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How a 22-year-old developer built a full 3D Jet Ski racing game in just 40 minutes with zero manual coding He used Claude Opus 5 to generate physics, WebGL 3D graphics, HUD, and audio in a single prompt and turned single-prompt gamedev into a high-margin income stream. Costs: $423...

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Ridark

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11,486 次观看 • 4 天前 •via X (Twitter)

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20 days ago, I connected Claude Code to my newly created instagram handle.. I gained 4.3M views and 6500+ followers in less than a month [ i post Ai generated animated stories ] Full workflow: i let claude study my account before i write another reel.. This is the cleanest content workflow i've built on claude. give it your IG first. 4 prompts handle the rest.. niche research, the reel script, the hook, and the daily automation.. the whole loop is basically, give claude your IG → find what's working → write retention-optimized scripts → engineer the hook → automate the daily output.. ▫️ Setup: give claude your instagram open claude code. claude code has a built-in web tool that browses any public URL. or install any agentic browser like Browser Harness or Firecrawl or Comet browser paste this with your handle filled in: "Browse and pull the last 30 reels and posts. Analyze my recurring topics, top-performing hooks, formats, and engagement patterns. Then map out my actual audience and what they consistently respond to." claude reads your profile, pulls every reel down, and now has the context to personalize every prompt below to YOUR account, not a generic niche. if you're on claude desktop, the same works with firecrawl MCP connected. ▫️ Prompt 1 find what actually goes viral in your niche: "Analyze the highest-performing Instagram Reels, TikToks, and Reddit posts in the [niche] niche from the last 30 days. Identify repeating hooks, visual styles, emotional triggers, and content formats that consistently generate high engagement. Then summarize the 5 strongest content angles optimized for AI-generated content and short-form videos." run this after the setup. you get 5 angles backed by what's already working in your niche, cross-checked against what's already working on YOUR account. ▫️ Prompt 2 write a high-retention reel script "Write a short-form Instagram Reel script about [topic] with an aggressive hook in the first 2 seconds. Create immediate curiosity, tension, or controversy to stop scrolling, then deliver a fast and satisfying payoff. Keep it under 30 seconds and optimize the structure for watch time, replays, comments, and shares. Finish with a subtle CTA." the line that matters: "optimize the structure for watch time, replays, comments, and shares." claude writes for the metrics, not just the word count. ▫️ Prompt 3 engineer better hooks "Study the top-performing Reels in [niche] and break down the hook structure, pacing, and emotional triggers used in the first 3 seconds. Then generate 5 new hook variations that are even more curiosity-driven, emotionally charged, and optimized to stop scrolling instantly. Focus on triggers like surprise, fear, ego, urgency, or desire." most reels die in the first 2 seconds. this prompt has claude reverse-engineer what already works, then give you 5 sharper versions to swap in. ▫️ Prompt 4 automate the whole workflow "Build a complete AI-powered content workflow for Instagram in the [niche] niche. The system should identify trending topics daily, generate high-retention scripts, create matching AI visuals, turn them into short-form videos, and generate optimized captions and hashtags. Structure everything as a repeatable workflow designed for consistent daily posting and growth." once the niche and script structure are validated, this turns it into a daily loop. one prompt that handles topic → script → visual → video → caption. these 4 prompts are the building blocks. the setup is what makes them yours. your real value is in the [niche] you plug in. content workflow built in one weekend, daily posting on autopilot from monday.
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20 days ago, I connected Claude Code to my newly created instagram handle.. I gained 4.3M views and 6500+ followers in less than a month [ i post Ai generated animated stories ] Full workflow: i let claude study my account before i write another reel.. This is the cleanest content workflow i've built on claude. give it your IG first. 4 prompts handle the rest.. niche research, the reel script, the hook, and the daily automation.. the whole loop is basically, give claude your IG → find what's working → write retention-optimized scripts → engineer the hook → automate the daily output.. ▫️ Setup: give claude your instagram open claude code. claude code has a built-in web tool that browses any public URL. or install any agentic browser like Browser Harness or Firecrawl or Comet browser paste this with your handle filled in: "Browse and pull the last 30 reels and posts. Analyze my recurring topics, top-performing hooks, formats, and engagement patterns. Then map out my actual audience and what they consistently respond to." claude reads your profile, pulls every reel down, and now has the context to personalize every prompt below to YOUR account, not a generic niche. if you're on claude desktop, the same works with firecrawl MCP connected. ▫️ Prompt 1 find what actually goes viral in your niche: "Analyze the highest-performing Instagram Reels, TikToks, and Reddit posts in the [niche] niche from the last 30 days. Identify repeating hooks, visual styles, emotional triggers, and content formats that consistently generate high engagement. Then summarize the 5 strongest content angles optimized for AI-generated content and short-form videos." run this after the setup. you get 5 angles backed by what's already working in your niche, cross-checked against what's already working on YOUR account. ▫️ Prompt 2 write a high-retention reel script "Write a short-form Instagram Reel script about [topic] with an aggressive hook in the first 2 seconds. Create immediate curiosity, tension, or controversy to stop scrolling, then deliver a fast and satisfying payoff. Keep it under 30 seconds and optimize the structure for watch time, replays, comments, and shares. Finish with a subtle CTA." the line that matters: "optimize the structure for watch time, replays, comments, and shares." claude writes for the metrics, not just the word count. ▫️ Prompt 3 engineer better hooks "Study the top-performing Reels in [niche] and break down the hook structure, pacing, and emotional triggers used in the first 3 seconds. Then generate 5 new hook variations that are even more curiosity-driven, emotionally charged, and optimized to stop scrolling instantly. Focus on triggers like surprise, fear, ego, urgency, or desire." most reels die in the first 2 seconds. this prompt has claude reverse-engineer what already works, then give you 5 sharper versions to swap in. ▫️ Prompt 4 automate the whole workflow "Build a complete AI-powered content workflow for Instagram in the [niche] niche. The system should identify trending topics daily, generate high-retention scripts, create matching AI visuals, turn them into short-form videos, and generate optimized captions and hashtags. Structure everything as a repeatable workflow designed for consistent daily posting and growth." once the niche and script structure are validated, this turns it into a daily loop. one prompt that handles topic → script → visual → video → caption. these 4 prompts are the building blocks. the setup is what makes them yours. your real value is in the [niche] you plug in. content workflow built in one weekend, daily posting on autopilot from monday.

