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opus 5.5 is f*cking cracked at motion design this entire video is code, 0 after effects im open sourcing the prompt template for these motion designs steal it to recreate these ↓ Ask me for: 8 to 12 UI states I want the shape to become (e.g. button, loader,... player, slider, toggle, tabs, chart, command palette, toast), pure black and white or one accent color, and a royalty-free song around 120 BPM (e.g. Mixkit, free for commercial use). Dribbble-level UI motion. One shape, never cut: every state is the same element morphing its size, radius and color while its content swaps with a short blur. A cursor drives every change with real clicks and drags. Light warm-gray canvas, black and white components, one clean UI font (Geist). Springs everywhere, a tiny overshoot at most. The camera zooms so each state fills the frame. The last frame is the first frame, so it loops. Banned: bouncy easing, particle bursts, glows, gradients on UI chrome, mismatched icon strokes, dead time, anything that looks like a template. 120 BPM, 7 bars, something happens on every beat. Button → loader → check → dynamic island → music player with a play/pause morph → scrub the progress bar → it becomes a volume slider that stretches when dragged past max → a toggle flips on the beat → the knob becomes a liquid tab indicator → the tabs open into a chart that draws itself, with a tooltip on hover → it collapses into ⌘K → type to filter → enter → toast → back to the button. 1. One HTML file, square 1440x1440. Every style is computed from time inside seek(t): no CSS transitions, no timers, no state carried between frames. 2. Springs are closed-form step responses. A value that changes target many times is the sum of one spring per change, so it stays a pure function of time. 3. The tab indicator's two edges ride different springs, so the leading edge stretches ahead of the trailing one. Same trick for the toggle knob. 4. Drags are direct manipulation: while the cursor is held, the value is computed from its position. On release it springs back from wherever it was. 5. Analyze the song with numpy for the beat grid and start on a downbeat. Place every UI sound by its measured peak. 6. Render with Playwright: 4 subframes per frame, blended with ffmpeg tmix for motion blur at 60fps. 7. Render one frame per beat before the full render. Fix anything off the grid, cramped or hard to read. Never put will-change on anything the camera scales or the text renders blurry. Text that swaps inside a morphing container needs its own enter and exit timing or it overlaps. Make the last frame identical to the first, cursor position and speed included, or the loop stutters. Ask me for the inputs, then show me the state list on the beat grid before you write any code.show more

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everyone's sharing motion graphic videos that Opus 5.5 made, and it's genuinely insane everyone says they created it with "one prompt", but my one prompt video looked mid so i went through a bunch of these videos to see how they were actually made, and found the workflow that works here's how to generate pro level motion graphic videos w/ opus: 1. get reference videos to direct from -> pick 1-2 videos whose style you want and tell opus to match them. naming a style works way better than describing one without a reference, opus falls back to its default look: centered text, gradient background, everything fading in that's why so many of these videos look the same. a reference gives it the pacing, the type and the transitions to copy 2. install HyperFrames or Remotion so opus can build the video both let opus write every scene as code and render it straight to mp4. no video editor without one, opus can only describe a video or hand you a rough html page you have to screen record with it, every frame is exact, and when you ask for a change it edits one line and re-renders instead of starting over 3. install 21st for high quality components in the video real buttons, cards and UI components made by design engineers, instead of whatever opus invents on the spot without it, opus draws your product UI from scratch and it looks off. wrong spacing, placeholder boxes, fake-looking buttons anyone who's used good software can feel it in a second, and the whole video reads as cheap 4. steps 1-3 were context + setup. now dump all of it into opus your brand (logo, colors, fonts), screenshots of your real product, the reference video, and a quick braindump of how you see the video then ask for 3 storyboard variants without this, opus guesses your colors, your font and what your product even does. the video could be for any startup with it, it could only be yours. and 3 variants means you pick a direction instead of fixing the first idea it had 5. pick the storyboard you like ask for one still frame per scene before anything moves. fixing a storyboard is way cheaper than fixing a render without this step, you only find out scene 4 is wrong after the whole thing is animated, and every fix means re-rendering. a still frame takes seconds to change 6. let claude cook then give notes like a director: "slow every zoom to 0.7x", "hard cut here", "push in on the button" without notes, the first render is usually 80% there, and that last 20% is what makes it look pro. vague notes like "make it better" get random changes. camera words get exactly the change you want everyone has the same model. the context you give it is what makes it look pro let it cooookk

Rexan Wong

452,203 görüntüleme • 1 gün önce

THE CLIMAX EVERYONE REMEMBERS IS BUILT FROM THE SECOND WHERE NOTHING HAPPENS A ronin. An army. One continuous shot. The frame most people miss is the one that makes everything land: a beat of pure black right before the explosion. Here’s what that sequence actually teaches: 1. Contrast is something you spend, not a setting you turn on. Stay bright the whole time and you’ve got nowhere left to go when it counts. Hold the darkness long enough and a single bright frame feels blinding. 2. One accent color in a monochrome world hits harder than a full palette. Charcoal, ash, pewter… and exactly one slash of violet neon. Your eye goes straight to it every time. Add a second accent and you halve the power of both. 3. The blackout isn’t just a transition — it’s the turning point. Everything before it is descent. Everything after is release. The structure is disguised as lighting. 4. Flat, faceless enemies aren’t lazy. They’re deliberate hierarchy. Identical grey soldiers and helmets make the ronin pop. Give them personality and you split the audience’s attention for nothing. 5. One continuous shot forces every decision to matter. No cuts to hide behind. Framing, movement, and light have to carry the whole thing — and that’s exactly why it holds up. 6. Quiet frames buy the loud ones. There’s a moment with zero neon — just black and white, hair streaming across the frame like spilled ink. It gives you nothing… which is why the next frame hits so hard. 7. End by taking the light away. Katana lowered, tip toward the ground, no glow left on the steel. The brightest thing in the entire piece disappears. That’s what makes it feel like an ending instead of just a stop. Why this matters: Most people build climaxes by adding more — more light, more motion, more color. This one does it by subtracting first. A dead beat costs nothing and pays off everything that follows. Restraint reads as confidence. Overloading reads as insecurity. Audiences feel it before they can explain it. You don’t need a bigger budget or a better model. You just need the discipline to hold back. The real lesson isn’t “make it dark.” It’s that impact is relative. You can’t have a true peak without spending time in the valley first — and most creators rush straight to the peak, then wonder why nothing lands. This version keeps all your original insights, tightens the flow, and sounds like a thoughtful filmmaker or editor sharing something they noticed — not like generated content. Ready to post.

