Motion

Motion AI

Motion AI

Motion AI

Motion AI

Motion AI

Motion AI

Motion AI

Motion AI

Motion AI

Motion AI

I tested Claude + After Effects so you don't have to guess anymore

I've been seeing a lot of curiosity and, honestly, a lot of hesitation around using Claude with After Effects. So many motion designers are in the "I've heard of it, but I don't really get what it does or how it works" camp. So I decided to go deep on it. I tested it across real motion design workflows and documented everything I found. I just put together a full breakdown that answers the questions I kept seeing over and over: What Claude can actually do inside After Effects. Where it helps, where it doesn't, and where it straight-up wastes your time. How setup works, because this was way less obvious than it should be, and most guides skip the parts that trip you up. Real use cases for motion designers and not generic "AI can help you brainstorm!" stuff. I'm talking about specific things like expression generation and workflow shortcuts that actually make a difference in daily work. There are things it's genuinely useful for and things that are still faster to do manually. If you're a motion designer who's been curious about Claude but hasn't taken the plunge because the info out there feels either too vague or too hype-y - this is for you. It's also for you if you've tried it once, got underwhelming results, and figured "yeah, not for me." There's a good chance you just didn't have the right setup or prompts. What this isn't: It's not a "Claude will replace you" video. It's not a sponsored thing. It's me sharing what I learned after actually using it in my workflow, so you can skip the trial-and-error phase. You can find the breakdown here if you're interested in learning more: https://youtu.be/ayZnTA4dnZk?si=y0ri5-rU5ejwK4QV Happy to answer any questions in the comments, too. submitted by /u/KashuAcademy [link] [comments]

How to Create a Night Car Selfie with GPT Image 2.0? Prompt Included!

We tested a darker, more editorial-style car selfie concept with GPT Image 2.0, and the result felt surprisingly realistic. Instead of making a direct AI portrait, I wanted the shot to feel like a late-night iPhone photo taken inside a car. The main frame only shows the hand holding the phone, while the girl’s face appears inside the iPhone camera preview. That small framing choice makes the image feel much more natural, like a real candid lifestyle shot rather than a typical generated portrait. What makes this prompt work: the subject is only visible through the phone screen dark premium car interior warm blurry city lights outside the window realistic low-light noise and slight motion blur iPhone-style framing without flash cinematic shadows and moody night atmosphere It gives the image a more believable “captured by accident” feeling. Go to GPT Image 2.0 Generator Write the full prompt given below Upload your reference image Click to the "Generate" and get the edited image Prompt: "The photo is taken inside a car at night. Only a woman’s hand and the iPhone are visible in the frame; the girl’s face appears only on the phone screen. The camera is positioned from the passenger seat side, aimed toward the windshield and the phone being held in one hand in front of her. In her hand is the latest black iPhone Pro in horizontal position. On the screen, the iPhone front camera interface is open with visible camera buttons, focus frames, and UI elements. On the phone screen, a close-up of the girl’s face inside the car is visible: her lips are slightly parted and she is touching her lower lip with a thin black object resembling a lip pencil. The girl on the screen is wearing black clothing, softly illuminated by the phone’s light. The hand holding the phone has long fingers with a short square French manicure. The rest of the frame is very dark; the car interior is black and premium-looking, with part of the window and dashboard visible. Outside the window is a nighttime street with warm blurry city lights, dark tree silhouettes, and subtle reflections of light on the glass. The shot is very dark with a cinematic night aesthetic and rich lifestyle mood, 9:16 ratio. Shot on an iPhone at night without flash, realistic photo, slight motion blur, high-contrast shadows, no filters, do not blur the background completely. Hair is voluminous." Would love to see other versions of this kind of indirect selfie / phone-screen framing. Share your similar night car iPhone selfie photos below! submitted by /u/DataGirlTraining [link] [comments]

Coffee, Claude, and Remotion is all you need to make launch videos.

