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Looking kinda short and stack Commando Robo Commander: 澄澈之冰【2】 Model Created: 🔞 LewdsSonk 🔞

VadamPlus🔞(Commissions : CLOSED)

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96,226 次观看 • 1 个月前 •via X (Twitter)

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Jiggle Wait (that sound is the boots, right?) Model By 🔞 LewdsSonk 🔞 Character By 澄澈之冰【2】 Sfx By OpenNSFW 🟣 Available Now

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Jiggle Wait (that sound is the boots, right?) Model By 🔞 LewdsSonk 🔞 Character By 澄澈之冰【2】 Sfx By OpenNSFW 🟣 Available Now

Kio Kashima

39,379 次观看 • 2 个月前

Finally got the robo coochmeister 🔞 (COMMISSIONS CLOSED) Jenny model made. Was concerned that the head would look off but it managed to turn out not too bad. This drive will focus on her boobs, butt and thighs so be sure to like and repost! The drive will only be active a week so be quick!

Finally got the robo coochmeister 🔞 (COMMISSIONS CLOSED) Jenny model made. Was concerned that the head would look off but it managed to turn out not too bad. This drive will focus on her boobs, butt and thighs so be sure to like and repost! The drive will only be active a week so be quick!

Random (Commissions full)

20,288 次观看 • 2 个月前

GPT Image 2 + Seedance 2.0 - Prompt Share Created on @MartiniArt_ Created the character in Midjourney, then built the storyboard around that character. This time I used a more detailed video prompt, but I wasn't describing the sequence shot-by-shot. The storyboard acted more as a visual guide for the action flow, camera language and overall narrative than as a strict frame-by-frame blueprint. Looking at the result, the model seems to preserve the key beats, escalation and composition of the storyboard while freely interpreting the transitions between them.

GPT Image 2 + Seedance 2.0 - Prompt Share Created on @MartiniArt_ Created the character in Midjourney, then built the storyboard around that character. This time I used a more detailed video prompt, but I wasn't describing the sequence shot-by-shot. The storyboard acted more as a visual guide for the action flow, camera language and overall narrative than as a strict frame-by-frame blueprint. Looking at the result, the model seems to preserve the key beats, escalation and composition of the storyboard while freely interpreting the transitions between them.

Kōda

44,234 次观看 • 4 个月前

Crimson Desert Adventure log: Day 10 ( 6 hours) - details in comments. 🔸Defeated Staglord without healing even once. 🔸Running short of money in game 🔸Figured out how to have constant health, stamina and Spirit regeneration. 🔸Figured out that you could merge 2 weapons of same type into a better rated weapon. 🔸Working on building my own farm and rench 🔸Sold a horse black market guy his own horse 🔸Defeated fort commander 🔸Caught my first fish 🔸Did some puzzles 🔸And helped a bunch of Greymanes What is your journey looking like fellow Greymanes?

Crimson Desert Adventure log: Day 10 ( 6 hours) - details in comments. 🔸Defeated Staglord without healing even once. 🔸Running short of money in game 🔸Figured out how to have constant health, stamina and Spirit regeneration. 🔸Figured out that you could merge 2 weapons of same type into a better rated weapon. 🔸Working on building my own farm and rench 🔸Sold a horse black market guy his own horse 🔸Defeated fort commander 🔸Caught my first fish 🔸Did some puzzles 🔸And helped a bunch of Greymanes What is your journey looking like fellow Greymanes?

SpawnYaard 🎮

196,567 次观看 • 6 个月前

Another Grizelda short animation by Manuel Alonso! I mentored him a bit on this shot, did the lighting and background, and we ended up getting a few other people involved to help finalize it. Hope you guys enjoy our collaboration! I really didn’t expect so many people to jump in on this one-shot—it started as just something fun for Manuel to animate my character. Creator: Jonathan Veiga Voice Actor: OolayTiger Studios 🔞🎙 Animation: Manuel Alonso Sound Engineer: Alex Lunstead Character Model: Bahman Zerei Character Design: Zeker_Art Rigging: alargewoodenbadger Compositor: ChrysAnim Music: Python Blue

Another Grizelda short animation by Manuel Alonso! I mentored him a bit on this shot, did the lighting and background, and we ended up getting a few other people involved to help finalize it. Hope you guys enjoy our collaboration! I really didn’t expect so many people to jump in on this one-shot—it started as just something fun for Manuel to animate my character. Creator: Jonathan Veiga Voice Actor: OolayTiger Studios 🔞🎙 Animation: Manuel Alonso Sound Engineer: Alex Lunstead Character Model: Bahman Zerei Character Design: Zeker_Art Rigging: alargewoodenbadger Compositor: ChrysAnim Music: Python Blue

Jonathan Veiga

77,948 次观看 • 2 个月前

Fable 5 comes back！It can now build playable game prototypes. I think it is actually a signal for where AI coding is going. Making a game is not just “write some code.” Even a small browser game needs: game loop；character movement；collision logic；scoring system；UI states；physics tuning；visual feedback；bug fixing；playtesting This is why game prototyping is a great test for AI models. A model cannot fake it with a pretty answer. Either the game runs, or it does not. What impressed me about Fable 5 is that it is useful for the messy middle: turning an idea into mechanics, turning mechanics into code, debugging broken interactions, and iterating until the prototype feels playable. But here is the practical part: I would not use the strongest model for every step. For game building, I would split the workflow: 1. Fable 5 for game design + architecture 2. a fast coding model for routine implementation 3. a vision-capable model for screenshot/UI feedback 4. a cheaper model for docs, test cases, and small fixes 5. fallback when latency, cost, or output quality becomes a problem That is the real AI coding stack. Not “one magic model does everything.” More like: the right model, for the right task, at the right cost, with fallback when things break. This is why I’ve been looking at ZenMux ZenMux. ZenMux gives developers one gateway to access multiple leading AI models, with OpenAI / Anthropic / Google Vertex compatible APIs, cost tracking, quality benchmarks, auto-routing, and compensation when output quality, latency, or throughput falls short. If AI can now make games, the next question is not just “which model is strongest?” It is:how do we manage the whole model workflow Fable 5 shows the creative ceiling. ZenMux is closer to the infrastructure layer you need when AI coding becomes a real production habit.