Axel Bitblaze 🪓

199,569 次观看 • 1 个月前

FABLE 5 + HIGGSFIELD TURN A $35,000 ANIMATED SITE INTO A ONE-SESSION, $12 BUILD. HERE'S EXACTLY HOW. a studio runs this across four people and three weeks. you run it across one chat window and one afternoon. THE BUILD, STAGE BY STAGE: STAGE 1 - THE CONCEPT Claude reads your brief and scripts the scroll before a line of code exists - what the visitor feels at second 3, 15, 40. prompt: "read this brief. script the scroll beat by beat, then scaffold the project with GSAP ScrollTrigger + Lenis." STAGE 2 - THE VISUALS (Higgsfield) every hero shot, transition, and ambient loop comes out of 30+ generative models - matched to the story, not pulled from a stock library. prompt: "generate the hero sting and one b-roll clip per section. 3-5s, high-res, cinematic." STAGE 3 - THE MOTION (Claude Code) Claude writes the ScrollTrigger timelines and Lenis smooth-scroll, extracts frames, optimizes every asset. zero hand-coded keyframes. prompt: "wire the scroll: pin the hero, scrub the video, reveal each section on scroll. keep it 60fps on mobile." STAGE 4 - THE POLISH six cinematic effects baked in, no config: film grain, particles, vignette, glass cards, color tints, scroll pacing. prompt: "bake in the cinematic layer, then QA load speed, mobile breakpoints, and whether the scroll actually lands - rewrite what doesn't." CONNECT HIGGSFIELD (MCP): add it as a custom connector in Claude Code: mcp_servers: higgsfield: url: " one OAuth flow. Claude generates and pulls clips directly - no exporting by hand. THE MATH: → what a studio charges: $6,000-$35,000+ → what it costs you: a Claude sub + a few dollars of Higgsfield credits → what it takes: 4 people + 3 weeks → 1 operator + 1 session the pipeline was the moat. it just became four prompts. Follow me, comment "MATH" and I'll send you the full step-by-step Playbook. full breakdown in the article 👇
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FABLE 5 + HIGGSFIELD TURN A $35,000 ANIMATED SITE INTO A ONE-SESSION, $12 BUILD. HERE'S EXACTLY HOW. a studio runs this across four people and three weeks. you run it across one chat window and one afternoon. THE BUILD, STAGE BY STAGE: STAGE 1 - THE CONCEPT Claude reads your brief and scripts the scroll before a line of code exists - what the visitor feels at second 3, 15, 40. prompt: "read this brief. script the scroll beat by beat, then scaffold the project with GSAP ScrollTrigger + Lenis." STAGE 2 - THE VISUALS (Higgsfield) every hero shot, transition, and ambient loop comes out of 30+ generative models - matched to the story, not pulled from a stock library. prompt: "generate the hero sting and one b-roll clip per section. 3-5s, high-res, cinematic." STAGE 3 - THE MOTION (Claude Code) Claude writes the ScrollTrigger timelines and Lenis smooth-scroll, extracts frames, optimizes every asset. zero hand-coded keyframes. prompt: "wire the scroll: pin the hero, scrub the video, reveal each section on scroll. keep it 60fps on mobile." STAGE 4 - THE POLISH six cinematic effects baked in, no config: film grain, particles, vignette, glass cards, color tints, scroll pacing. prompt: "bake in the cinematic layer, then QA load speed, mobile breakpoints, and whether the scroll actually lands - rewrite what doesn't." CONNECT HIGGSFIELD (MCP): add it as a custom connector in Claude Code: mcp_servers: higgsfield: url: " one OAuth flow. Claude generates and pulls clips directly - no exporting by hand. THE MATH: → what a studio charges: $6,000-$35,000+ → what it costs you: a Claude sub + a few dollars of Higgsfield credits → what it takes: 4 people + 3 weeks → 1 operator + 1 session the pipeline was the moat. it just became four prompts. Follow me, comment "MATH" and I'll send you the full step-by-step Playbook. full breakdown in the article 👇

ZEUS⚡️

47,174 次观看 • 29 天前

OPUS 5 + HIGGSFIELD TURNED RETRO MARIO INTO A HYPER-REALISTIC NEXT-GEN SHOOTER I built this entire concept without relying on traditional game engines, hiring expensive 3D artists, or waiting through a decade-long development cycle, simply because I decided to take a harmless 8-bit childhood platformer and ask a single question: what if it was rebuilt today as an aggressive AAA tactical FPS? this is what came back. 00:00 - the pixelated 2D platformer abruptly morphs into a photorealistic tactical arena right as the camera swings into action 00:03 - the hero pulls up a custom assault rifle featuring a glowing 'M' emblem while racking live ammunition in sharp first-person view 00:06 - I strafe behind a crumbling brick wall to spray full-auto rounds directly at terrifying, realistic Goomba-monsters charging forward 00:09 - a final grenade launch hits a burning Bowser castle while real-time hit-markers flash continuously across the screen The sequence delivers true first-person bodycam immersion complete with heavy weapon recoil and a fully functional tactical HUD tracking ammo, health, and minimap positioning. Every single frame was generated seamlessly in real time without a single 3D model being crafted by hand. While Nintendo spends tens of millions of dollars dragging out massive production cycles, I pulled this entire demo off in one afternoon using a single text prompt and zero lines of code.
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OPUS 5 + HIGGSFIELD TURNED RETRO MARIO INTO A HYPER-REALISTIC NEXT-GEN SHOOTER I built this entire concept without relying on traditional game engines, hiring expensive 3D artists, or waiting through a decade-long development cycle, simply because I decided to take a harmless 8-bit childhood platformer and ask a single question: what if it was rebuilt today as an aggressive AAA tactical FPS? this is what came back. 00:00 - the pixelated 2D platformer abruptly morphs into a photorealistic tactical arena right as the camera swings into action 00:03 - the hero pulls up a custom assault rifle featuring a glowing 'M' emblem while racking live ammunition in sharp first-person view 00:06 - I strafe behind a crumbling brick wall to spray full-auto rounds directly at terrifying, realistic Goomba-monsters charging forward 00:09 - a final grenade launch hits a burning Bowser castle while real-time hit-markers flash continuously across the screen The sequence delivers true first-person bodycam immersion complete with heavy weapon recoil and a fully functional tactical HUD tracking ammo, health, and minimap positioning. Every single frame was generated seamlessly in real time without a single 3D model being crafted by hand. While Nintendo spends tens of millions of dollars dragging out massive production cycles, I pulled this entire demo off in one afternoon using a single text prompt and zero lines of code.