THE CLIMAX EVERYONE REMEMBERS IS BUILT FROM THE SECOND WHERE NOTHING HAPPENS A ronin. An army. One continuous shot. The frame most people miss is the one that makes everything land: a beat of pure black right before the explosion. Here’s what that sequence actually teaches: 1. Contrast is something you spend, not a setting you turn on. Stay bright the whole time and you’ve got nowhere left to go when it counts. Hold the darkness long enough and a single bright frame feels blinding. 2. One accent color in a monochrome world hits harder than a full palette. Charcoal, ash, pewter… and exactly one slash of violet neon. Your eye goes straight to it every time. Add a second accent and you halve the power of both. 3. The blackout isn’t just a transition — it’s the turning point. Everything before it is descent. Everything after is release. The structure is disguised as lighting. 4. Flat, faceless enemies aren’t lazy. They’re deliberate hierarchy. Identical grey soldiers and helmets make the ronin pop. Give them personality and you split the audience’s attention for nothing. 5. One continuous shot forces every decision to matter. No cuts to hide behind. Framing, movement, and light have to carry the whole thing — and that’s exactly why it holds up. 6. Quiet frames buy the loud ones. There’s a moment with zero neon — just black and white, hair streaming across the frame like spilled ink. It gives you nothing… which is why the next frame hits so hard. 7. End by taking the light away. Katana lowered, tip toward the ground, no glow left on the steel. The brightest thing in the entire piece disappears. That’s what makes it feel like an ending instead of just a stop. Why this matters: Most people build climaxes by adding more — more light, more motion, more color. This one does it by subtracting first. A dead beat costs nothing and pays off everything that follows. Restraint reads as confidence. Overloading reads as insecurity. Audiences feel it before they can explain it. You don’t need a bigger budget or a better model. You just need the discipline to hold back. The real lesson isn’t “make it dark.” It’s that impact is relative. You can’t have a true peak without spending time in the valley first — and most creators rush straight to the peak, then wonder why nothing lands. This version keeps all your original insights, tightens the flow, and sounds like a thoughtful filmmaker or editor sharing something they noticed — not like generated content. Ready to post.

Nexlow

12,812 görüntüleme • 2 ay önce

Beauty ads just changed forever. Free Claude Opus 4.8 + GPT Image 2 + Seedance 2.0 workflow to spin up 100s of video ads. No studio, no model, no macro lens, no shoot day. Here's what nobody in beauty marketing wants to say out loud. That glossy lip shot. The droplet hitting the surface in slow motion. The whip-pan into the next scene. The crystalline product splash. All the stuff that used to need a real set, a real camera op, and a full shoot day. You can generate every frame of it from a text prompt now, and stitch it into a finished ad before your coffee goes cold. The workflow is almost stupidly simple: → Tell Claude Opus 4.8 the beauty shot you want (dewy skin macro, gloss-on-lips contact, ripple transition, the works) → Claude turns it into a shot-by-shot storyboard plus a prompt for every frame → GPT Image 2 generates the photoreal stills, frame by frame → Seedance 2.0 animates each one into a clip with that buttery slow-mo glide → You drop the clips into HeyOz and assemble the full ad in one place The real unlock is volume. This isn't one hero video. Once the workflow is dialed, you spin up hundreds of variations. Different shades, different models, different hooks, different transitions. The exact creative volume Meta rewards, minus the production cost that used to make it impossible. Old way: one shoot, one look, $10k+, weeks of waiting. New way: a hundred angles, any look, a few dollars each, same afternoon. I wrote up the entire workflow. The Claude storyboard prompt, the GPT Image 2 frame prompts, the Seedance motion settings, the full assembly flow. Completely free, no email gate. Want it? Comment "GLOSS" and I'll send it straight over. (make sure you're following so it can actually reach you)

Beauty ads just changed forever. Free Claude Opus 4.8 + GPT Image 2 + Seedance 2.0 workflow to spin up 100s of video ads. No studio, no model, no macro lens, no shoot day. Here's what nobody in beauty marketing wants to say out loud. That glossy lip shot. The droplet hitting the surface in slow motion. The whip-pan into the next scene. The crystalline product splash. All the stuff that used to need a real set, a real camera op, and a full shoot day. You can generate every frame of it from a text prompt now, and stitch it into a finished ad before your coffee goes cold. The workflow is almost stupidly simple: → Tell Claude Opus 4.8 the beauty shot you want (dewy skin macro, gloss-on-lips contact, ripple transition, the works) → Claude turns it into a shot-by-shot storyboard plus a prompt for every frame → GPT Image 2 generates the photoreal stills, frame by frame → Seedance 2.0 animates each one into a clip with that buttery slow-mo glide → You drop the clips into HeyOz and assemble the full ad in one place The real unlock is volume. This isn't one hero video. Once the workflow is dialed, you spin up hundreds of variations. Different shades, different models, different hooks, different transitions. The exact creative volume Meta rewards, minus the production cost that used to make it impossible. Old way: one shoot, one look, $10k+, weeks of waiting. New way: a hundred angles, any look, a few dollars each, same afternoon. I wrote up the entire workflow. The Claude storyboard prompt, the GPT Image 2 frame prompts, the Seedance motion settings, the full assembly flow. Completely free, no email gate. Want it? Comment "GLOSS" and I'll send it straight over. (make sure you're following so it can actually reach you)

Ahad Shams | AI Ads Guy

11,303 görüntüleme • 3 ay önce

sorry, they just did WHAT someone gave a machine one disease name, the leading cause of blindness in the developed world with 1.5 million americans already in its path, and it came back pointing at a drug that has sat in pharmacies for years under a different label: 551 papers read in 30 minutes against the 294 hours a human would have needed, and the loop that did it is public on GitHub most agent setups answer one question at a time, so the ceiling on the work is the quality of the question you happened to think of this one was handed a single question and wrote the second one itself. turns out that follow-up is where the real find was: a target called ABCA1, upregulated threefold, in an experiment no human ordered i read the whole paper looking for the trick, and the trick is structural. that is the second question, and it is the gap between an assistant and a factory: - hand the loop a field rather than a task: it was given a disease, and choosing the mechanism was part of its job - make it rank before it spends: 151 papers in, ten candidate mechanisms out, scored against each other before anything touched a bench - split reading from judging, so the agent that forms the theory is a different agent from the one grading it - close every cycle on physical reality: the verdict was an experiment, and another model's opinion was never allowed to stand in for one - feed each result back as the next question rather than a log line, which is the step almost nobody builds - search what already passed inspection first: the winner was an approved compound with a safety file already on record - write down what the round learned before opening the next one, so round two starts where round one stopped my read, and i think it is the uncomfortable one: reading was the entire bottleneck in that field, and everybody spent the decade optimising the writing. people ran every physical experiment here, the analysis agent needs a domain expert writing its prompts, and the authors decline to call this the leap it resembles. the thinking got replaced, and the hands did not so the question i cannot answer for my own setup: which step of your loop still stops dead until you sit down and type something bookmark this one. the four parts that turn one model into a line that runs like this, the queue, the rooms, the write permissions and the gate, are built file by file in the piece below ↓

