this is pure f*cking treasure Netflix engineers posted 20 GitHub repos that turn your Opus 5.5 into a f*cking motion empire: built for Opus 5.5 1. claude-motion: taste, checks its own frames, sound written in code. the author built it for Opus 5.5 2. claude-motion-design: HTML, Playwright and ffmpeg, no Remotion. the demo was made on Opus 5.5 3. motion-graphics-skills: 13 skills for launch videos. the README tells you to run it on Opus 5.5 4. claude-remotion-skill: a Remotion editor from one prompt. captions, B-roll, sound 5. klik-anim-skill-creation: the laws of animation and camera, built on Opus 6. hyperframes-motion-reel-skill: a showreel cut to the beat at 120 BPM, on HyperFrames 7. product-launch-motion: a full launch film. voice-over, camera, color grade 8. animate: a short clip in one canvas file, storyboarded as it goes 9. motion-design-skills: timing, color, typography, logos, Remotion 10. hyperframes-student-kit: 14 skills and 406 motion graphics cards the engines these skills render with 11. hyperframes: HTML to MP4. the one almost everyone installs 12. remotion: video written in React 13. remotion skills: the official Remotion skills for your agent 14. GSAP: the timelines HyperFrames runs on. skills: npx skills add greensock/gsap-skills taste and everything around it 15. motion-design-skill: motion principles that work with any engine 16. awesome-motion-design-agent-skills: a map of GSAP, Remotion, Lottie and Rive skills 17. motion-graphics: B-roll built straight from your transcript 18. motion-dev-animations-skill: web animation on not video 19. motion: Motion itself, formerly Framer Motion 20. lottie-web: when you need Lottie, not MP4 the stack in one line: Opus 5.5 writes the code, a skill sets the timing and the camera, an engine renders the MP4. check each repo before you install. save this.

me after a video call with the first AI that passed the Turing test tavus gave me early access to griffin, and i'm still processing it 48% of people who talked to it live thought they'd talked to a real human. every system before it, tavus's own included, topped out at 2% it's not a chatbot with a face. it's a human interaction model: video in, video out, fully duplex. it listens while it talks and reads your pauses and expressions, not just your words: > waves back before you say a word > notices someone walking in behind you before you turn around > cuts you off mid-ramble and tells you what you left out NVIDIA's independent benchmark: griffin 3.83 out of 5. a real human: 3.92. next best system: 2.80 and this was griffin-lite, the research preview. the more powerful model isn't out yet @tavus @hassaanrza

I found the optimal stack. Jev + Picsart, and it turned into a factory for AI girls on social. before this I was paying $277 a month to keep eleven tabs open. $3,300 a year, most of them idle between projects, all of them billing anyway. but money was never the thing that broke. consistency was. one model makes the face. another makes the body move. a third does the voice. every handoff is an export and a re-upload, and by the fourth post the character is not the same person anymore. that is what kills an account. not quality. drift so I stopped treating it as a prompt problem and built it as a graph. Jev decides. Picsart runs. 188 models from 34 providers behind one bar, and Jev picks which one gets each job instead of me opening another tab. eight seats, one rule each: > BRIEF holds who she is. it never regenerates, everything else reads from it > ROUTER picks the model per job and shows the credit cost before it spends > IMAGE runs 3 directions through 3 different models, side by side, not 9 variations from one > VIDEO only ever inherits an approved frame. it never starts from a fresh guess > AUDIO runs in the same environment, same session, same asset > EDIT transforms without leaving the workspace > REVIEW checks against the brief, not against "does it look cool" EXPORT the rule the whole factory hangs on: never let one generation decide the post. one prompt that returns a finished clip is not a workflow. it is a coin flip with good production values. when the face shifts, you have no idea which stage shifted it. three things that make it actually hold: the approved frame is the contract. video does not get a description of her, it gets the exact image the last stage signed off. that is how the character survives twenty posts instead of three. cost is visible before you commit. change duration or resolution and the credit number updates on the button. you find out what a clip costs before you spend it, not after. output saves with metadata and opens straight in the editor. no export, no re-upload, no asset you cannot trace back to the model that made it. and label the output as AI. the platforms have a flag for it, using it costs nothing, and an account that hides it gets found eventually. what none of this solves: whether she is actually interesting. a factory that makes posting cheap will let you post past the point anyone cares and never mention it. the models make her look real. only you decide if she is worth following. eleven invoices, or one graph

SHE TURNED KIDS YOUTUBE INTO A CASH MACHINE USING CLAUDE. $25,000/MONTH. THE WORKFLOW IS 40 SECONDS before: find a channel. hire animators. weeks of production after: screenshot → claude → higgsfield → animated video. 40 seconds this isn't a productivity hack. it's a new content pipeline > find a top performing kids channel. screenshot it. paste to claude. > claude analyzes the style. higgsfield MCP generates the animation. one prompt > finished animated video. same niche. same format. from scratch to done in under a minute

he spent $1,999 once. saved $21,000 in a year. NVIDIA DGX Spark on his desk was paying $180/month for cloud GPUs. every experiment had a price tag. every overnight run cost money. then he bought the machine 128GB unified memory. 1 petaflop at FP4. runs Qwen, Llama, Mistral locally. nothing leaves the machine. ever stack: Ollama. PyTorch. HuggingFace. vLLM. all compatible out of the box cloud cost per year: $2,160+. DGX Spark: $1,999 once. electricity after that: basically zero Jensen Huang hand-delivered the first units to Elon Musk at Starbase. fits next to a monitor. no rack. no server room. no cooling setup the math only works once. after that every inference run is free

THIS AGENT LEARNS FROM EVERY TASK. BUILDS ITS OWN SKILLS. REMEMBERS EVERYTHING 100K GitHub stars in 53 days. 160K+ now. 26K forks. 1,000+ contributors. what makes it different: > completes a task > writes a reusable skill from experience > gets faster next use > three-tier memory: remembers your projects, preferences, environment across every session > 200+ models via OpenRouter. Claude, GPT, Grok, Gemini, Ollama local one command > Telegram, Discord, WhatsApp, iMessage one gateway > built-in cron scheduler. sub-agents. MCP servers. web search. code execution > runs on a $5 VPS. 24/7. from your phone most AI agents: blank slate every session. Hermes: remembers everything. always