well — define each shot, camera angle, action, and mood before touching Seedance at all. This is the step most people skip and it's the reason clips look disconnected ▪ Define a color palette and lighting mood early — golden afternoon light, soft warm tones, dramatic shadows. Lock those values and repeat them across every prompt ▪ Take each storyboard frame into Seedance 2.0 as the reference image — one frame becomes one clip ▪ Write the Seedance prompt around the character action, not the scene description. The scene is already in the image. The prompt handles motion, camera behavior, and timing ▪ Keep clip duration between 4-6 seconds per shot — shorter clips give more control over pacing and reduce motion drift on character faces ▪ Match camera movement type across consecutive clips — if one shot dollies in, the next should hold or pull back, not dolly again The consistency across these frames comes from the character design sheet, not from luck. Seedance reads the reference image and the prompt together — if the reference is detailed enough, the output stays on-model. This video was created by ALOKXMEHTA 📥 tomorrow: the exact ChatGPT Image 2 prompt structure used to generate a multi-angle character design sheet like this one 🔖One article covers the entire workflow — it is pinned below, do not scroll past it.

📖THE STEP MOST CREATORS SKIP IS WHY THEIR AI ANIMATION LOOKS INCONSISTENT Consistency across clips doesn't come from prompting — it comes from the reference image. The pipeline, step by step: ▪ Start with ChatGPT Image 2 — generate a full character design sheet first, not just a single frame. Multiple angles, expressions, and outfit variations in one image keeps the character consistent across every scene ▪ Build a storyboard inside ChatGPT Image 2 as well — define each shot, camera angle, action, and mood before touching Seedance at all. This is the step most people skip and it's the reason clips look disconnected ▪ Define a color palette and lighting mood early — golden afternoon light, soft warm tones, dramatic shadows. Lock those values and repeat them across every prompt ▪ Take each storyboard frame into Seedance 2.0 as the reference image — one frame becomes one clip ▪ Write the Seedance prompt around the character action, not the scene description. The scene is already in the image. The prompt handles motion, camera behavior, and timing ▪ Keep clip duration between 4-6 seconds per shot — shorter clips give more control over pacing and reduce motion drift on character faces ▪ Match camera movement type across consecutive clips — if one shot dollies in, the next should hold or pull back, not dolly again The consistency across these frames comes from the character design sheet, not from luck. Seedance reads the reference image and the prompt together — if the reference is detailed enough, the output stays on-model. This video was created by ALOKXMEHTA 📥 tomorrow: the exact ChatGPT Image 2 prompt structure used to generate a multi-angle character design sheet like this one 🔖One article covers the entire workflow — it is pinned below, do not scroll past it.

Zentrix⌚️

14,015 views • 3 months ago

We have FINALLY solved how to generate infinite static ads completely on autopilot (including copy) and put them in a Drive folder, with ZERO human intervention necessary: 1. Generate ChatGPT headlines and push them into Google Sheets with Zapier. 2. Create one or several design templates for your brand in using their Canva integration 3. Set up a Zap that pushes your Google Sheet copy into the Switchboard template. It will automatically handle text formatting and centering 4. Auto upload the completed design file into Google Drive with Zapier You can do this with infinite design templates and different sources of copy (website reviews, subreddits, etc) so you ALWAYS have brand new ads READY TO GO, organized in the Drive folder of your choice for someone on your team to review and launch. The copy refreshes weekly in this zap according to your prompt. Comment if you want the SOP document for this that outlines it step-by-step!

Peter Czepiga

26,129 views • 1 year ago

MiniMax H3 Instead of sharing the prompts for each of these videos, I thought it would be more useful to share how I created that prompts. All of the videos were generated with text-to-video. First, find an image with the kind of scene, composition and mood you want to recreate. I used a few YouTube playlist thumbnails as references but Pinterest is also a great place to find inspiration. You can even use your own old or nostalgic photographs. Then upload the image to ChatGPT and ask it to describe the scene. The description it gives you can essentially become your text-to-video prompt. From there, you can generate completely new scenes with a similar composition, atmosphere and cinematic language. You can of course use the reference image directly with image-to-video or as a first frame. But if the original image isn't yours, I prefer using it only as visual inspiration and recreating the scene through text-to-video. This is the prompt I use with ChatGPT: "Describe the scene in this image in English, focusing primarily on what is happening, the characters, their actions and body language, the setting and the overall atmosphere. Also briefly describe the composition, framing, camera angle, approximate lens choice, lighting, color palette and cinematic aesthetic. Keep it concise and scene-focused rather than overly technical."