sorry, they just did WHAT someone gave a machine one disease name, the leading cause of blindness in the developed world with 1.5 million americans already in its path, and it came back pointing at a drug that has sat in pharmacies for years under a different label: 551 papers read in 30 minutes against the 294 hours a human would have needed, and the loop that did it is public on GitHub most agent setups answer one question at a time, so the ceiling on the work is the quality of the question you happened to think of this one was handed a single question and wrote the second one itself. turns out that follow-up is where the real find was: a target called ABCA1, upregulated threefold, in an experiment no human ordered i read the whole paper looking for the trick, and the trick is structural. that is the second question, and it is the gap between an assistant and a factory: - hand the loop a field rather than a task: it was given a disease, and choosing the mechanism was part of its job - make it rank before it spends: 151 papers in, ten candidate mechanisms out, scored against each other before anything touched a bench - split reading from judging, so the agent that forms the theory is a different agent from the one grading it - close every cycle on physical reality: the verdict was an experiment, and another model's opinion was never allowed to stand in for one - feed each result back as the next question rather than a log line, which is the step almost nobody builds - search what already passed inspection first: the winner was an approved compound with a safety file already on record - write down what the round learned before opening the next one, so round two starts where round one stopped my read, and i think it is the uncomfortable one: reading was the entire bottleneck in that field, and everybody spent the decade optimising the writing. people ran every physical experiment here, the analysis agent needs a domain expert writing its prompts, and the authors decline to call this the leap it resembles. the thinking got replaced, and the hands did not so the question i cannot answer for my own setup: which step of your loop still stops dead until you sit down and type something bookmark this one. the four parts that turn one model into a line that runs like this, the queue, the rooms, the write permissions and the gate, are built file by file in the piece below ↓

Argona

32,475 görüntüleme • 1 ay önce

SOMEONE BUILT A WAY TO SEND FILES BETWEEN TWO PHONES WITH NO WIFI, NO BLUETOOTH AND NO NETWORK AT ALL it works with just a screen flashing qr codes at a camera one device plays an endless stream of animated qr codes, the other one points its camera at the screen and rebuilds the file out of the light no pairing, no app, no shared network, nothing but the camera > its pulling about 129 kb/s, a full 2mb image transfers in seconds > theres no back channel, the receiver can never ask for a missed frame, so it uses fountain codes, every frame is a scrambled mix of the file rather than one piece of it > that means the receiver just collects any frames it can catch in any order, and once it has enough it can reconstruct the whole file > dropping frames costs you a little time but never breaks the transfer > you can point the camera at a stream thats already running and it just locks on mid flight he was building a cached mp3 player because he was sick of ads and bad streaming quality. he wanted a way to send tracks phone to phone without both phones being on the same network, and realized flashing qr codes could carry the data he built the whole working prototype in one night with claude code its open source and already at 375 stars

SOMEONE BUILT A WAY TO SEND FILES BETWEEN TWO PHONES WITH NO WIFI, NO BLUETOOTH AND NO NETWORK AT ALL it works with just a screen flashing qr codes at a camera one device plays an endless stream of animated qr codes, the other one points its camera at the screen and rebuilds the file out of the light no pairing, no app, no shared network, nothing but the camera > its pulling about 129 kb/s, a full 2mb image transfers in seconds > theres no back channel, the receiver can never ask for a missed frame, so it uses fountain codes, every frame is a scrambled mix of the file rather than one piece of it > that means the receiver just collects any frames it can catch in any order, and once it has enough it can reconstruct the whole file > dropping frames costs you a little time but never breaks the transfer > you can point the camera at a stream thats already running and it just locks on mid flight he was building a cached mp3 player because he was sick of ads and bad streaming quality. he wanted a way to send tracks phone to phone without both phones being on the same network, and realized flashing qr codes could carry the data he built the whole working prototype in one night with claude code its open source and already at 375 stars

Om Patel

951,210 görüntüleme • 1 ay önce

This guy built a visual scanner that reads 468 points on his face and 42 points on his hands from a regular webcam and turns them into a cloud of thousands of particles right between his palms. Inside, MediaPipe and TouchDesigner are linked: the first captures hands and face from the webcam with high accuracy, the second turns those coordinates into a live plane and feeds it into a POP system that instantly generates a swarm of particles in the shape of a head. No studio, no render farmer, no VR headset. Just a laptop, a webcam, and 1 TouchDesigner session. And traditional VJ studios keep teams of 5 people on a setup with lighting, custom hardware, and commercial plugins, while his expenses are only a TouchDesigner subscription and a regular USB camera. One laptop runs MediaPipe and TouchDesigner simultaneously, holds the camera stream at 60 FPS without drops, and in parallel processes 468 face points + 21 points on each hand. The camera captures frame after frame, MediaPipe in real time sends TouchDesigner the finger coordinates and face geometry, and the POP operator inside the engine translates those numbers into thousands of particle points with colors from bright pink to gold. This setup immediately defines the role of the tool and the limits of its autonomy. It knows where the fingertips are at every moment of the frame. It knows how to read the face geometry at any angle to the camera. It knows how to draw a swarm of particles between them with the right color and contour. → MediaPipe pulls 468 points from the face and 21 points from each hand, 60 times per second → TouchDesigner receives those coordinates, builds a virtual rectangle between the fingertips, and feeds it into the POP system → POP generates thousands of particle points in the shape of a head, coloring them in a gradient from bright pink to gold → The HUD layer adds green corners and a blue neon frame, styling the image like an AR interface → All layers assemble into 1 real-time frame that projects back onto the video in the camera window → The final image is recorded to a file or broadcast to a projector for a live installation And only when the guy spreads his hands wider does the plane between the palms stretch; brings them together, it narrows. Otherwise the system runs on its own. And when he moves from his home room to a concert hall, the same laptop with the same webcam launches the same TouchDesigner session in just 5 minutes, without reconfiguration, without a new team, and without a single line of new code. In his work setup there is no studio of his own and no team for assembly. On the desk sits a laptop with a webcam, on top run MediaPipe and TouchDesigner with POP operators, and the same setup through a USB camera moves to any concert without a new configuration. Out of everything I have seen this year, this is the cleanest Creative Coding setup on 1 laptop: 0 render farms, 0 studio lighting, and between them 3 libraries, thousands of particle points, and 1 webcam.