Fable 5 comes back！It can now build playable game prototypes. I think it is actually a signal for where AI coding is going. Making a game is not just “write some code.” Even a small browser game needs: game loop；character movement；collision logic；scoring system；UI states；physics tuning；visual feedback；bug fixing；playtesting This is why game prototyping is a great test for AI models. A model cannot fake it with a pretty answer. Either the game runs, or it does not. What impressed me about Fable 5 is that it is useful for the messy middle: turning an idea into mechanics, turning mechanics into code, debugging broken interactions, and iterating until the prototype feels playable. But here is the practical part: I would not use the strongest model for every step. For game building, I would split the workflow: 1. Fable 5 for game design + architecture 2. a fast coding model for routine implementation 3. a vision-capable model for screenshot/UI feedback 4. a cheaper model for docs, test cases, and small fixes 5. fallback when latency, cost, or output quality becomes a problem That is the real AI coding stack. Not “one magic model does everything.” More like: the right model, for the right task, at the right cost, with fallback when things break. This is why I’ve been looking at ZenMux ZenMux. ZenMux gives developers one gateway to access multiple leading AI models, with OpenAI / Anthropic / Google Vertex compatible APIs, cost tracking, quality benchmarks, auto-routing, and compensation when output quality, latency, or throughput falls short. If AI can now make games, the next question is not just “which model is strongest?” It is:how do we manage the whole model workflow Fable 5 shows the creative ceiling. ZenMux is closer to the infrastructure layer you need when AI coding becomes a real production habit.

Rachel🥥

61,994 次观看 • 3 个月前

Flawless finish, every stroke meet the ultimate brush set for your everyday glam, Created with Seedance 2.5 Prompt: High end commercial makeup ad, multi scene video sequence. Scene 1: Close-up of a blonde model with flawless skin and light makeup looking at the camera. Scene 2: Macro shot of a luxurious white-and-gold makeup brush sweeping into a compact of soft pink powder, creating a slight dust cloud. Scene 3: A red-haired model having blusher applied to her cheek with a brush in a professional studio setup. Scene 4: Precision eye makeup application, applying golden eyeshadow with a fine brush. Scene 5: Close-up of glossy dark-pink lips being perfected with a lip brush. Scene 6: A brush highlighting the collarbone with shimmer powder. Scene 7: Close-up portrait of an Asian model with elegant dark hair and polished makeup. Scene 8: Three diverse models (Asian, blonde, and red-haired) wearing stylish professional business suits and white pants, confidently walking forward holding makeup brushes. Scene 9: Product showcase of the "adelice" white-and-gold makeup brush set neatly arranged with containers and compacts on a reflective glass surface. Cinematic studio lighting, photorealistic, 8k resolution, smooth camera pans and transitions.

Flawless finish, every stroke meet the ultimate brush set for your everyday glam, Created with Seedance 2.5 Prompt: High end commercial makeup ad, multi scene video sequence. Scene 1: Close-up of a blonde model with flawless skin and light makeup looking at the camera. Scene 2: Macro shot of a luxurious white-and-gold makeup brush sweeping into a compact of soft pink powder, creating a slight dust cloud. Scene 3: A red-haired model having blusher applied to her cheek with a brush in a professional studio setup. Scene 4: Precision eye makeup application, applying golden eyeshadow with a fine brush. Scene 5: Close-up of glossy dark-pink lips being perfected with a lip brush. Scene 6: A brush highlighting the collarbone with shimmer powder. Scene 7: Close-up portrait of an Asian model with elegant dark hair and polished makeup. Scene 8: Three diverse models (Asian, blonde, and red-haired) wearing stylish professional business suits and white pants, confidently walking forward holding makeup brushes. Scene 9: Product showcase of the "adelice" white-and-gold makeup brush set neatly arranged with containers and compacts on a reflective glass surface. Cinematic studio lighting, photorealistic, 8k resolution, smooth camera pans and transitions.

Avelyrah

15,760 次观看 • 1 个月前

Sora 2, “ family guy dark humor” My personal thoughts: This is clearly the best AI video model for replicating animated shows. This truly gives credence to the fact that within the decade AI generated short films/shows is looking like a real possibility. My important caveats. Model trained only on video can get visually indistinguishable for most viewers most of the time. It can match texture, lighting, motion, and camera language so well that only careful inspection gives it away. However For sustained quality at the level of a full scene or an episode, video alone hits a ceiling IMO. Pure video likelihood drives the model toward what is frequent, not toward the rare timing and payoff choices that make the best jokes land. It has weak grasp of long arc causality, character memory, and joke structure. It also does not see intent, off screen context, or prosody unless you give it those signals. So you get something that looks right but drifts on beats that matter. I don’t know what the potential solution would be other than to have an AGI just animate the show for me. Any others ? Credit for the sora clip: figure

Sora 2, “ family guy dark humor” My personal thoughts: This is clearly the best AI video model for replicating animated shows. This truly gives credence to the fact that within the decade AI generated short films/shows is looking like a real possibility. My important caveats. Model trained only on video can get visually indistinguishable for most viewers most of the time. It can match texture, lighting, motion, and camera language so well that only careful inspection gives it away. However For sustained quality at the level of a full scene or an episode, video alone hits a ceiling IMO. Pure video likelihood drives the model toward what is frequent, not toward the rare timing and payoff choices that make the best jokes land. It has weak grasp of long arc causality, character memory, and joke structure. It also does not see intent, off screen context, or prosody unless you give it those signals. So you get something that looks right but drifts on beats that matter. I don’t know what the potential solution would be other than to have an AGI just animate the show for me. Any others ? Credit for the sora clip: figure

