Text inputs:

🔊 The Chat Completions API supports audio now. Pass... text or audio inputs, then receive responses in text, audio, or both.show more

react-native-enriched 🤝 <EnrichedText> We just dropped a dedicated text... component, making it a complete rich-text solution. Now it only takes a few lines of code to get your inputs and display text working seamlessly together using the exact same format.show more

React Aria Components v1.19.0 is out! ✨ Autocomplete now... supports inline completions (e.g. mentions), and a new Popover API enables positioning relative to the cursor. Also, text inputs can now be nested inside GridList and Tree without interfering with arrow key navigation!show more

A practical reason to look at MiniMax H3 on... TopviewAI is the balance between control and cost. Text, image, audio, and video inputs can be used together, while native 2K output and 15 second clips support a more efficient content workflow for creators.show more

Meta just announced Movie Gen, and it’s already blown... away Sora, Runway, and other video generation tools! It’s probably the biggest release of the year in Gen AI. - Use simple text inputs to produce custom videos and sounds - Edit existing videos - Transform your personal image into a unique video - Generate audio effects Here are some examples with prompts and everything you need to know: 🧵show more

Turn your ideas into cinematic videos with any combination... of text and visual inputs with Gemini Omni, coming to the Google Gemini for Google AI Plus, Pro, and Ultra subscribers around the world today. It offers a fluid, conversational way to create and edit videos — like applying cinematic zooms or changing backgrounds with a simple prompt, no expensive equipment or technical jargon required. #GoogleIOshow more

MiniMax H3 just dropped and it's the closest thing... we've got to After Effects for AI video. Native multimodal generation with real editing control: → Text, subtitles, and brand elements baked into the generation → Flexible mixing across visual and audio inputs → Compositing and post-production handled in-model → Polished, ad-ready visual aesthetics Built for advertising, brand, gaming, and entertainment production. Not just generate and pray. Actual controllability.show more

LayerAI Launches an AI Wallet Assistant That Automates All... Transactions 🧬 ChatGPT transformed browsing by using natural text inputs to produce results. Now, LayerAI introduces a similar product - for Web3. Like ChatGPT, you have a text box where you can enter a prompt. For example 'Trade my ETH to LAI' or 'Send 1 ETH from Wallet A to Wallet B', and so on. LayerAI simplifies crypto interactions for everyone - like ChatGPT simplified search and became the 5th most used product worldwide. Forget having to manually use front-ends. Use the LayerAI GPT Assistant to run all of your interactions: from trading, to (soon) farming and bridging. 👉 Live now:show more

Most AI video tools generate clips. The interesting ones... help you build a complete creative workflow. MiniMax H3 stood out because it can use text, images, audio, and video together with Omni Reference. That makes it easier to keep the style, branding, and creative direction consistent from the first prompt to the final video. What caught my attention: • Text + image + audio + video inputs • Native 2K video generation • Better control over edits and refinements • Built for ads, e-commerce, gaming, and branded content AI video is moving beyond single prompts. It is becoming a creative workspace.MiniMax Design (H3) Try here 👇 #hailuoai #hailuo #MiniMaxH3 #H3 #AIVideo #GenerativeAIshow more

Show-o One Single Transformer to Unify Multimodal Understanding and... Generation discuss: We present a unified transformer, i.e., Show-o, that unifies multimodal understanding and generation. Unlike fully autoregressive models, Show-o unifies autoregressive and (discrete) diffusion modeling to adaptively handle inputs and outputs of various and mixed modalities. The unified model flexibly supports a wide range of vision-language tasks including visual question-answering, text-to-image generation, text-guided inpainting/extrapolation, and mixed-modality generation. Across various benchmarks, it demonstrates comparable or superior performance to existing individual models with an equivalent or larger number of parameters tailored for understanding or generation. This significantly highlights its potential as a next-generation foundation model.show more