This guy built a visual scanner that reads 468 points on his face and 42 points on his hands from a regular webcam and turns them into a cloud of thousands of particles right between his palms. Inside, MediaPipe and TouchDesigner are linked: the first captures hands and face from the webcam with high accuracy, the second turns those coordinates into a live plane and feeds it into a POP system that instantly generates a swarm of particles in the shape of a head. No studio, no render farmer, no VR headset. Just a laptop, a webcam, and 1 TouchDesigner session. And traditional VJ studios keep teams of 5 people on a setup with lighting, custom hardware, and commercial plugins, while his expenses are only a TouchDesigner subscription and a regular USB camera. One laptop runs MediaPipe and TouchDesigner simultaneously, holds the camera stream at 60 FPS without drops, and in parallel processes 468 face points + 21 points on each hand. The camera captures frame after frame, MediaPipe in real time sends TouchDesigner the finger coordinates and face geometry, and the POP operator inside the engine translates those numbers into thousands of particle points with colors from bright pink to gold. This setup immediately defines the role of the tool and the limits of its autonomy. It knows where the fingertips are at every moment of the frame. It knows how to read the face geometry at any angle to the camera. It knows how to draw a swarm of particles between them with the right color and contour. → MediaPipe pulls 468 points from the face and 21 points from each hand, 60 times per second → TouchDesigner receives those coordinates, builds a virtual rectangle between the fingertips, and feeds it into the POP system → POP generates thousands of particle points in the shape of a head, coloring them in a gradient from bright pink to gold → The HUD layer adds green corners and a blue neon frame, styling the image like an AR interface → All layers assemble into 1 real-time frame that projects back onto the video in the camera window → The final image is recorded to a file or broadcast to a projector for a live installation And only when the guy spreads his hands wider does the plane between the palms stretch; brings them together, it narrows. Otherwise the system runs on its own. And when he moves from his home room to a concert hall, the same laptop with the same webcam launches the same TouchDesigner session in just 5 minutes, without reconfiguration, without a new team, and without a single line of new code. In his work setup there is no studio of his own and no team for assembly. On the desk sits a laptop with a webcam, on top run MediaPipe and TouchDesigner with POP operators, and the same setup through a USB camera moves to any concert without a new configuration. Out of everything I have seen this year, this is the cleanest Creative Coding setup on 1 laptop: 0 render farms, 0 studio lighting, and between them 3 libraries, thousands of particle points, and 1 webcam.

Blaze

38,242 görüntüleme • 4 ay önce

004/100 Buttons. A bit of the process on building an animation. When looking at a finished animation or in this example a finished button, it can look quite complex inside the CSS. But when building it, it’s more like a lot of simple steps, one after another. Here I had the idea to make some kind of text animation like the footer logo on the Osmo site. I try to add the base animation with no complex easing, for example transition: translate 0.4s ease. Starting with just moving the one text from bottom to top and the other text to top. Adding a stagger, play around with it. Searching for a way to make it more circular. On the research I found the sin() function inside CSS which can build a more smooth non linear curve for the stagger which creates this circular effect. And step by step adding more complexity like, different easing for hover/hover-out, opacity, 3D transform and more. I use also the sin() function to rotate the letters, so the middle ones are getting more rotated than the outer ones. Another thing which helps is to add a small delay on hover, for example 0.05s or 0.1s, you don’t really see the difference, but when you hover pretty fast on and out it doesn’t get that jumpy. I’m using here GSAP’s SplitText to split every char into spans. And then I’m adding a CSS index variable to every span, starting from the center. SplitText can provide CSS index variables, but you cannot tell it from which direction. For the sin() it’s also important to have a max length, so I add another CSS variable with the max char number on it. Crafting 100 Buttons with Osmo ⏳ Total time: 63h

Eduard Bodak

166,023 görüntüleme • 4 ay önce

From International Space Station, we have a unique vantage point to observe lightning storms from directly above them. Much like from the ground, they appear as rapid, intense flashes that are over as quickly as they started. But, with the help of handheld cameras and a bit of practice (and, admittedly, some luck), we can capture these intense flashes with a very high frame rate then slow them down to see how the lightning propagates. It's lightning, in slow motion. This is one that occurred over Indonesia on 6/28. I shot it for a half second at 1/120 frame rate, so, 60 separate frames. I then compiled those frames into two videos: the first, in real-time, and the second, slowed down so that each frame is a half second to highlight the progression of the flash. Note the incredible burst of light in one frame, which includes what is likely a TLE on the upper left. A TLE, or Transient Luminous Event, is an electrical discharge that happens in the upper atmosphere above lightning storms (if you haven’t seen Ferry Farhan TLE photo from last week, head over to her page). As their name implies, these are transient, or short lived, phenomena – it appeared in only one frame of the sequence, lasting for no more than 1/120th of a second. The last photo is that single frame. Absolutely incredible.

From International Space Station, we have a unique vantage point to observe lightning storms from directly above them. Much like from the ground, they appear as rapid, intense flashes that are over as quickly as they started. But, with the help of handheld cameras and a bit of practice (and, admittedly, some luck), we can capture these intense flashes with a very high frame rate then slow them down to see how the lightning propagates. It's lightning, in slow motion. This is one that occurred over Indonesia on 6/28. I shot it for a half second at 1/120 frame rate, so, 60 separate frames. I then compiled those frames into two videos: the first, in real-time, and the second, slowed down so that each frame is a half second to highlight the progression of the flash. Note the incredible burst of light in one frame, which includes what is likely a TLE on the upper left. A TLE, or Transient Luminous Event, is an electrical discharge that happens in the upper atmosphere above lightning storms (if you haven’t seen Ferry Farhan TLE photo from last week, head over to her page). As their name implies, these are transient, or short lived, phenomena – it appeared in only one frame of the sequence, lasting for no more than 1/120th of a second. The last photo is that single frame. Absolutely incredible.