https://reddit.com/link/1tik0qe/video/9bh6ypr3ca2h1/player A few hours, Claude Code + Remotion, 4 black coffees, no design tools, no After Effects, no editor. The whole trick: Remotion is React for video. You write JSX, you get an mp4. Every animation is interpolate(frame, [start, end], [from, to]). That means Claude Code can write the entire video for you — it already knows React, animation is just numbers, and you can iterate the same way you iterate on a landing page. Change a value, re-render, see what happens. That feedback loop is the whole unlock. I described the scenes I wanted, Claude wrote them, I tweaked timing and cut whatever felt slow. 5 small things that made it not look like a dev made it: Crossfade every cut. Don't hard-cut between scenes. Overlap them and blur-fade. Instantly stops feeling like a slideshow. One easing curve everywhere. cubic-bezier(0.22, 1, 0.36, 1) (expo-out) on every animation. Consistency in motion is 80% of "looks designed." Film grain + vignette overlay. Two dumb components on top of everything — SVG noise at 2% opacity, soft dark vignette. Cheapest cinematic upgrade in existence. Layered audio, not one track. Background music low, plus targeted SFX - whoosh only on chapter cuts, typing during the hook, pop on the CTA. Overdoing SFX is the #1 amateur tell. Cut ruthlessly. If a scene doesn't earn its place in 3 seconds, kill it. The first cut is always too long. Stack: Remotion, React, TypeScript, Claude Code, Google Fonts (DM Sans + Crimson Pro), a few SFX from freesound.org, one royalty-free background track. $0 in tools. Bonus meta thing: the video isn't a screen recording of my product. It's a Remotion-built launch video that features a real video output from my product (the Cultured AF deck one). So I used InkMotion to make the demo footage inside the launch video. Probably should've just used InkMotion to make the whole launch video and saved the 4 coffees. Next time. Happy to answer specifics in the comments. submitted by /u/Top_Commission_8567 [link] [comments]

How to Create Viral Stadium Fan Cam Storyboards with GPT Image 2? Prompt Below!

This was one of the most realistic storyboard styles I’ve generated recently with GPT Image 2. The goal was to recreate the feeling of a real televised football broadcast mixed with cinematic commercial production — authentic crowd emotion, live camera imperfections, shallow telephoto depth of field, broadcast overlays, and natural sponsor integration. What makes this style work so well: realistic stadium crowd energy sports TV broadcast aesthetics cinematic advertisement framing emotional candid reactions ultra realistic lighting and skin texture natural product placement that feels like a real sponsorship commercial The storyboard panels can later be animated inside Seedance, Kling, Veo, or similar AI video tools to create a full fan-cam style commercial sequence. Tools used: GPT Image 2 → storyboard generation Seedance / Kling → animation & motion Prompt: "Hyper-realistic cinematic storyboard sheet for a 15-second sports broadcast commercial, beautiful stylish woman with natural blonde wavy hair wearing a cream sleeveless turtleneck knit top and pearl earrings sitting naturally among real football audience inside a packed stadium, yellow and blue fans cheering in background, realistic live sports broadcast camera perspective, authentic stadium lighting, soft cinematic blur, realistic skin texture and facial details, natural candid expressions, she watches the football match intensely while holding a blue Japanese premium beverage can naturally in her hand, realistic crowd interaction, broadcast scoreboard overlays, sports network watermark, smooth TV-commercial camera shots, ultra realistic photography style, documentary sports coverage aesthetic, realistic depth of field, live match atmosphere, product integrated naturally like real sponsorship footage, final shot close-up where she smiles and blows a flying kiss toward the camera, emotional crowd energy, cinematic realism, premium advertisement production storyboard layout, professional shot sequence panels, real broadcast feeling, highly detailed realistic storyboard sheet --ar 16:9" Would love to see more people experimenting with this format. submitted by /u/DataGirlTraining [link] [comments]

Waiting for your prompt to finish? ssh vimarcade.app to play games in terminal!

open a terminal type: ssh vimarcade.app type yes and begin playing! These games were designed to assist with learning vim motions, so hjkl are the primary movements. See if you can top the leaderboard! (Vim Flyer has a secret menu too - type w for WASD mode and i for Insantity mode). (I created the vision, game selection, and vibe, claude opus built the plan and claude code executed.) submitted by /u/awriterabroad [link] [comments]

I replicated Anthropic's Generator-Evaluator harness to build a website through 12 adversarial AI iterations - here's the result and what I learned