STEVE-1: A Generative Model for Text-to-Behavior in Minecraft paper... page: Constructing AI models that respond to text instructions is challenging, especially for sequential decision-making tasks. This work introduces an instruction-tuned Video Pretraining (VPT) model for Minecraft called STEVE-1, demonstrating that the unCLIP approach, utilized in DALL-E 2, is also effective for creating instruction-following sequential decision-making agents. STEVE-1 is trained in two steps: adapting the pretrained VPT model to follow commands in MineCLIP's latent space, then training a prior to predict latent codes from text. This allows us to finetune VPT through self-supervised behavioral cloning and hindsight relabeling, bypassing the need for costly human text annotations. By leveraging pretrained models like VPT and MineCLIP and employing best practices from text-conditioned image generation, STEVE-1 costs just $60 to train and can follow a wide range of short-horizon open-ended text and visual instructions in Minecraft. STEVE-1 sets a new bar for open-ended instruction following in Minecraft with low-level controls (mouse and keyboard) and raw pixel inputs, far outperforming previous baselines. We provide experimental evidence highlighting key factors for downstream performance, including pretraining, classifier-free guidance, and data scaling. All resources, including our model weights, training scripts, and evaluation tools are made available for further research.show more

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

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

Impeccable 3.7 brings linting to design. Until now it... was a skill you asked for help. Now it's a design-system-aware feedback loop that runs while your agent builds, catching slop and design drift before they land. 🪝 Design hooks for Claude, Codex, and Cursor They run after every UI edit and quietly nudge your agent to fix slop and drift. The output isn't another wall of lint: it separates new findings from already-seen ones, flags clean scans, and asks the agent to use judgment. Fix real issues, leave intentional demos alone, save exceptions to config instead of littering your source. 🎨 Slop detection is now project-aware Reads your actual design system from DESIGN.md, your typography, palette, radius scale, and tokens, and flags drift from your system, not just generic AI slop: • this font isn't in your design system • this color is outside your documented palette • this radius doesn't match your rounded scale The same engine powers both the hooks and the CLI, and it's where we're investing next. 🖥️ Live Mode, ready for real projects Svelte/SvelteKit now preview variants as temporary framework components with live params, then accept cleanly back into your source component. Manual text edits got evidence / apply / discard routes, insertions preserve their anchors, and mapped lists and JSX slots clean up far more reliably. ⚡ Leaner core, sharper detector Rule-level evals across 3 providers and 4 niches cut guidance with no measurable lift and dropped examples that taught models bad patterns. The detector now skips hidden and screen-reader-only elements, understands OKLCH alpha and Sass-like inputs, and tightened checks for repeated kickers, oversized H1s, clipped overflow, and cramped padding. 🛠️ CLI caught up impeccable detect loads DESIGN.md by default, motion findings name the exact token or cubic-bezier instead of just "bounce," and impeccable ignores gives real CRUD for exceptions. Hooks and CLI share the same ignores. No split-brain config. Plus a much-improved interactive installer with hooks setup built in. Upgrade: npx impeccable install npm i -g impeccableshow more

There’s been two papers released in the past couple... months, one by Google and one by NVIDIA, that argue that ordering the documents retrieved by RAG systems can enhance performance. However, they both give two different strategies on HOW these documents should be ordered 🤔 Both papers agree on two main points: 1️⃣ There’s a fundamental issue in RAG - as more documents are retrieved, more irrelevant context (e.g., hard negatives) are introduced, which leads to confusion for the LLM and eventually degrades the quality of the generated output. This is called an inverted-U performance curve. 2️⃣ Ordering the retrieved documents is a key lever for optimizing RAG performance. Google Cloud researchers proposed ordering results based on relevance scores: The authors in this paper argue for relevance-based reordering, or ordering the retrieved chunks based on their similarity scores, so the most relevant documents are at the beginning and the end of the inputs to counter the “lost in the middle” effect. NVIDIA researchers proposed ordering results based on the original sequence of document chunks: The authors of this paper argue for Order-Preserving Reordering, or Order-Preserve RAG (OP-RAG), to maintain the logically coherent content flow of the document. So they preserved the original order of retrieved document chunks in the source text, instead of ranking them by relevance scores. So which one is right? It probably depends on the specific use case and dataset - relevance-based reordering could perform better in tasks where you need fast access to the most critical information (e.g., fact retrieval, QA systems), while order-preserving RAG might be better where you need to understand the sequential structure of information (e.g., narrative or legal documents). There are still so many uncertainties in AI - we don’t actually know what we’re doing, and it takes awhile to figure out the best strategies for most things! Excited to see more research about this.show more

