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Have you ever watched this meme? Now it’s transformed into a full meme video using Seedance 2.0 on Renoise Sell your favorite product with a meme Oh yes… Domino’s wins every time PROMPT: 0:00–0:03: Close-up of a bland salad on a table → meme appears (shouting woman vs confused...

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Shami

17,214 subscribers

44,919 Aufrufe • vor 2 Monaten •via X (Twitter)

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Seedance 2 is so good at character and scene consistency across cuts. I tested it with a short film on something I learned last week about cats. The key is detailed prompts - take your story to an LLM to nail the structure, then to Krea to make the video. Take my prompt 👇 Style: high-quality 3D animation, expressive character acting, cozy lighting, cinematic composition Timestamped structure: 0:00 - 0:02 Open on a cozy desk setup in a home office. A black cat, Finn, is near the desk, alert and curious. The woman with brown hair and blue eyes sits at her computer, pulling up a YouTube-style bird video. The screen shows colorful birds hopping on branches. Finn immediately locks in on the movement. 0:03 - 0:05 The woman smiles knowingly and adjusts the monitor so Finn can see better. She lightly gestures toward the screen. Finn jumps onto the chair or desk edge and sits facing the monitor, ears forward, body still, completely mesmerized by the birds. 0:05 - 0:07 The woman gets up from the desk and walks away casually, leaving Finn watching. Finn remains seated, staring at the bird video with total concentration. Maybe a subtle tail flick or tiny head movement tracks the birds on screen. 0:07 - 0:10 Cut to later in the day, in the exact same office. The woman is now back at the desk in a Zoom meeting, still with the single monitor on the same desk. The computer screen now shows a video call grid or work presentation instead of the birds. She is mid-conversation, professional and focused, seated upright, saying "and now we'll move to the next slide" 0:10 - 0:12 Finn suddenly jumps up onto the desk from below, entering frame with urgency. He looks at the screen, confused and dissatisfied that the birds are gone. He starts pawing at the screen and the woman is shocked and embarrassed
0:11

Seedance 2 is so good at character and scene consistency across cuts. I tested it with a short film on something I learned last week about cats. The key is detailed prompts - take your story to an LLM to nail the structure, then to Krea to make the video. Take my prompt 👇 Style: high-quality 3D animation, expressive character acting, cozy lighting, cinematic composition Timestamped structure: 0:00 - 0:02 Open on a cozy desk setup in a home office. A black cat, Finn, is near the desk, alert and curious. The woman with brown hair and blue eyes sits at her computer, pulling up a YouTube-style bird video. The screen shows colorful birds hopping on branches. Finn immediately locks in on the movement. 0:03 - 0:05 The woman smiles knowingly and adjusts the monitor so Finn can see better. She lightly gestures toward the screen. Finn jumps onto the chair or desk edge and sits facing the monitor, ears forward, body still, completely mesmerized by the birds. 0:05 - 0:07 The woman gets up from the desk and walks away casually, leaving Finn watching. Finn remains seated, staring at the bird video with total concentration. Maybe a subtle tail flick or tiny head movement tracks the birds on screen. 0:07 - 0:10 Cut to later in the day, in the exact same office. The woman is now back at the desk in a Zoom meeting, still with the single monitor on the same desk. The computer screen now shows a video call grid or work presentation instead of the birds. She is mid-conversation, professional and focused, seated upright, saying "and now we'll move to the next slide" 0:10 - 0:12 Finn suddenly jumps up onto the desk from below, entering frame with urgency. He looks at the screen, confused and dissatisfied that the birds are gone. He starts pawing at the screen and the woman is shocked and embarrassed

Justine Moore

27,336 Aufrufe • vor 3 Monaten

Wait? Is this Seedance 2 Mini? The mystery Seedance 2 model IS HERE! No wait this time. About 55% of the cost of the 2.0 Fast is about 75% Quality wise they say to expect Fast level output and that seems fair? Thoughts? Left is Seedance 2 Mini Right is Seedance 2.0 Full Prompt Image To Video Use the uploaded images as identity, costume, and lighting reference. Same naturally confident charismatic age 25 woman throughout performing a sold-out arena concert. Real concert documentary feel, handheld camera energy, practical stage lighting only. gritty, real, fun, clean editing, english language high energy modern concert performance 0: 00–0:01 — Open tight on her face mid-performance, eyes bright, genuine laugh breaking through a lyric, hair catching stage light. Crowd noise swells under faint mic hum. 0: 01–0:06 — She steps forward to the edge of the stage, singing into the mic with playful energy, she frowns, sad "Wait! Is this Seedance 2 Mini?" and then smiles and laughs free hand reaching out toward the crowd, fingers brushing toward raised phones and hands. The crowd repeats the lyric back to her as she laughs. Warm stage lights and lens flares in the background, slight handheld camera sway following her movement. 0: 06–0:09 — Wide shot from a low angle near the crowd barrier — she's framed against haze and colored stage lights, band visible in soft focus behind her (guitarist stage left, keyboard player stage right). She throws her free arm out wide, laughing between lines, hair whipping slightly as she turns. 0: 10–0:12 — Cut to a closer three-quarter shot — she playfully points the mic toward the crowd for a sing-along moment, grinning, eyes scanning the audience with real warmth and connection. Visible sweat sheen, natural skin texture, slight camera shake as if shot from the pit. — Final beat: close-up, she looks directly into the lens with a bright, genuine smile Camera: handheld documentary-style movement throughout, natural focus pulls, no perfectly smooth gimbal shots, slight shake and imperfection consistent with a real concert film crew. Practical stage lighting only — no CGI glow, no artificial bloom, no glossy skin, visible natural texture and sweat under hot lights. Audio: live arena ambience — crowd roar, cheering, phone-camera flash pops, her voice carrying clearly over a full live band mix (electric guitar, keys, drums, bass). No studio polish — natural live-mix dynamics, slight room reverb. No on-screen text, no logos, no subtitles, no watermarks, no identity drift, no extra performers beyond the band already in frame, no slideshow stillness, no overly smooth or glossy rendering, no jarring cuts, no akward moments, no jerky moves, no duplicates, no clones
0:12

Wait? Is this Seedance 2 Mini? The mystery Seedance 2 model IS HERE! No wait this time. About 55% of the cost of the 2.0 Fast is about 75% Quality wise they say to expect Fast level output and that seems fair? Thoughts? Left is Seedance 2 Mini Right is Seedance 2.0 Full Prompt Image To Video Use the uploaded images as identity, costume, and lighting reference. Same naturally confident charismatic age 25 woman throughout performing a sold-out arena concert. Real concert documentary feel, handheld camera energy, practical stage lighting only. gritty, real, fun, clean editing, english language high energy modern concert performance 0: 00–0:01 — Open tight on her face mid-performance, eyes bright, genuine laugh breaking through a lyric, hair catching stage light. Crowd noise swells under faint mic hum. 0: 01–0:06 — She steps forward to the edge of the stage, singing into the mic with playful energy, she frowns, sad "Wait! Is this Seedance 2 Mini?" and then smiles and laughs free hand reaching out toward the crowd, fingers brushing toward raised phones and hands. The crowd repeats the lyric back to her as she laughs. Warm stage lights and lens flares in the background, slight handheld camera sway following her movement. 0: 06–0:09 — Wide shot from a low angle near the crowd barrier — she's framed against haze and colored stage lights, band visible in soft focus behind her (guitarist stage left, keyboard player stage right). She throws her free arm out wide, laughing between lines, hair whipping slightly as she turns. 0: 10–0:12 — Cut to a closer three-quarter shot — she playfully points the mic toward the crowd for a sing-along moment, grinning, eyes scanning the audience with real warmth and connection. Visible sweat sheen, natural skin texture, slight camera shake as if shot from the pit. — Final beat: close-up, she looks directly into the lens with a bright, genuine smile Camera: handheld documentary-style movement throughout, natural focus pulls, no perfectly smooth gimbal shots, slight shake and imperfection consistent with a real concert film crew. Practical stage lighting only — no CGI glow, no artificial bloom, no glossy skin, visible natural texture and sweat under hot lights. Audio: live arena ambience — crowd roar, cheering, phone-camera flash pops, her voice carrying clearly over a full live band mix (electric guitar, keys, drums, bass). No studio polish — natural live-mix dynamics, slight room reverb. No on-screen text, no logos, no subtitles, no watermarks, no identity drift, no extra performers beyond the band already in frame, no slideshow stillness, no overly smooth or glossy rendering, no jarring cuts, no akward moments, no jerky moves, no duplicates, no clones