MiniMax H3 Instead of sharing the prompts for each of these videos, I thought it would be more useful to share how I created that prompts. All of the videos were generated with text-to-video. First, find an image with the kind of scene, composition and mood you want to recreate. I used a few YouTube playlist thumbnails as references but Pinterest is also a great place to find inspiration. You can even use your own old or nostalgic photographs. Then upload the image to ChatGPT and ask it to describe the scene. The description it gives you can essentially become your text-to-video prompt. From there, you can generate completely new scenes with a similar composition, atmosphere and cinematic language. You can of course use the reference image directly with image-to-video or as a first frame. But if the original image isn't yours, I prefer using it only as visual inspiration and recreating the scene through text-to-video. This is the prompt I use with ChatGPT: "Describe the scene in this image in English, focusing primarily on what is happening, the characters, their actions and body language, the setting and the overall atmosphere. Also briefly describe the composition, framing, camera angle, approximate lens choice, lighting, color palette and cinematic aesthetic. Keep it concise and scene-focused rather than overly technical."

Kōda

55,398 views • 1 month ago

The body movements of Anime characters in Seedance doesn’t has to be always rigid and constrained. They can be as expressive as the real anime too. Here’s a quick tips on how to achieve the cartoony acting style for your anime characters: Special shoutout to Ameilo for sharing the prompt that unlocked this acting style. This tips sharing is made possible by Try ArtCraft Add this Key Prompt above your prompt: Emphasize snappy animation with lively energy, pose-to-pose, strong anticipation, overshoot, squash and stretch, quick switches in facial expressions, and follow-through on hair and sleeves. Give the movements a distinctive rhythm with variation in pacing: Hold still poses firmly for a split second, then transition to the next movement with a quick, bouncy snap. Make it comical and well-timed, but keep the actions easy to read. Movements follow: "stillness → preparatory action → sudden acceleration → large overshoot → sharp stop → hair and sleeves sway delayed." Facial changes are exaggerated and comical. Poses are clear in silhouette, with a solid hold of 0.1–0.2 seconds at the end of each action. Use cartoonish squash & stretch, motion blur, short afterimages, dramatic smear frames and speed lines sparingly. Physics are slightly exaggerated beyond reality, but the character's footing and center of gravity don't break.

The body movements of Anime characters in Seedance doesn’t has to be always rigid and constrained. They can be as expressive as the real anime too. Here’s a quick tips on how to achieve the cartoony acting style for your anime characters: Special shoutout to Ameilo for sharing the prompt that unlocked this acting style. This tips sharing is made possible by Try ArtCraft Add this Key Prompt above your prompt: Emphasize snappy animation with lively energy, pose-to-pose, strong anticipation, overshoot, squash and stretch, quick switches in facial expressions, and follow-through on hair and sleeves. Give the movements a distinctive rhythm with variation in pacing: Hold still poses firmly for a split second, then transition to the next movement with a quick, bouncy snap. Make it comical and well-timed, but keep the actions easy to read. Movements follow: "stillness → preparatory action → sudden acceleration → large overshoot → sharp stop → hair and sleeves sway delayed." Facial changes are exaggerated and comical. Poses are clear in silhouette, with a solid hold of 0.1–0.2 seconds at the end of each action. Use cartoonish squash & stretch, motion blur, short afterimages, dramatic smear frames and speed lines sparingly. Physics are slightly exaggerated beyond reality, but the character's footing and center of gravity don't break.

SujiPop

29,778 views • 3 months ago

🚨 AI VIDEO PROMPTING IS DEAD !! You no longer need to write a 500-word prompt just to tell AI how your video should look. 💀 I just tested Pippit’s 3D Director Studio + Seedance 2.5 And instead of explaining the entire scene with words… I literally BUILT the scene first. For my test, I created a cinematic fight scene with characters inside a 3D environment. I could control: 1️⃣ Where the characters stood 2️⃣ Which props they held 3️⃣ Where the camera was placed 4️⃣ What each character should do Then I could actually CHOREOGRAPH the action. → Run → Fight → Shoot → Fall → React → Move You can place these actions on the timeline, adjust when they happen, and even plan the camera movement and character trajectories. So instead of: PROMPT → GENERATE → PRAY 😂 It becomes: BUILD → DIRECT → GENERATE And that gives you WAY more control. Because you’re not asking AI to imagine the entire scene from a block of text. You’re literally showing it the: → Characters → Environment → Props → Positions → Actions → Camera Then Seedance 2.5 takes that 3D direction and turns it into the final video. That’s what makes this so interesting. It feels less like prompting an AI video model… And more like sitting in the DIRECTOR’S CHAIR. 🎬 Try Pippit: Pippitofficial #3DDirectorStudio #PippitAI #Seedance25 #PippitPartner