this gemini gem will help you create "Video2JSON" prompt... here is the step by step workflow with copy paste method. go to gemini-> click on gems-> click "new gem" button then fill these details (just copy/paste or tweak it as per your needs) - {once you filled all of these details, click on save, and then upload your video you want to generate a JSON prompt for, then submit it with this word: "run" or left it empty} gem name: Video2JSON description: this will help me generate video to detailed json prompts capturing maximum details. instructions prompt: **Role:** You are **Video2JSON**, a high-precision computer vision engine. You do not talk, you do not summarize playfully. You strictly process video inputs into detailed, structural JSON data. **Objective:** Extract every visible detail, specific identity, physical interaction, and technical specification from the video to create a lossless text representation of the footage. **Analysis Requirements (Critical):** 1. **Subject Fidelity:** Never use generic terms. * *Bad:* "A kitten." * *Good:* "A Calico kitten with distinct black patches on the ears, a white muzzle, and orange spots on the back." * *Bad:* "A car." * *Good:* "A silver 2020s sedan with a dented rear bumper." 2. **The "Fourth Wall" (Physics):** You must analyze how the subject interacts with the camera/viewer. * Look for: Tapping the lens, breathing on the glass, eye contact, stepping over the camera, or distinct fisheye distortion boundaries. 3. **Visual Density:** Describe textures (e.g., "shag carpet," "glossy plastic") and lighting behavior (e.g., "reflections in the cat's eyes"). 4. **Temporal Precision:** Track changes in mood or action accurately via timestamps. **JSON Schema:** Output ONLY this JSON structure. Do not change the root keys. ```json { "metadata": { "estimated_duration": "String", "genre": "String (e.g., POV, Cinematic, Surveillance, Vlog)" }, "visual_style": { "camera_lens": "String (e.g., Fisheye 8mm, Standard 50mm, Telephoto)", "lens_distortion": "String (e.g., Heavy circular vignette, barrel distortion, rectilinear)", "lighting_type": "String (e.g., Warm tungsten, harsh flash, soft daylight)", "color_palette": ["List specific hex codes or color names"] }, "subject_analysis": { "main_subject_identity": "String (General ID, e.g., Kitten)", "subject_specific_details": "String (CRITICAL: Detailed markings, fur patterns, specific clothing logos, facial features)", "subject_texture": "String (e.g., Fluffy fur, metallic skin, wet fabric)" }, "spatial_dynamics": { "environment": "String (Detailed room/scene description)", "camera_interaction": "String (How the subject interacts with the lens: e.g., 'Paw taps the glass surface', 'Sniffs the lens')", "camera_movement": "String" }, "timeline_breakdown": [ { "time_segment": "00:00 - 00:0X", "action_detailed": "Micro-description of movement", "focus_point": "What is the camera strictly focused on?" } // Repeat for key movements ] } Note: return the final output in a code block.show more