COL Anne McClain

82,113 görüntüleme • 1 yıl önce

i tried Framer Agent to build a brand guidelines website template, something i've meant to do for a while. a site i can swap a logo, nudge a colour, update the type, and send a link instead of re-exporting a pdf nobody opens. a few observations on the AI process: - i started with references. pulled together a handful of minimalist grid layouts and had Claude describe the visual style back to me. you can do the same inside Framer, feeding it the references directly. - from there i had it write a detailed prompt aimed at that exact style, then asked it to break it into a few smaller steps. then i fed those into the agent, one at a time. - the scaffolding stage is the satisfying part. for something this grid-driven (the columns, the spans, the whole underlying structure) watching it land in seconds is hard to look away from. - but then i still have to sweat the details: text alignment, line-heights, image sizes. i don't mind it at all; it's the part i like, making these design decisions. the strength of the agent is the mundane work. point it at the stuff that eats your time: cleaning up the build, adding responsiveness, dropping in small effects, checking text and colour styles stay consistent, writing alt text for every image. then i get the time back for the parts of web design that are actually fun. 🤝

charlota

37,427 görüntüleme • 3 ay önce

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

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

Zentrix⌚️

14,015 görüntüleme • 2 ay önce

Fable 5 just dropped and someone already built a $10k-looking website with it. Here's the full workflow The smooth scrolling, the animations, the transitions, all built with Fable 5. Here's how to get the same result instead of a generic template. 1. Steal a direction first. Go to Mobbin or Godly and find a site whose feel you love. Screenshot it. Fable copies a vibe far better than it invents one. 2. Grab real components. Pull animation-ready blocks from Aceternity UI or Magic UI. These are built for exactly this look (scroll reveals, parallax, gradients). 3. Prompt Fable with all of it. Switch to Fable 5 in Claude Code, drop in your screenshots and the component references, and describe the sections and scroll behavior you want. Let it plan before it builds. 4. Animate with the right tools. Tell it to use GSAP with ScrollTrigger for scroll effects, or Framer Motion for React. This is what separates "nice" from "$10k." 5. Iterate on feel, not code. Preview it, then give Fable specific notes ("slower fade, more spacing on mobile") until it lands. Bookmark this

Fable 5 just dropped and someone already built a $10k-looking website with it. Here's the full workflow The smooth scrolling, the animations, the transitions, all built with Fable 5. Here's how to get the same result instead of a generic template. 1. Steal a direction first. Go to Mobbin or Godly and find a site whose feel you love. Screenshot it. Fable copies a vibe far better than it invents one. 2. Grab real components. Pull animation-ready blocks from Aceternity UI or Magic UI. These are built for exactly this look (scroll reveals, parallax, gradients). 3. Prompt Fable with all of it. Switch to Fable 5 in Claude Code, drop in your screenshots and the component references, and describe the sections and scroll behavior you want. Let it plan before it builds. 4. Animate with the right tools. Tell it to use GSAP with ScrollTrigger for scroll effects, or Framer Motion for React. This is what separates "nice" from "$10k." 5. Iterate on feel, not code. Preview it, then give Fable specific notes ("slower fade, more spacing on mobile") until it lands. Bookmark this

Fokki

72,175 görüntüleme • 2 ay önce

You don't understand... Higgsfield MCP + Claude just automated AI film making. Every single step you used to grind through to make an AI movie, you can now do 10x faster. Drop the script into Claude Opus 4.8 and say: "Here's my script. Break it into a full shotlist. Shot number, scene, shot type, camera move and the action in each frame." Now the whole film is mapped, shot by shot. - Pull your assets. Ask Claude: "From this shotlist, list every character, every location and every prop across the whole film." That's your build list. The stuff you would need to generate and give as references in next steps. - Build the character sheets. Higgsfield MCP is connected, so Claude has hands now to do stuff directly. It generates the images itself. Have the full body, back view and close up in the character sheet. One per character. Each sheet becomes the locked reference for that face. Same move for locations, generate the empty plate for each one before anyone steps into it. - Generate the frames. Feed Claude the references plus the shot and have it write and fire the Seedance 2.0 prompt. "Using the lead's character sheet and the alley plate, generate shot 4 in Seedance 2.0. Low angle, slow push-in, rain." Claude builds the prompt, calls Seedance 2.0 and the frame lands back in chat. Use a Seedance 2.0 skill to teach Claude how to prompt it properly. Now, there are 3 ways to make the shots. Pick one per scene. - Pure prompting. Fastest one. You describe the action in words and let Seedance interpret it. For consistency across a sequence, feed it a frame from the previous shot so the look carries. - Storyboarding. You hand it a panel and it matches that composition exactly. Way more control over how the shot is framed. The tradeoff is that it can introduce more cuts than you actually want. - Path Control System This is the latest technique Seedance 2.0 technique. Generate a still base plate of the scene. Draw a red line across it to mark the exact path of the movement, then describe what's happening. Seedance follows that line for the action. Also ask Claude to remove the red line when animating. This is the one for anything where motion has to land precisely. The output reads like real live action. - Lastly, generate every clip you need, then cut them together. Get it to Capcut for editing and audio design. And that's it. The pipeline that used to need a full crew and a studio can now run from one Claude chat. 2026 is gonna be wild

You don't understand... Higgsfield MCP + Claude just automated AI film making. Every single step you used to grind through to make an AI movie, you can now do 10x faster. Drop the script into Claude Opus 4.8 and say: "Here's my script. Break it into a full shotlist. Shot number, scene, shot type, camera move and the action in each frame." Now the whole film is mapped, shot by shot. - Pull your assets. Ask Claude: "From this shotlist, list every character, every location and every prop across the whole film." That's your build list. The stuff you would need to generate and give as references in next steps. - Build the character sheets. Higgsfield MCP is connected, so Claude has hands now to do stuff directly. It generates the images itself. Have the full body, back view and close up in the character sheet. One per character. Each sheet becomes the locked reference for that face. Same move for locations, generate the empty plate for each one before anyone steps into it. - Generate the frames. Feed Claude the references plus the shot and have it write and fire the Seedance 2.0 prompt. "Using the lead's character sheet and the alley plate, generate shot 4 in Seedance 2.0. Low angle, slow push-in, rain." Claude builds the prompt, calls Seedance 2.0 and the frame lands back in chat. Use a Seedance 2.0 skill to teach Claude how to prompt it properly. Now, there are 3 ways to make the shots. Pick one per scene. - Pure prompting. Fastest one. You describe the action in words and let Seedance interpret it. For consistency across a sequence, feed it a frame from the previous shot so the look carries. - Storyboarding. You hand it a panel and it matches that composition exactly. Way more control over how the shot is framed. The tradeoff is that it can introduce more cuts than you actually want. - Path Control System This is the latest technique Seedance 2.0 technique. Generate a still base plate of the scene. Draw a red line across it to mark the exact path of the movement, then describe what's happening. Seedance follows that line for the action. Also ask Claude to remove the red line when animating. This is the one for anything where motion has to land precisely. The output reads like real live action. - Lastly, generate every clip you need, then cut them together. Get it to Capcut for editing and audio design. And that's it. The pipeline that used to need a full crew and a studio can now run from one Claude chat. 2026 is gonna be wild