🚨 AI VIDEO PROMPTING IS DEAD !! You no longer need to write a 500-word prompt just to tell AI how your video should look. 💀 I just tested Pippit’s 3D Director Studio + Seedance 2.5 And instead of explaining the entire scene with words… I literally BUILT the scene first. For my test, I created a cinematic fight scene with characters inside a 3D environment. I could control: 1️⃣ Where the characters stood 2️⃣ Which props they held 3️⃣ Where the camera was placed 4️⃣ What each character should do Then I could actually CHOREOGRAPH the action. → Run → Fight → Shoot → Fall → React → Move You can place these actions on the timeline, adjust when they happen, and even plan the camera movement and character trajectories. So instead of: PROMPT → GENERATE → PRAY 😂 It becomes: BUILD → DIRECT → GENERATE And that gives you WAY more control. Because you’re not asking AI to imagine the entire scene from a block of text. You’re literally showing it the: → Characters → Environment → Props → Positions → Actions → Camera Then Seedance 2.5 takes that 3D direction and turns it into the final video. That’s what makes this so interesting. It feels less like prompting an AI video model… And more like sitting in the DIRECTOR’S CHAIR. 🎬 Try Pippit: Pippitofficial #3DDirectorStudio #PippitAI #Seedance25 #PippitPartner

SANI BULA

95,462 views • 22 days ago

this effect is all over tiktok right now and nobody's explaining how to actually do it properly... the 3d balloon character thing. where someone turns into a shiny inflatable version of themselves that still moves and talks. looks pretty smooth in feeds. the workflow is stupid simple once you see it. step 1: take any photo. drop it into an image gen tool (nano banana pro). prompt it with something like "make the person in the photo a plastic blow up balloon character with a shiny surface. keep the face details as 3d balloon details including the person in the background. don't change background" that's it for the image. don't overcomplicate the prompt. shorter = more consistent results. (learned this after wasting like 2 hours trying to get "perfect" prompts that kept giving me garbage) step 2: take that balloon image + your original video and drop both into kling motion control. prompt: "turn the motion and detailed mouth movement of the video to the setting of the image" that's literally it. kling maps the motion from the real video onto the balloon character. mouth moves. head turns. expressions transfer. the whole thing renders in a few minutes. the result looks like a $500 custom animation and costs you maybe $0.30 in kling credits. people are getting 500k+ views with these because the scroll-stop factor is insane. nobody expects to see a shiny inflatable version of someone giving a real speech or doing a product review. the play here is obvious btw. run this for client content (mix with the hook and real body, check the results yourself) or use it on your own faceless channels as a hook pattern before the algo catches up...

this effect is all over tiktok right now and nobody's explaining how to actually do it properly... the 3d balloon character thing. where someone turns into a shiny inflatable version of themselves that still moves and talks. looks pretty smooth in feeds. the workflow is stupid simple once you see it. step 1: take any photo. drop it into an image gen tool (nano banana pro). prompt it with something like "make the person in the photo a plastic blow up balloon character with a shiny surface. keep the face details as 3d balloon details including the person in the background. don't change background" that's it for the image. don't overcomplicate the prompt. shorter = more consistent results. (learned this after wasting like 2 hours trying to get "perfect" prompts that kept giving me garbage) step 2: take that balloon image + your original video and drop both into kling motion control. prompt: "turn the motion and detailed mouth movement of the video to the setting of the image" that's literally it. kling maps the motion from the real video onto the balloon character. mouth moves. head turns. expressions transfer. the whole thing renders in a few minutes. the result looks like a $500 custom animation and costs you maybe $0.30 in kling credits. people are getting 500k+ views with these because the scroll-stop factor is insane. nobody expects to see a shiny inflatable version of someone giving a real speech or doing a product review. the play here is obvious btw. run this for client content (mix with the hook and real body, check the results yourself) or use it on your own faceless channels as a hook pattern before the algo catches up...

KNOX

25,773 views • 7 months ago

$Google Gemini Nano Banana pro 3.0 Prompt { "subject": { "description": "A young Western woman with an elegant, serious expression", "features": { "eyes": "Large, dark, expressive with soft eyeliner", "lips": "Glossy berry-toned lipstick", "hair": "Dark blonde hair styled in a sophisticated low bun with face-framing curtain bangs", "makeup": "Dewy skin, soft peach blush, and groomed eyebrows" } }, "attire": { "type": "Formal military dress uniform", "color": "Stark white", "details": [ "Mandarin collar with gold buttons", "Black shoulder epaulettes with gold embroidered insignias", "Ornate gold braided aiguillette (honor cord) draped across the shoulder and chest", "Multiple silver service medals pinned to the left chest with striped ribbons (red, green, white)", "Pilot wings/aviation insignia on the right chest", "Name tag reading 'JAVERIA' with a small Pakistan flag pin above it" ] }, "composition": { "shot_type": "Medium close-up portrait", "lighting": { "style": "Cinematic directional lighting", "effects": "Strong key light from the front-left creating a soft shadow on the wall behind", "mood": "Stately, professional, and graceful" }, "background": "Minimalist off-white paneled wall with a hint of a crystal chandelier in the blurred upper-left corner" }, "technical_tags": [ "High resolution", "Photorealistic", "8k", "Shallow depth of field", "Soft bokeh", "Uniform precision" ] }$