Shadow Nick

15,838 次观看 • 17 天前

FABLE 5 + HIGGSFIELD JUST KILLED THE $35,000 WEB AGENCY. The same animated website that used to cost between $6,000 and $35,000 can now be built in a single session for around $12 in AI credits. Claude Code handles the website. Higgsfield creates the visuals. Together, they can build a complete scroll-driven website from a simple prompt. Claude writes the layout, GSAP ScrollTrigger animations, Lenis smooth scrolling, responsive pages, and checks everything before you ship. Higgsfield generates the hero videos, cinematic transitions, ambient loops, thumbnails, and every visual asset you need. By the end of one session, you have a fully animated website with cinematic motion, smooth scrolling, optimized assets, responsive layouts, and polished visual effects like film grain, particles, vignette, glass cards, and color tints. Getting started only takes a few minutes. Add Higgsfield as an MCP server inside Claude Code, complete the OAuth login once, and Claude can generate and pull videos directly into your project without manually exporting anything. The prompts are simple. Give Claude your project brief and ask it to script the entire scroll experience. Tell it to generate a hero video and supporting clips for every section. Ask it to add the finishing touches like film grain, particles, glass cards, and scroll pacing. Then let it review the site, improve loading speed, fix mobile layouts, and rewrite anything that doesn’t work. This replaces work that usually looks like this: A $6,000-$35,000 web agency. An $800-$2,000 motion designer. A $2,000-$10,000 front-end developer. And weeks of back-and-forth before launch. Now it’s Fable 5, Higgsfield, a subscription, a few dollars in AI credits, and one session. The pipeline used to be the advantage. Now it’s just a prompt. Full breakdown in the article below.
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FABLE 5 + HIGGSFIELD JUST KILLED THE $35,000 WEB AGENCY. The same animated website that used to cost between $6,000 and $35,000 can now be built in a single session for around $12 in AI credits. Claude Code handles the website. Higgsfield creates the visuals. Together, they can build a complete scroll-driven website from a simple prompt. Claude writes the layout, GSAP ScrollTrigger animations, Lenis smooth scrolling, responsive pages, and checks everything before you ship. Higgsfield generates the hero videos, cinematic transitions, ambient loops, thumbnails, and every visual asset you need. By the end of one session, you have a fully animated website with cinematic motion, smooth scrolling, optimized assets, responsive layouts, and polished visual effects like film grain, particles, vignette, glass cards, and color tints. Getting started only takes a few minutes. Add Higgsfield as an MCP server inside Claude Code, complete the OAuth login once, and Claude can generate and pull videos directly into your project without manually exporting anything. The prompts are simple. Give Claude your project brief and ask it to script the entire scroll experience. Tell it to generate a hero video and supporting clips for every section. Ask it to add the finishing touches like film grain, particles, glass cards, and scroll pacing. Then let it review the site, improve loading speed, fix mobile layouts, and rewrite anything that doesn’t work. This replaces work that usually looks like this: A $6,000-$35,000 web agency. An $800-$2,000 motion designer. A $2,000-$10,000 front-end developer. And weeks of back-and-forth before launch. Now it’s Fable 5, Higgsfield, a subscription, a few dollars in AI credits, and one session. The pipeline used to be the advantage. Now it’s just a prompt. Full breakdown in the article below.

MIKE

140,288 次观看 • 1 个月前

american guy made $10,845 in a single month from a kids youtube channel he built with AI no camera, no animation skills, no voiceover. openart writes the script, generates the characters, renders the whole episode the numbers sound fake. first month the channel barely moved. that's where most people quit then one short hit 800K views. youtube started pushing it. and month three came in over $10,845 his whole workflow fits in one prompt. he opens openart, types a description of a 3D animated nursery scene, and the tool spits out a full short film ready to upload he showed it live. one prompt, one render, one upload. he can also vibe direct longer videos up to 5 minutes for higher CPM kids content is the highest-paying niche on youtube. parents leave it on autoplay for hours. one video can loop all day and the algorithm keeps feeding it to new viewers cocomelon does 10M views per video with a full studio. he does 100-200K views per video with a laptop. the CPM on kids content does the math for him no face on camera. no brand deals. no editing software. just prompts and uploads this used to take a pixar-level team and months of rendering. now it takes one AI tool and a free afternoon full workflow is in the video - save it before youtube patches the meta
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american guy made $10,845 in a single month from a kids youtube channel he built with AI no camera, no animation skills, no voiceover. openart writes the script, generates the characters, renders the whole episode the numbers sound fake. first month the channel barely moved. that's where most people quit then one short hit 800K views. youtube started pushing it. and month three came in over $10,845 his whole workflow fits in one prompt. he opens openart, types a description of a 3D animated nursery scene, and the tool spits out a full short film ready to upload he showed it live. one prompt, one render, one upload. he can also vibe direct longer videos up to 5 minutes for higher CPM kids content is the highest-paying niche on youtube. parents leave it on autoplay for hours. one video can loop all day and the algorithm keeps feeding it to new viewers cocomelon does 10M views per video with a full studio. he does 100-200K views per video with a laptop. the CPM on kids content does the math for him no face on camera. no brand deals. no editing software. just prompts and uploads this used to take a pixar-level team and months of rendering. now it takes one AI tool and a free afternoon full workflow is in the video - save it before youtube patches the meta

0xbobaa

48,037 次观看 • 15 天前

Claude Fable 5 + Claude Design is f*cking insane 🤯 Anthropic just dropped its most intelligent model ever, and the first thing I pointed it at was email design. I built a complete email campaign design in Claude Design, and the difference is night and day: tighter layouts, cleaner hierarchy, on-brand from the first generation. All inside Claude Design with Fable 5. Perfect for DTC brands and agencies who are still paying email agencies $3-5K/month for campaign designs that take 2 weeks to ship. If your campaign calendar is packed but every new email means briefing a designer, waiting on mockups, sending notes, and waiting again... This workflow eliminates the entire bottleneck: → Load your brand design system into Claude Design once (colors, fonts, logo, button styling) → Switch the model to Claude Fable 5 — Anthropic's new state-of-the-art model with the best vision of any AI → Prompt the campaign email section by section: header, hero, headline, offer block, CTA → Fable 5 nails layout and brand details that older models fumbled → Iterate inline — swap images, adjust styling, color-pick directly in the canvas → Export the finished email and hand off to your ESP No briefing a designer. No 2-week turnaround on a single campaign. No paying an agency $4K/month for 4 emails. What you get: → Campaign emails designed in minutes, not weeks → A reusable design system every new email pulls from automatically → Noticeably smarter design decisions from Fable 5's upgraded vision → Full inline editing before anything touches your ESP Built 100% with Claude Design + Claude Fable 5. I recorded a full walkthrough showing exactly how this works. Want it for free? > Like this post > Comment "FABLE" And I'll send it over (must be following so I can DM)
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Claude Fable 5 + Claude Design is f*cking insane 🤯 Anthropic just dropped its most intelligent model ever, and the first thing I pointed it at was email design. I built a complete email campaign design in Claude Design, and the difference is night and day: tighter layouts, cleaner hierarchy, on-brand from the first generation. All inside Claude Design with Fable 5. Perfect for DTC brands and agencies who are still paying email agencies $3-5K/month for campaign designs that take 2 weeks to ship. If your campaign calendar is packed but every new email means briefing a designer, waiting on mockups, sending notes, and waiting again... This workflow eliminates the entire bottleneck: → Load your brand design system into Claude Design once (colors, fonts, logo, button styling) → Switch the model to Claude Fable 5 — Anthropic's new state-of-the-art model with the best vision of any AI → Prompt the campaign email section by section: header, hero, headline, offer block, CTA → Fable 5 nails layout and brand details that older models fumbled → Iterate inline — swap images, adjust styling, color-pick directly in the canvas → Export the finished email and hand off to your ESP No briefing a designer. No 2-week turnaround on a single campaign. No paying an agency $4K/month for 4 emails. What you get: → Campaign emails designed in minutes, not weeks → A reusable design system every new email pulls from automatically → Noticeably smarter design decisions from Fable 5's upgraded vision → Full inline editing before anything touches your ESP Built 100% with Claude Design + Claude Fable 5. I recorded a full walkthrough showing exactly how this works. Want it for free? > Like this post > Comment "FABLE" And I'll send it over (must be following so I can DM)