Brent Lynch

12,501 Aufrufe • vor 1 Monat

Here we go GPT Image 2 and Seedance 2.0 is now live on insMind #insmind #insmindai Generated this GRWM video using the Prompt : Aesthetic “Get Ready With Me – Gym Edition” storyboard layout, minimal neutral-toned design, soft beige and cream color palette, clean editorial grid. Top header text: “GET READY WITH ME” subtitle: “gym edition” in elegant script font subheading: “step-by-step activewear routine” The layout is divided into 4 blocks, each showing a sequence (1–8 steps per row), featuring the same young woman throughout with consistent face, natural makeup, athletic toned body, hair tied in a messy bun or sleek ponytail. BLOCK 1 – BASE (1–8) 1–2: putting on a fitted sports bra 3–4: wearing high-waisted gym leggings 5–6: adjusting waistband / smoothing fit 7–8: mirror check, relaxed confident pose BLOCK 2 – LAYERS (9–16) 1–2: putting on oversized gym t-shirt or cropped top 3–4: adding lightweight zip-up hoodie or jacket 5–6: tying hair tighter / adjusting outfit 7–8: slight movement pose (stretching arms or twisting body) BLOCK 3 – DETAILS (17–24) 1–2: wearing smartwatch / fitness band 3–4: adding minimal jewelry (thin chain, studs) 5–6: putting on gym gloves or lifting straps 7–8: wearing sunglasses or tying hair final look BLOCK 4 – FINISH (25–32) 1–2: putting on training shoes (clean white sneakers) 3–4: grabbing gym bag / water bottle 5–6: holding headphones / protein shaker 7–8: full-body mirror shot, confident final look Side icons representing categories: base, layers, accessories, shoes, final look. Soft natural lighting, indoor minimal room or modern apartment, neutral background, clean shadows, editorial fashion photography style, consistent framing across all panels. Footer text: “You’re ready. Go own your workout.” Video prompt : Use provided storyboard image as reference CONCEPT: Get Ready With Me — Gym Edition TIMELINE: 0 : 00–0:04 Sports bra on High-waisted leggings wear Waistband adjustment Mirror check 0 : 04–0:08 Oversized tee / cropped top Lightweight hoodie or jacket Hair tie (ponytail/bun) Light stretch / body turn 0: 08–0:12 Smartwatch / fitness band Minimal jewelry Gym gloves / lifting straps Sunglasses on 0: 12–0:15 Training shoes Grab gym bag + water bottle Headphones / shaker Walk-out + final confident look STYLE: Minimal, neutral tones, soft beige/grey palette, natural indoor lighting, clean modern interior, athletic aesthetic CAMERA: Close-up + mid shots, soft focus, shallow depth of field, steady framing, subtle handheld realism TRANSITIONS: Match cuts, outfit snap transitions, fabric motion cuts, quick clean jump cuts synced to movement OUTPUT: Loopable, smooth pacing, satisfying flow, social media ready (vertical 9 : 16
0:14

Here we go GPT Image 2 and Seedance 2.0 is now live on insMind #insmind #insmindai Generated this GRWM video using the Prompt : Aesthetic “Get Ready With Me – Gym Edition” storyboard layout, minimal neutral-toned design, soft beige and cream color palette, clean editorial grid. Top header text: “GET READY WITH ME” subtitle: “gym edition” in elegant script font subheading: “step-by-step activewear routine” The layout is divided into 4 blocks, each showing a sequence (1–8 steps per row), featuring the same young woman throughout with consistent face, natural makeup, athletic toned body, hair tied in a messy bun or sleek ponytail. BLOCK 1 – BASE (1–8) 1–2: putting on a fitted sports bra 3–4: wearing high-waisted gym leggings 5–6: adjusting waistband / smoothing fit 7–8: mirror check, relaxed confident pose BLOCK 2 – LAYERS (9–16) 1–2: putting on oversized gym t-shirt or cropped top 3–4: adding lightweight zip-up hoodie or jacket 5–6: tying hair tighter / adjusting outfit 7–8: slight movement pose (stretching arms or twisting body) BLOCK 3 – DETAILS (17–24) 1–2: wearing smartwatch / fitness band 3–4: adding minimal jewelry (thin chain, studs) 5–6: putting on gym gloves or lifting straps 7–8: wearing sunglasses or tying hair final look BLOCK 4 – FINISH (25–32) 1–2: putting on training shoes (clean white sneakers) 3–4: grabbing gym bag / water bottle 5–6: holding headphones / protein shaker 7–8: full-body mirror shot, confident final look Side icons representing categories: base, layers, accessories, shoes, final look. Soft natural lighting, indoor minimal room or modern apartment, neutral background, clean shadows, editorial fashion photography style, consistent framing across all panels. Footer text: “You’re ready. Go own your workout.” Video prompt : Use provided storyboard image as reference CONCEPT: Get Ready With Me — Gym Edition TIMELINE: 0 : 00–0:04 Sports bra on High-waisted leggings wear Waistband adjustment Mirror check 0 : 04–0:08 Oversized tee / cropped top Lightweight hoodie or jacket Hair tie (ponytail/bun) Light stretch / body turn 0: 08–0:12 Smartwatch / fitness band Minimal jewelry Gym gloves / lifting straps Sunglasses on 0: 12–0:15 Training shoes Grab gym bag + water bottle Headphones / shaker Walk-out + final confident look STYLE: Minimal, neutral tones, soft beige/grey palette, natural indoor lighting, clean modern interior, athletic aesthetic CAMERA: Close-up + mid shots, soft focus, shallow depth of field, steady framing, subtle handheld realism TRANSITIONS: Match cuts, outfit snap transitions, fabric motion cuts, quick clean jump cuts synced to movement OUTPUT: Loopable, smooth pacing, satisfying flow, social media ready (vertical 9 : 16

Smiling Khan

31,594 Aufrufe • vor 2 Monaten

We have released Seedance 2.0. Due to the 2500-character limit, please translate the following prompts into Chinese before use. [Technical Specs] Generate a 10-second, 16:9, 720p cinematic video. Smooth continuous camera motion with no cuts. The overall pacing is fast and tightly compressed, with rapid escalation from start to finish. Audio evolves quickly from a high-performance engine idle into intricate mechanical shifting and clicks, culminating in a soft electronic chime and the distinct sound of a "mwah" blowing kiss. [Global Constraints] Only the evolving mechanical character appears; no other humans or characters. All transformations must follow physical logic and maintain structural continuity. No object should pass through or intersect with other solid objects. Every robotic component must originate from visible parts of the Porsche 911 (doors, hood, wheels, chassis) through unfolding, splitting, or reconfiguration. [Scene Setup — 0:00–0:01] A sleek, metallic silver Porsche 911 sits on a rain-slicked futuristic city street at night, neon lights reflecting off its polished surface. The camera starts at a low-angle front-quarter view and begins a fast, smooth tracking-arc towards the side. [Rapid Transformation Initiation — 0:01–0:03] Transformation triggers instantly. The car’s suspension drops, and the frame begins to fracture into a complex grid of panels. The doors swing open and begin to segment into articulated arm structures. The front hood splits down the center, folding inward to reveal a glowing internal core. The headlights flicker and start to reorient as the "eyes." [Accelerated Feminine Reconfiguration — 0:03–0:07] The mechanical action is dense, overlapping, and fluid, emphasizing graceful but powerful motion. Lower Body: The rear wheels and wheel arches split and rotate downward, reassembling into slender, high-heeled mechanical legs. Torso: The roof and rear engine cover slide and compress, forming a sleek, curvaceous hourglass torso that retains the car’s aerodynamic lines. Arms & Hands: The side mirrors and door panels unfold into delicate but strong hands and fingers. Head: The front bumper and emblem area segment and rise, folding into a feminine-shaped head with a sleek metallic "helmet" visor. [Logical Transformation Constraints — No Spontaneous Appearance] The robot’s "skin" is composed of the car's outer silver panels. The internal frame and wiring emerge from the engine and undercarriage. No parts appear out of thin air; every joint is a reconfigured automotive component. [Transformation Completion — 0:07–0:08.5] The robot stands tall and elegant. The silver panels lock into place with a satisfying "click," revealing glowing blue LED accents in the seams. The silhouette is clearly feminine, humanoid, and sophisticated, reflecting the premium design of the original vehicle. [Final Hero Ending — 0:08.5–0:10] As the robot stabilizes, the camera performs a rapid, smooth zoom-in (Dolly-In) directly to her face. The robot tilts its head slightly, and the optic sensors (eyes) brighten. It brings its mechanical hand to its metallic lips and performs a graceful blowing kiss (fly-kiss) gesture toward the camera. The video ends with a close-up of the face, capturing the reflection of neon lights in its visor just as the kiss is released. [Cinematography Notes] Continuous Motion: No cuts or fades; the camera must transition from the car-tracking shot to the face-zoom seamlessly. Material Consistency: The robot must maintain the exact metallic silver paint, texture, and reflections of the Porsche. Energy: The transformation should feel high-energy and "force-driven," while the final gesture is soft and charismatic.
0:10