Google Gemini Nano Banana pro 3.0 Prompt { "subject": { "description": "A young Western woman with an elegant, serious expression", "features": { "eyes": "Large, dark, expressive with soft eyeliner", "lips": "Glossy berry-toned lipstick", "hair": "Dark blonde hair styled in a sophisticated low bun with face-framing curtain bangs", "makeup": "Dewy skin, soft peach blush, and groomed eyebrows" } }, "attire": { "type": "Formal military dress uniform", "color": "Stark white", "details": [ "Mandarin collar with gold buttons", "Black shoulder epaulettes with gold embroidered insignias", "Ornate gold braided aiguillette (honor cord) draped across the shoulder and chest", "Multiple silver service medals pinned to the left chest with striped ribbons (red, green, white)", "Pilot wings/aviation insignia on the right chest", "Name tag reading 'JAVERIA' with a small Pakistan flag pin above it" ] }, "composition": { "shot_type": "Medium close-up portrait", "lighting": { "style": "Cinematic directional lighting", "effects": "Strong key light from the front-left creating a soft shadow on the wall behind", "mood": "Stately, professional, and graceful" }, "background": "Minimalist off-white paneled wall with a hint of a crystal chandelier in the blurred upper-left corner" }, "technical_tags": [ "High resolution", "Photorealistic", "8k", "Shallow depth of field", "Soft bokeh", "Uniform precision" ] }

Sharon Riley

545,983 views • 8 months ago

This is not camera footage. It is a Blender character with 8K skin, detailed wrinkles, wet eyes, facial controls and enough micro-detail to make your brain keep waiting for the person to behave like a person. HumanPro packages that skin workflow into a Blender add-on instead of making artists rebuild it from scratch every time. The interesting AI angle is not “AI made a realistic girl.” A reusable 3D human can keep the same face across thousands of shots, then be relit, reposed, animated and dropped into completely different scenes without the identity drifting every six frames. Add Claude through Blender MCP and the workflow gets stranger: the model can help assemble scenes, adjust cameras and lighting, inspect renders and correct obvious visual problems, while the character system handles the skin and facial structure. It still does not remove the artist. Someone has to control expression, motion, lighting and the exact moment realism quietly turns into a very expensive mannequin. Most AI influencer projects are still fighting prompt consistency one image at a time. A rigged digital human is less magical, but probably much closer to how this becomes an actual production system.

This is not camera footage. It is a Blender character with 8K skin, detailed wrinkles, wet eyes, facial controls and enough micro-detail to make your brain keep waiting for the person to behave like a person. HumanPro packages that skin workflow into a Blender add-on instead of making artists rebuild it from scratch every time. The interesting AI angle is not “AI made a realistic girl.” A reusable 3D human can keep the same face across thousands of shots, then be relit, reposed, animated and dropped into completely different scenes without the identity drifting every six frames. Add Claude through Blender MCP and the workflow gets stranger: the model can help assemble scenes, adjust cameras and lighting, inspect renders and correct obvious visual problems, while the character system handles the skin and facial structure. It still does not remove the artist. Someone has to control expression, motion, lighting and the exact moment realism quietly turns into a very expensive mannequin. Most AI influencer projects are still fighting prompt consistency one image at a time. A rigged digital human is less magical, but probably much closer to how this becomes an actual production system.

Rina

109,872 views • 2 months ago

n8n + Arcads = AI UGC machine most DTC brands waste hours writing product video scripts, then wait days for creators to deliver UGC content. this automation eliminates both bottlenecks entirely. just add your product details to a Google Sheet -> n8n workflow activates -> google Gemini generates 5 complete video scripts (each using a different proven angle) -> scripts automatically feed into Arcads product Showcase -> you get 5 scroll-stopping product videos in minutes here’s how it works. 1. script generation - reads product name and description from your spreadsheet - gemini creates 5 different 75-90 word scripts using proven angles 2. UGC Production - scripts automatically trigger Arcads Product Showcase - AI places your product in conversion-optimized UGC environments - generates authentic spokesperson interactions - creates 5 complete videos ready for paid social all automated in under 10 minutes. i built the complete n8n workflow + Arcads setup so you can deploy this yourself. comment "AUTO" below and I'll send the full guide. (must be following so I can DM)

n8n + Arcads = AI UGC machine most DTC brands waste hours writing product video scripts, then wait days for creators to deliver UGC content. this automation eliminates both bottlenecks entirely. just add your product details to a Google Sheet -> n8n workflow activates -> google Gemini generates 5 complete video scripts (each using a different proven angle) -> scripts automatically feed into Arcads product Showcase -> you get 5 scroll-stopping product videos in minutes here’s how it works. 1. script generation - reads product name and description from your spreadsheet - gemini creates 5 different 75-90 word scripts using proven angles 2. UGC Production - scripts automatically trigger Arcads Product Showcase - AI places your product in conversion-optimized UGC environments - generates authentic spokesperson interactions - creates 5 complete videos ready for paid social all automated in under 10 minutes. i built the complete n8n workflow + Arcads setup so you can deploy this yourself. comment "AUTO" below and I'll send the full guide. (must be following so I can DM)

J.B.