Mike Futia

42,932 次观看 • 2 个月前

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.
0:17

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

22,390 次观看 • 1 个月前

This guy built an AI pipeline that generates hyperrealistic fashion models in 47 minutes and now dropshippers pay him $1,400 to clone the entire system. He got tired of watching e-com brands lose $8K per photoshoot when a single product angle changed so he built a 9-node workflow that generates 127 product videos from one Pinterest photo without hiring a single model. Here's the exact breakdown: → Claude writes a 34-parameter JSON brand DNA before any image is touched target psychographics, price anchor, vibe matrix, anti-inspiration blacklist → Pinterest becomes the model source library but you can't just download and animate → Kling 2.6 takes that static JPG and turns it into 5-second video but only after the prompt architecture is locked → Negative prompt node runs 41 exclusion terms: no plastic skin, no CGI glow, no symmetry artifacts, no doll face, no synthetic lighting → That one step kills the "AI look" that tanks engagement by 67% in the first 3 seconds → TikTok Studio uploads 19 videos in one batch with zero manual captioning because the brand voice was pre-programmed in step one → Atlas scrapes Amazon product links and auto-generates a Shopify store with hero images, pricing tiers, scarcity copy, and mobile-optimized checkout in 90 seconds → The store goes live before the first TikTok video finishes processing The key move 94% of people skip: you can't animate the photo before you inject the negative prompt. If you send a raw Pinterest image straight into image-to-video the face morphs into a wax figure. The fabric loses texture. The hands grow extra fingers. The whole thing screams "AI" and your CTR dies. His system runs the exclusion filter first so the model moves like she's shot on an iPhone 15 Pro in natural light. One brand hit 2.6M views on TikTok in 11 days with zero paid ads and converted at 3.7% because the videos looked like organic UGC not polished studio content. Brands now pay him $1,400 for the full pipeline setup + $340/month to keep the store synced with new product drops and seasonal video batches. The entire system runs on $23/month in API costs and one laptop. No photographer. No model agency. No product samples. Just a prompt template, a Pinterest account, and the discipline to filter out the AI artifacts before you render movement.
0:10

This guy built an AI pipeline that generates hyperrealistic fashion models in 47 minutes and now dropshippers pay him $1,400 to clone the entire system. He got tired of watching e-com brands lose $8K per photoshoot when a single product angle changed so he built a 9-node workflow that generates 127 product videos from one Pinterest photo without hiring a single model. Here's the exact breakdown: → Claude writes a 34-parameter JSON brand DNA before any image is touched target psychographics, price anchor, vibe matrix, anti-inspiration blacklist → Pinterest becomes the model source library but you can't just download and animate → Kling 2.6 takes that static JPG and turns it into 5-second video but only after the prompt architecture is locked → Negative prompt node runs 41 exclusion terms: no plastic skin, no CGI glow, no symmetry artifacts, no doll face, no synthetic lighting → That one step kills the "AI look" that tanks engagement by 67% in the first 3 seconds → TikTok Studio uploads 19 videos in one batch with zero manual captioning because the brand voice was pre-programmed in step one → Atlas scrapes Amazon product links and auto-generates a Shopify store with hero images, pricing tiers, scarcity copy, and mobile-optimized checkout in 90 seconds → The store goes live before the first TikTok video finishes processing The key move 94% of people skip: you can't animate the photo before you inject the negative prompt. If you send a raw Pinterest image straight into image-to-video the face morphs into a wax figure. The fabric loses texture. The hands grow extra fingers. The whole thing screams "AI" and your CTR dies. His system runs the exclusion filter first so the model moves like she's shot on an iPhone 15 Pro in natural light. One brand hit 2.6M views on TikTok in 11 days with zero paid ads and converted at 3.7% because the videos looked like organic UGC not polished studio content. Brands now pay him $1,400 for the full pipeline setup + $340/month to keep the store synced with new product drops and seasonal video batches. The entire system runs on $23/month in API costs and one laptop. No photographer. No model agency. No product samples. Just a prompt template, a Pinterest account, and the discipline to filter out the AI artifacts before you render movement.

Shade

536,735 次观看 • 2 个月前

Claude Cowork Sub-Agents are f*cking cracked 🤯 One prompt → 50 competitor ads analyzed, hooks extracted, and a full creative brief generated. 10 AI agents running in parallel, under 5 minutes. All inside Claude Cowork. Perfect for DTC brands and agencies who are still doing creative research and ad production one task at a time inside Claude. If you're analyzing competitor ads one by one, copying hooks into a spreadsheet manually, writing brief after brief from scratch, and watching Claude's output quality fall off a cliff after the 15th variation because the context window is completely bloated... Sub-agents eliminate the entire bottleneck: → Drop in a spreadsheet of 50 competitor ads and spin up 10 parallel sub-agents → Each sub-agent analyzes 5 ads simultaneously — hooks, angles, CTAs, emotional tone, creative format → They report structured summaries back to the main agent without bloating the context → The main agent synthesizes patterns across all 50 ads into a competitive intel brief → Then spin up another round of sub-agents to generate 30 ad copy variations across 10 personas → Each sub-agent writes for 1-2 personas in a fresh context — so variation 30 is as sharp as variation 1 No analyzing ads one at a time. No context window blowing up halfway through. No copy quality degrading after the first dozen variations. What this gives you: → 50 competitor ads broken down in minutes — hooks, angles, CTAs, formats, all structured → Pattern analysis across the full dataset that you'd miss reviewing ads individually → 30+ ad copy variations with persona-specific messaging that actually stays sharp → A workflow you can save as reusable skills and trigger with one command next time → The same output quality on the last task as the first Built 100% inside Claude Cowork with sub-agents. I put together a full DTC playbook: 5 bulk workflows with copy-paste prompts, the exact sub-agent prompting pattern, batching guidelines, and an honest breakdown of when this setup is worth it vs. when a simpler approach is the better move. Want it for free? > Like this post > Comment "AGENTS" And I'll send it over (must be following so I can DM)
0:10

Claude Cowork Sub-Agents are f*cking cracked 🤯 One prompt → 50 competitor ads analyzed, hooks extracted, and a full creative brief generated. 10 AI agents running in parallel, under 5 minutes. All inside Claude Cowork. Perfect for DTC brands and agencies who are still doing creative research and ad production one task at a time inside Claude. If you're analyzing competitor ads one by one, copying hooks into a spreadsheet manually, writing brief after brief from scratch, and watching Claude's output quality fall off a cliff after the 15th variation because the context window is completely bloated... Sub-agents eliminate the entire bottleneck: → Drop in a spreadsheet of 50 competitor ads and spin up 10 parallel sub-agents → Each sub-agent analyzes 5 ads simultaneously — hooks, angles, CTAs, emotional tone, creative format → They report structured summaries back to the main agent without bloating the context → The main agent synthesizes patterns across all 50 ads into a competitive intel brief → Then spin up another round of sub-agents to generate 30 ad copy variations across 10 personas → Each sub-agent writes for 1-2 personas in a fresh context — so variation 30 is as sharp as variation 1 No analyzing ads one at a time. No context window blowing up halfway through. No copy quality degrading after the first dozen variations. What this gives you: → 50 competitor ads broken down in minutes — hooks, angles, CTAs, formats, all structured → Pattern analysis across the full dataset that you'd miss reviewing ads individually → 30+ ad copy variations with persona-specific messaging that actually stays sharp → A workflow you can save as reusable skills and trigger with one command next time → The same output quality on the last task as the first Built 100% inside Claude Cowork with sub-agents. I put together a full DTC playbook: 5 bulk workflows with copy-paste prompts, the exact sub-agent prompting pattern, batching guidelines, and an honest breakdown of when this setup is worth it vs. when a simpler approach is the better move. Want it for free? > Like this post > Comment "AGENTS" And I'll send it over (must be following so I can DM)