We have released Seedance 2.0. Due to the 2500-character limit, please translate the following prompts into Chinese before use. [Technical Specs] Generate a 10-second, 16:9, 720p cinematic video. Smooth continuous camera motion with no cuts. The overall pacing is fast and tightly compressed, with rapid escalation from start to finish. Audio evolves quickly from a high-performance engine idle into intricate mechanical shifting and clicks, culminating in a soft electronic chime and the distinct sound of a "mwah" blowing kiss. [Global Constraints] Only the evolving mechanical character appears; no other humans or characters. All transformations must follow physical logic and maintain structural continuity. No object should pass through or intersect with other solid objects. Every robotic component must originate from visible parts of the Porsche 911 (doors, hood, wheels, chassis) through unfolding, splitting, or reconfiguration. [Scene Setup — 0:00–0:01] A sleek, metallic silver Porsche 911 sits on a rain-slicked futuristic city street at night, neon lights reflecting off its polished surface. The camera starts at a low-angle front-quarter view and begins a fast, smooth tracking-arc towards the side. [Rapid Transformation Initiation — 0:01–0:03] Transformation triggers instantly. The car’s suspension drops, and the frame begins to fracture into a complex grid of panels. The doors swing open and begin to segment into articulated arm structures. The front hood splits down the center, folding inward to reveal a glowing internal core. The headlights flicker and start to reorient as the "eyes." [Accelerated Feminine Reconfiguration — 0:03–0:07] The mechanical action is dense, overlapping, and fluid, emphasizing graceful but powerful motion. Lower Body: The rear wheels and wheel arches split and rotate downward, reassembling into slender, high-heeled mechanical legs. Torso: The roof and rear engine cover slide and compress, forming a sleek, curvaceous hourglass torso that retains the car’s aerodynamic lines. Arms & Hands: The side mirrors and door panels unfold into delicate but strong hands and fingers. Head: The front bumper and emblem area segment and rise, folding into a feminine-shaped head with a sleek metallic "helmet" visor. [Logical Transformation Constraints — No Spontaneous Appearance] The robot’s "skin" is composed of the car's outer silver panels. The internal frame and wiring emerge from the engine and undercarriage. No parts appear out of thin air; every joint is a reconfigured automotive component. [Transformation Completion — 0:07–0:08.5] The robot stands tall and elegant. The silver panels lock into place with a satisfying "click," revealing glowing blue LED accents in the seams. The silhouette is clearly feminine, humanoid, and sophisticated, reflecting the premium design of the original vehicle. [Final Hero Ending — 0:08.5–0:10] As the robot stabilizes, the camera performs a rapid, smooth zoom-in (Dolly-In) directly to her face. The robot tilts its head slightly, and the optic sensors (eyes) brighten. It brings its mechanical hand to its metallic lips and performs a graceful blowing kiss (fly-kiss) gesture toward the camera. The video ends with a close-up of the face, capturing the reflection of neon lights in its visor just as the kiss is released. [Cinematography Notes] Continuous Motion: No cuts or fades; the camera must transition from the car-tracking shot to the face-zoom seamlessly. Material Consistency: The robot must maintain the exact metallic silver paint, texture, and reflections of the Porsche. Energy: The transformation should feel high-energy and "force-driven," while the final gesture is soft and charismatic.

underwood

19,462 Aufrufe • vor 3 Monaten

📢 Today marks a major milestone as Cat is one step closer to decentralizing $MEOW. Cat has officially set the sell tax for the token to 0% and renounced control over the contract. This means the token tax can no longer be altered, and Cat no longer has the ability to blacklist/whitelist addresses or pause token transfers. However, this also means that any funds sent to the contract address cannot be recovered, so exercise caution and avoid sending funds to $MEOW's token address. With that being said, let Cat leave you with some reasons on why you should hyper gamble on $MEOW 👇 🔸 We have held our position as the leading meme coin on ZKsync, both pre- and post-airdrop. 🔸 $MEOW never faded; it simply traded within a wide range for months, forming a solid bottom on the chart, followed by a steady trend of higher lows over several months. 🔸 The Zeek Army stood strong through a 90% dip. Cat's battle-hardened community never wavered, continuing to vibe and hang out even during the toughest times. 🔸 Despite a low market cap, Cat maintained significant mindshare on ZKsync. 🔸 $MEOW's market cap is currently low enough to have realistic potential for a 10x gain. 🔸 Wealth transfer happened. After 10 months of token distribution, all jeets are out. The supply is now held mostly by long-term believers. Before the $ZK airdrop, 25% of $MEOW was staked, and that number increased to 40% post-airdrop—(3,3) vibes. 🔸 We are not a Cabal coin. While a Cabal can fake trading volume and exchange listings, they can’t fake time. Cat has always been transparent and grinding alongside everyone in the trenches since day one. Now, imagine where $MEOW's price would be if ZKsync catches a narrative and hits the $2B market cap mark. There is no second best.
0:10

📢 Today marks a major milestone as Cat is one step closer to decentralizing $MEOW. Cat has officially set the sell tax for the token to 0% and renounced control over the contract. This means the token tax can no longer be altered, and Cat no longer has the ability to blacklist/whitelist addresses or pause token transfers. However, this also means that any funds sent to the contract address cannot be recovered, so exercise caution and avoid sending funds to $MEOW's token address. With that being said, let Cat leave you with some reasons on why you should hyper gamble on $MEOW 👇 🔸 We have held our position as the leading meme coin on ZKsync, both pre- and post-airdrop. 🔸 $MEOW never faded; it simply traded within a wide range for months, forming a solid bottom on the chart, followed by a steady trend of higher lows over several months. 🔸 The Zeek Army stood strong through a 90% dip. Cat's battle-hardened community never wavered, continuing to vibe and hang out even during the toughest times. 🔸 Despite a low market cap, Cat maintained significant mindshare on ZKsync. 🔸 $MEOW's market cap is currently low enough to have realistic potential for a 10x gain. 🔸 Wealth transfer happened. After 10 months of token distribution, all jeets are out. The supply is now held mostly by long-term believers. Before the $ZK airdrop, 25% of $MEOW was staked, and that number increased to 40% post-airdrop—(3,3) vibes. 🔸 We are not a Cabal coin. While a Cabal can fake trading volume and exchange listings, they can’t fake time. Cat has always been transparent and grinding alongside everyone in the trenches since day one. Now, imagine where $MEOW's price would be if ZKsync catches a narrative and hits the $2B market cap mark. There is no second best.

Zeek

42,353 Aufrufe • vor 1 Jahr

🚨🇸🇪 MUSLIM WOMAN IN SWEDEN DROPS THE MASK AND SAYS THE QUIET PART OUT LOUD: "I EXPLOIT, LOOT, AND DESTROY AS MUCH AS POSSIBLE!" This woman in hijab just went full savage on camera, basically admitting she’s here to loot, not to live. No guilt, no shame, just pure “thanks for the free money, losers” energy. “I don’t belong here. Have I ever told you I belong here? That’s exactly why I destroy everything, disturb as much as I can, and milk every single dime of your tax money. Every benefit you have in Sweden, I exploit it to the absolute maximum. Maximum. And you can’t do a damn thing about it.” This is the end-stage of unlimited welfare + open borders + 0 assimilation demands. You import people who openly say they don’t belong, then pay them to hate you and wreck the place. Sweden isn’t a country anymore, it’s an ATM with a flag. And the worst part? She’s 100% right. They literally can’t stop her. Wake up, Europe. This is what surrender looks like. Source: Breaking911
0:17

🚨🇸🇪 MUSLIM WOMAN IN SWEDEN DROPS THE MASK AND SAYS THE QUIET PART OUT LOUD: "I EXPLOIT, LOOT, AND DESTROY AS MUCH AS POSSIBLE!" This woman in hijab just went full savage on camera, basically admitting she’s here to loot, not to live. No guilt, no shame, just pure “thanks for the free money, losers” energy. “I don’t belong here. Have I ever told you I belong here? That’s exactly why I destroy everything, disturb as much as I can, and milk every single dime of your tax money. Every benefit you have in Sweden, I exploit it to the absolute maximum. Maximum. And you can’t do a damn thing about it.” This is the end-stage of unlimited welfare + open borders + 0 assimilation demands. You import people who openly say they don’t belong, then pay them to hate you and wreck the place. Sweden isn’t a country anymore, it’s an ATM with a flag. And the worst part? She’s 100% right. They literally can’t stop her. Wake up, Europe. This is what surrender looks like. Source: Breaking911

Mario Nawfal

381,707 Aufrufe • vor 7 Monaten

We've donated over 13,000 dollars to National Children's Advocacy Center going towards preventing sexual and physical abuse of children. What started as a simple meme evolved into something beautiful thanks to this comment: It's because of you we were able to donate life changing money to support children in a time where epstein's crimes are not being prosecuted. I got to speak with the woman on the phone and learn a small part of her story and understand where the funds are going. NCAC is the FIRST child support centre in the world, and the money is being used for both current trauma support, and preventative methods with 0 cost to the victims or their family. It's all funded by donations, like ours. Our token address: 7p8hs6fZwTXvSYESCdHgWCXVsVcxTrX1cs3Q4yNGpump
0:14

We've donated over 13,000 dollars to National Children's Advocacy Center going towards preventing sexual and physical abuse of children. What started as a simple meme evolved into something beautiful thanks to this comment: It's because of you we were able to donate life changing money to support children in a time where epstein's crimes are not being prosecuted. I got to speak with the woman on the phone and learn a small part of her story and understand where the funds are going. NCAC is the FIRST child support centre in the world, and the money is being used for both current trauma support, and preventative methods with 0 cost to the victims or their family. It's all funded by donations, like ours. Our token address: 7p8hs6fZwTXvSYESCdHgWCXVsVcxTrX1cs3Q4yNGpump

_______

29,708 Aufrufe • vor 5 Monaten

I haven’t missed a workout in over 10+ years 0 misses in over 3650 days is great discipline if you zoom out however, that doesn’t mean each workout is perfect, not rested, don’t have enough food, not enough time, etc but none of that matters if you showed up and tried your best finishing this, then flying with the team to vegas for the weekend! we’re focusing on the 1st workout of this 4 week period, attempting to push my limits here, going for 110% attempts usually I stay around 80-90% work for most then try to max out once in a while but we hit our max today!!!! ahhhh find what works for you and stay consistent to it id say it’s been a key factor to an understanding that life and business is a marathon it may not be a sprint but we can work on running on pace faster and without pain health is wealth
0:11

I haven’t missed a workout in over 10+ years 0 misses in over 3650 days is great discipline if you zoom out however, that doesn’t mean each workout is perfect, not rested, don’t have enough food, not enough time, etc but none of that matters if you showed up and tried your best finishing this, then flying with the team to vegas for the weekend! we’re focusing on the 1st workout of this 4 week period, attempting to push my limits here, going for 110% attempts usually I stay around 80-90% work for most then try to max out once in a while but we hit our max today!!!! ahhhh find what works for you and stay consistent to it id say it’s been a key factor to an understanding that life and business is a marathon it may not be a sprint but we can work on running on pace faster and without pain health is wealth