11,000 views • 8 months ago

$Nano Banana Pro + Kling 2.6 on Social Sight: Video prompt: { "cinematic_video_request": { "meta": { "title": "Sadie Sink - Night Drive Portrait", "style_preset": "Cinematic Realism", "duration_seconds": 10, "resolution": "4K", "aspect_ratio": "16:9" }, "prompts": { "main_prompt": "Ultra-realistic cinematic animation of Sadie Sink sitting in the backseat of a luxury car at night. The city lights outside the window create soft motion blur with passing traffic and glowing streetlights. Subtle camera push-in shot from medium frame to close-up. Gentle movement in her hair as the car moves. She slowly shifts her gaze toward the window, blinking naturally, then looks back toward the camera with a calm, slightly mysterious expression.", "visual_modifiers": "4K cinematic quality, realistic skin texture, natural facial micro-expressions, smooth motion, dramatic nighttime mood, film-grade color grading, soft contrast, subtle handheld camera feel, shallow depth of field, bokeh city lights, detailed leather texture.", "lighting_prompt": "Soft ambient lighting from streetlights flickers across her face, creating dynamic shadows and warm highlights. Background traffic lights streak smoothly past the window." }, "scene_specifications": { "subject": { "name": "Sadie Sink", "action": "Sitting, gazing out window, turning head to camera, blinking", "expression": "Calm, mysterious, natural micro-expressions", "details": "Gentle hair movement, realistic skin texture" }, "environment": { "setting": "Backseat of luxury car", "time": "Night", "exterior": "City streets, passing traffic", "details": "Detailed leather interior, window reflections" }, "camera": { "movement": "Slow push-in (dolly forward)", "stabilization": "Slight handheld feel (organic motion)", "framing": "Medium shot to Close-up", "focus": "Shallow depth of field with background bokeh" }, "lighting": { "type": "Dynamic/Transient", "sources": "Passing streetlights, city glow", "characteristics": "Warm highlights, soft contrast, rhythmic flickering shadows" } } } } Image prompt: { "prompt": { "subject": { "description": "Sadie Sink with shoulder-length, blonde red hair and bangs. She has fair skin with light freckles, winged eyeliner. Her expression is sultry, with her index finger resting gently on her lower lip.", "clothing": "She is wearing a tight, long-sleeved black mini dress and sheer black pantyhose.", "pose": "She is seated in the back of a car with her legs crossed and pulled up towards her chest, displaying bare feet in sheer hose." }, "environment": { "location": "Interior of a luxury car, specifically the back seat.", "details": "Light grey leather seats with diamond stitching patterns visible on the side door panel. A 'B' logo (likely Bentley) is embroidered on the headrest.", "background": "Through the car window, a night cityscape is visible with blurred city lights, street lamps, and streaks of light from moving traffic on a highway." }, "lighting_and_quality": { "lighting": "Direct flash photography illuminating the subject against a darker background.", "resolution": "4K HD quality, highly detailed textures on the hair, skin, and leather seats.", "style": "Candid, urban night aesthetic, realistic photo." } } }$