Mike Futia

50,124 次观看 • 5 个月前

I Combined ChatGPT 5.5 Image-2 + Claude Fable 5… And Built This FULL Game in JUST 8 Hours 😱 The World Has Officially Changed Forever Guys… I still can’t believe what I just pulled off. I took ChatGPT 5.5’s new Image-2 to generate every single visual characters, environments, UI, particles, everything and paired it with Claude Fable 5 for the entire codebase. The result? A complete, polished, fully playable game… finished in only 8 hours. No massive team. No months of crunch. No expensive asset packs. Image-2 created mind-blowing art assets on demand. Fable 5 turned those images into real, working code mechanics, physics, AI, animations, menus everything. This hybrid combo is straight-up sorcery. The world has truly changed. We are no longer waiting years for games to be made. One person + these two god-tier AIs just built something that used to require entire studios and huge budgets… in less than a single workday. This is the next level of human civilization. This is what creation looks like from now on. But here’s the crazy part: This free access ends June 22, 2026. After that, you’ll have to pay/subscribe to keep using it. If you’ve been waiting to see what the future of game dev actually looks like… THIS IS IT. Go try it right now before the paywall hits. Don’t sleep on this. Seriously. Drop in the comments: What game should I build next with this insane Image-2 + Fable 5 hybrid? Like if your mind is blown too 🔥 And tag a friend who NEEDS to see this before it’s gone. The future isn’t coming… It’s already here. And it’s free for one more day only. #Fable5 #ChatGPT55 #Image2 #AIHybrid #GameDevRevolution
0:16

I Combined ChatGPT 5.5 Image-2 + Claude Fable 5… And Built This FULL Game in JUST 8 Hours 😱 The World Has Officially Changed Forever Guys… I still can’t believe what I just pulled off. I took ChatGPT 5.5’s new Image-2 to generate every single visual characters, environments, UI, particles, everything and paired it with Claude Fable 5 for the entire codebase. The result? A complete, polished, fully playable game… finished in only 8 hours. No massive team. No months of crunch. No expensive asset packs. Image-2 created mind-blowing art assets on demand. Fable 5 turned those images into real, working code mechanics, physics, AI, animations, menus everything. This hybrid combo is straight-up sorcery. The world has truly changed. We are no longer waiting years for games to be made. One person + these two god-tier AIs just built something that used to require entire studios and huge budgets… in less than a single workday. This is the next level of human civilization. This is what creation looks like from now on. But here’s the crazy part: This free access ends June 22, 2026. After that, you’ll have to pay/subscribe to keep using it. If you’ve been waiting to see what the future of game dev actually looks like… THIS IS IT. Go try it right now before the paywall hits. Don’t sleep on this. Seriously. Drop in the comments: What game should I build next with this insane Image-2 + Fable 5 hybrid? Like if your mind is blown too 🔥 And tag a friend who NEEDS to see this before it’s gone. The future isn’t coming… It’s already here. And it’s free for one more day only. #Fable5 #ChatGPT55 #Image2 #AIHybrid #GameDevRevolution

Zayro.ETH

27,929 次观看 • 2 个月前

$KNDX 🤖 Theres 3 big narratives that are sending coins left right and centre rn. 🚀 #AI, #Gamefi, & #NFTs 🔹Theres 50% mindshare for #AI. 🤖 🔹#GameFi mcap is hitting ATH's with #OfftheGrid, $XBG and $SUPER making spectacular moves. 🎮 🔹NFTs and the #Metaverse are making a strong comeback with $APE up 100% over the weekend. 🐵 What if there's a project that touches all these trending narratives with groundbreaking technology to disrupt all 3 of them? 🔥 💡- That's where $KNDX comes in. -💡 Kondux is a cutting-edge Web3 SaaS platform, combining NVIDIA’s Omniverse, AI, Blockchain, and dynamic NFTs to revolutionize secure asset management across industries. 👏 Their flagship product, kNFTs, are 3D digital assets usable across Metaverse and Gaming platforms, AR/VR/XR environments, and manufacturing applications. Kondux’s scalable model opens new revenue streams by enabling effective digital asset monetization. 💰 Kondux is the first Web3 project to integrate VFX pipelines with NVIDIA’s Omniverse and bringing it onto the Blockchain. ⛓️ It is also the only Web3 project with a *Select Status Partnership* with NVIDIA, operating under NVIDIA NDAs and working with them directly for more than 2 years. About their NVIDIA Integrations: 🤖 🔹There are three areas of the Kondux tech stack that coincide with three divisions of NVIDIA: 📡GDN (Graphics Delivery Network, the backbone of GeForce Now) 💡Omniverse for 3D aspects such as, geospatial data, real world physics, lighting, and raytracing 🤖NVIDIA AI Foundation, which covers many aspects of #AI, including inference and deployment scaling. The convergence of all these components lie within .USD file format . 🔹 They are the first blockchain project to integrate NVIDIA’s Omniverse Cloud and Graphics Delivery Network (GDN) to provide high-quality 3D content accessible on any device without requiring high-end hardware. 🔹 This setup streamlines content management, democratises access to resource-intensive 3D content, and enables real-time interaction with 3D NFTs. Now, I haven’t seen any crypto project so deeply connected with NVIDIA and NVIDIA technology. GDN is a HUGE competitive advantage. With it, the need for #GPU’s basically goes out the window. 🤯 Now lets take a look at some of the other main features... 👀 OpenUSD (Universal Scene Description): 📽️ 🔹 Kondux is leveraging USD technology, developed by Pixar and used by Meta, Apple, Microsoft and other industry leaders to enhance 3D graphics and interoperability within its creative ecosystem. 🔹 Originally created for high-end film production, USD now supports a variety of applications, including gaming and virtual reality, making it a key asset for Kondux. kNFT's: 🎨 🔹 Kondux is pioneering a new category of NFTs known as kNFTs, which aim to redefine NFT utility through innovative features. 🔹 A standout feature is the upgradeable aspect provided by Kondux DNA, allowing kNFTs to transform and combine with other NFTs, creating limitless possibilities in art, gaming, and music. 🔹Through the Kondux AI portal it will be possible to communicate with kNFTs. They can learn and adapt. This AI technology is revolutionary because it makes human to kNFT interaction possible, turning it into a unique, personalized experience. Check out the clip of kNFTs in Unreal Engine 5 gameplay below. 👇 Kondux is a very obvious utility play with huge upside because it’s multi narrative. 📈 It's seriously groundbreaking stuff that they’re about to launch. 🚀 After speaking with the team there’s no doubt in my mind this will do crazy big numbers in the next months. 🤑
0:17