۟

12,993 Aufrufe • vor 8 Monaten

Can Adobe Firefly's "Edit" feature compete with Photoshop? Select a region, describe the fix, keep the rest of the frame intact. That's the pitch. Here’s what we observed across 4 sessions and 8 targeted edits with real working creatives: >1 edit landed cleanly 🥇 >5 landed partial 🥈 >2 missed entirely ❌ Firefly understood the request almost every time, it just couldn't hold the rest of the image still while executing it. Avoiding drift is hard. In the partials, the target moved but something else broke. >A product lost prominence / focus >A shadow stayed broken >A new artifact appeared on the wall while the format crept closer to the brief Social was the hardest category. 0 clean wins across 4 attempts. With social content, crop, pose, and product placement ARE the deliverable, so any drift fails the job. TLDR: Firefly can edit like Photoshop, but avoiding drifts is the real challenge. I personally use this feature a lot, how have you solved for this ?
0:16

Can Adobe Firefly's "Edit" feature compete with Photoshop? Select a region, describe the fix, keep the rest of the frame intact. That's the pitch. Here’s what we observed across 4 sessions and 8 targeted edits with real working creatives: >1 edit landed cleanly 🥇 >5 landed partial 🥈 >2 missed entirely ❌ Firefly understood the request almost every time, it just couldn't hold the rest of the image still while executing it. Avoiding drift is hard. In the partials, the target moved but something else broke. >A product lost prominence / focus >A shadow stayed broken >A new artifact appeared on the wall while the format crept closer to the brief Social was the hardest category. 0 clean wins across 4 attempts. With social content, crop, pose, and product placement ARE the deliverable, so any drift fails the job. TLDR: Firefly can edit like Photoshop, but avoiding drifts is the real challenge. I personally use this feature a lot, how have you solved for this ?

ben

13,452 Aufrufe • vor 1 Monat

Created on seedance 2.0 Prompt: Create a luxurious, hyper-realistic 9-second cinematic food video in vertical 9:16 format, warm golden lighting, shallow depth of field, and mouthwatering detail. Show the satisfying step-by-step process of making perfect cinnamon rolls. Scene-by-scene breakdown: 0-1s: Close-up of hands using a wooden rolling pin to roll out soft dough on a floured wooden countertop, flour gently flying in the air. 1-2s: A metal spatula smoothly spreads rich, glossy butter in elegant swirls across the flattened dough. 2-3s: Dramatic slow-motion shot of fragrant brown cinnamon sugar pouring and cascading from above, forming a beautiful mound on the buttered dough. 3-4s: Hands gently roll the dough into a tight spiral log, revealing the perfect cinnamon swirl inside. 4-5s: A thin thread slices cleanly through the rolled dough, cutting it into even cinnamon roll portions on the wooden board. 5-6s: Golden baked cinnamon rolls rising in a warm oven with soft steam rising around them. 6-7s: Thick, creamy white icing poured slowly from above, dripping luxuriously down the sides of a fresh, warm cinnamon roll on a plate. 7-8s: Extreme close-up of the glossy icing slowly dripping over the golden, flaky layers. 8-9s: A pair of hands pulls apart a warm cinnamon roll, revealing a perfect heart-shaped cinnamon swirl in the soft, fluffy interior, with steam gently rising. Final shot of a beautifully glazed cinnamon roll on a wooden plate with more rolls softly blurred in the background. Style: Professional food cinematography, ultra-detailed textures, cozy bakery atmosphere, appetizing colors, smooth camera movements, high-end 4K quality, satisfying and ASMR-style visual storytelling."
0:10

Created on seedance 2.0 Prompt: Create a luxurious, hyper-realistic 9-second cinematic food video in vertical 9:16 format, warm golden lighting, shallow depth of field, and mouthwatering detail. Show the satisfying step-by-step process of making perfect cinnamon rolls. Scene-by-scene breakdown: 0-1s: Close-up of hands using a wooden rolling pin to roll out soft dough on a floured wooden countertop, flour gently flying in the air. 1-2s: A metal spatula smoothly spreads rich, glossy butter in elegant swirls across the flattened dough. 2-3s: Dramatic slow-motion shot of fragrant brown cinnamon sugar pouring and cascading from above, forming a beautiful mound on the buttered dough. 3-4s: Hands gently roll the dough into a tight spiral log, revealing the perfect cinnamon swirl inside. 4-5s: A thin thread slices cleanly through the rolled dough, cutting it into even cinnamon roll portions on the wooden board. 5-6s: Golden baked cinnamon rolls rising in a warm oven with soft steam rising around them. 6-7s: Thick, creamy white icing poured slowly from above, dripping luxuriously down the sides of a fresh, warm cinnamon roll on a plate. 7-8s: Extreme close-up of the glossy icing slowly dripping over the golden, flaky layers. 8-9s: A pair of hands pulls apart a warm cinnamon roll, revealing a perfect heart-shaped cinnamon swirl in the soft, fluffy interior, with steam gently rising. Final shot of a beautifully glazed cinnamon roll on a wooden plate with more rolls softly blurred in the background. Style: Professional food cinematography, ultra-detailed textures, cozy bakery atmosphere, appetizing colors, smooth camera movements, high-end 4K quality, satisfying and ASMR-style visual storytelling."

ayzalnoor

12,142 Aufrufe • vor 10 Tagen

Kiss Cam! Image: GPT Image 2 Video: Seedance in Dreamina Prompt: Style: Hyper-realistic live NBA broadcast footage, authentic televised sports coverage, realistic arena lighting, telephoto broadcast lens compression, shallow depth of field, imperfect live-camera framing, natural crowd reactions, realistic skin textures, subtle compression artifacts, slight interlacing grain, live TV color grading. Duration: 14 seconds Aspect Ratio: 16:9 Camera: Fixed live broadcast camera only, no cinematic cuts, no camera movement. IMPORTANT: The scene must feel EXACTLY like a real NBA live TV Kiss Cam broadcast. The woman: — unbelievably attractive Korean woman in her 20s — long black hair — flawless skin — elegant glamorous makeup — luxurious aura — sophisticated red low-cut summer outfit — subtle jewelry — natural smile and realistic reactions The man: — overweight unattractive man — casual black jacket — glasses — awkward but wholesome energy IMPORTANT AUDIO RULE: ABSOLUTELY NO SOUND OR DIALOGUE FROM THE COUPLE. The couple never speaks. Only arena crowd cheering and distant stadium ambience. Broadcast details: — realistic NBA scoreboard overlay — sports network watermark — live broadcast graphics — crowd in background — realistic arena LEDs — slight motion blur — realistic televised image quality [00:00-00:03] Fixed Kiss Cam shot appears on the arena jumbotron. The woman notices the camera and immediately laughs shyly while casually waving her hand “no” toward the screen. The overweight man beside her looks shocked and confused, then smiles nervously. Crowd begins cheering loudly. No talking from either person. [00:03-00:06] The crowd cheers louder encouraging them. The woman smiles warmly while glancing at the man. He looks overwhelmed and frozen in disbelief. The broadcast camera slightly struggles to keep framing centered realistically like a real live operator. [00:06-00:09] The woman suddenly grabs the man’s cheeks gently and kisses him. The man becomes completely shocked and overjoyed, eyes wide, almost emotionally frozen. Arena crowd erupts cheering and laughing loudly. Realistic shallow depth of field isolates them from crowd. [00:09-00:12] The woman pulls away laughing silently and gives a playful thumbs up directly toward the Kiss Cam. The man still looks stunned and unbelievably happy. Crowd continues cheering loudly while broadcast overlays remain visible. End exactly like a real televised NBA Kiss Cam moment. [00:12-00:14] The man looks in awe and surprise to the camera , the woman smiles with shyness. Audio: ONLY loud NBA arena ambience, crowd cheering, distant announcer echo, stadium reactions, sneaker squeaks, arena music muffled in background. ABSOLUTELY NO dialogue, whispering, laughing audio, or vocal sounds from the couple. Negative prompts: No cinematic movie look, no dialogue, no talking, no subtitles, no unrealistic beauty filter, no anime style, no perfect framing, no dramatic camera movement, no unrealistic reactions, no extra people interacting directly with camera.
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Kiss Cam! Image: GPT Image 2 Video: Seedance in Dreamina Prompt: Style: Hyper-realistic live NBA broadcast footage, authentic televised sports coverage, realistic arena lighting, telephoto broadcast lens compression, shallow depth of field, imperfect live-camera framing, natural crowd reactions, realistic skin textures, subtle compression artifacts, slight interlacing grain, live TV color grading. Duration: 14 seconds Aspect Ratio: 16:9 Camera: Fixed live broadcast camera only, no cinematic cuts, no camera movement. IMPORTANT: The scene must feel EXACTLY like a real NBA live TV Kiss Cam broadcast. The woman: — unbelievably attractive Korean woman in her 20s — long black hair — flawless skin — elegant glamorous makeup — luxurious aura — sophisticated red low-cut summer outfit — subtle jewelry — natural smile and realistic reactions The man: — overweight unattractive man — casual black jacket — glasses — awkward but wholesome energy IMPORTANT AUDIO RULE: ABSOLUTELY NO SOUND OR DIALOGUE FROM THE COUPLE. The couple never speaks. Only arena crowd cheering and distant stadium ambience. Broadcast details: — realistic NBA scoreboard overlay — sports network watermark — live broadcast graphics — crowd in background — realistic arena LEDs — slight motion blur — realistic televised image quality [00:00-00:03] Fixed Kiss Cam shot appears on the arena jumbotron. The woman notices the camera and immediately laughs shyly while casually waving her hand “no” toward the screen. The overweight man beside her looks shocked and confused, then smiles nervously. Crowd begins cheering loudly. No talking from either person. [00:03-00:06] The crowd cheers louder encouraging them. The woman smiles warmly while glancing at the man. He looks overwhelmed and frozen in disbelief. The broadcast camera slightly struggles to keep framing centered realistically like a real live operator. [00:06-00:09] The woman suddenly grabs the man’s cheeks gently and kisses him. The man becomes completely shocked and overjoyed, eyes wide, almost emotionally frozen. Arena crowd erupts cheering and laughing loudly. Realistic shallow depth of field isolates them from crowd. [00:09-00:12] The woman pulls away laughing silently and gives a playful thumbs up directly toward the Kiss Cam. The man still looks stunned and unbelievably happy. Crowd continues cheering loudly while broadcast overlays remain visible. End exactly like a real televised NBA Kiss Cam moment. [00:12-00:14] The man looks in awe and surprise to the camera , the woman smiles with shyness. Audio: ONLY loud NBA arena ambience, crowd cheering, distant announcer echo, stadium reactions, sneaker squeaks, arena music muffled in background. ABSOLUTELY NO dialogue, whispering, laughing audio, or vocal sounds from the couple. Negative prompts: No cinematic movie look, no dialogue, no talking, no subtitles, no unrealistic beauty filter, no anime style, no perfect framing, no dramatic camera movement, no unrealistic reactions, no extra people interacting directly with camera.