Nano Banana Pro + Kling 2.6 on Social Sight: Video prompt: { "cinematic_video_request": { "meta": { "title": "Sadie Sink - Night Drive Portrait", "style_preset": "Cinematic Realism", "duration_seconds": 10, "resolution": "4K", "aspect_ratio": "16:9" }, "prompts": { "main_prompt": "Ultra-realistic cinematic animation of Sadie Sink sitting in the backseat of a luxury car at night. The city lights outside the window create soft motion blur with passing traffic and glowing streetlights. Subtle camera push-in shot from medium frame to close-up. Gentle movement in her hair as the car moves. She slowly shifts her gaze toward the window, blinking naturally, then looks back toward the camera with a calm, slightly mysterious expression.", "visual_modifiers": "4K cinematic quality, realistic skin texture, natural facial micro-expressions, smooth motion, dramatic nighttime mood, film-grade color grading, soft contrast, subtle handheld camera feel, shallow depth of field, bokeh city lights, detailed leather texture.", "lighting_prompt": "Soft ambient lighting from streetlights flickers across her face, creating dynamic shadows and warm highlights. Background traffic lights streak smoothly past the window." }, "scene_specifications": { "subject": { "name": "Sadie Sink", "action": "Sitting, gazing out window, turning head to camera, blinking", "expression": "Calm, mysterious, natural micro-expressions", "details": "Gentle hair movement, realistic skin texture" }, "environment": { "setting": "Backseat of luxury car", "time": "Night", "exterior": "City streets, passing traffic", "details": "Detailed leather interior, window reflections" }, "camera": { "movement": "Slow push-in (dolly forward)", "stabilization": "Slight handheld feel (organic motion)", "framing": "Medium shot to Close-up", "focus": "Shallow depth of field with background bokeh" }, "lighting": { "type": "Dynamic/Transient", "sources": "Passing streetlights, city glow", "characteristics": "Warm highlights, soft contrast, rhythmic flickering shadows" } } } } Image prompt: { "prompt": { "subject": { "description": "Sadie Sink with shoulder-length, blonde red hair and bangs. She has fair skin with light freckles, winged eyeliner. Her expression is sultry, with her index finger resting gently on her lower lip.", "clothing": "She is wearing a tight, long-sleeved black mini dress and sheer black pantyhose.", "pose": "She is seated in the back of a car with her legs crossed and pulled up towards her chest, displaying bare feet in sheer hose." }, "environment": { "location": "Interior of a luxury car, specifically the back seat.", "details": "Light grey leather seats with diamond stitching patterns visible on the side door panel. A 'B' logo (likely Bentley) is embroidered on the headrest.", "background": "Through the car window, a night cityscape is visible with blurred city lights, street lamps, and streaks of light from moving traffic on a highway." }, "lighting_and_quality": { "lighting": "Direct flash photography illuminating the subject against a darker background.", "resolution": "4K HD quality, highly detailed textures on the hair, skin, and leather seats.", "style": "Candid, urban night aesthetic, realistic photo." } } }

Iris

74,089 views • 8 months ago

Kling 2.6 Motion Control is absolutely insane 🤯 Take any reference video and transfer the exact motion onto an AI character: full-body sync, facial expressions, hand gestures, everything. All with just a few clicks. Perfect for e-comm brands and agencies creating AI video ads that don't look like AI. Here's the problem: AI-generated video ads still look robotic. The movements are stiff, the expressions are flat. Your audience clocks it as AI instantly and keeps scrolling. Kling 2.6 Motion Control fixes it: → Start with any reference clip (stock footage, existing UGC, motion reference) → Upload to Kling → Map the exact movement onto any AI character → Full-body motion, hand gestures, facial expressions—all transferred → Generate up to 30 seconds of video No stiff AI movements, no uncanny valley, no instant "skip this ad" reaction. What this unlocks: - Use one winning UGC motion → swap in different AI creators - Pull reference clips from anywhere → generate branded variations - Create dynamic AI video ads with real human movement - Test multiple "creators" without filming anyone new I recorded a quick walkthrough showing how to do this step-by-step. Want access? > Comment "KLING" > Like this post And I'll send it over (must be following so I can DM)

Kling 2.6 Motion Control is absolutely insane 🤯 Take any reference video and transfer the exact motion onto an AI character: full-body sync, facial expressions, hand gestures, everything. All with just a few clicks. Perfect for e-comm brands and agencies creating AI video ads that don't look like AI. Here's the problem: AI-generated video ads still look robotic. The movements are stiff, the expressions are flat. Your audience clocks it as AI instantly and keeps scrolling. Kling 2.6 Motion Control fixes it: → Start with any reference clip (stock footage, existing UGC, motion reference) → Upload to Kling → Map the exact movement onto any AI character → Full-body motion, hand gestures, facial expressions—all transferred → Generate up to 30 seconds of video No stiff AI movements, no uncanny valley, no instant "skip this ad" reaction. What this unlocks: - Use one winning UGC motion → swap in different AI creators - Pull reference clips from anywhere → generate branded variations - Create dynamic AI video ads with real human movement - Test multiple "creators" without filming anyone new I recorded a quick walkthrough showing how to do this step-by-step. Want access? > Comment "KLING" > Like this post And I'll send it over (must be following so I can DM)

Mike Futia

25,247 views • 8 months ago

Happy Horse 1.1 is finally here!! And HorsePower AI Cinema Awards Submissions Are Now Open! The Golden Acorn Heist was generated entirely with HappyHorse 1.1. A two-scene, 5-second heist sequence featuring Barnaby — a hyper-expressive flying squirrel - descending through a laser grid before triggering a plasma lava trap. The film pushes HappyHorse 1.5's core upgrades hard: orbital spiral camera moves with stable mid-air wire physics, violent whip-pan transitions, and a goggle-snap panic expression that lands in a single frame. Character detail stays locked across every cut via R2V multi-image referencing. Complex prompt. Zero drift. Whats New with Happy Horse 1.1? — Fluid motion under complex camera choreography (orbital spiral, whip-pan) — Consistent character detail across rapid scene cuts — Expressive facial rendering under high-motion conditions — Accurate response to multi-camera shot scheduling in a single prompt See what it can do >. HappyHorse #happyhorse #HorsepowerAICinema #happyhorse1.1 #ai