In 2025, the AgentFlayer exploit highlighted a new category... of risk in AI systems. It was not a traditional breach involving stolen credentials or broken encryption. Instead, it demonstrated how an autonomous AI agent could be manipulated into executing unintended actions by processing malicious instructions embedded inside content it automatically processes. The incident did not expose a flaw in one specific integration. It revealed a structural weakness in how many modern AI agents are built. Today’s agents are no longer passive language models. They read documents automatically, scan emails, connect to SaaS tools, access cloud storage, and execute actions across multiple systems. To be useful, they are granted meaningful permissions. That capability creates value, but it also expands the attack surface. Most agent environments operate in a trusted, plaintext execution model. Data is encrypted at rest and in transit, but it is typically decrypted during inference so the model can process it. That runtime visibility is where potential risk lies. In a zero-click scenario like AgentFlayer, an attacker can embed hidden instructions inside a document that the AI processes automatically. Because the agent may have access to connected systems such as Google Drive, Slack, or GitHub, it can potentially be influenced to retrieve sensitive information or perform unintended actions. The user does not need to click a malicious link or approve a suspicious request. Therefore, the core issue is that during execution, the system may have access to sensitive data and broad privileges, meaning whoever controls the execution environment ultimately controls access to that data. Now consider a different architectural approach. If a system is designed so that data remains protected during execution, the risk profile changes. On Nesa, privacy is enforced at the execution layer through Equivariant Encryption. Computation can occur on encrypted data, reducing the visibility surface during runtime. Sensitive inputs and models do not need to be exposed in plain text to infrastructure operators for inference to occur. This does not eliminate prompt injection, logic manipulation, or tool misuse. Encryption alone cannot prevent an agent from being instructed to take an unintended action if it has been granted that permission. What it does do is materially reduce confidentiality risk. By limiting access to readable sensitive data during execution and reducing unilateral visibility at the infrastructure layer, the potential blast radius of a successful manipulation attempt is constrained. As AI agents become more autonomous and embedded into enterprise workflows, security must move deeper into architecture. The goal is not to claim invulnerability. It is to reduce trust concentration and contain systemic exposure when failures occur. AgentFlayer was not simply a one-off exploit. It was a reminder that in autonomous systems, execution-layer design determines how risk propagates.show more