Keskin

78,545 Aufrufe • vor 2 Monaten

Softmax vs Sigmoid ✍️ Interact 👉 = Softmax = Softmax is how deep networks turn raw scores into a probability distribution — the final layer of every classifier, and the core of every attention head in a transformer. To see what it does, picture five boba tea shops on the same block, all competing for your dollar. Five candidates: a, b, c, d, e — different chains, different brewing styles, different pearls. A boba reviewer hands you a 𝘤𝘩𝘦𝘸𝘪𝘯𝘦𝘴𝘴 𝘴𝘤𝘰𝘳𝘦 for each — higher means perfectly chewy "QQ" pearls with the right bite (ask a Taiwanese friend to find out what QQ means). Negative scores are real: mushy bobas, overcooked pearls, a batch left sitting too long. How do you turn five chewiness scores into an allocation that adds to a whole dollar? You could spend everything at the chewiest shop, but that ignores how good the runners-up are. Softmax is the smooth alternative. Read the diagram left to right. First, raise each score to e^{x} — this does two things: it turns negative chewiness into small positives, and it stretches the gaps between scores exponentially. Then sum all five into a single total Z. Finally, divide each e^{x} by Z to get a probability. The five probabilities add up to one, so you can read them as percentages of your dollar. The chewiest shop gets the biggest slice — but never the whole dollar. That's the point of softmax: it ranks confidently while still leaving room for the others. = Sigmoid = Sigmoid squashes any real number into a probability between 0 and 1 — the classic activation for binary classification, and still the gating function inside LSTMs and GRUs. Same boba block as the previous Softmax example, narrowed to just two contenders — a hot new shop `a` with chewiness score x, and your usual go-to `b` whose score is pinned at zero (the neutral baseline you've come to expect). Sigmoid is just softmax with two players, one of them pinned to zero. Read the diagram left to right. First, raise each score to e^{x} — for the usual shop `b` whose score is zero, this is just e^0 = 1 (the constant baseline). Then sum the two into a total Z. Finally, divide each e^{x} by Z to get a probability. The two probabilities add up to one — the new shop wins more of your dollar when its pearls get chewier, and your usual keeps the rest. That's the point of sigmoid: it turns a single chewiness score into a clean 0-to-1 chance you'll try the new place over your usual. --- AI Math, Algorithms, Architectures by hand ✍️ Subscribe to my 60K+ reader newsletter 👉
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Softmax vs Sigmoid ✍️ Interact 👉 = Softmax = Softmax is how deep networks turn raw scores into a probability distribution — the final layer of every classifier, and the core of every attention head in a transformer. To see what it does, picture five boba tea shops on the same block, all competing for your dollar. Five candidates: a, b, c, d, e — different chains, different brewing styles, different pearls. A boba reviewer hands you a 𝘤𝘩𝘦𝘸𝘪𝘯𝘦𝘴𝘴 𝘴𝘤𝘰𝘳𝘦 for each — higher means perfectly chewy "QQ" pearls with the right bite (ask a Taiwanese friend to find out what QQ means). Negative scores are real: mushy bobas, overcooked pearls, a batch left sitting too long. How do you turn five chewiness scores into an allocation that adds to a whole dollar? You could spend everything at the chewiest shop, but that ignores how good the runners-up are. Softmax is the smooth alternative. Read the diagram left to right. First, raise each score to e^{x} — this does two things: it turns negative chewiness into small positives, and it stretches the gaps between scores exponentially. Then sum all five into a single total Z. Finally, divide each e^{x} by Z to get a probability. The five probabilities add up to one, so you can read them as percentages of your dollar. The chewiest shop gets the biggest slice — but never the whole dollar. That's the point of softmax: it ranks confidently while still leaving room for the others. = Sigmoid = Sigmoid squashes any real number into a probability between 0 and 1 — the classic activation for binary classification, and still the gating function inside LSTMs and GRUs. Same boba block as the previous Softmax example, narrowed to just two contenders — a hot new shop `a` with chewiness score x, and your usual go-to `b` whose score is pinned at zero (the neutral baseline you've come to expect). Sigmoid is just softmax with two players, one of them pinned to zero. Read the diagram left to right. First, raise each score to e^{x} — for the usual shop `b` whose score is zero, this is just e^0 = 1 (the constant baseline). Then sum the two into a total Z. Finally, divide each e^{x} by Z to get a probability. The two probabilities add up to one — the new shop wins more of your dollar when its pearls get chewier, and your usual keeps the rest. That's the point of sigmoid: it turns a single chewiness score into a clean 0-to-1 chance you'll try the new place over your usual. --- AI Math, Algorithms, Architectures by hand ✍️ Subscribe to my 60K+ reader newsletter 👉

Tom Yeh

73,787 Aufrufe • vor 2 Monaten

Ferrari’s $640k Luce EV is straight-up embarrassing, looks like a fancy Prius that costs as much as a small fleet of actual rockets. That video hits hard: top panel, this red Ferrari “Luce” they’re hyping at $640,000 with 2.5s 0-100 and 530km range. Bottom: some Chinese MGIM (or whatever IM Motors variant) for $43k that does 2.74s and 655km. One Ferrari buys you 15 of the Chinese ones that beat it on paper. Brutal. Ferrari completely botched this. They took their first proper EV, stripped out all the V12 screaming soul that actually justifies the stupid prices, and built a heavy four-door liftback with safe, Jony Ive-ish styling that could pass for a loaded Chinese exec sedan from half a block away. No drama, no aggression, just “most comfortable Ferrari ever” marketing fluff. Who the hell pays supercar money for that? It’s like they forgot what made them special and chased generic luxury EV vibes instead. This is exactly why Ferrari’s losing the plot in the electric world. Acceleration and range are now commoditized, you don’t get to charge Ferrari tax for specs that $40k cars match or crush. Tesla kicked the door down first: showed the world EVs could smoke supercars on the drag strip while pushing constant software updates and real innovation at scale. Then the Chinese (BYD, MG, Zeekr, Xiaomi, IM Motors) went nuclear, battery dominance, insane production speed, yearly refreshes, and they stuff premium features into cars that cost a fraction. 0-100 under 3 seconds? Ferrari’s hedging with hybrids and low EV targets because they know the truth: their heritage premium is getting absolutely demolished by spreadsheet reality. Tesla made high-performance EVs cool and accessible. China turned it into a ruthless value machine. Maranello’s overpriced electric mediocrity just proves the game’s changed and the old guard is still playing catch-up with badges instead of brains.
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Ferrari’s $640k Luce EV is straight-up embarrassing, looks like a fancy Prius that costs as much as a small fleet of actual rockets. That video hits hard: top panel, this red Ferrari “Luce” they’re hyping at $640,000 with 2.5s 0-100 and 530km range. Bottom: some Chinese MGIM (or whatever IM Motors variant) for $43k that does 2.74s and 655km. One Ferrari buys you 15 of the Chinese ones that beat it on paper. Brutal. Ferrari completely botched this. They took their first proper EV, stripped out all the V12 screaming soul that actually justifies the stupid prices, and built a heavy four-door liftback with safe, Jony Ive-ish styling that could pass for a loaded Chinese exec sedan from half a block away. No drama, no aggression, just “most comfortable Ferrari ever” marketing fluff. Who the hell pays supercar money for that? It’s like they forgot what made them special and chased generic luxury EV vibes instead. This is exactly why Ferrari’s losing the plot in the electric world. Acceleration and range are now commoditized, you don’t get to charge Ferrari tax for specs that $40k cars match or crush. Tesla kicked the door down first: showed the world EVs could smoke supercars on the drag strip while pushing constant software updates and real innovation at scale. Then the Chinese (BYD, MG, Zeekr, Xiaomi, IM Motors) went nuclear, battery dominance, insane production speed, yearly refreshes, and they stuff premium features into cars that cost a fraction. 0-100 under 3 seconds? Ferrari’s hedging with hybrids and low EV targets because they know the truth: their heritage premium is getting absolutely demolished by spreadsheet reality. Tesla made high-performance EVs cool and accessible. China turned it into a ruthless value machine. Maranello’s overpriced electric mediocrity just proves the game’s changed and the old guard is still playing catch-up with badges instead of brains.