Happy Horse 1.1 is finally here!! And HorsePower AI Cinema Awards Submissions Are Now Open! The Golden Acorn Heist was generated entirely with HappyHorse 1.1. A two-scene, 5-second heist sequence featuring Barnaby — a hyper-expressive flying squirrel - descending through a laser grid before triggering a plasma lava trap. The film pushes HappyHorse 1.5's core upgrades hard: orbital spiral camera moves with stable mid-air wire physics, violent whip-pan transitions, and a goggle-snap panic expression that lands in a single frame. Character detail stays locked across every cut via R2V multi-image referencing. Complex prompt. Zero drift. Whats New with Happy Horse 1.1? — Fluid motion under complex camera choreography (orbital spiral, whip-pan) — Consistent character detail across rapid scene cuts — Expressive facial rendering under high-motion conditions — Accurate response to multi-camera shot scheduling in a single prompt See what it can do >. HappyHorse #happyhorse #HorsepowerAICinema #happyhorse1.1 #ai

ben.

21,377 views • 3 months ago

I noticed something weird about the ads that get 10M+ views. While most brands randomly animate everything hoping something works, viral ads follow a precise psychology pattern. Here's what I discovered: → They never animate text (protects cognitive load - Daniel Kahneman's research) → They focus motion on ONE primary element (leverages attention theory from William James) → They use specific timing that triggers dopamine release (based on BF Skinner's variable reward schedules) Most animated ads fail because they create visual chaos that overloads working memory. The brands crushing it with animated ads use Notch's Animated Ads feature, where they're giving away a master prompt system built on decades of behavioral psychology research from Stanford and MIT. No more guessing what motion will work. No more paying $5K for animations that flop. No more static ads getting buried in feeds. I'm giving away their complete Animated Ads Master Prompt guide that reveals the exact animation psychology. This is the same system top agencies use to create scroll-stopping animated creatives that convert. Want the viral animation playbook? Comment "NOTCH" + RT + Like I'll DM you the master prompt guide (Must be following so I can DM) Skip this and keep wondering why your animated ads don't work.

I noticed something weird about the ads that get 10M+ views. While most brands randomly animate everything hoping something works, viral ads follow a precise psychology pattern. Here's what I discovered: → They never animate text (protects cognitive load - Daniel Kahneman's research) → They focus motion on ONE primary element (leverages attention theory from William James) → They use specific timing that triggers dopamine release (based on BF Skinner's variable reward schedules) Most animated ads fail because they create visual chaos that overloads working memory. The brands crushing it with animated ads use Notch's Animated Ads feature, where they're giving away a master prompt system built on decades of behavioral psychology research from Stanford and MIT. No more guessing what motion will work. No more paying $5K for animations that flop. No more static ads getting buried in feeds. I'm giving away their complete Animated Ads Master Prompt guide that reveals the exact animation psychology. This is the same system top agencies use to create scroll-stopping animated creatives that convert. Want the viral animation playbook? Comment "NOTCH" + RT + Like I'll DM you the master prompt guide (Must be following so I can DM) Skip this and keep wondering why your animated ads don't work.

Samruddhi Mokal

105,508 views • 1 year ago

$📖 MOST CREATORS PROMPT SEEDANCE 2.0 WRONG — HERE'S THE JSON METHOD THAT FIXES IT This workflow takes about 12 minutes and most creators are still skipping it. Here's the exact pipeline: Generate the base frame in ChatGPT Image — keep the prompt specific: subject, lighting, camera distance, mood Drop that image into Seedance 2.0 as the reference frame Use a structured JSON prompt instead of a plain text description — Seedance reads structured data far more consistently than freeform sentences Define camera movement, duration, and motion intensity as separate fields, not buried in a paragraph Example JSON prompt: { "scene": "vast sky realm with floating stone islands and waterfalls pouring into clouds below", "subject": "lone hooded figure in weathered cloak and armor, standing on a cliff edge", "action": "raises both arms to open a glowing circular portal etched with ancient symbols", "camera_movement": "slow push in, low angle tilting up toward the portal", "duration": "6s", "motion_intensity": "medium", "environment": "floating islands suspended in golden haze, twin waterfalls cascading into a sea of clouds", "lighting": "warm backlit sun, golden hour haze, glowing rim light on the figure", "effects": "swirling energy ring, light particles streaming upward, faint lens flare", "color_palette": "teal sky, amber light, desaturated stone tones", "mood": "epic, mystical, ancient power awakening" } Feed that JSON straight into Seedance 2.0's prompt field Render and review — if motion feels off, adjust motion_intensity first, that field controls most of the result Stack 3-4 clips with matching lighting fields for a consistent short sequence The reason JSON beats plain text here: each field is isolated, so the model doesn't have to guess which word controls what. Camera behavior becomes predictable instead of random. This is also where the monetization angle gets interesting — brands paying for short cinematic ads care less about the prompt method and more about consistency across multiple clips. JSON prompting is what makes that consistency repeatable at scale. ~ The video was created by Aastha ~ 📥Tomorrow I will reveal something so underrated it almost did not make it into a post. 🔖Every step of this workflow is documented in the pinned article below.$