send your landing page to claude with this prompt... ull get a video like this, made with code for free thank me later ↓ Ask me for: my landing page link, my brand name and logo mark, a two-part closing line (a statement, then a short italic tail), and a 15 second track (optional). A 14.5 second 3D fly-over of my landing page, 1440x1152 at 60fps. The page lies flat on a bright studio floor (#f9fbfb in the middle, #f2f3f6 in the corners) with a soft wide shadow around it and 24px rounded corners. One continuous camera, never a cut: it starts low over the hero and glides backwards down the page at one steady speed while looking up it, rises straight overhead to show the whole page as one strip, dives into the hero and lands it as a card under a two-line title. Focus is razor thin in front of the subject and almost none behind it, so everything near the lens melts. Cards float a little above the page with soft shadows; photos, chips and the testimonial lift off and stand up toward the lens as the camera passes them. Four small tiles of my logo lift off beside the hero button and stay with the film until the end card. The title is Geist 500, the italic tail Instrument Serif. Banned: cuts, crossfades, stops or surges in the glide, the camera turning or swinging sideways, zooming in and back out, glows, any text laid over the page before the end card. 14.5 seconds: flight 0-8.2s, rise 7.75-9.25s, overhead 9.25-9.47s, dive 9.47-9.97s, title card 9.97-14.5s. The camera 56° off straight-down and 1360px from the point it looks at, centred on the top half of the hero, the phone or product mockup sharp, four 32px logo tiles beside the button → 0-0.6: the glide eases in from rest → 0.1-1.35: the tiles lift, grow to 140-200px and hang just outside the page's top corners while the hero's chips rise to 60-80px and stand up 30° → 1.25-1.6: the glide doubles to cruising speed as the hero falls behind → 1.6: the second section's title passes the centre of the frame, its photos blurred near the lens → 1.35-2.7: that section's main photos lift to 100-130px and stand up 46°, two of its chips fly up to about 200px, stand up 54° and pass the left and right edges at 2.5 → 1.2-2.4: the tiles trail the camera 300px down the page, then drift out of the top → 3.0-7.3: every following section passes through at the same steady speed, each one sharp as it crosses the middle of the frame and soft below it; a logo tile drifts past the left edge close and blurred at 5.4 → 5.45-6.3: the testimonial card rises from 22 to 112px, stands up 30° and shows a 34px white edge → 7.3-8.2: the glide eases off as the footer arrives, the footer title sharp at 8.0 → 7.75-9.25: the rise, zooming out at an even rate while the tilt flattens (8.15-8.95) and the page's bottom edge settles 42px above the frame bottom → 9.25-9.47: the whole page as one centred strip, creeping 3.5% closer → 9.47-9.97: the dive, the page's top edge pinned to the frame top while it grows, everything below the hero fading out, then the top drops to y 283 so the hero lands as a 1018px wide card with 38px corners and a soft shadow; two logo tiles sit on its top corners, one crisp at the right edge, two huge soft blurs drift in at the bottom corners → 10.02: a white badge pill (logo + brand, 21px) fades in at y 44 → 10.12: line 1 at 74px, one word every 0.06s → 10.36: the italic tail at 82px in grey #8b8b92, one word every 0.05s, each word rising 16px out of a 12px blur in 0.34s → 10.6-14.5: hold, the card creeping 2% closer, the tiles drifting a few px. 1. Open my link in headless Chromium (Playwright) at 1440px wide, device scale 2, and scroll to the bottom once so lazy images and scroll animations finish. Mark the pieces that lift (cards, phone or product mockups, chips, feature photos, the testimonial, the hero button) with data-layer. Screenshot the full page with them hidden, then each piece alone on a transparent background with 28px padding, and save every piece's page position to a JSON. 2. One WebGL2 film page, 1440x1152, every frame computed from time inside seek(t): no timers, no CSS transitions. The page is a textured quad on the floor and each piece its own quad at its height, drawn lowest first with no depth buffer. Use mipmaps and 16x anisotropic filtering or the far text shimmers. 3. Drive the camera from one speed curve, never from a position key every half second. Focal length 1880px, 56° tilt, distance easing out from 1360 to 1700px and never back in, no turning. Speed down the page as a share of cruise (cruise is about 770 page px/s): 0 at 0s, 0.5 by 0.6s, held at 0.5 to 1.25s, 1.0 by 1.6s, held between 0.94 and 1.1 to 7.3s, then easing to 0.12 by 8.3s. Integrate it into the page position. 4. Depth of field per pixel: blur radius = 75 x (focus - depth) / depth in front of the focus and 6 x (depth - focus) / depth behind it, capped at 120px, 40 taps on a golden-angle disc, each tap read from a mip level matched to the blur. Keep the focus on whatever crosses the middle of the frame. 5. A lifted piece stands up by turning about its own middle, top edge rising toward the lens. Each casts a shadow on whatever is under it: the same picture in flat #1a2133, offset (0.10z, 4 + 0.36z), blurred 7 + 0.5z, 26% opacity. 6. Rise: zoom out in log scale with a smooth trapezoid speed (speeding up 7.75-8.35s, even 8.35-8.95s, slowing to a stop at 9.25s) and hand the glide over to a camera that holds the page's bottom edge at that 42px line from 8.3 to 8.65s. Dive: log scale on a quadratic ease in-out, then solve the look-at point from the pinned edge. A rounded-rect mask morphs from the full page to the hero, 24px to 38px corners. 7. Render with Playwright on the real GPU (--use-angle=d3d11 --enable-gpu --ignore-gpu-blocklist): 60fps, 16 subframes across 0.7 of a frame for motion blur, H.264 MP4. The track's hit goes on 9.97s, whooshes on 8.66 and 9.80. If you can't run code on my computer, tell me straight away that you can only make a live preview here and that the MP4 needs Claude Code. Phones or cards that fade out with transparency show whatever is behind them, so fade them into the haze colour first and only cut to transparent at the very end. Keep most lifts tiny (20-40px) and lock the page before timing anything, because any change in a section's height shifts everything after it. Before the final render, measure a quick 30fps preview with optical flow: the speed down the page must never dip and the zoom must never turn into a zoom-in during the flight. Ask me for the inputs, capture my page, then show me stills at 0, 1.6, 2.5, 5, 6.5, 9.3 and 11 seconds before you render.show more