$KNDX 🤖 Theres 3 big narratives that are sending coins left right and centre rn. 🚀 #AI, #Gamefi, & #NFTs 🔹Theres 50% mindshare for #AI. 🤖 🔹#GameFi mcap is hitting ATH's with #OfftheGrid, $XBG and $SUPER making spectacular moves. 🎮 🔹NFTs and the #Metaverse are making a strong comeback with $APE up 100% over the weekend. 🐵 What if there's a project that touches all these trending narratives with groundbreaking technology to disrupt all 3 of them? 🔥 💡- That's where $KNDX comes in. -💡 Kondux is a cutting-edge Web3 SaaS platform, combining NVIDIA’s Omniverse, AI, Blockchain, and dynamic NFTs to revolutionize secure asset management across industries. 👏 Their flagship product, kNFTs, are 3D digital assets usable across Metaverse and Gaming platforms, AR/VR/XR environments, and manufacturing applications. Kondux’s scalable model opens new revenue streams by enabling effective digital asset monetization. 💰 Kondux is the first Web3 project to integrate VFX pipelines with NVIDIA’s Omniverse and bringing it onto the Blockchain. ⛓️ It is also the only Web3 project with a *Select Status Partnership* with NVIDIA, operating under NVIDIA NDAs and working with them directly for more than 2 years. About their NVIDIA Integrations: 🤖 🔹There are three areas of the Kondux tech stack that coincide with three divisions of NVIDIA: 📡GDN (Graphics Delivery Network, the backbone of GeForce Now) 💡Omniverse for 3D aspects such as, geospatial data, real world physics, lighting, and raytracing 🤖NVIDIA AI Foundation, which covers many aspects of #AI, including inference and deployment scaling. The convergence of all these components lie within .USD file format . 🔹 They are the first blockchain project to integrate NVIDIA’s Omniverse Cloud and Graphics Delivery Network (GDN) to provide high-quality 3D content accessible on any device without requiring high-end hardware. 🔹 This setup streamlines content management, democratises access to resource-intensive 3D content, and enables real-time interaction with 3D NFTs. Now, I haven’t seen any crypto project so deeply connected with NVIDIA and NVIDIA technology. GDN is a HUGE competitive advantage. With it, the need for #GPU’s basically goes out the window. 🤯 Now lets take a look at some of the other main features... 👀 OpenUSD (Universal Scene Description): 📽️ 🔹 Kondux is leveraging USD technology, developed by Pixar and used by Meta, Apple, Microsoft and other industry leaders to enhance 3D graphics and interoperability within its creative ecosystem. 🔹 Originally created for high-end film production, USD now supports a variety of applications, including gaming and virtual reality, making it a key asset for Kondux. kNFT's: 🎨 🔹 Kondux is pioneering a new category of NFTs known as kNFTs, which aim to redefine NFT utility through innovative features. 🔹 A standout feature is the upgradeable aspect provided by Kondux DNA, allowing kNFTs to transform and combine with other NFTs, creating limitless possibilities in art, gaming, and music. 🔹Through the Kondux AI portal it will be possible to communicate with kNFTs. They can learn and adapt. This AI technology is revolutionary because it makes human to kNFT interaction possible, turning it into a unique, personalized experience. Check out the clip of kNFTs in Unreal Engine 5 gameplay below. 👇 Kondux is a very obvious utility play with huge upside because it’s multi narrative. 📈 It's seriously groundbreaking stuff that they’re about to launch. 🚀 After speaking with the team there’s no doubt in my mind this will do crazy big numbers in the next months. 🤑

Altcoin Miyagi🇯🇵

17,323 次观看 • 1 年前

I learned this the hard way: do NOT use SwiftUI if you want your app to look and feel amazing. At least when coding with AI. (sorry, Apple colleagues reading this 😅) I'm sharing my process vibe coding this calorie tracker. I get a lot of questions about the fluid transition in the video. Here's the whole story. Initially, Claude built the grid with SwiftUI. It was quick and easy, and looked good! But the transition to the day view was a boring navigation push/pop. No fun. I wanted something custom. I asked Claude to make it a fluid transition that remaps the food tiles from their source to destination positions. All hell broke loose. Claude tried a bunch of horrible things. Initially it used matched geometry effects, which worked OK but didn't lend themselves well to gesture-driven animations. So it resorted to SwiftUI preference keys + geometry readers to figure out the source and destination positions and calculate the interpolated position based on gesture progress, coordinating across grid and day views. But this meant it had to write a custom layout because it couldn't reposition tiles inside the native SwiftUI grid. And it had to do an awkward handoff between views, which always created ugly pops or jumps. And don't get me started on trying to put it on a bouncy spring, that only made the math 10x buggier. Fortunately, Claude Fable was smart enough to see that this was becoming a disaster (and discover most of the issues itself, in the simulator), so it pivoted away from SwiftUI. Opus might not be so wise, so you'll have to pay attention and intervene. Ultimately, it rewrote it in plain UIKit and everything turned out great. After that, we moved from 2D images to 3D assets, which introduced a new set of performance challenges and yet another rewrite to a single Metal layer, which is what you see below. I can write more about the 2D-to-3D saga if anyone's interested. If I were to do it again, I'd just say "Don't use SwiftUI" from the very first prompt, and save a few hours of headaches. SwiftUI can be amazing for a human iterating directly in code. But agents don't benefit from any of its advantages. Plus, agents have seen decades of UIKit training data, so they're great at writing it, and it's far more flexible. Here's hoping we see more agent-friendly iterations of SwiftUI in the future. Till then, I'm probably going to avoid it.
0:12

I learned this the hard way: do NOT use SwiftUI if you want your app to look and feel amazing. At least when coding with AI. (sorry, Apple colleagues reading this 😅) I'm sharing my process vibe coding this calorie tracker. I get a lot of questions about the fluid transition in the video. Here's the whole story. Initially, Claude built the grid with SwiftUI. It was quick and easy, and looked good! But the transition to the day view was a boring navigation push/pop. No fun. I wanted something custom. I asked Claude to make it a fluid transition that remaps the food tiles from their source to destination positions. All hell broke loose. Claude tried a bunch of horrible things. Initially it used matched geometry effects, which worked OK but didn't lend themselves well to gesture-driven animations. So it resorted to SwiftUI preference keys + geometry readers to figure out the source and destination positions and calculate the interpolated position based on gesture progress, coordinating across grid and day views. But this meant it had to write a custom layout because it couldn't reposition tiles inside the native SwiftUI grid. And it had to do an awkward handoff between views, which always created ugly pops or jumps. And don't get me started on trying to put it on a bouncy spring, that only made the math 10x buggier. Fortunately, Claude Fable was smart enough to see that this was becoming a disaster (and discover most of the issues itself, in the simulator), so it pivoted away from SwiftUI. Opus might not be so wise, so you'll have to pay attention and intervene. Ultimately, it rewrote it in plain UIKit and everything turned out great. After that, we moved from 2D images to 3D assets, which introduced a new set of performance challenges and yet another rewrite to a single Metal layer, which is what you see below. I can write more about the 2D-to-3D saga if anyone's interested. If I were to do it again, I'd just say "Don't use SwiftUI" from the very first prompt, and save a few hours of headaches. SwiftUI can be amazing for a human iterating directly in code. But agents don't benefit from any of its advantages. Plus, agents have seen decades of UIKit training data, so they're great at writing it, and it's far more flexible. Here's hoping we see more agent-friendly iterations of SwiftUI in the future. Till then, I'm probably going to avoid it.