Anthropic recently published their harness design for long-running apps — a multi-agent architecture inspired by GANs where a Generator builds code and an Evaluator critiques it in a loop. I built my own version using Kiro CLI and used it to generate a marketing website for my project Mnemo (persistent memory for AI coding agents). The architecture: Planner (runs once) → Generator ↔ Evaluator (12 iterations) Each agent is a separate CLI process with zero shared context. They communicate only through files (spec.md, eval-report.md). The Evaluator uses Playwright to actually browse the live site — not just read code. What made it work: Clean slate per invocation — each agent starts fresh, reads only its input files. Prevents context anxiety. Playwright MCP for testing — the evaluator navigates, clicks, resizes viewports. Catches visual bugs code review never would. Anthropic's frontend design skill — explicitly penalizes generic AI patterns (Inter font, purple gradients, card layouts). Forces creative risk-taking. Continuous iteration, not retry-on-failure— all 12 rounds run regardless. Each one improves. The progression was wild: Iteration 1: Exactly what you'd expect from AI — functional but forgettable Iteration 4: Generator pivoted to "Terminal Noir" — IBM Plex Mono, amber on black, grain textures, scanlines. This is the kind of creative leap that doesn't happen in single-shot generation. Iterations 5-12: Polish, accessibility, responsive fixes, reduced-motion support Stats: Total time: 3h 20min Iterations: 12 (generator + evaluator each) Manual code written: 0 lines (I fixed a few visual issues after) Tech: Next.js, Tailwind, Framer Motion, TypeScript Live result: https://mnemo-mcp.github.io/Mnemo/ Documentation : https://github.com/Mnemo-mcp/Harness Key takeaway: The model is the engine. The harness — the constraints, feedback loops, and adversarial structure around it — is what determines whether you get AI slop or something genuinely distinctive. submitted by /u/killerexelon [link] [comments]

Claude picks theta measured from the top of the pendulum while GPT-4o picks it from the bottom, and you can see the difference in about two seconds

I was running the same double pendulum prompt through Claude and GPT-4o side by side, both panels rendering through the same host drawer, and within seconds the two simulations looked like completely different physical systems. Took me a minute to figure out what was happening. Claude measured theta from the up vertical (so theta=0 means the arm is pointing straight up). GPT-4o measured theta from the down vertical (theta=0 means the arm hangs straight down). The host renderer in public/workers/simulator-host.js just reads info.theta1 and info.theta2 and draws, so whatever convention the model chose is exactly what you see on screen. No drawing tricks, no style differences. The visual mismatch is a real physics mismatch. The thing that made this click for me is that both conventions are technically valid. Most classical mechanics textbooks use theta from the down vertical because it makes the equilibrium point theta=0, which is tidier for small angle approximations. But theta from the up vertical is also standard in plenty of references. Claude just... picked the other one. And it committed to it consistently through the equations of motion, the initial conditions, everything. It wasn't wrong, it just made a different choice than GPT-4o on an ambiguous part of the prompt. What's interesting from a Claude behavior perspective is that this isn't a reasoning failure or a hallucination. The code Claude produced was internally consistent. The equations of motion were correct for its chosen convention. The Runge Kutta integration was clean. It just interpreted "theta" differently than the other model did, and because both panels render through one shared host drawer, that interpretive difference became immediately visible. I noticed this while working on Physics Bench, an open source side by side benchmark where every model gets the same generation contract (function createSimulator(...) in lib/prompt.ts) and the host owns all rendering. Models only implement step, getInfo, and reset. They never touch draw. So any visual difference between panels is guaranteed to come from a real difference in the simulation logic, not from cosmetic rendering choices. Built with Verdent. The conversation inspector on each panel shows the full transcript, and Claude's reasoning about the coordinate system is right there in the code comments it generated. It explicitly noted its convention choice. GPT-4o did not comment on its choice at all, just used the other one silently. I keep going back to the fact that a unit test of the math would not have caught this. Both models produce correct physics for their respective conventions. You only see the split when you render them next to each other through the same drawing code. submitted by /u/Ill_Awareness6706 [link] [comments]

Looking for an AI / system to basically manage my entire life 😭 Does this even exist?

Hi everyone, I genuinely feel overwhelmed and I’m wondering if there’s an AI tool, app, or system that can help me organize basically my entire life. I’m juggling a demanding full-time job, university, building a business from scratch, personal finances, and full wedding planning, and I feel like I need a personal chief of staff / executive assistant for life 😭 I’m looking for something that could help with: Work/project management (prioritizing, deadlines, helping me think through work) Calendar & scheduling (actually time-blocking and organizing my days realistically) Finances/budgeting and helping me stay on track financially Entrepreneurship/business building from scratch (planning, prioritization, next steps) University/studying support Wedding planning (timelines, vendors, budgets, to-do lists, reminders, etc.) Personal goals, habits, routines, and becoming a more organized/productive version of myself What I’m looking for is something that feels like a life operating system, not just a chatbot that answers questions. Ideally, I’d love something that: helps me decide what to prioritize reorganizes things when I inevitably fall behind 😅 integrates with calendars/tasks feels proactive instead of reactive I struggle a lot with overwhelm and procrastination when too many things pile up, so if you’ve found a setup that genuinely changed your life, I would LOVE recommendations. What are you actually using? One tool? A stack of tools? AI agents? Claude, ChatGPT, Motion, Notion, Reclaim, Goblin Tools, Sunsama, something else? And most importantly: what actually works in real life? submitted by /u/Lucky_Lie_917 [link] [comments]

Can we acknowledge that Anthropic watches open sourcers and copies them?