Rez Karim

10,951 görüntüleme • 3 ay önce

Elon Musk gave the entire entertainment industry its expiration date, and he is the one building the thing that kills it. Musk: “My guess is that we see the first compelling half hour, pure AI show next year.” Next year. A complete show generated entirely by AI. No writers. No actors. No cameras. No sets. No crew. No studio. Just a prompt and enough compute to render a reality that never physically existed. And shows are the easy part. Musk: “I say probably we’re maybe three years away from AI does the whole video game.” A show plays the same way every time. A game has to generate a living world that reacts to every decision in real time across every single frame. That is a fundamentally harder class of problem. And Musk put three years on it. Right now a single AAA title takes seven years and half a billion dollars across thousands of engineers and artists just to ship it. Musk is describing a world where one person types a paragraph and gets something comparable. The entire value proposition of a multi-billion dollar industry lives inside that gap. And it closes in thirty-six months. But the prediction is not the story. The person making it is. This is not an analyst speculating from the sidelines. This is the man building the largest AI compute clusters on the planet. The man who built xAI from zero in under two years. The man stacking hundreds of thousands of GPUs into facilities designed to do exactly what he is describing. When Musk says three years, he is not guessing about what someone else might eventually ship. He is reading you a delivery date off his own roadmap. Every media company on Earth is valued on a single assumption. That quality content is expensive and difficult to produce at scale. That one assumption is the structural foundation underneath every studio, every network, and every publisher in existence. Musk is dismantling it with raw compute. The studios still parading thousand-person production teams are not demonstrating strength. They are advertising the exact cost structure that one person with a prompt and a GPU allocation is about to make irrelevant. And it does not stop at entertainment. If AI can generate an interactive world that responds to human input in real time, it can generate anything. Advertising. Architecture. Training simulations. Product design. Every industry built on humans manually constructing visual experiences frame by frame is sitting on the same countdown Musk just read out loud. Now zoom out. Because this is not just an industry story. For the entire history of human civilization, the distance between imagining a world and actually creating one required thousands of people, millions of hours, and billions of dollars. That distance built Hollywood. That distance built the gaming industry. That distance made content scarce and studios powerful. Musk is collapsing that distance to zero. When the gap between imagining something and it existing disappears, every business model built on the difficulty of creation disappears with it. That is not disruption. That is a full inversion of how human beings create. Musk did not make a casual prediction on that podcast. He told you what he is building. He told you the timeline. And he told you which industries do not survive it. The entertainment industry is still debating whether this future is real. Musk is not part of that debate. He is building. And he just told you the delivery date.

Elon Musk gave the entire entertainment industry its expiration date, and he is the one building the thing that kills it. Musk: “My guess is that we see the first compelling half hour, pure AI show next year.” Next year. A complete show generated entirely by AI. No writers. No actors. No cameras. No sets. No crew. No studio. Just a prompt and enough compute to render a reality that never physically existed. And shows are the easy part. Musk: “I say probably we’re maybe three years away from AI does the whole video game.” A show plays the same way every time. A game has to generate a living world that reacts to every decision in real time across every single frame. That is a fundamentally harder class of problem. And Musk put three years on it. Right now a single AAA title takes seven years and half a billion dollars across thousands of engineers and artists just to ship it. Musk is describing a world where one person types a paragraph and gets something comparable. The entire value proposition of a multi-billion dollar industry lives inside that gap. And it closes in thirty-six months. But the prediction is not the story. The person making it is. This is not an analyst speculating from the sidelines. This is the man building the largest AI compute clusters on the planet. The man who built xAI from zero in under two years. The man stacking hundreds of thousands of GPUs into facilities designed to do exactly what he is describing. When Musk says three years, he is not guessing about what someone else might eventually ship. He is reading you a delivery date off his own roadmap. Every media company on Earth is valued on a single assumption. That quality content is expensive and difficult to produce at scale. That one assumption is the structural foundation underneath every studio, every network, and every publisher in existence. Musk is dismantling it with raw compute. The studios still parading thousand-person production teams are not demonstrating strength. They are advertising the exact cost structure that one person with a prompt and a GPU allocation is about to make irrelevant. And it does not stop at entertainment. If AI can generate an interactive world that responds to human input in real time, it can generate anything. Advertising. Architecture. Training simulations. Product design. Every industry built on humans manually constructing visual experiences frame by frame is sitting on the same countdown Musk just read out loud. Now zoom out. Because this is not just an industry story. For the entire history of human civilization, the distance between imagining a world and actually creating one required thousands of people, millions of hours, and billions of dollars. That distance built Hollywood. That distance built the gaming industry. That distance made content scarce and studios powerful. Musk is collapsing that distance to zero. When the gap between imagining something and it existing disappears, every business model built on the difficulty of creation disappears with it. That is not disruption. That is a full inversion of how human beings create. Musk did not make a casual prediction on that podcast. He told you what he is building. He told you the timeline. And he told you which industries do not survive it. The entertainment industry is still debating whether this future is real. Musk is not part of that debate. He is building. And he just told you the delivery date.

Dustin

22,458 görüntüleme • 2 ay önce

$166,000 neurons from a dead fruit fly now hunt drones from a bedroom desk. A white drone the size of a palm lifts off the carpet, and a black 3D-printed turret on the desk swings after it, with the fly's brain working its 2 stepper motors, 1 camera and 2 barrels through a nest of rainbow wires on a breadboard. That brain sits in the top-left corner of the screen and flares every time the drone jerks. A Patriot interceptor costs about $4 million, and armies are firing them at drones worth a fraction of that. This brain is smaller than a poppy seed, and Google put it online for free 2 weeks ago. Then someone gave it a job it was built for. Most of a fly's brain exists to see motion, and this brain is male, and male flies spend their lives chasing whatever moves. The drone climbs past the ceiling fan and the crosshair climbs with it, then drops when it drops. For 18 seconds it never loses the drone. In the last shot the drone flies home to the guy holding the controller, and the crosshair follows it right onto his chest. It spent its whole life dodging swatters. Now it's the one aiming.$

166,000 neurons from a dead fruit fly now hunt drones from a bedroom desk. A white drone the size of a palm lifts off the carpet, and a black 3D-printed turret on the desk swings after it, with the fly's brain working its 2 stepper motors, 1 camera and 2 barrels through a nest of rainbow wires on a breadboard. That brain sits in the top-left corner of the screen and flares every time the drone jerks. A Patriot interceptor costs about $4 million, and armies are firing them at drones worth a fraction of that. This brain is smaller than a poppy seed, and Google put it online for free 2 weeks ago. Then someone gave it a job it was built for. Most of a fly's brain exists to see motion, and this brain is male, and male flies spend their lives chasing whatever moves. The drone climbs past the ceiling fan and the crosshair climbs with it, then drops when it drops. For 18 seconds it never loses the drone. In the last shot the drone flies home to the guy holding the controller, and the crosshair follows it right onto his chest. It spent its whole life dodging swatters. Now it's the one aiming.