Anshu

114,381 次观看 • 1 个月前

This guy cracked the code on AI virtual influencers using real-time face filters and now D2C brands pay him $2,000 per UGC video. He got tired of watching D2C brands burn $4,000 on a single creator who takes 2 weeks to deliver one angle, so he built a setup that runs hyperrealistic AI girls in real-time from his own webcam, generating viral content without actresses, studios, or makeup artists. His monthly revenue hit $89,000 last month from a network of 7 AI personas across TikTok and Instagram, while the average UGC creator caps at $6K juggling 4 brand deals. Here is the exact breakdown: → The hardware is the moat, but most people butcher the setup in the first frame. You need the face mesh locked at 60fps with zero artifacting → Persona comes first, and if you mess this up nothing saves it. Name, backstory, voice tone, niche before a single clip is shot → Face selection is not random. You A/B test features (eye spacing, jawline, hair contrast with face-framing highlights) because some faces convert better in 9:16 → You are picking who your audience trusts, not who looks cool. That is your targeting baked into bone structure → Real-time physics run before the script, and this is what kills the uncanny valley that destroys watch time in 2 seconds → The filter has to survive the strap of a tank top, the texture of a knit cardigan, the hair flick. → Batching is the move 96 percent skip: one performance, multiple personas, three platforms. → The system pushes 12 pieces of content before lunch, while traditional brands test 2 creators per week and wonder why their CPAs are stuck at $94 The economics are stupid: each video costs him $4 in compute, sells for $1,500 to $3,000, and takes 14 minutes to produce. That is a 37,500 percent margin, while UGC agencies pay creators $400 to $800 per clip and net $200 after revisions. One supplement brand generated 14 variants with 7 personas in 4 hours and found a winner in 36 hours without flying a creator to LA. They were previously paying $1,200 per UGC video and burning $6,000 per week on content that did not scale. Now they spend $210 for 14 variants and their CPA dropped from $89 to $27. The avatars hold real products. Warm window light on the persona, cold neon on the operator. Mouth shapes sync to consonants, not just vowels. Just a webcam, a tracked face, and the discipline to move enough that the filter never has a chance to break.
0:10

This guy cracked the code on AI virtual influencers using real-time face filters and now D2C brands pay him $2,000 per UGC video. He got tired of watching D2C brands burn $4,000 on a single creator who takes 2 weeks to deliver one angle, so he built a setup that runs hyperrealistic AI girls in real-time from his own webcam, generating viral content without actresses, studios, or makeup artists. His monthly revenue hit $89,000 last month from a network of 7 AI personas across TikTok and Instagram, while the average UGC creator caps at $6K juggling 4 brand deals. Here is the exact breakdown: → The hardware is the moat, but most people butcher the setup in the first frame. You need the face mesh locked at 60fps with zero artifacting → Persona comes first, and if you mess this up nothing saves it. Name, backstory, voice tone, niche before a single clip is shot → Face selection is not random. You A/B test features (eye spacing, jawline, hair contrast with face-framing highlights) because some faces convert better in 9:16 → You are picking who your audience trusts, not who looks cool. That is your targeting baked into bone structure → Real-time physics run before the script, and this is what kills the uncanny valley that destroys watch time in 2 seconds → The filter has to survive the strap of a tank top, the texture of a knit cardigan, the hair flick. → Batching is the move 96 percent skip: one performance, multiple personas, three platforms. → The system pushes 12 pieces of content before lunch, while traditional brands test 2 creators per week and wonder why their CPAs are stuck at $94 The economics are stupid: each video costs him $4 in compute, sells for $1,500 to $3,000, and takes 14 minutes to produce. That is a 37,500 percent margin, while UGC agencies pay creators $400 to $800 per clip and net $200 after revisions. One supplement brand generated 14 variants with 7 personas in 4 hours and found a winner in 36 hours without flying a creator to LA. They were previously paying $1,200 per UGC video and burning $6,000 per week on content that did not scale. Now they spend $210 for 14 variants and their CPA dropped from $89 to $27. The avatars hold real products. Warm window light on the persona, cold neon on the operator. Mouth shapes sync to consonants, not just vowels. Just a webcam, a tracked face, and the discipline to move enough that the filter never has a chance to break.

Shade

135,682 次观看 • 2 个月前

A 17 year old high schooler told his mom he needed a Steam Deck for school. She said no, it's a gaming console. He said it runs Linux. She didn't know what that means. Bought it for his birthday. $280. He never installed a single game on it. Opened the terminal, installed Claude Code and typed his first command while holding the device like a PlayStation controller. Thumbsticks on both sides. Code editor in the middle. The most ridiculous dev setup anyone has ever seen. At second 0:09 you can read what he typed into the terminal: claude your code looks like absolute shit Claude didn't argue. Just started rewriting the shader, adding bloom effects, fixing chromatic aberration and improving the particle system. On a gaming console held in two hands on a couch. His friends play Fortnite on their Steam Decks. He builds software on his while lying in bed. He set up Claude Code with custom skills, hooks that auto run tests every time a file is saved and memory that remembers every project across sessions. The stuff most developers pay $200 a month for and use at maybe 20% capacity. He runs it on a $280 handheld and squeezes out every feature. Within three weeks he had built and sold four small apps to local businesses. A booking page for a barber shop, an inventory tracker for a vape store, a menu site for a taco truck and a scheduling tool for a dog groomer. All built on a Steam Deck in his bedroom. All coded by Claude while he gave instructions with his thumbs. Made over $13,000 in his first month. His mom still thinks he plays games on it. His teacher caught him using it during study hall. Looked at the screen expecting a game. Saw green code scrolling and Claude asking: Do you want to make this edit to main.js ? Teacher had no idea what she was looking at. Told him to put it away. He closed the lid. Claude kept running inside. A $280 gaming console that his mom bought thinking it was a toy is now a development workstation that earns more per month than her car payment. Setup time: 20 minutes once. Time he saves every day: 3 to 5 hours. Money made in month one: $13,000. Games installed: zero. His grandpa asked him to install FIFA last weekend. He said the console is busy. Grandpa asked doing what. He said working. Grandpa didn't ask again.
0:10