I’ve been seeing over the past few months an interesting phenomenon, an open sourcer makes a tool or MCP < Anthropic adds functionality for that exact thing a couple weeks later < repeat. The biggest examples are Openclaw (like 5 features, including cowork), persistent memory across chats, and latest example of the “goal” feature being added. This is obvious and I’m not really saying anything that’s revolutionary here, I’m sure we’ve all noticed it. My larger observation, no credit is given, they’re just copying and then providing a direct replacement for things open sourcers thought of. At this level, we’re all learning from each other. AI like it is right now is very new and you could even argue that they’re not copying, that we’re all just thinking the same things. The deeper issue though is that this shows a dystopian effect of AI, the big companies get the credit widely for things others have done. More people have heard about Claude cowork than have heard about Openclaw, and the result of the guy who made it was getting a job at OpenAI. He wasn’t able to make this into a business, it’s not how open source has been for the past 20 years where an idea can be copied but not completely absorbed. Ideas are being absorbed, the person who made it doesn’t get credit by the masses, then gets hired by the companies that take their ideas. Is this a bad thing per se? Hard to fully know yet but it creates a weird dynamic where anything you put out there about MCPs or AI is gonna be absorbed and you won’t get credit for it. What if this expands into other industries and professions? Is this something that would be good in the scientific field? Imagine if Newton discovered the laws of motion but he used AI to formalize the equations, the AI companies saw the chats, took the idea directly from him, and he gets no credit. We’re sprinting towards a future where all that exists is the big companies, they get the credit and make the decisions. Sounds a lot like we’re becoming the coal miners living in company towns again, not owning anything or getting any credit, just being a cog in the machine. Edit: grammar submitted by /u/TheOnlyVibemaster [link] [comments]

Sharing OpenPets, a live usage and task-status for Claude code and Cowork

Codex Pets are fun and I'm often switching between Claude Code and OpenCode so I built OpenPets, an open-source project with a native macOS desktop app providing a CLI and an MCP server to connect any agents. A Swift library is included you can use to embed the system in your own apps. There's also a plugin system to build on top. This is how you get live weekly Claude usage or the battery status. You can imagine how cool it can become when you extend the animation sprites to support more motions or ambient animations: - a weather plugin and the right assets could bring rain to your character - a low battery could make your character go to sleep. Pure fun project but notifications and quick data in the cloudy bubbles are really useful to me. https://github.com/alterhq/openpets submitted by /u/samuelroy_ [link] [comments]

I told Claude to create a PS5 controller in blender

nice try submitted by /u/Ok-One1885 [link] [comments]

V-JEPA 2.1's dense features are partitioned: a robustness study across all four model sizes [R]

I ran a pre-registered robustness study on Meta's V-JEPA 2.1 across all four released model sizes (80M → 2B). 322-cell sweep Three findings worth flagging: 1. Dense features are partitioned. M2 (representational drift between clean and perturbed clips, measured as cosine distance on temporal-gradient vectors) predicts downstream task failure on DAVIS for temporal corruption (frame drops r=0.37 [0.30, 0.44], occlusion r=0.35 [0.28, 0.42]). For image-noise corruption, the correlation is statistically indistinguishable from zero (Gaussian r=−0.06, motion blur r=+0.09, low-light r=+0.05; all CIs cross zero). The two perturbation families are statistically separable at 95% confidence (closest CI gap +0.106). Aggregate r=0.16 [0.13, 0.20] is below both the pre-registered ambiguous threshold (0.30) and confirmation threshold (0.50). 2. Bigger is not reliably better. Every Tier 1 perturbation showed non-monotonic robustness. The 2B "gigantic" model is less robust than the 1B "giant" variant on three of the five perturbations. All jumps >5× their pooled CI half-width. 3. V-JEPA 2.1 is meaningfully orientation-sensitive. Horizontal flip preserves all temporal structure but disrupts representations comparably to playing the video backwards (M2 = 0.91 across all models vs. predicted upper bound of 0.30). Not orientation-equivariant out of the box. Six hypotheses pre-registered with explicit numerical decision rules. Two confirmed, three refuted, one partially withdrawn during analysis - the M1 component of H2 turned out to be ill-defined under reverse playback (M1 assumes preserved frame ordering, which time-axis perturbations break). Documented and not buried. Proposed mechanism for the non-monotonic scaling result: hub marginalization in deep ViTs (arXiv:2511.21635). Deeper models can over-shoot from "single hub aggregator" to a regime where extra layers scramble information rather than refine it. V-JEPA's dense predictive loss explicitly pushes against single-hub aggregation; if the 2B variant has crossed into the over-communication regime while the distilled 300M retains controlled mixing, the pattern is what hub marginalization predicts. Code, reproducibility manifest, raw shards: https://github.com/poisson-labs/vjepa-stress Full writeup: https://poissonlabs.ai/research/vjepa-2-1-robustness Happy to discuss methodology, the partitioning interpretation, or the hub-marginalization argument. The image-noise side of partitioning (gaussian/motion blur/low-light CIs all crossing zero) is the part I'd most like skeptical eyes on. submitted by /u/poisson_labs [link] [comments]