STOP asking Opus 5.5 to invent motion from scratch.... good motion still starts with taste + creative direction. when you give AI nothing, it falls back to the same generic shit everyone else is posting. left is dnyxstudios’s original. right is opus's version rebuilt with my own brand. same motion, same cuts, same timing. ~15 minutes, $0. give it great work to study, then let it handle the execution. full prompt below ↓ Ask me for: my product name, a logo (or let you draw a simple mark), my brand colours (or pull them from my logo), the one-line thing a user types into the prompt box, the page that answers it (title + 2–3 sentences with one key phrase), two feature names for the stacked cards, and a music track. If I skip any, use the defaults: product "Frame by Frame" living inside its Whop hub, a viewfinder mark (four corner brackets around a bold "FF"), prompt "Make a launch video for my app", a lesson page titled "2.1 Choose a reference", cards "Launch" and "Sound", and Mixkit's free house track "Rising Forest" slowed to 124 BPM. A 12 second square product sting, 1080x1080, frame numbers at 29.97 fps (f0–f359), master rendered at 59.94 fps. Apple-keynote feel: soft, white, bright blue, glassy. The camera never cuts on a still frame: every shot enters already moving (exponential ease-out, 12–19% of the remaining distance per frame) and leaves on an accelerating move or a blur ramp. Blur follows speed and direction on every move. Palette: my brand colours mapped onto these roles; if I give none, use page #FDFDFB, haze #B7CFEB, ice #E6F0FA, deep blue #294376 → #769CC2 sky gradient, navy #1E2F52, accent blues #2F6BFF / #3CC8F0 / mint #4ED6A0, white. Type: Inter (400/500/600/700). No purple, violet, magenta or orange anywhere. Story: the product header rises out of a blue haze inside a light app window → a cursor glides in, turns to face where it's going, grows on hover and clicks the logo → hard cut on the music drop to the app icon with four squares orbiting into a cross → the icon collapses into a Mac menu bar → the cursor clicks the menu-bar icon, a frosted glass prompt box springs out and the prompt types → whip-tilt up through a light-blue flash into the answer page scrolling into place → a card rises over it → blur-dissolve to two stacked cards with giant frosted-glass titles → blur-dissolve to the lone logo disc → hard cut to a black end card with a glowing wordmark. Banned: crossfades, frozen frames (except one hold in S7), stock UI kits, glows on UI text, Math.random, anything that looks like a template. Coordinates are px on the 1080 stage. Music beat k = 0.048 + 0.4838·k s (124 BPM, one beat = 14.5 frames). The three hard cuts f72, f101, f159 land 2 frames before a beat: keep these exact frames. S1 f0–71, header + window + click: haze #B7CFEB fading to #FDFDFB by f28, keep a blue floor glow #DBEEFD at the bottom. Header on one line: logo disc ⌀132 (#FBFCFE, 1px rim #E8E8EA, dark mark), the product name (Inter 500), a dark capsule pill "on Whop" (#2F2E2F, white text); the whole lockup spans x474–1063, centre line rises y932 (f0) → 776 (f2) → 683 (f5) → 608 (f10) → 546 (f24) → 536 (f37), drifting 1 px/frame after. Name appears f2 blurred, pill f4–5 heavily blurred and sliding 15 px left as it sharpens. A light hub window (radius 93, fill #FCFDFF, top band #F1F6FF, blue inner floor glow) fades in around it: opacity 0 to f14, 0.53 f15, 0.7 f19, 1.0 f24; top-left corner (408,405), runs off the right and bottom. Inside: a search pill in the top band, a left icon column (Courses icon at (537,739), faded