Chris

150,286 次观看 • 1 年前

🚀ASST TO $700 PER SHARE?!?🚀 YOU THINK I'M JOKING? THINK AGAIN, BUCKO. Current ASST snapshot: BTC holdings: 15,000.5 BTC BTC price: $80,593 Bitcoin NAV: $1.21B Total debt: $10M Preferred outstanding: $495.95M Debt + preferred: $505.95M Amplification ratio: 41.9% Current stock price: $15.85 Now here’s the model, and this isn't MOONBOY NONSENSE, kids. This is with Bitcoin at $750k in 2036, not $1 million in 2034. ASST maintains their current 41.9% amplification ratio for 10 years. Translation for normal people: For every $1.00 of Bitcoin NAV, ASST keeps roughly $0.419 of senior claims through debt/preferred financing. The bears hear that and immediately start sweating through a Men’s Wearhouse suit. But this is the actual machine. As Bitcoin rises, the Bitcoin NAV rises. When the NAV rises, the old preferred stack becomes smaller relative to the treasury. So ASST issues more SATA to keep amplification at 41.9%. That new SATA capital buys more Bitcoin. Then Bitcoin goes up again. Then the NAV goes up again. Then the amplification ratio drops again. Then they issue more SATA again. Then they buy more Bitcoin again. This is how you turn a balance sheet into a legally registered orange crocodile. Now we add the funding mix: 75% of new Bitcoin accumulation comes from SATA. 25% comes from issuing common stock. And the common stock is issued at 1.2x EV mNAV. Meaning they are selling equity at a 20% premium to the enterprise value of the Bitcoin stack. That matters. Because issuing common below NAV is financial self-harm. Issuing common above NAV is accretive treasury sorcery. Now assume Bitcoin compounds at 25% per year for 10 years. BTC price goes from: $80,593 today to roughly: $750,579 in year 10 That is a 9.3x move in Bitcoin. Now what happens to ASST? Starting BTC stack: 15,000.5 BTC Projected year 10 BTC stack: 143,425 BTC That is 9.6x more Bitcoin. Starting Bitcoin NAV: $1.21B Projected year 10 Bitcoin NAV: $107.65B That is 89x larger. Now the bears will say: “BUT THE PREFERREDS!” Yes, Carl. The preferreds are the point. Senior claims rise from $505.95M to $45.11B because the model intentionally keeps amplification at 41.9%. That sounds terrifying until you remember the Bitcoin NAV grew to $107.65B. The stack got bigger. The senior claims got bigger. The common equity claim got bigger too. This is where CEBE comes in. CEBE = Common Equity Bitcoin Exposure. It answers the only question that matters: After debt and preferred holders get their claim, how much Bitcoin exposure does the common shareholder really own? Today: Gross BPS: 20,222 sats CEBE/share: 11,759 sats Year 10: Gross BPS: 95,380 sats CEBE/share: 55,416 sats That means common-equity Bitcoin exposure per share rises about 4.7x. Even after common issuance. Even after maintaining the preferred stack. Even after the bears finish their sacred ritual of screaming “DILUTION” into a spreadsheet they opened sideways. Now the share count. Current implied diluted shares: 74.2M Projected year 10 shares: 150.4M So yes, the share count roughly doubles in this model. But the Bitcoin stack goes 9.6x. This is the entire game. If Bitcoin holdings grow much faster than shares outstanding, the common shareholder’s Bitcoin exposure goes up. The bears think all issuance is bad because they learned finance from a Yahoo message board during a divorce. The actual question is: Does issuance increase Bitcoin per share after senior claims? In this model, yes. Now the stock price. Strict 1.2x EV mNAV model gets ASST to about: $559/share But if we anchor the model to today’s actual ASST price of $15.85, the same growth path gets you to roughly: $696/share Call it $700. There it is. ASST to $700 per share is not “vibes.” It is a model. BTC compounds at 25%. SATA funds 75% of accumulation. Common funds 25% at 1.2x EV mNAV. Amplification stays at 41.9%. BTC stack grows from 15,000 BTC to 143,425 BTC. Bitcoin NAV goes from $1.21B to $107.65B. CEBE/share goes from 11,759 sats to 55,416 sats. The stock goes from $15.85 to roughly $700. This is why small Bitcoin treasury companies are so insane. Strategy is the Death Star. ASST is the weird little orange lab experiment in the basement where someone accidentally discovers corporate finance methamphetamine. Tiny denominator. Preferred financing. Bitcoin accumulation. Premium equity issuance. CEBE expansion. A compounding treasury loop. The bear case is that dilution kills the common. The bull case is that accretive dilution plus preferred financing creates a Bitcoin-per-share machine that eats capital markets and leaves behind a pile of traumatized short sellers asking why their model still says “book value.” ASST to $700? If the machine works, yes. If Bitcoin does 25% CAGR, absolutely possible. If SATA scales and common gets issued above NAV, the goblin gets fed. And once the goblin gets fed, the spreadsheet starts looking like it was written by Saylor, Dylan LeClair, and a sleep-deprived Austrian economist locked inside a treasury dashboard with three Celsius energy drinks. This is not financial advice. This is FINANCIAL ENTERTAINMENT:

🚀ASST TO $700 PER SHARE?!?🚀 YOU THINK I'M JOKING? THINK AGAIN, BUCKO. Current ASST snapshot: BTC holdings: 15,000.5 BTC BTC price: $80,593 Bitcoin NAV: $1.21B Total debt: $10M Preferred outstanding: $495.95M Debt + preferred: $505.95M Amplification ratio: 41.9% Current stock price: $15.85 Now here’s the model, and this isn't MOONBOY NONSENSE, kids. This is with Bitcoin at $750k in 2036, not $1 million in 2034. ASST maintains their current 41.9% amplification ratio for 10 years. Translation for normal people: For every $1.00 of Bitcoin NAV, ASST keeps roughly $0.419 of senior claims through debt/preferred financing. The bears hear that and immediately start sweating through a Men’s Wearhouse suit. But this is the actual machine. As Bitcoin rises, the Bitcoin NAV rises. When the NAV rises, the old preferred stack becomes smaller relative to the treasury. So ASST issues more SATA to keep amplification at 41.9%. That new SATA capital buys more Bitcoin. Then Bitcoin goes up again. Then the NAV goes up again. Then the amplification ratio drops again. Then they issue more SATA again. Then they buy more Bitcoin again. This is how you turn a balance sheet into a legally registered orange crocodile. Now we add the funding mix: 75% of new Bitcoin accumulation comes from SATA. 25% comes from issuing common stock. And the common stock is issued at 1.2x EV mNAV. Meaning they are selling equity at a 20% premium to the enterprise value of the Bitcoin stack. That matters. Because issuing common below NAV is financial self-harm. Issuing common above NAV is accretive treasury sorcery. Now assume Bitcoin compounds at 25% per year for 10 years. BTC price goes from: $80,593 today to roughly: $750,579 in year 10 That is a 9.3x move in Bitcoin. Now what happens to ASST? Starting BTC stack: 15,000.5 BTC Projected year 10 BTC stack: 143,425 BTC That is 9.6x more Bitcoin. Starting Bitcoin NAV: $1.21B Projected year 10 Bitcoin NAV: $107.65B That is 89x larger. Now the bears will say: “BUT THE PREFERREDS!” Yes, Carl. The preferreds are the point. Senior claims rise from $505.95M to $45.11B because the model intentionally keeps amplification at 41.9%. That sounds terrifying until you remember the Bitcoin NAV grew to $107.65B. The stack got bigger. The senior claims got bigger. The common equity claim got bigger too. This is where CEBE comes in. CEBE = Common Equity Bitcoin Exposure. It answers the only question that matters: After debt and preferred holders get their claim, how much Bitcoin exposure does the common shareholder really own? Today: Gross BPS: 20,222 sats CEBE/share: 11,759 sats Year 10: Gross BPS: 95,380 sats CEBE/share: 55,416 sats That means common-equity Bitcoin exposure per share rises about 4.7x. Even after common issuance. Even after maintaining the preferred stack. Even after the bears finish their sacred ritual of screaming “DILUTION” into a spreadsheet they opened sideways. Now the share count. Current implied diluted shares: 74.2M Projected year 10 shares: 150.4M So yes, the share count roughly doubles in this model. But the Bitcoin stack goes 9.6x. This is the entire game. If Bitcoin holdings grow much faster than shares outstanding, the common shareholder’s Bitcoin exposure goes up. The bears think all issuance is bad because they learned finance from a Yahoo message board during a divorce. The actual question is: Does issuance increase Bitcoin per share after senior claims? In this model, yes. Now the stock price. Strict 1.2x EV mNAV model gets ASST to about: $559/share But if we anchor the model to today’s actual ASST price of $15.85, the same growth path gets you to roughly: $696/share Call it $700. There it is. ASST to $700 per share is not “vibes.” It is a model. BTC compounds at 25%. SATA funds 75% of accumulation. Common funds 25% at 1.2x EV mNAV. Amplification stays at 41.9%. BTC stack grows from 15,000 BTC to 143,425 BTC. Bitcoin NAV goes from $1.21B to $107.65B. CEBE/share goes from 11,759 sats to 55,416 sats. The stock goes from $15.85 to roughly $700. This is why small Bitcoin treasury companies are so insane. Strategy is the Death Star. ASST is the weird little orange lab experiment in the basement where someone accidentally discovers corporate finance methamphetamine. Tiny denominator. Preferred financing. Bitcoin accumulation. Premium equity issuance. CEBE expansion. A compounding treasury loop. The bear case is that dilution kills the common. The bull case is that accretive dilution plus preferred financing creates a Bitcoin-per-share machine that eats capital markets and leaves behind a pile of traumatized short sellers asking why their model still says “book value.” ASST to $700? If the machine works, yes. If Bitcoin does 25% CAGR, absolutely possible. If SATA scales and common gets issued above NAV, the goblin gets fed. And once the goblin gets fed, the spreadsheet starts looking like it was written by Saylor, Dylan LeClair, and a sleep-deprived Austrian economist locked inside a treasury dashboard with three Celsius energy drinks. This is not financial advice. This is FINANCIAL ENTERTAINMENT:

Adam Livingston

66,707 次观看 • 5 个月前

I created this short ad video using GPT Imagge 2 + Seedance 2.0 on Creatify AI Prompt Create a 12-shot cinematic luxury fashion film in a pure white infinity studio. The video MUST follow ALL 12 panels EXACTLY in order from 1 to 12 without skipping, combining ultra-realistic fashion editorial cinematography, luxury commercial aesthetics, warm cinematic tones, glossy baby pink metallic textures, dramatic studio lighting, reflective chrome highlights, soft haze, slow-motion elegance, and premium Vogue-style camera work. SHOT 1 — Panther Close-Up Ultra-realistic extreme close-up portrait of a majestic black panther staring directly into camera with piercing yellow-green eyes, dramatic high-contrast lighting, ultra-sharp black fur texture, white background, subtle breathing movement, cinematic macro lens. SHOT 2 — Chrome Logo Reveal Highly detailed 3D metallic chrome “CREATIFY” logo in elegant cursive script floating above reflective white surface, reflective baby pink and silver tones, soft studio shadows, slow cinematic rotation, luxury branding commercial style. SHOT 3 — Standing Model in Dress Full-body fashion editorial shot of beautiful blonde model wearing flowing floor-length baby pink metallic gown, standing gracefully on white seamless background, soft studio lighting, elegant pose, slow push-in camera movement. SHOT 4 — Model Walking With Panther Elegant model in shimmering baby pink metallic gown walking side-by-side with sleek black panther toward camera, graceful synchronized movement, flowing dress motion, white studio environment, luxury fashion cinematography. SHOT 5 — Liquid Metal Fabric Extreme macro close-up of luxurious liquid metallic baby pink fabric with glossy folds and reflective chrome-like texture, dramatic lighting, soft movement rippling across material, high-end fashion product commercial look. SHOT 6 — Luxury Handbag Reveal Luxury baby pink metallic handbag with sleek curved design displayed on transparent glass pedestal with perfect reflection underneath, minimalist white studio setting, slow rotating product shot, soft cinematic spotlight. SHOT 7 — Kneeling With Panther Stunning blonde model in baby pink metallic crop top and skirt kneeling beside large black panther, gently touching its face, intimate emotional connection, dramatic editorial lighting, white studio background. SHOT 8 — Flowing Hair & Fabric Dynamic fashion shot of model with long blonde hair dramatically flowing into waves of shiny baby pink metallic fabric, strong wind effect, fluid sculptural motion, high-fashion editorial cinematography. SHOT 9 — Jewelry Showcase Luxury baby pink metallic jewelry collection including thick rings, cuffs, and chain necklace arranged on reflective white surface, sparkling highlights, macro camera glide, premium luxury product-commercial aesthetic. SHOT 10 — Lying With Panther Beautiful model in shiny baby pink metallic outfit lying beside relaxed black panther on white seamless floor, both looking directly at camera, intimate luxury editorial atmosphere, soft dramatic shadows. SHOT 11 — Motion Walking With Panther High-fashion motion shot of model confidently walking with black panther, flowing baby pink metallic gown trailing behind, subtle motion blur on legs and fabric, dynamic runway energy, cinematic tracking shot. SHOT 12 — Final Hero Shot Elegant full-body composition of model in long baby pink metallic dress standing gracefully beside small black panther, both facing forward in minimal white studio setting, soft glowing light, final chrome “CREATIFY” logo appearing elegantly as ending frame.

Sharon Riley

29,976 次观看 • 4 个月前

Luxury is in the details. 🖤✨ The iconic Miu Miu sunglasses, where timeless elegance meets modern confidence. Created with GPT image 2 and Seedance 2.0 on Thank You AI PROMPT: A luxurious high-fashion studio commercial featuring the girl from the reference image modeling the exact black Miu Miu sunglasses from the product reference. Clean seamless white studio backdrop with cinematic lighting, soft spotlight, glossy floor reflections, and a premium editorial aesthetic. The model has deep burgundy hair, flawless natural makeup, and wears a fitted black turtleneck. Shot 1 (0–4s): Extreme close-up of the Miu Miu sunglasses resting on a glossy white surface. A slow cinematic slider movement reveals the gold Miu Miu logo on the temples. Dramatic reflections, shallow depth of field, and luxury product lighting. Shot 2 (4–9s): The model gracefully picks up the sunglasses and puts them on while looking directly into the camera. Slow motion with subtle hair movement from a gentle studio fan. Elegant, confident expression. Shot 3 (9–13s): Medium shot as she slowly turns her head from side profile to front, highlighting the iconic gold logo on the temples. Soft rim lighting accentuates the frame's shape with premium fashion editorial vibes. Shot 4 (13–15s): Tight beauty close-up. She lowers her chin slightly and gives a confident, sophisticated gaze toward the camera. The screen fades to white with the Miu Miu logo centered. Style: Luxury fashion campaign, cinematic studio lighting, Vogue editorial, premium product commercial, ultra-realistic, photorealistic, 8K, smooth camera movements, shallow depth of field, high-end color grading, soft highlights, elegant reflections. Audio: No voiceover. Only elegant modern luxury ambient music with subtle orchestral and electronic elements.