📖 MOST CREATORS PROMPT SEEDANCE 2.0 WRONG — HERE'S THE JSON METHOD THAT FIXES IT This workflow takes about 12 minutes and most creators are still skipping it. Here's the exact pipeline: Generate the base frame in ChatGPT Image — keep the prompt specific: subject, lighting, camera distance, mood Drop that image into Seedance 2.0 as the reference frame Use a structured JSON prompt instead of a plain text description — Seedance reads structured data far more consistently than freeform sentences Define camera movement, duration, and motion intensity as separate fields, not buried in a paragraph Example JSON prompt: { "scene": "vast sky realm with floating stone islands and waterfalls pouring into clouds below", "subject": "lone hooded figure in weathered cloak and armor, standing on a cliff edge", "action": "raises both arms to open a glowing circular portal etched with ancient symbols", "camera_movement": "slow push in, low angle tilting up toward the portal", "duration": "6s", "motion_intensity": "medium", "environment": "floating islands suspended in golden haze, twin waterfalls cascading into a sea of clouds", "lighting": "warm backlit sun, golden hour haze, glowing rim light on the figure", "effects": "swirling energy ring, light particles streaming upward, faint lens flare", "color_palette": "teal sky, amber light, desaturated stone tones", "mood": "epic, mystical, ancient power awakening" } Feed that JSON straight into Seedance 2.0's prompt field Render and review — if motion feels off, adjust motion_intensity first, that field controls most of the result Stack 3-4 clips with matching lighting fields for a consistent short sequence The reason JSON beats plain text here: each field is isolated, so the model doesn't have to guess which word controls what. Camera behavior becomes predictable instead of random. This is also where the monetization angle gets interesting — brands paying for short cinematic ads care less about the prompt method and more about consistency across multiple clips. JSON prompting is what makes that consistency repeatable at scale. ~ The video was created by Aastha ~ 📥Tomorrow I will reveal something so underrated it almost did not make it into a post. 🔖Every step of this workflow is documented in the pinned article below.

Zentrix⌚️

34,615 views • 3 months ago

I just built a Claude prompt library that runs your entire DTC marketing operation 🤯 100+ prompts organized by function: competitor research, creative briefs, ad copy, hooks, landing pages, performance analysis, customer review mining, and more. Perfect for DTC brands and agencies who are still prompting Claude from scratch every time they open a new chat, rewriting the same context, and getting generic output that sounds like every other AI-generated ad. This prompt library eliminates the entire loop: → Competitor Research: scrape and analyze competitor ads, extract winning hooks, map creative strategies, build competitive battlecards → Creative Briefs: generate data-backed briefs from ad performance, write iteration briefs, new concept briefs, test plans → Ad Copy & Hooks: 20 hooks across 10 frameworks, full ad copy variations, persona-specific angles, fatigue-busting rewrites → Landing Pages: audit any landing page against DR best practices, clone high-converting advertorial structures, write product page copy → Performance Analysis: audit Google Ads accounts, find wasted spend, build visual dashboards, weekly narrative reports → Customer Intelligence: mine reviews for ad copy language, extract objections, find unexpected use cases, build persona cards from real data → SEO & Content: find keyword gaps, write content in your brand voice, optimize product listings for AI shopping (ChatGPT, Gemini) → Email & SMS: launch sequences, weekly newsletters, abandoned cart flows, post-purchase nurture No more blank-page prompting. No more re-explaining your brand every session. No more generic AI output that sounds like a template. What you get: →100+ copy-paste prompts organized by the 8 functions DTC teams actually run →Every prompt pre-loaded with the context structure Claude needs to give you real output →Prompts that reference your brand voice, your ICPs, and your real data — not generic placeholders →A living library you can customize once and reuse across every campaign I put together the full prompt library as a single downloadable playbook: organized by section, ready to copy-paste into Claude today. Want it for free? > Like this post >Comment "PROMPTS" And I'll send it over (must be following so I can DM)

Mike Futia

34,929 views • 5 months ago