Alvin Foo

18,491 Aufrufe • vor 1 Monat

Made this cinematic AI video in minutes using Getvivix Prompt used below 👇 STORYBOARD 1 "THE KNIGHT" PROJECT TYPE: 10-second cinematic fantasy storyboard CHARACTER LOCK: single consistent knight — original fictional STYLIZED fantasy warrior (not a real person). Full ornate plate armor, VISOR DOWN the entire sequence (face never visible — safe by design), tattered surcoat + banner, mounted on an armored warhorse. Identical armor/horse across all frames. STYLE: epic dark-fantasy, cinematic, painterly film stills PACING & FLOW: slow, weighty, EPIC — no rush. One continuous charge → clash → melee → hero arc. Gradual camera moves; the action carries unbroken from frame to frame (each beat is the next instant of the last). Transitions are match-on-motion — the horse's stride and the sword's arc bridge every cut, never a hard jump. FRAMES (8 shots, 0–10s) — angle | lens | motion | lighting | environment | → into next: 1 (0–1.5s): wide establishing | 24mm | knight reined at a hill crest, banner snapping, slow push-in | cold dawn backlight, mist | battlefield below → camera drifts down as the horse shifts weight 2 (1.5–3s): 3/4-rear tracking | 35mm | horse breaks into a canter down the slope | low sun raking | churned mud, distant ranks → match-on-stride into the gallop 3 (3–4.5s): side tracking | 50mm | full gallop toward the enemy line, dust plume | side rim light, haze | spears + banners ahead → he lowers the lance, carrying the motion 4 (4.5–6s): low-angle hero | 35mm | lance leveled mid-gallop, visor catching light | backlit dust glow | closing on the line → impact begins 5 (6–7s): impact wide | 50mm | lance strikes, enemy hurled back, splinters | harsh flash + sparks | clash of the lines → horse rears from the hit 6 (7–8s): low 3/4 | 35mm | warhorse rears amid the melee, sword drawn | embers, torchlight | swirling battle → the blade sweeps down 7 (8–9s): tracking the blade | 50mm | sweeping arc through foes, motion-blur trail | sparks on steel | bodies + banners → camera settles, pulls back 8 (9–10s): hero hold | 24mm | horse reared, sword raised, banner behind, silhouette | dramatic backlight, battle haze | the field beyond → freeze LAYOUT: film sheet — left: 3 dynamic mounted poses (charging 3/4, rearing, mid-swing — in-scene, visor down); center: 8-frame grid; right: director notes; bottom: 0–10s. VISUAL STYLE: cinematic dark-fantasy, painterly, volumetric dawn light, dust + embers + mist, shallow DOF, motion blur, anamorphic; stylized — NOT photorealistic, not real human skin; FACE NEVER SHOWN (visor down). Seedance on Getvivix lets you generate high-end cinematic visuals for around 1000 credits (~$1), making pro-level video creation cheap and scalable. Try it here:
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Made this cinematic AI video in minutes using Getvivix Prompt used below 👇 STORYBOARD 1 "THE KNIGHT" PROJECT TYPE: 10-second cinematic fantasy storyboard CHARACTER LOCK: single consistent knight — original fictional STYLIZED fantasy warrior (not a real person). Full ornate plate armor, VISOR DOWN the entire sequence (face never visible — safe by design), tattered surcoat + banner, mounted on an armored warhorse. Identical armor/horse across all frames. STYLE: epic dark-fantasy, cinematic, painterly film stills PACING & FLOW: slow, weighty, EPIC — no rush. One continuous charge → clash → melee → hero arc. Gradual camera moves; the action carries unbroken from frame to frame (each beat is the next instant of the last). Transitions are match-on-motion — the horse's stride and the sword's arc bridge every cut, never a hard jump. FRAMES (8 shots, 0–10s) — angle | lens | motion | lighting | environment | → into next: 1 (0–1.5s): wide establishing | 24mm | knight reined at a hill crest, banner snapping, slow push-in | cold dawn backlight, mist | battlefield below → camera drifts down as the horse shifts weight 2 (1.5–3s): 3/4-rear tracking | 35mm | horse breaks into a canter down the slope | low sun raking | churned mud, distant ranks → match-on-stride into the gallop 3 (3–4.5s): side tracking | 50mm | full gallop toward the enemy line, dust plume | side rim light, haze | spears + banners ahead → he lowers the lance, carrying the motion 4 (4.5–6s): low-angle hero | 35mm | lance leveled mid-gallop, visor catching light | backlit dust glow | closing on the line → impact begins 5 (6–7s): impact wide | 50mm | lance strikes, enemy hurled back, splinters | harsh flash + sparks | clash of the lines → horse rears from the hit 6 (7–8s): low 3/4 | 35mm | warhorse rears amid the melee, sword drawn | embers, torchlight | swirling battle → the blade sweeps down 7 (8–9s): tracking the blade | 50mm | sweeping arc through foes, motion-blur trail | sparks on steel | bodies + banners → camera settles, pulls back 8 (9–10s): hero hold | 24mm | horse reared, sword raised, banner behind, silhouette | dramatic backlight, battle haze | the field beyond → freeze LAYOUT: film sheet — left: 3 dynamic mounted poses (charging 3/4, rearing, mid-swing — in-scene, visor down); center: 8-frame grid; right: director notes; bottom: 0–10s. VISUAL STYLE: cinematic dark-fantasy, painterly, volumetric dawn light, dust + embers + mist, shallow DOF, motion blur, anamorphic; stylized — NOT photorealistic, not real human skin; FACE NEVER SHOWN (visor down). Seedance on Getvivix lets you generate high-end cinematic visuals for around 1000 credits (~$1), making pro-level video creation cheap and scalable. Try it here:

Zoraiz Ai

10,748 Aufrufe • vor 1 Monat

Day 436 of documenting 0-1M Checkmate. For the last 436 days I publicly built this store online, X was mainly the daily notebook, but i also made several videos revealing nearly everything about this store without fully doxxing. We started with 1 product and finished with 37 as we crossed this finish line. It’s not the craziest numbers, but the i hope it shows people who are just starting out, the game of consistency. While documenting this publicly, it was the complete opposite of a straight line up. Many times we ran it up, thought we were in the clear, made mistakes, got reset back to virtually zero. In fact, we actually were set back to nearly zero twice and it’s all documented somewhere in the X posts. I could have easily faked revenue to say i got here earlier, I could have easily jumped on X only posting our best days, but that’s gay and what most people do. We did not miss a single post throughout building this store. Every single day we posted. The reason I used these photos/videos instead a regular ss is because this is the exact table & chair that I made my first $ online in. This broken chair, a broken table, and this laptop that would proceed to break in the process (have a new one now) but it felt like a more full circle moment This store was in the jewlery & fashion accessories niche. Margins were healthy due to low cogs, and it cash flowed nicely but were most likely going to move on from it. Have much better plans in place but was a sick ride. Over 23,000 total orders. This was ZERO to a million. 436 days without a single day missed of posting, which also means not a single day missed of work. Despite the internet turning into a world of fake weird shit, authentic shit still exists. Gonna start dropping some archives of all the shit I filmed behind the scenes that never got posted. Real 🅿️, put a milly up on the board publicly. 🧘‍♂️
0:04

Day 436 of documenting 0-1M Checkmate. For the last 436 days I publicly built this store online, X was mainly the daily notebook, but i also made several videos revealing nearly everything about this store without fully doxxing. We started with 1 product and finished with 37 as we crossed this finish line. It’s not the craziest numbers, but the i hope it shows people who are just starting out, the game of consistency. While documenting this publicly, it was the complete opposite of a straight line up. Many times we ran it up, thought we were in the clear, made mistakes, got reset back to virtually zero. In fact, we actually were set back to nearly zero twice and it’s all documented somewhere in the X posts. I could have easily faked revenue to say i got here earlier, I could have easily jumped on X only posting our best days, but that’s gay and what most people do. We did not miss a single post throughout building this store. Every single day we posted. The reason I used these photos/videos instead a regular ss is because this is the exact table & chair that I made my first $ online in. This broken chair, a broken table, and this laptop that would proceed to break in the process (have a new one now) but it felt like a more full circle moment This store was in the jewlery & fashion accessories niche. Margins were healthy due to low cogs, and it cash flowed nicely but were most likely going to move on from it. Have much better plans in place but was a sick ride. Over 23,000 total orders. This was ZERO to a million. 436 days without a single day missed of posting, which also means not a single day missed of work. Despite the internet turning into a world of fake weird shit, authentic shit still exists. Gonna start dropping some archives of all the shit I filmed behind the scenes that never got posted. Real 🅿️, put a milly up on the board publicly. 🧘‍♂️