Natalia

12,986 次观看 • 2 个月前

What is Pi App Studio’s New creation Pricing Model? Pi Network (Pi Network) has announced a major change to how creators will pay to build and edit apps through Pi App Studio, with the new pricing taking effect on August 24, 2026. Until now, Pi heavily subsidized App Studio for all creators. Under the introductory model, creators paid just 0.25 Pi to create an app and another 0.25 Pi to edit one. But Pi says the actual cost of providing AI-powered app creation is significantly higher than what creators were paying. That means Pi Network has been covering much of the difference. Now, after collecting data on how creators use App Studio, Pi is changing that approach. So, what exactly is changing? (1) Standard App Studio pricing is coming From August 24, creators will face a standard creation and editing price that more closely reflects the underlying cost of the AI services used. The exact price will not necessarily be fixed. Pi says it may vary depending on the resources required to create or edit an app. Importantly, Pi says it will not add a markup to the underlying AI service costs. (2) Some creators can still receive subsidized pricing The biggest change is that Pi is not completely removing its subsidies. Instead, subsidies will become more selective. Creators whose apps attract a sufficient number of real users can continue receiving the previous subsidized pricing. The focus will be on apps demonstrating genuine utility and usage from distinct users, rather than activity generated by the creator alone. (3) Pi wants creators to build useful apps This is arguably the most important part of the new model. Pi says not every app created through App Studio becomes something that other people actually use. Some apps are created simply for experimentation, testing or potentially spam. Continuing to subsidize every creation equally could therefore consume significant network resources without producing meaningful utility. The new model is designed to change that incentive. Creators now have a stronger reason to build apps that solve problems, attract users and provide value to the Pi ecosystem. (4) Subsidized pricing is not permanent or guaranteed Qualifying for the subsidy will not be a one-time decision. Pi plans to regularly review creator eligibility using newly generated data. A creator who does not qualify when the new pricing model launches could qualify later if their app attracts more genuine users. Likewise, the qualification criteria, pricing structure and number of creator tiers could change as App Studio develops. (5) Existing creators have a short window to improve The new pricing model begins on August 24, 2026. Before then, existing creators can improve their apps and encourage more Pioneers to use them. Pi specifically recommends improving an app's usefulness and usability, collecting feedback and continuing to build products that solve real problems. Pi wants its resources going toward apps that people actually use. At its core, the change turns App Studio from a broadly subsidized experimentation tool into a more usage-driven ecosystem.

What is Pi App Studio’s New creation Pricing Model? Pi Network (Pi Network) has announced a major change to how creators will pay to build and edit apps through Pi App Studio, with the new pricing taking effect on August 24, 2026. Until now, Pi heavily subsidized App Studio for all creators. Under the introductory model, creators paid just 0.25 Pi to create an app and another 0.25 Pi to edit one. But Pi says the actual cost of providing AI-powered app creation is significantly higher than what creators were paying. That means Pi Network has been covering much of the difference. Now, after collecting data on how creators use App Studio, Pi is changing that approach. So, what exactly is changing? (1) Standard App Studio pricing is coming From August 24, creators will face a standard creation and editing price that more closely reflects the underlying cost of the AI services used. The exact price will not necessarily be fixed. Pi says it may vary depending on the resources required to create or edit an app. Importantly, Pi says it will not add a markup to the underlying AI service costs. (2) Some creators can still receive subsidized pricing The biggest change is that Pi is not completely removing its subsidies. Instead, subsidies will become more selective. Creators whose apps attract a sufficient number of real users can continue receiving the previous subsidized pricing. The focus will be on apps demonstrating genuine utility and usage from distinct users, rather than activity generated by the creator alone. (3) Pi wants creators to build useful apps This is arguably the most important part of the new model. Pi says not every app created through App Studio becomes something that other people actually use. Some apps are created simply for experimentation, testing or potentially spam. Continuing to subsidize every creation equally could therefore consume significant network resources without producing meaningful utility. The new model is designed to change that incentive. Creators now have a stronger reason to build apps that solve problems, attract users and provide value to the Pi ecosystem. (4) Subsidized pricing is not permanent or guaranteed Qualifying for the subsidy will not be a one-time decision. Pi plans to regularly review creator eligibility using newly generated data. A creator who does not qualify when the new pricing model launches could qualify later if their app attracts more genuine users. Likewise, the qualification criteria, pricing structure and number of creator tiers could change as App Studio develops. (5) Existing creators have a short window to improve The new pricing model begins on August 24, 2026. Before then, existing creators can improve their apps and encourage more Pioneers to use them. Pi specifically recommends improving an app's usefulness and usability, collecting feedback and continuing to build products that solve real problems. Pi wants its resources going toward apps that people actually use. At its core, the change turns App Studio from a broadly subsidized experimentation tool into a more usage-driven ecosystem.

BSCN

28,165 次观看 • 1 个月前

Ling-3.0-flash is built as a fast, reliable execution engine for agent workflows. It shines in long-running tasks, tool calling, and high-volume production work where speed and stability matter more than massive reasoning depth. At 124B parameters with only 5.1B active, it keeps costs low while delivering quick responses and strong instruction following I ran a practical task using Ling-3.0-flash capabilities (based on its documented agent strengths in coding and tool use). Real Test Run I tested Ling-3.0-flash on a content creation task that matches your workflow. I asked it to generate a short, viral-style YouTube Shorts script for football highlights, including captions, title suggestions, and thumbnail ideas. Input Prompt: "Create a 30-second YouTube Shorts script for a dramatic Ronaldo goal from the 2026 World Cup qualifiers. Include engaging English narration, 3 multilingual caption versions (English, Spanish, Portuguese), a catchy title, and thumbnail description. Make it feel real and exciting for football fans." Process: The model first outlined the structure: intro hook, key action description, emotional peak, and call to action. It generated the script, then created caption variants, optimized the title for clicks, and suggested a thumbnail layout. It handled iterations well when I asked for adjustments, such as making it more dramatic or adding player stats. Total interaction took under 2 minutes with low token use. Result: - Script: Solid, ready-to-record narration with natural flow. - Captions: High-quality and culturally adapted. - Title & Thumbnail: Click-worthy and on brand. This demonstrates its strength for creators who need fast, high-quality content assets. Official account : Demo:

Pee✌️💜

22,488 次观看 • 2 个月前

$📖 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 次观看 • 3 个月前

$Hi my friends 🌞 let’s make this Sunday more interesting with latex style🔥 Freya Allan, Lexi Marvel, My Model, Sabrina Carpenter 🔥 👉🏻Subscribe for more content ⚡️ Nano Banana 2 & MiniMax H3 via Hailuo AI Prompt: { "member": "Freya Allan", "subject": { "identity": "facial structure, complexion and proportions of Freya Allan", "hair": "long dark wavy hair over the shoulders", "expression": "head tilted down toward right shoulder, eyes looking up at the lens", "pose": "leaning forward, torso tilted right, right hand on upper right thigh, left arm on metal railing behind her, left knee raised on a step, right leg cropped at the bottom edge" }, "wardrobe": { "silhouette": "short form-fitting high-neck halter dress, gathered at bust, open sides, ruched seams, mid-thigh hem", "material": "glossy latex", "color": "neon pink", "footwear": "only the top edge of a neon pink heel visible on the left foot, rest cropped", "accessories": "opaque black tights, thin hoop earrings partly hidden by hair" }, "scene": { "location": "dilapidated industrial stairwell", "atmosphere": "hazy dusty air catching the light", "background": "peeling paint and rusted wall on the left with small graffiti 'Keor' near a red dragon drawing, metal staircase with diamond-plate treads and weathered railing on the right", "composition": "tall vertical frame, slightly low angle looking up, subject centered and filling the height", "lighting": "dramatic high-contrast warm light from upper left, angular rays on the wall and face, lower right staircase in deep shadow", "camera": "50mm, f/2.8", "grade": "high-contrast, warm highlights, rich shadows" }, "compiled_prompt": "Portrait of Freya Allan, one continuous full-frame exposure. Long dark wavy hair over her shoulders. Head tilted down toward her right shoulder, eyes looking up at the lens. Leaning forward, torso tilted right, right hand on her thigh, left arm on a metal railing. Left knee raised on a step, right leg cropped at the bottom. Short form-fitting glossy neon-pink latex high-neck halter dress with open sides and ruched seams, mid-thigh hem. Opaque black tights, thin hoop earrings. Only the top of a neon-pink heel visible. Dilapidated industrial stairwell, hazy dusty air. Textured peeling wall with small 'Keor' graffiti near a red dragon drawing on the left, metal staircase on the right. Tall vertical low-angle composition. Warm directional light from upper left with specular highlights on the latex, deep shadows on the stairs. 50mm f/2.8, dramatic high-contrast grade." } ```$

Hi my friends 🌞 let’s make this Sunday more interesting with latex style🔥 Freya Allan, Lexi Marvel, My Model, Sabrina Carpenter 🔥 👉🏻Subscribe for more content ⚡️ Nano Banana 2 & MiniMax H3 via Hailuo AI Prompt: { "member": "Freya Allan", "subject": { "identity": "facial structure, complexion and proportions of Freya Allan", "hair": "long dark wavy hair over the shoulders", "expression": "head tilted down toward right shoulder, eyes looking up at the lens", "pose": "leaning forward, torso tilted right, right hand on upper right thigh, left arm on metal railing behind her, left knee raised on a step, right leg cropped at the bottom edge" }, "wardrobe": { "silhouette": "short form-fitting high-neck halter dress, gathered at bust, open sides, ruched seams, mid-thigh hem", "material": "glossy latex", "color": "neon pink", "footwear": "only the top edge of a neon pink heel visible on the left foot, rest cropped", "accessories": "opaque black tights, thin hoop earrings partly hidden by hair" }, "scene": { "location": "dilapidated industrial stairwell", "atmosphere": "hazy dusty air catching the light", "background": "peeling paint and rusted wall on the left with small graffiti 'Keor' near a red dragon drawing, metal staircase with diamond-plate treads and weathered railing on the right", "composition": "tall vertical frame, slightly low angle looking up, subject centered and filling the height", "lighting": "dramatic high-contrast warm light from upper left, angular rays on the wall and face, lower right staircase in deep shadow", "camera": "50mm, f/2.8", "grade": "high-contrast, warm highlights, rich shadows" }, "compiled_prompt": "Portrait of Freya Allan, one continuous full-frame exposure. Long dark wavy hair over her shoulders. Head tilted down toward her right shoulder, eyes looking up at the lens. Leaning forward, torso tilted right, right hand on her thigh, left arm on a metal railing. Left knee raised on a step, right leg cropped at the bottom. Short form-fitting glossy neon-pink latex high-neck halter dress with open sides and ruched seams, mid-thigh hem. Opaque black tights, thin hoop earrings. Only the top of a neon-pink heel visible. Dilapidated industrial stairwell, hazy dusty air. Textured peeling wall with small 'Keor' graffiti near a red dragon drawing on the left, metal staircase on the right. Tall vertical low-angle composition. Warm directional light from upper left with specular highlights on the latex, deep shadows on the stairs. 50mm f/2.8, dramatic high-contrast grade." } ```

KeorUnreal

32,670 次观看 • 1 个月前

$🚨 I TURNED OPUS 5.5 INTO A 24/7 TRADING AGENT Opus 5.5 surprised me. In my trading setup, it is outperforming what I was getting from Fable 5.1 - at a fraction of the cost. I wanted an AI system that could research a strategy, build it, challenge it, test it, deploy it and keep watching it after I go to sleep. Here is the architecture: 1. REASONING ENGINE Opus 5.5 sits at the center of the stack. Long-horizon reasoning, large context, code execution, computer use. The ability to work through complex quantitative problems without collapsing into one giant prompt. 2. 10-AGENT RESEARCH TEAM AgentKit turns Opus 5.5 into a 10-agent specialist team running through Claude Code: Conductor Backend Architect Test Engineer Code Reviewer Data Engineer Debugger Frontend Infra / DevOps Performance & Observability Security Same model. Ten different disciplines. 3. GATED WORKFLOW The agent cannot simply decide to build something and ship it. Every project moves through: Brainstorm → Architecture → Plan → Build → Review → Ship Operator approval sits between the stages. Nothing gets built before the spec is approved. 4. QUANTITATIVE CORE The system is not built around generic indicators. The production stack includes: - Ornstein-Uhlenbeck for statistical arbitrage - Avellaneda-Stoikov for market making - Hawkes processes for order-flow dynamics - Heston for volatility modeling Different market problems. Different mathematical frameworks. 5. PRODUCTION ARCHITECTURE Every strategy wraps into six independent layers: Data → Signal → Decision → Risk → Execution → Monitoring That separation matters. A broken feed should not become a fake signal. A valid signal should not automatically become an order. A disconnected WebSocket should not quietly kill the strategy at 2 AM. 6. RISK ENGINE Kelly-based sizing. 5% drawdown kill switch. Delta-neutral hedging where required. Execution constraints. Intent blotter before orders. The numbers are not theoretical. An earlier chatbot trading script managed to blow through 12% in one session. That changed the architecture completely. 7. VALIDATION BEFORE CAPITAL A backtest looking good is not enough. The strategy has to clear the gates: - Sharpe > 1.5 - Drawdown 55% - t-stat > 2.0 - 5 years of walk-forward validation Fail one? Back to research. No capital. 8. OBSERVABILITY The agent does not just produce trades. It produces a machine-readable decision trail: - Instrument - Strategy - Market regime - Signal - Confidence - Sharpe - Drawdown - Action window - Kelly-sized position Every decision can be traced back through the stack. 9. FINAL INTERFACE Telegram becomes the control surface. My phone gets the final output instead of thousands of logs: - What is trading - Why it is trading - How large the position is - What invalidates the thesis - When the opportunity expires Opus 5.5 is outperforming Fable 5.1 in my setup - at about 40% of the cost. That changes what is economically possible when you are running agents 24/7. Turns out, the hardest part of AI trading wasn't the AI. It was everything around it: Data, validation, risk, execution, monitoring. Build those layers properly, and one model can operate like an entire quant desk. This is only version one. The next build gets posted here first. Follow and turn on notifications.$