A New Way to Explore Tech With Claude

Hi r/ClaudeAI, This project I developed was inspired by the heavy hallucinating and lazy searching that Claude and other AIs experience when searching for products. I built this website with Claude Code (praise to its Vercel and Supabase skills :) specapis.com is a new way for you to interact with Claude to find specs, release dates, reviews and more. Now live with 5000+ monitors that makes finding your perfect fit one prompt away! You can test it by pasting this into Claude: Use https://specapis.com/. My monitor question: best oled 27in It is free forever and I am planning on expanding the specs beyond monitors; to PC parts, speakers and more! submitted by /u/Consistent_Sky5871 [link] [comments]

Any implementations similar to D4RT? [D]

Deepmind released a paper on D4RT at the start of this year which crucially enabled a “4D” understanding of the world via structure from motion and generating: 1. Point cloud reconstruction from 2D videos (not static scenes) 2. Camera pose estimation You could pass in a video of a dog walking on a beach and it would estimate the 3d representation of the beach and the dog at any point in time. They did not release the model though. Are there any open source, available implementations of anything similar now? submitted by /u/reddysteady [link] [comments]

Introducing AI finetuner, Source available and free Claude skill to fine tune your vibe coded UI with live preview

​ Fine-tuning UI with AI right now: "Make the shadow softer." "Stronger." "No, less." "Go back." "A bit more." 17 messages later, you've spent more tokens than the shadow is soft. I built something that breaks the loop. AI Fine-Tuner — free, source-available — a plugin that teaches AI coding agents to stop chatting and hand you an actual GUI for your component. Sliders. Color pickers. Live preview. Drag until it feels right. The AI agent automatically opens the editor window for you on your default browser once ready. Then the magic part: you click one button. The tuner outputs a structured handoff with your exact tuned values mapped to their targets in your code. Paste it back to your AI — it reads the mapping, opens your source, and applies everything precisely. No CSS guesswork, no syntax translation, nothing for you to interpret. Why it's not just another slider playground: Bespoke controls — no raw CSS names Sliders are named in plain English: "Glow softness", "Card lift", "Hover intensity" — not "box-shadow-spread-radius" A single slider can drive multiple properties at once. The AI doesn't expose CSS to you; it wires meaningful, human-named controls to your element. 3 prebuilt editor templates — guaranteed polish, every time The AI doesn't design the editor. It picks one of three prebuilt templates and fills in your component: - single.html — 1 control, full-screen preview - small.html — 2-4 controls, preview + bottom grid - full.html — 5+ controls, grouped sidebar + preview Slider chrome, color picker, layout, animations, infinite canvas with zoom/pan — all pre-built. No "the AI generated an ugly panel" failure mode. And once it's open, you tune in pure browser JS — no AI sitting in the loop per drag. Color picker + hex paste Pick it or paste it. Done. Animation tuning Not just static styles — timing, easing, keyframes too. Works on ANY platform — language-agnostic Flutter, SwiftUI, React Native, Tailwind, vanilla CSS, SVG — the AI is meta-prompted to rebuild your component in HTML/CSS for the tuning preview (the web is where sliders work). When you copy back, the AI applies the tuned values to your real source, in your component's original framework. You never leave Flutter to tune Flutter. Infinite canvas + multiple previews Drop 5 variations side-by-side and tune them together. The template is a starting point — experiment freely. Contextually named presets Every tuner ships with thoughtful presets ("Subtle," "Bold," "Brutalist," whatever fits) so you can ping-pong through variations in one click. No new software It's a skill, not an app. Full install guides for Claude Code. One command and you're in. Website and Live demos: https://muhamadjawdatsalemalakoum.github.io/aifinetuner Free. Source-available. #AI #DeveloperTools #ClaudeCode #BuildInPublic #OpenSource #AITools #FrontendDev submitted by /u/keonakoum [link] [comments]