Dennis DeMarino

18,998 Aufrufe • vor 1 Jahr

I just built a Claude skill that audits your entire Meta Ads account in under 5 minutes 🤯 Export your CSV from Ads Manager → drop it into Claude → get back an account health score, a wasted spend breakdown, and a prioritized fix list telling you exactly what to change this week. All inside Claude Cowork. Perfect for DTC brands and agencies who are running Meta Ads but have no idea which creatives are bleeding budget, which audiences stopped converting, or why CPA crept up 40% last month. If your weekly Meta workflow still looks like this — open Ads Manager, stare at the dashboard, sort by spend, squint at CTR columns, export a CSV you never actually analyze, close the tab and hope for the best... This skill runs the full audit for you: → Reads your Meta Ads CSV export (campaign, ad set, and ad-level data) → Scores your account 0-100 across 6 dimensions: creative health, audience efficiency, budget allocation, funnel performance, fatigue signals, and offer effectiveness → Calculates your exact wasted spend in dollars: every ad with spend and zero purchases → Identifies creative fatigue before it tanks your CPA (declining CTR + rising frequency + increasing cost) → Flags audience overlap and saturation across ad sets → Delivers a top-5 fix list ranked by how much money each fix saves you No API connection. No third-party tool access to your ad account. No risk of Meta flagging your account. What you get: →A full account health score (0-100) with a grade for each dimension →Your exact wasted spend in dollars (not a vague "you're overspending") →Creative fatigue signals with specific ads to kill or refresh this week →Audience efficiency analysis showing which ad sets are cannibalizing each other →A prioritized fix list ranked by budget impact (do #1 first, save the most money) One CSV export, one prompt. Five minutes. I put together the full playbook with the skill file, the scoring methodology, and the exact CSV export steps from Ads Manager. Want it for free? > Like this post > Comment "META" And I'll send it over (must be following so I can DM)
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I just built a Claude skill that audits your entire Meta Ads account in under 5 minutes 🤯 Export your CSV from Ads Manager → drop it into Claude → get back an account health score, a wasted spend breakdown, and a prioritized fix list telling you exactly what to change this week. All inside Claude Cowork. Perfect for DTC brands and agencies who are running Meta Ads but have no idea which creatives are bleeding budget, which audiences stopped converting, or why CPA crept up 40% last month. If your weekly Meta workflow still looks like this — open Ads Manager, stare at the dashboard, sort by spend, squint at CTR columns, export a CSV you never actually analyze, close the tab and hope for the best... This skill runs the full audit for you: → Reads your Meta Ads CSV export (campaign, ad set, and ad-level data) → Scores your account 0-100 across 6 dimensions: creative health, audience efficiency, budget allocation, funnel performance, fatigue signals, and offer effectiveness → Calculates your exact wasted spend in dollars: every ad with spend and zero purchases → Identifies creative fatigue before it tanks your CPA (declining CTR + rising frequency + increasing cost) → Flags audience overlap and saturation across ad sets → Delivers a top-5 fix list ranked by how much money each fix saves you No API connection. No third-party tool access to your ad account. No risk of Meta flagging your account. What you get: →A full account health score (0-100) with a grade for each dimension →Your exact wasted spend in dollars (not a vague "you're overspending") →Creative fatigue signals with specific ads to kill or refresh this week →Audience efficiency analysis showing which ad sets are cannibalizing each other →A prioritized fix list ranked by budget impact (do #1 first, save the most money) One CSV export, one prompt. Five minutes. I put together the full playbook with the skill file, the scoring methodology, and the exact CSV export steps from Ads Manager. Want it for free? > Like this post > Comment "META" And I'll send it over (must be following so I can DM)

Mike Futia

35,858 Aufrufe • vor 3 Monaten

Karpathy method + Claude Code reading your whole Obsidian vault is the smartest second brain on earth. The method is simple and brutal. If you can’t build a thing from scratch, you don’t know it. Tutorials are fake learning and your brain deletes them in 3 days. Most people ignore this. They build a second brain that just sits there, folders of notes nobody reopens, dead text. Point Claude Code at the vault and it wakes up. 5,000 notes, one mind. It reads all of it and answers in your own words and your own proofs, not a model’s guess. Then the loop closes. Want to understand neural nets? Skip the 3-hour video and ask Claude Code to build a tiny one. 200 lines from scratch. Watch it train, break a layer, watch it fail, fix it. It clicks in 20 minutes instead of 3 weeks. The second it lands the note gets written. One idea per file, linked to 10 others, dropped into the vault while the memory is still hot. Now it compounds. Month 1: is 60 notes. Month 6 is 900. Every new note pulls in old ones, so you ask anything and the answer comes from your brain, not the internet. Before: 40 tabs, 6 half read PDF, 0 retained. After: build it once, own it for life. Setup takes 4 minutes. Plain text, no lock-in. A second brain nobody reads is a graveyard. Yours just started thinking.
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Karpathy method + Claude Code reading your whole Obsidian vault is the smartest second brain on earth. The method is simple and brutal. If you can’t build a thing from scratch, you don’t know it. Tutorials are fake learning and your brain deletes them in 3 days. Most people ignore this. They build a second brain that just sits there, folders of notes nobody reopens, dead text. Point Claude Code at the vault and it wakes up. 5,000 notes, one mind. It reads all of it and answers in your own words and your own proofs, not a model’s guess. Then the loop closes. Want to understand neural nets? Skip the 3-hour video and ask Claude Code to build a tiny one. 200 lines from scratch. Watch it train, break a layer, watch it fail, fix it. It clicks in 20 minutes instead of 3 weeks. The second it lands the note gets written. One idea per file, linked to 10 others, dropped into the vault while the memory is still hot. Now it compounds. Month 1: is 60 notes. Month 6 is 900. Every new note pulls in old ones, so you ask anything and the answer comes from your brain, not the internet. Before: 40 tabs, 6 half read PDF, 0 retained. After: build it once, own it for life. Setup takes 4 minutes. Plain text, no lock-in. A second brain nobody reads is a graveyard. Yours just started thinking.

West Lord

585,056 Aufrufe • vor 17 Tagen

[Discrete Fourier Transform] by Hand ✍️ In signal processing, the Discrete Fourier Transform (DFT) is no doubt the most important method. But the math involved is extremely complex, literally, involving a summation over a complex number term e^(-iwt). I developed this exercise to demonstrate that underneath such complexity, DFT is just a series of matrix multiplications you can calculate by hand. ✍️ Once you see that, it should not surprise you that a deep neural network, which is also a series of matrix multiplications, with activation functions in-between, can learn to perform DFT to process and analyze signals so effectively. How does DFT work? [1] Given ↳ Signals A, B, and C in the 🟧 frequency domain: ◦ A = cos(w) + 2cos(2w) ◦ B = cos(w) + cos(3w) + cos(4w) ◦ C = -cos(2w) + cos(3w) ◦ Each signal is a weighed sum of four cosine waves at frequencies 1w, 2w, 3w, and 4w. ◦ We will apply Inverse DFT to convert the signals to time domain representations, and then demonstrate DFT can convert back to their original frequency domain representations. ↳ Signal X in the 🟩 time domain. X is sampled at 10 time points 1t, 2t, …, 10t: ◦ X = [-2.5, -1.8, 3, -0.7, -1.0, -0.7, 3, -1.8, -2.5, 5] ◦ Suppose X is also a weighted sum of the same four cosine waves, but we don’t already know their weights. We will apply DFT to discover them. [2] 🟧 Frequency Matrix (F) ↳ Write the coefficients of A, B, C as a matrix F. Each signal is a row. Each frequency is a column. ↳ A → [1, 2, 0, 0] ↳ B → [1, 0, 1, 1] ↳ C → [0, 1-, 1, 0] [3] Cosine → Discrete ↳ Sample from the continuous cosine waves at discrete time points 1t, 2t, 3t, to 10t. [4] Cosine Matrix (W) ↳ Write the samples as a matrix, Each frequency is a row. Each time point is a column. [5] Inverse DFT: 🟧 Frequency → 🟩 Time ↳ Multiply the frequency matrix F and the cosine matrix W. ↳ The meaning of this multiplication is to linearly combine the four cosine waves (rows in W) into time-domain signals (rows in T) using the weights specified in F. ↳ The result is matrix T, which are signals A, B, C converted to the time domain. Each signal is a row. Each time point is a column. [6] Transpose ↳ Transpose T, converting each signal’s time domain representation from a row to a column. [7] DFT: 🟩 Time → 🟧 Frequency ↳ Multiply the cosine matrix W with the transpose of matrix T. ↳ The purpose of this multiplication is to take a dot-product between each time-domain signal (columns in the transpose of T) and each cosine wave (rows in W), which has the effect of projecting the signal onto a cosine wave to determine how much they are correlated. Zero means not correlated at all. ↳ The result is an intermediate version of the “recovered” frequency matrix where each column corresponds to a signal and each row corresponds to a frequency. ↳ Compared to the original frequency matrix F, this intermediate matrix has non-zero weights in the correct places, but scaled up by a factor of 5 (n/2, n=10). For example, signal A, originally [1,2,0,0], is recovered at [5,10,0,0]. [8] Scale ↳ Multiply each value by 2/n = 1/5 to scale down the intermediate matrix to match the magnitude of the original frequency matrix F. [9] Transpose ↳ Transpose the recovered frequency matrix back to the same orientation of the original frequency matrix F. ↳ Like magic 🪄, the result is identical to the original F, which means DFT successfully recovered the frequency components of signals A, B, C. [10] Apply DFT to X: 🟩 Time → 🟧 Frequency ↳ Now that we have some confidence in DFT’s ability to recover frequency components, we apply DFT to X’s time-domain representation by multiplying W with X. ↳ The result is the an intermediate matrix. [11] Scale ↳ Similarly, we scale down by a factor of 5 to obtain the recovered frequency components of X (a column). [12] Transpose ↳ Similarly, we transpose the recovered column to row to match the orientation of the frequency matrix. ↳ Using the coefficients [0,0,3,2], we can write the equation of X as 3cos(3w) + 2cos(4w). Notes: I hope this by hand exercise helps you understand the essence of DFT. But there is more technical details, such as: • Sine: The complete DFT math also includes sine waves that follow a similar calculation process. • Phase: Here, we assume all the cosine waves are aligned at the origin, namely, phase is 0. If a phase p is added, for example, cos(w+p), we will need to calculate the sine component and use their ratio to figure out what p is. • Magnitude: If phase is not zero, the magnitude will need to be calculated by combining both cosine and sine terms.
0:12