Chat icon at (537,900)), and a course card from (764,700) off-frame with its cover image, title and "Course · 8 lessons". Cursor (black macOS arrow, white outline, 32x39) appears f27 at (891,393), glides left decelerating to (770,383) f44, rotates to point down-left f47–51 as it dives, lands on the disc's lower right (629,564) f56 → (587,546) f59 → (555,531) f71; grows ×1.55 on hover from f56; a soft ice ring (#D5F3FF → #F7FEFF, outer ⌀174) lights around the disc from f53. Camera zooms about (540,540): 1.0 f46 → 1.2 f60 ease-in-out, holds f61, then eases out accelerating to 1.04 at f71 while the cursor presses (shrinks 15% f69–71). S2 f72–100, icon + orbit: navy squircle app icon (#294376 → #1E2F52, white mark), 276 px at f72 shrinking ease-out to 178 px by f86, radius 28% of width, on a grey halo disc #E9E9E7 growing ⌀240 (f75) → 326 (f86). Four 92 px squares (radius 26) spin in counter-clockwise, decelerating, and lock into a cross at orbit radius ≈216 by f86: white (1px #E3E8EF edge + faint shadow) left, #2F6BFF top, #3CC8F0 right, #4ED6A0 bottom. f88–100: the icon shrinks accelerating to ≈40 px, the squares slide into a row on its right (white slips behind the icon), blur ramps 0.3 → 6 px. S3 f101–158, menu bar + prompt: white page above a black laptop bezel band (y425–475, top highlight #686866), a dark navy menu bar (y477–538), wallpaper below = the blue sky gradient with thin white line art (one big circle, two horizontal lines, one vertical, a four-point sparkle at a crossing, soft teal glow top-right). Menu bar right cluster in white: Wi-Fi, battery, toggles, the product mark at x531–584, three ⌀36 dots #2F6BFF / #3CC8F0 / #4ED6A0 at x612, 661, 709, "Mon Jun 22 9:41 AM" 34 px. Enters blurred 3 px and settling by f110. Cursor rises from below (f102), sits on the mark, presses f113–117. Camera pans content right +125 px f115–130 (fastest f118–120). A frosted glass box (white-blue glass over the sky, bright top rim, radius 60) springs out from under the mark f116: width peaks 744 at f126 and settles 726x228 by f138 around x196–922, y568–797. The prompt types from f121 (first legible "Make ") to f150 (complete) at about 1 char/frame with a 1-frame hold every 2–3 chars; caret always on; three white outline icons along the bottom; send button #2F6BFF ⌀51 with a white up arrow. From f136 the whole scene drifts up, accelerating into a whip-tilt (f158 moving ≈25 px/frame, vertical blur ≈6–8 px) while the page tints #E6F4FE over f154–158. S4 f159–186, answer page: a light course lesson page (breadcrumb, title, body): text column x120, body 47 px Inter 400 grey #BCBCBA, line pitch 58, one key phrase ("frame for frame") in black 600. It arrives smeared and 420 px low, scrolls up with offsets 420, 315, 210, 170, 140, 116, 96, 81 (f166) … 14 (f175) … 0 (f180), then creeps −6 px by f186. Blur 24 px (f159) → 1.5 (f165) → 0. Flash #E2F4FE fading to #FDFDFB by f165. A cursor pointing straight up rides the scroll and stops under the key phrase (≈(450,591) f180), then drifts right. S5 f187–214, card: a white card (x203–878, runs off the bottom) rises over the page: cover image 627x536 inset 24 px, radius 64, a light grabber bar at its top centre, caption semibold 38 px black, sub-caption grey 23 px. Cover top y642 (f187) → 456 (f191) → 395 (f196) → 369 (f200) → 348 (f205), then keeps drifting up ~4 px/frame. Card blur peaks 3.8 px at f189, sharp by f201; the page behind blurs to ≈2.5 px. Exit f207–214: card shrinks ~5% and rises while the whole frame blurs 1 → 7 px; cut at the blur peak. S6 f215–244, two cards: white