🚨 I TURNED OPUS 5.5 INTO A 24/7 TRADING AGENT Opus 5.5 surprised me. In my trading setup, it is outperforming what I was getting from Fable 5.1 - at a fraction of the cost. I wanted an AI system that could research a strategy, build it, challenge it, test it, deploy it and keep watching it after I go to sleep. Here is the architecture: 1. REASONING ENGINE Opus 5.5 sits at the center of the stack. Long-horizon reasoning, large context, code execution, computer use. The ability to work through complex quantitative problems without collapsing into one giant prompt. 2. 10-AGENT RESEARCH TEAM AgentKit turns Opus 5.5 into a 10-agent specialist team running through Claude Code: Conductor Backend Architect Test Engineer Code Reviewer Data Engineer Debugger Frontend Infra / DevOps Performance & Observability Security Same model. Ten different disciplines. 3. GATED WORKFLOW The agent cannot simply decide to build something and ship it. Every project moves through: Brainstorm → Architecture → Plan → Build → Review → Ship Operator approval sits between the stages. Nothing gets built before the spec is approved. 4. QUANTITATIVE CORE The system is not built around generic indicators. The production stack includes: - Ornstein-Uhlenbeck for statistical arbitrage - Avellaneda-Stoikov for market making - Hawkes processes for order-flow dynamics - Heston for volatility modeling Different market problems. Different mathematical frameworks. 5. PRODUCTION ARCHITECTURE Every strategy wraps into six independent layers: Data → Signal → Decision → Risk → Execution → Monitoring That separation matters. A broken feed should not become a fake signal. A valid signal should not automatically become an order. A disconnected WebSocket should not quietly kill the strategy at 2 AM. 6. RISK ENGINE Kelly-based sizing. 5% drawdown kill switch. Delta-neutral hedging where required. Execution constraints. Intent blotter before orders. The numbers are not theoretical. An earlier chatbot trading script managed to blow through 12% in one session. That changed the architecture completely. 7. VALIDATION BEFORE CAPITAL A backtest looking good is not enough. The strategy has to clear the gates: - Sharpe > 1.5 - Drawdown 55% - t-stat > 2.0 - 5 years of walk-forward validation Fail one? Back to research. No capital. 8. OBSERVABILITY The agent does not just produce trades. It produces a machine-readable decision trail: - Instrument - Strategy - Market regime - Signal - Confidence - Sharpe - Drawdown - Action window - Kelly-sized position Every decision can be traced back through the stack. 9. FINAL INTERFACE Telegram becomes the control surface. My phone gets the final output instead of thousands of logs: - What is trading - Why it is trading - How large the position is - What invalidates the thesis - When the opportunity expires Opus 5.5 is outperforming Fable 5.1 in my setup - at about 40% of the cost. That changes what is economically possible when you are running agents 24/7. Turns out, the hardest part of AI trading wasn't the AI. It was everything around it: Data, validation, risk, execution, monitoring. Build those layers properly, and one model can operate like an entire quant desk. This is only version one. The next build gets posted here first. Follow and turn on notifications.

Nonzee

11,616 次观看 • 14 天前

Created with GPT image 2 + Kling 3.0 on OpenArt Prompt: Ultra-realistic football broadcast screenshot — slightly soft and naturally compressed quality, as if captured directly from a live TV feed during an Argentina national football team match. The uploaded person is sitting naturally among the crowd, casually watching the game and not looking directly at the camera. Fans are seated closely around her, creating an authentic stadium atmosphere. A realistic live sports broadcast overlay is visible, with Argentina football broadcast-style scoreboard and graphics in the top corners. This must feel like a genuine frame grab from a live football broadcast itself — NOT a photo of a screen, NOT a model shoot, NOT influencer photography, NOT beauty photography. Natural candid posture, subtle facial asymmetry, realistic skin texture, flyaway hair, slight motion blur, visible compression artifacts, interlaced live TV quality, soft zoom-lens focus, uneven stadium lighting, and authentic crowd energy. The person is wearing a casual Argentina national team jersey in sky blue and white, sitting comfortably while reacting to the match, with scarves, drinks, or cheering items nearby. South American football broadcast aesthetic, accidental crowd-cam moment, realistic sports production frame. NO enlarged eyes. Aspect Ratio 4:3. Negative prompt: garbled text, unreadable letters, distorted typography, messy subtitles, broken words, misspelled words. Video Prompt: Use the attached photo as the exact facial reference. Create an ultra realistic 16:9 Argentina football broadcast crowd shot during a summer evening match at Camp Nou. Preserve the original face and hairstyle naturally without over-editing. Wide shot from an eye-level spectator perspective using a super telephoto broadcast zoom lens and then zoom in on her face . The person is sitting comfortably, naturally watching the match.

Created with GPT image 2 + Kling 3.0 on OpenArt Prompt: Ultra-realistic football broadcast screenshot — slightly soft and naturally compressed quality, as if captured directly from a live TV feed during an Argentina national football team match. The uploaded person is sitting naturally among the crowd, casually watching the game and not looking directly at the camera. Fans are seated closely around her, creating an authentic stadium atmosphere. A realistic live sports broadcast overlay is visible, with Argentina football broadcast-style scoreboard and graphics in the top corners. This must feel like a genuine frame grab from a live football broadcast itself — NOT a photo of a screen, NOT a model shoot, NOT influencer photography, NOT beauty photography. Natural candid posture, subtle facial asymmetry, realistic skin texture, flyaway hair, slight motion blur, visible compression artifacts, interlaced live TV quality, soft zoom-lens focus, uneven stadium lighting, and authentic crowd energy. The person is wearing a casual Argentina national team jersey in sky blue and white, sitting comfortably while reacting to the match, with scarves, drinks, or cheering items nearby. South American football broadcast aesthetic, accidental crowd-cam moment, realistic sports production frame. NO enlarged eyes. Aspect Ratio 4:3. Negative prompt: garbled text, unreadable letters, distorted typography, messy subtitles, broken words, misspelled words. Video Prompt: Use the attached photo as the exact facial reference. Create an ultra realistic 16:9 Argentina football broadcast crowd shot during a summer evening match at Camp Nou. Preserve the original face and hairstyle naturally without over-editing. Wide shot from an eye-level spectator perspective using a super telephoto broadcast zoom lens and then zoom in on her face . The person is sitting comfortably, naturally watching the match.