Spike 1%

35,159 görüntüleme • 9 gün önce

A wrist force sensor fires at 100Hz. The policy only ever sees it at 30Hz, downsampled to land on the same control step as the camera and the joint state. That's not a bug, it's the whole point, and it sits inside a bigger pattern in VLA research this year. Every major release has been Markovian at its core, mapping the current frame straight to the next action. The fix everyone reaches for is more vision: more history frames, longer image context. FM-VLA makes a clean case that the fix is the wrong channel for a whole class of tasks. Press a button three times and stop. A camera watching that has almost nothing to work with, the scene barely changes between press one and press three. Force doesn't have that ambiguity problem. Each press is a sharp, distinct spike in the wrench signal, whether or not the camera noticed anything at all. So FM-VLA doesn't add more frames. It compresses the wrench history into eight tokens with a VAE, pretrained purely on reconstructing force signals, frozen before it ever touches the policy, then hands those tokens to the action expert alongside a short window of joint state. That's the entire memory system. Averaged across three contact-rich tasks, FM-VLA hits 83.3 percent success against 33.3 percent for the strongest vision-memory baseline on the button-counting task specifically, where the ambiguity problem is worst, 72.2 percent for FM-VLA there. Strip out the short-state window and force-only performance drops well below the combined system, so force alone isn't the answer either. The two channels are doing different jobs. The field has defaulted to one memory channel for every kind of temporal problem. This is a clean data point that the channel should match the ambiguity you're actually trying to resolve, not just get bigger. Source: Paper: Credit to the teams at Tsinghua University, Microsoft Research, and Fudan University. #Robotics #PhysicalAI #RobotLearning

A wrist force sensor fires at 100Hz. The policy only ever sees it at 30Hz, downsampled to land on the same control step as the camera and the joint state. That's not a bug, it's the whole point, and it sits inside a bigger pattern in VLA research this year. Every major release has been Markovian at its core, mapping the current frame straight to the next action. The fix everyone reaches for is more vision: more history frames, longer image context. FM-VLA makes a clean case that the fix is the wrong channel for a whole class of tasks. Press a button three times and stop. A camera watching that has almost nothing to work with, the scene barely changes between press one and press three. Force doesn't have that ambiguity problem. Each press is a sharp, distinct spike in the wrench signal, whether or not the camera noticed anything at all. So FM-VLA doesn't add more frames. It compresses the wrench history into eight tokens with a VAE, pretrained purely on reconstructing force signals, frozen before it ever touches the policy, then hands those tokens to the action expert alongside a short window of joint state. That's the entire memory system. Averaged across three contact-rich tasks, FM-VLA hits 83.3 percent success against 33.3 percent for the strongest vision-memory baseline on the button-counting task specifically, where the ambiguity problem is worst, 72.2 percent for FM-VLA there. Strip out the short-state window and force-only performance drops well below the combined system, so force alone isn't the answer either. The two channels are doing different jobs. The field has defaulted to one memory channel for every kind of temporal problem. This is a clean data point that the channel should match the ambiguity you're actually trying to resolve, not just get bigger. Source: Paper: Credit to the teams at Tsinghua University, Microsoft Research, and Fudan University. #Robotics #PhysicalAI #RobotLearning

Stephen James

11,658 görüntüleme • 1 ay önce

I FOUND A BEACH ACCOUNT WITH 900K FOLLOWERS POSTING GOLDEN HOUR BIKINI VIDEOS FROM A TROPICAL ISLAND. I RECOGNIZED THE BUTTERFLY. IT IS THE SAME TATTOO FROM THE 7-ELEVEN GIRL. SAME OPERATOR. DIFFERENT BODY. Blue micro bikini. Butterfly tattoo on the chest. Text tattoo on the ribs. Roses on the arms. She is standing on a tropical beach at golden hour. Sand. Water. Driftwood. Trees behind her. The sun is low and backlights her from the right. Haze in the air. She moves slowly. Adjusts the top. Runs her hands through her hair. Lifts her arms above her head. Twelve seconds. It looks like a photographer's reel from Tulum. I have seen that butterfly before. The 7-Eleven girl. Different face. Different body. Different hair. Same butterfly on the same spot on the chest. Same wings. Same position between the collarbones. Two accounts. Two "girls." One tattoo pulled from the same prompt. The golden hour is not aesthetic. It is camouflage. Backlight creates lens flare. Lens flare softens edges. Soft edges hide the places where AI skin meets AI hair and the render breaks down. Every frame has a warm haze across it that the viewer reads as "cinematic" and the operator uses as a filter to cover artifacts. This is not a girl filmed at sunset. This is a render passed through the sun. The bikini is the smallest amount of fabric the algorithm will not flag. The strings are nearly invisible. The triangles cover the minimum. The operator did not pick blue for aesthetics. Blue metallic fabric catches golden hour light and shifts color between frames. It goes from royal blue to purple to copper depending on the angle. The viewer's eye tracks the color shift the way it tracked the metallic bikini in the mirror clip. The fabric is the distraction. The skin is the content. She never faces the camera straight on. In twelve seconds she is always turned fifteen degrees to the side. Profile shots. Three-quarter shots. One moment where she raises her arms and looks at the camera but her chin is tilted down so her eyes are in shadow. The operator learned what the mirror girl knew and what the car girl knew. Straight angles break AI faces. Every other angle sells them. The text tattoo on her ribs says something in cursive. It is readable in one frame, blurred in the next, and different in the third. The words change mid-video. Real ink does not rewrite itself. Prompts do. But nobody pauses a golden hour beach video to read a rib tattoo. The operator knows the viewer is not looking at the ribs. The driftwood on the sand behind her is the same log in every frame. But the sand around it changes. The waterline shifts forward in one cut and backward in the next. Real tides do not reverse in twelve seconds. The beach is a backdrop that was rendered once and the water was animated on top of it without tracking the continuity. The butterfly connects everything. The 7-Eleven. The beach. Two characters that were never in the same prompt but came from the same operator who never changed the tattoo line. One detail. One mistake. Two accounts exposed. That beach has never had a sunset. That sand has never been wet. But the butterfly is still the same.

framexin

13,921 görüntüleme • 22 gün önce

On your open perpetual position can become an NFT and live in your wallet. You open a position like normal than mint it. The program issues a token 2022 nft, that points to your exact position. Normally your open position lives inside the protocol. With Percolator Trade your able to wrap it into an NFT that sits in your own wallet. A real, ownable object instead of a line in a dashboard. Because it’s a token, it moves like one. Send the NFT to another wallet and the position goes with it. Whoever holds the NFT owns the trade. One NFT = one position If you burn it, your back to holding the position directly. That’s the composable part, a position use to be stuck inside one account. Because it’s a token, it can now be sold OTC or used by anything that speaks tokens. Why does it matter? Because the position is bound to the NFT, it can’t be drained out from under whoever holds it so there is no middleman that holds your position. The NFT does, and the NFT is yours. One important safety detail; transfers run through a token 2022 hook that checks the positions health first, so it can’t be handed off in a broken state & the NFT layer is a separate program so it can never touch the funds the core engine holds.