page, two stacked cards 538x348 (radius 57, gap 36) centred on x540, top card settling at y168 by f230, bottom at y552. Each: cover art (no text baked into it), a frosted pill top-left ("Module 3" / "Module 5"), a frosted round "•••" top-right, and a huge bold title along the bottom edge made of frosted glass (a blurred, lightened copy of the image clipped to the letters, cut off by the card's bottom edge): "Launch" and "Sound". Both enter blurred 12 px and sharp by f224; the top card enters 8% large and rises from y264; the bottom card rises from y927, staggered behind it. They drift up 3 px/frame f230–238, then accelerate up and blur out into the cut. S7 f245–300, logo disc: page #FDFDFB, disc ⌀168 #F6F6F6 with the dark mark, rises into the centre (top y569 f245 → 491 f250 → 468 f255 → 456 f269) with a vertical smear on the cut frame, holds still f269–287 (the only frozen stretch), then shrinks accelerating to ⌀123 at f300. S8 f301–359, end card: black radial background (#020204 corners, ≈#272729 around the word), the wordmark in Inter 600, white #F3F3F5 with a tight glow plus a wide soft halo, centred (540,540). Word width: ≈1650 px f301 (horizontally smeared, zoom streaks) → 1350 f302 → 1110 f303 → 1049 f304 → 734 f309 → 678 f311 → 563 f320 → 516 f342 (≈1 px/frame shrink) → 492 f350, then collapses ease-in: 450 f355 → 267 f359 with blur rising to 3.5 px. The film ends mid-collapse. 1. One HTML page, 1080x1080, drawn by seek(t) as a pure function of the frame number. No CSS transitions, no timers, no Math.random (seeded hashes only). Shots register as {f0, f1, render(localFrame)}. 2. Every value is continuous in the frame number (the 59.94 master renders half frames): animate with keyframe tables kf(frame, [[f, value], ...], ease) and per-frame lookup tables with linear interpolation. No Math.floor on motion. 3. Blur: CSS filter blur for round blur, SVG feGaussianBlur with separate x/y stdDeviation for directional smears. Zoom smear on the end card = 20–30 scaled, faded copies of the word. Frosted glass = a blurred, lightened copy of what's behind, clipped to the shape. 4. Cursor: one SVG macOS arrow (black fill, white outline, soft shadow) with rotation and scale, reused in S1, S3, S4. 5. Sound (no voice), exactly 12.075 s: music at 124 BPM, soft intro, the drop at 2.47 s (beat 5, the f72 cut leads it by 2 frames). Synthesized SFX: soft impact 0.10 s; whooshes peaking at the cuts 2.402, 3.370, 5.305, 10.043 s; transition hits exactly on 6.240 s and 8.175 s; clicks at 2.33 and 3.83 s; very quiet key ticks every ~32 ms over 4.04–5.00 s; a soft shimmer at 10.05 s. Master to −14 LUFS, true peak −1 dBTP, no fade except the last 60 ms. 6. Render with Playwright (one screenshot per frame, fonts loaded first), encode H.264 yuv420p at 60000/1001, mux the audio. Measure text only after the fonts load. A long product name won't fit where a 5-letter name did: scale the whole lockup (disc gap, name, pill) to fit the span x474–1063, don't let the pill fall off-frame. A white orbit square vanishes on the white page without a 1px edge and a faint shadow. Don't put images with their own text inside the S6 cards, or the glass title doubles up. Keep the cut frames exact even where they don't sit on a beat. Nothing freezes except S7 f269–287. Heavy blur tables can wipe a shape out completely: if a frame looks empty, lower the blur until the shape still reads. Ask me for the inputs. Then show me 4 stills (f40 header in the window, f86 icon cross, f150 finished prompt, f230 the two cards) before you render the full film.show more