Zara

22,356 次观看 • 4 个月前

Prompt: Create a professional Hollywood advertising storyboard sheet for a premium moisturizing face cream commercial, inspired by the visual language of world-class luxury skincare and high-end beauty campaigns, while remaining completely original and brand-neutral. FORMAT & LAYOUT - One large white presentation board - 10 ultra-realistic cinematic storyboard frames arranged in a perfectly aligned 2 × 5 grid - Each frame has a thin, elegant black border - Consistent spacing, precise editorial layout, premium advertising-agency presentation aesthetic - Every frame clearly displays a bold scene number (01–10), a duration timestamp, and a short production caption underneath - Clean typography, subtle black/gray text, sophisticated art-direction notes - No logos, trademarks, brand names, or copyrighted campaign elements - The final image should look like an authentic Hollywood commercial director's storyboard / agency treatment board VISUAL STYLE Ultra-realistic cinematic photography, luxury beauty advertising, photorealistic skin texture, natural pores and fine details, sophisticated beauty lighting, soft diffused highlights, controlled shadows, subtle volumetric light, premium glossy product photography, shallow depth of field, anamorphic cinematic composition, elegant neutral color palette, creamy whites, warm skin tones, subtle champagne highlights, high dynamic range, realistic reflections, polished color grading, 8K-level detail, sophisticated production design. STORYBOARD SEQUENCE — 10 FRAMES 01 — 0:00–0:03 | THE FIRST DROP Extreme macro shot of a crystal-clear water droplet falling onto a pristine cream-colored surface, creating delicate concentric ripples. Luxurious soft lighting, dramatic slow-motion feeling. 02 — 0:03–0:06 | PURE HYDRATION Macro beauty shot of a rich moisturizing cream being elegantly lifted from a premium minimalist glass jar. Silky texture, glossy highlights, luxurious studio lighting. 03 — 0:06–0:09 | TOUCH Close-up of a beautiful adult female model gently applying the cream to her cheek with her fingertips. Natural expression, immaculate but realistic skin, intimate cinematic framing. 04 — 0:09–0:12 | ABSORPTION Ultra-close beauty shot showing the cream melting seamlessly into the skin. Emphasize realistic skin texture, moisture, luminous highlights, and a fresh hydrated appearance. 05 — 0:12–0:15 | HYDRATION GLOW Cinematic portrait of the model beside a softly illuminated window. Her skin appears naturally radiant, supple, fresh, and deeply moisturized. Elegant morning atmosphere. 06 — 0:15–0:18 | THE FORMULA Premium product-science visual: suspended water droplets, translucent botanical-inspired elements, creamy textures, and elegant fluid motion surrounding the face cream jar. Sophisticated, believable cosmetic advertising aesthetic. 07 — 0:18–0:21 | ALL-DAY COMFORT The model outdoors in soft golden-hour light, looking confident and relaxed. Close cinematic framing emphasizes comfortable, hydrated, naturally glowing skin. 08 — 0:21–0:24 | LUMINOUS DETAIL Extreme macro shot of hydrated facial skin with beautifully controlled highlights and realistic pores. Skin looks healthy, smooth, supple, and luminous rather than artificially airbrushed. 09 — 0:24–0:27 | HERO PRODUCT Hero shot of the premium face cream jar centered on a sculptural stone surface, surrounded by subtle water reflections and soft atmospheric mist. Luxury beauty commercial lighting, dramatic but minimal. 10 — 0:27–0:30 | THE FINAL REVEAL Final cinematic composition combining the hero product and the model in the background, softly out of focus. Elegant glowing light, premium minimalist environment, strong visual hierarchy, sophisticated end-card composition with clean negative space for a tagline.

Huma Ai

13,476 次观看 • 1 个月前

Created with Google Flow Character Sheet Prompt : Create a professional, ultra-photorealistic CHARACTER CONSISTENCY SHEET using the uploaded reference image as the ONLY source for the character's identity. IMPORTANT — IDENTITY LOCK: The uploaded reference image is the definitive identity reference. Reproduce the EXACT SAME PERSON throughout the entire character sheet. Do NOT redesign, beautify, reinterpret, or replace the character. Preserve with maximum accuracy: - exact facial identity - facial structure and proportions - eyes, eye shape and iris color - eyebrows and natural brow shape - nose shape and proportions - lips, mouth shape and fullness - cheeks, jawline and chin - skin tone and natural skin texture - age appearance - hairline - exact hair color, length, texture and hairstyle - body proportions and physique - distinctive facial characteristics CHARACTER SHEET LAYOUT: Create one clean, professional reference-board image containing: 1. LARGE FRONT PORTRAIT / FACE CLOSE-UP 2. FRONT VIEW 3. 3/4 LEFT VIEW 4. 3/4 RIGHT VIEW 5. LEFT PROFILE 6. RIGHT PROFILE 7. BACK VIEW 8. FULL-BODY FRONT VIEW 9. FULL-BODY 3/4 VIEW 10. FULL-BODY SIDE VIEW 11. FULL-BODY BACK VIEW 12. NEUTRAL EXPRESSION 13. NATURAL SMILE 14. SOFT SMILE 15. LOOKING LEFT 16. LOOKING RIGHT 17. EYE CLOSE-UP 18. EYEBROW CLOSE-UP 19. NOSE CLOSE-UP 20. LIPS CLOSE-UP 21. HAIR CLOSE-UP 22. ACCESSORIES CLOSE-UP 23. OUTFIT / CLOTHING REFERENCE 24. COLOR PALETTE / SKIN-TONE REFERENCE CONSISTENCY: Every panel must show the SAME person. Keep facial identity, proportions, hairstyle, skin tone and physical characteristics consistent across every angle, pose and expression. For views not clearly visible in the reference, intelligently reconstruct the character while staying strictly faithful to the visible reference. Do not invent major distinguishing features. PRESENTATION: Premium professional film-production character bible / modeling reference sheet. Clean editorial layout. Neutral studio background. Soft realistic studio lighting. Sharp facial details. Natural skin pores and texture. Individual hair strands. Realistic fabric and accessories. Accurate human anatomy. Consistent camera perspective. Clean thin borders between panels. Professional labels underneath each view. Minimal elegant typography. High-end fashion/model reference aesthetic. PHOTOREALISM: Ultra-photorealistic. Real human appearance. Natural skin texture. Realistic eyes. Physically accurate hair. Natural shadows. Realistic proportions. No CGI appearance. No plastic skin. No doll-like face. No artificial beauty-face replacement. NEGATIVE PROMPT: different person, different face, face morphing, identity drift, altered facial structure, different eyes, different nose, different lips, different jawline, different skin tone, different hairstyle, inconsistent body proportions, duplicate person, distorted anatomy, asymmetrical face, plastic skin, waxy skin, CGI, 3D render, cartoon, anime, illustration, over-beautification, excessive makeup, unrealistic eyes, blurry face, low detail, extra fingers, missing fingers, malformed hands, bad anatomy. OUTPUT: One complete, highly detailed professional CHARACTER CONSISTENCY SHEET in a single image, suitable as a master reference for generating consistent images and videos of this character.

M. Asif

85,616 次观看 • 27 天前