[Discrete Fourier Transform] by Hand ✍️ In signal processing, the Discrete Fourier Transform (DFT) is no doubt the most important method. But the math involved is extremely complex, literally, involving a summation over a complex number term e^(-iwt). I developed this exercise to demonstrate that underneath such complexity, DFT is just a series of matrix multiplications you can calculate by hand. ✍️ Once you see that, it should not surprise you that a deep neural network, which is also a series of matrix multiplications, with activation functions in-between, can learn to perform DFT to process and analyze signals so effectively. How does DFT work? [1] Given ↳ Signals A, B, and C in the 🟧 frequency domain: ◦ A = cos(w) + 2cos(2w) ◦ B = cos(w) + cos(3w) + cos(4w) ◦ C = -cos(2w) + cos(3w) ◦ Each signal is a weighed sum of four cosine waves at frequencies 1w, 2w, 3w, and 4w. ◦ We will apply Inverse DFT to convert the signals to time domain representations, and then demonstrate DFT can convert back to their original frequency domain representations. ↳ Signal X in the 🟩 time domain. X is sampled at 10 time points 1t, 2t, …, 10t: ◦ X = [-2.5, -1.8, 3, -0.7, -1.0, -0.7, 3, -1.8, -2.5, 5] ◦ Suppose X is also a weighted sum of the same four cosine waves, but we don’t already know their weights. We will apply DFT to discover them. [2] 🟧 Frequency Matrix (F) ↳ Write the coefficients of A, B, C as a matrix F. Each signal is a row. Each frequency is a column. ↳ A → [1, 2, 0, 0] ↳ B → [1, 0, 1, 1] ↳ C → [0, 1-, 1, 0] [3] Cosine → Discrete ↳ Sample from the continuous cosine waves at discrete time points 1t, 2t, 3t, to 10t. [4] Cosine Matrix (W) ↳ Write the samples as a matrix, Each frequency is a row. Each time point is a column. [5] Inverse DFT: 🟧 Frequency → 🟩 Time ↳ Multiply the frequency matrix F and the cosine matrix W. ↳ The meaning of this multiplication is to linearly combine the four cosine waves (rows in W) into time-domain signals (rows in T) using the weights specified in F. ↳ The result is matrix T, which are signals A, B, C converted to the time domain. Each signal is a row. Each time point is a column. [6] Transpose ↳ Transpose T, converting each signal’s time domain representation from a row to a column. [7] DFT: 🟩 Time → 🟧 Frequency ↳ Multiply the cosine matrix W with the transpose of matrix T. ↳ The purpose of this multiplication is to take a dot-product between each time-domain signal (columns in the transpose of T) and each cosine wave (rows in W), which has the effect of projecting the signal onto a cosine wave to determine how much they are correlated. Zero means not correlated at all. ↳ The result is an intermediate version of the “recovered” frequency matrix where each column corresponds to a signal and each row corresponds to a frequency. ↳ Compared to the original frequency matrix F, this intermediate matrix has non-zero weights in the correct places, but scaled up by a factor of 5 (n/2, n=10). For example, signal A, originally [1,2,0,0], is recovered at [5,10,0,0]. [8] Scale ↳ Multiply each value by 2/n = 1/5 to scale down the intermediate matrix to match the magnitude of the original frequency matrix F. [9] Transpose ↳ Transpose the recovered frequency matrix back to the same orientation of the original frequency matrix F. ↳ Like magic 🪄, the result is identical to the original F, which means DFT successfully recovered the frequency components of signals A, B, C. [10] Apply DFT to X: 🟩 Time → 🟧 Frequency ↳ Now that we have some confidence in DFT’s ability to recover frequency components, we apply DFT to X’s time-domain representation by multiplying W with X. ↳ The result is the an intermediate matrix. [11] Scale ↳ Similarly, we scale down by a factor of 5 to obtain the recovered frequency components of X (a column). [12] Transpose ↳ Similarly, we transpose the recovered column to row to match the orientation of the frequency matrix. ↳ Using the coefficients [0,0,3,2], we can write the equation of X as 3cos(3w) + 2cos(4w). Notes: I hope this by hand exercise helps you understand the essence of DFT. But there is more technical details, such as: • Sine: The complete DFT math also includes sine waves that follow a similar calculation process. • Phase: Here, we assume all the cosine waves are aligned at the origin, namely, phase is 0. If a phase p is added, for example, cos(w+p), we will need to calculate the sine component and use their ratio to figure out what p is. • Magnitude: If phase is not zero, the magnitude will need to be calculated by combining both cosine and sine terms.

Tom Yeh

116,622 Aufrufe • vor 2 Jahren

IF I WAS FORCED to build a $20K/month AI creative agency using nothing but Photoshop, starting from 0, here's exactly what I would do in steps: The production setup (Days 1–3) 1. Download the Higgsfield plugin inside Photoshop — takes 5 minutes 2. You now have: sketch-to-image, layer decomposer, mockup studio, relight, upscale, face swap, character swap, background removal, AI stylist — all in 1 tool 3. Old creative agency workflow: designer + photographer + editor + 3–5 day turnaround 4. New workflow: 1 person, Photoshop, 30 minutes per deliverable The offer (Days 3–7) 5. Pick 1 niche — ecom brands, real estate agents, or course creators all need visuals constantly 6. Build a simple offer: "10 ad creatives delivered in 24 hours — $500" 7. Old agencies charge $2,000–$5,000/month for the same output 8. Your cost to deliver: $0 beyond the plugin. Pure margin. 9. Create 3 sample mockups using the tool — drop a product image in, generate 9 variations, pick the best 3 10. That's your portfolio. Built in under 1 hour. Cost: $0. The client machine (Days 7–20) 11. Go on X and search "[niche] + need a designer" or "[niche] + creatives" 12. DM 50 people per day — "I'll make you 3 free ad creatives in 24 hours, no catch" 13. Deliver them in 30 minutes using the plugin 14. 50 DMs/day × 14 days = 700 outreach messages 15. Conservative 3% conversion = 21 people see the free work 16. Close 5 of them at $500 = $2,500 in week 3 The scale (Days 20–30) 17. Upsell every client to a $1,500/month retainer — 10 creatives/week, unlimited revisions 18. 1 client per day in Photoshop takes 45 minutes max 19. 10 retainer clients × $1,500 = $15,000/month 20. Add 3 one-off clients at $500/month = $1,500 21. Add a $997 "AI creative system" course teaching other people this exact workflow = $3,000+/month from 3 sales The math: 50 DMs/day × 30 days = 1,500 outreach messages 3% book a call = 45 calls 40% close at $1,500/month retainer = 18 clients 18 × $1,500 = $27,000/month recurring Time per client per day: 45 minutes Total daily work: 4–5 hours Every mockup — AI. Every restyle — AI. Every layer rebuild — AI. Every variation — AI. No photographer. No designer and no reshoot. Start it here. 👇
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IF I WAS FORCED to build a $20K/month AI creative agency using nothing but Photoshop, starting from 0, here's exactly what I would do in steps: The production setup (Days 1–3) 1. Download the Higgsfield plugin inside Photoshop — takes 5 minutes 2. You now have: sketch-to-image, layer decomposer, mockup studio, relight, upscale, face swap, character swap, background removal, AI stylist — all in 1 tool 3. Old creative agency workflow: designer + photographer + editor + 3–5 day turnaround 4. New workflow: 1 person, Photoshop, 30 minutes per deliverable The offer (Days 3–7) 5. Pick 1 niche — ecom brands, real estate agents, or course creators all need visuals constantly 6. Build a simple offer: "10 ad creatives delivered in 24 hours — $500" 7. Old agencies charge $2,000–$5,000/month for the same output 8. Your cost to deliver: $0 beyond the plugin. Pure margin. 9. Create 3 sample mockups using the tool — drop a product image in, generate 9 variations, pick the best 3 10. That's your portfolio. Built in under 1 hour. Cost: $0. The client machine (Days 7–20) 11. Go on X and search "[niche] + need a designer" or "[niche] + creatives" 12. DM 50 people per day — "I'll make you 3 free ad creatives in 24 hours, no catch" 13. Deliver them in 30 minutes using the plugin 14. 50 DMs/day × 14 days = 700 outreach messages 15. Conservative 3% conversion = 21 people see the free work 16. Close 5 of them at $500 = $2,500 in week 3 The scale (Days 20–30) 17. Upsell every client to a $1,500/month retainer — 10 creatives/week, unlimited revisions 18. 1 client per day in Photoshop takes 45 minutes max 19. 10 retainer clients × $1,500 = $15,000/month 20. Add 3 one-off clients at $500/month = $1,500 21. Add a $997 "AI creative system" course teaching other people this exact workflow = $3,000+/month from 3 sales The math: 50 DMs/day × 30 days = 1,500 outreach messages 3% book a call = 45 calls 40% close at $1,500/month retainer = 18 clients 18 × $1,500 = $27,000/month recurring Time per client per day: 45 minutes Total daily work: 4–5 hours Every mockup — AI. Every restyle — AI. Every layer rebuild — AI. Every variation — AI. No photographer. No designer and no reshoot. Start it here. 👇

ALEX SUZUKI

20,557 Aufrufe • vor 1 Monat