On your open perpetual position can become an NFT and live in your wallet. You open a position like normal than mint it. The program issues a token 2022 nft, that points to your exact position. Normally your open position lives inside the protocol. With Percolator Trade your able to wrap it into an NFT that sits in your own wallet. A real, ownable object instead of a line in a dashboard. Because it’s a token, it moves like one. Send the NFT to another wallet and the position goes with it. Whoever holds the NFT owns the trade. One NFT = one position If you burn it, your back to holding the position directly. That’s the composable part, a position use to be stuck inside one account. Because it’s a token, it can now be sold OTC or used by anything that speaks tokens. Why does it matter? Because the position is bound to the NFT, it can’t be drained out from under whoever holds it so there is no middleman that holds your position. The NFT does, and the NFT is yours. One important safety detail; transfers run through a token 2022 hook that checks the positions health first, so it can’t be handed off in a broken state & the NFT layer is a separate program so it can never touch the funds the core engine holds.

Percolator

12,359 görüntüleme • 3 ay önce

A CHINESE GUY STOPPED PAYING WEB DESIGNERS $1,800 PER LANDING PAGE AND BUILT THE SAME KIND OF SITE WITH CLAUDE CODE IN ONE AFTERNOON FOR LESS THAN $70 claude was not just writing code. it was doing the job of a designer, copywriter and frontend dev in one window. in 4 hours, one rough idea turned into the layout, color system, font choices, page sections and the full html/css/js build the real trick was the references. he gave claude 5 screenshots, made it ask 7 questions first, then pushed it toward one clear visual direction instead of accepting another generic ai-looking template the first version already looked solid. the second pass made it feel expensive: better typography, a darker palette, mobile cleanup, cursor effects, 6 micro-interactions and custom hero visuals his old workflow was burning $1,200 on design, $500 on frontend work and another $150 on small fixes every time he needed a new page. now the whole test costs less than $70 and the site still looks like something a $5,000 agency would ship the edge is not “ai builds websites.” the edge is that one person can now brief, critique, polish and launch in one afternoon without waiting 10 days for a designer to send version one

A CHINESE GUY STOPPED PAYING WEB DESIGNERS $1,800 PER LANDING PAGE AND BUILT THE SAME KIND OF SITE WITH CLAUDE CODE IN ONE AFTERNOON FOR LESS THAN $70 claude was not just writing code. it was doing the job of a designer, copywriter and frontend dev in one window. in 4 hours, one rough idea turned into the layout, color system, font choices, page sections and the full html/css/js build the real trick was the references. he gave claude 5 screenshots, made it ask 7 questions first, then pushed it toward one clear visual direction instead of accepting another generic ai-looking template the first version already looked solid. the second pass made it feel expensive: better typography, a darker palette, mobile cleanup, cursor effects, 6 micro-interactions and custom hero visuals his old workflow was burning $1,200 on design, $500 on frontend work and another $150 on small fixes every time he needed a new page. now the whole test costs less than $70 and the site still looks like something a $5,000 agency would ship the edge is not “ai builds websites.” the edge is that one person can now brief, critique, polish and launch in one afternoon without waiting 10 days for a designer to send version one

Gipp 🦅

304,719 görüntüleme • 3 ay önce

this is worth more than most five figure courses 16 claude agents audit an entire repo at once, a second fleet re-checks every finding on fresh context, and the whole thing runs off one diagram instead of a prompt i ran it against my own code and got back 11 endpoints where i never checked who was logged in, 3 of which the verifier threw out before they ever reached me this is Graph Engineering, the layer above prompting, and it runs on the agent you already pay for: - write your plan out, then ask one question at every "and then": does the next step actually read what the previous one produced - the seams that fail that question were never dependencies, so those jobs run at the same time - the arrows that survive are your real edges, and the longest chain of them is your floor that no number of agents shortens - want it faster, cut a false edge instead of adding a worker - fan the independent work out, one agent per item, no shared state between them - send every finding to a separate agent on fresh context, because a model recognises its own writing 73.5% of the time and grades it kinder once it does - make that verifier check a real signal like a passing test, never the worker's own word that it finished - shard the fleet across worktrees so parallel workers stop overwriting each other, one rule frozen into every worker: never git stash, never git reset - merge only what came back verified, into one report instead of twenty open chats the catch is the ceiling. at 95% independent work 16 agents return 9.14x rather than the 16 you would guess, and even 256 only reach 18.6x, because the merge and the verify stay serial however wide you fan coordination itself is free plain code and every agent underneath it is billed, so start at twenty files and widen once it works bookmark this, the whole method with all six ready-to-run graphs is written out in the article ↓

this is worth more than most five figure courses 16 claude agents audit an entire repo at once, a second fleet re-checks every finding on fresh context, and the whole thing runs off one diagram instead of a prompt i ran it against my own code and got back 11 endpoints where i never checked who was logged in, 3 of which the verifier threw out before they ever reached me this is Graph Engineering, the layer above prompting, and it runs on the agent you already pay for: - write your plan out, then ask one question at every "and then": does the next step actually read what the previous one produced - the seams that fail that question were never dependencies, so those jobs run at the same time - the arrows that survive are your real edges, and the longest chain of them is your floor that no number of agents shortens - want it faster, cut a false edge instead of adding a worker - fan the independent work out, one agent per item, no shared state between them - send every finding to a separate agent on fresh context, because a model recognises its own writing 73.5% of the time and grades it kinder once it does - make that verifier check a real signal like a passing test, never the worker's own word that it finished - shard the fleet across worktrees so parallel workers stop overwriting each other, one rule frozen into every worker: never git stash, never git reset - merge only what came back verified, into one report instead of twenty open chats the catch is the ceiling. at 95% independent work 16 agents return 9.14x rather than the 16 you would guess, and even 256 only reach 18.6x, because the merge and the verify stay serial however wide you fan coordination itself is free plain code and every agent underneath it is billed, so start at twenty files and widen once it works bookmark this, the whole method with all six ready-to-run graphs is written out in the article ↓

Argona

157,312 görüntüleme • 2 ay önce