A 17 year old high schooler told his mom he needed a Steam Deck for school. She said no, it's a gaming console. He said it runs Linux. She didn't know what that means. Bought it for his birthday. $280. He never installed a single game on it. Opened the terminal, installed Claude Code and typed his first command while holding the device like a PlayStation controller. Thumbsticks on both sides. Code editor in the middle. The most ridiculous dev setup anyone has ever seen. At second 0:09 you can read what he typed into the terminal: claude your code looks like absolute shit Claude didn't argue. Just started rewriting the shader, adding bloom effects, fixing chromatic aberration and improving the particle system. On a gaming console held in two hands on a couch. His friends play Fortnite on their Steam Decks. He builds software on his while lying in bed. He set up Claude Code with custom skills, hooks that auto run tests every time a file is saved and memory that remembers every project across sessions. The stuff most developers pay $200 a month for and use at maybe 20% capacity. He runs it on a $280 handheld and squeezes out every feature. Within three weeks he had built and sold four small apps to local businesses. A booking page for a barber shop, an inventory tracker for a vape store, a menu site for a taco truck and a scheduling tool for a dog groomer. All built on a Steam Deck in his bedroom. All coded by Claude while he gave instructions with his thumbs. Made over $13,000 in his first month. His mom still thinks he plays games on it. His teacher caught him using it during study hall. Looked at the screen expecting a game. Saw green code scrolling and Claude asking: Do you want to make this edit to main.js ? Teacher had no idea what she was looking at. Told him to put it away. He closed the lid. Claude kept running inside. A $280 gaming console that his mom bought thinking it was a toy is now a development workstation that earns more per month than her car payment. Setup time: 20 minutes once. Time he saves every day: 3 to 5 hours. Money made in month one: $13,000. Games installed: zero. His grandpa asked him to install FIFA last weekend. He said the console is busy. Grandpa asked doing what. He said working. Grandpa didn't ask again.

Marlow

3,237,526 次观看 • 3 个月前

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.
0:10

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 次观看 • 3 个月前

This Chinese developer launched Llama 70B locally on a MacBook on a plane and for a full 11 hours without internet ran client projects. He was sitting by the window on a transatlantic flight with a MacBook Pro M4 with 64 GB of memory. WiFi on board cost $25 for the flight. He declined. No cloud API, no connection to Anthropic or OpenAI servers, no internet at all. Just a local Llama 3.3 70B on bf16 and his own orchestrator script. The model runs through llama.cpp. Generation speed, 71 tokens per second. Context around 60,000 tokens. Memory usage, 48.6 GiB out of 64. Battery at takeoff, 3 hours 21 minutes. And he gave the orchestrator this system prompt before takeoff: "You are an offline orchestrator running on a single MacBook. There is no network. The only resources you have are local files in /Users/dev/work, the Llama 70B inference server at localhost:8080, and a battery budget of 3 hours 21 minutes. Process the queue at /Users/dev/work/queue.jsonl (one client task per line). For each task: draft → run local evals → save artefact to /Users/dev/work/done/. Save context checkpoints every 12 tasks so you can resume after a battery swap. Stop only on empty queue or when battery drops below 5%." So the system knows exactly what resources it is running on. It knows it has no connection to the outside world for the next 11 hours. It knows it has finite memory and a finite battery. It knows the human will not intervene until the plane lands. The system runs in 1 loop. Takes a task from the queue, runs it through inference, saves the artifact, writes a checkpoint. Task after task, just like that. And only when the battery drops below 5% does the orchestrator automatically pause, waits for the laptop to switch to the backup power bank, and continues from the last checkpoint. Here is what the system actually writes in his log during the flight: "saved context checkpoint 8 of 12 (pos_min = 488, pos_max = 50118, size = 62.813 MiB)" "restored context checkpoint (pos_min = 488, pos_max = 50118)" "prompt processing progress: n_tokens = 50 / 60 818" "task 37016 done | tps = 71 s tokens text → /Users/dev/work/done/proposal_westside.md" Outside the window, clouds, blue sky, and no WiFi. On the tray, 1 MacBook, an open terminal on 2 screens, and an inference server on localhost. From what I have observed, this is the cleanest offline AI workflow I have seen in the past year: 11 hours of flight, $0 for WiFi, and the entire client queue closed before landing.
0:10

This Chinese developer launched Llama 70B locally on a MacBook on a plane and for a full 11 hours without internet ran client projects. He was sitting by the window on a transatlantic flight with a MacBook Pro M4 with 64 GB of memory. WiFi on board cost $25 for the flight. He declined. No cloud API, no connection to Anthropic or OpenAI servers, no internet at all. Just a local Llama 3.3 70B on bf16 and his own orchestrator script. The model runs through llama.cpp. Generation speed, 71 tokens per second. Context around 60,000 tokens. Memory usage, 48.6 GiB out of 64. Battery at takeoff, 3 hours 21 minutes. And he gave the orchestrator this system prompt before takeoff: "You are an offline orchestrator running on a single MacBook. There is no network. The only resources you have are local files in /Users/dev/work, the Llama 70B inference server at localhost:8080, and a battery budget of 3 hours 21 minutes. Process the queue at /Users/dev/work/queue.jsonl (one client task per line). For each task: draft → run local evals → save artefact to /Users/dev/work/done/. Save context checkpoints every 12 tasks so you can resume after a battery swap. Stop only on empty queue or when battery drops below 5%." So the system knows exactly what resources it is running on. It knows it has no connection to the outside world for the next 11 hours. It knows it has finite memory and a finite battery. It knows the human will not intervene until the plane lands. The system runs in 1 loop. Takes a task from the queue, runs it through inference, saves the artifact, writes a checkpoint. Task after task, just like that. And only when the battery drops below 5% does the orchestrator automatically pause, waits for the laptop to switch to the backup power bank, and continues from the last checkpoint. Here is what the system actually writes in his log during the flight: "saved context checkpoint 8 of 12 (pos_min = 488, pos_max = 50118, size = 62.813 MiB)" "restored context checkpoint (pos_min = 488, pos_max = 50118)" "prompt processing progress: n_tokens = 50 / 60 818" "task 37016 done | tps = 71 s tokens text → /Users/dev/work/done/proposal_westside.md" Outside the window, clouds, blue sky, and no WiFi. On the tray, 1 MacBook, an open terminal on 2 screens, and an inference server on localhost. From what I have observed, this is the cleanest offline AI workflow I have seen in the past year: 11 hours of flight, $0 for WiFi, and the entire client queue closed before landing.

Blaze

1,841,161 次观看 • 3 个月前

I told you to claim your free 16GB NVIDIA GPU for learning Local LLMs. Now I’m going to show you how to double its inference speed without touching the hardware. Google Colab gives you an enterprise grade NVIDIA Tesla T4 GPU for free, roughly 4 hours every single day. It is the absolute perfect sandbox for learning AI engineering, testing inference flags, and pushing massive context windows. The local AI timeline is moving way too fast. If you aren't using Multi Token Prediction (MTP) yet, you are leaving massive performance on the table. I just pushed DeepMind’s Gemma 4 26B to 64.9 t/s on this exact free tier. Let's look at the raw benchmark data running on an Ubuntu Linux environment with the latest compiled llama.cpp binaries and quantized GGUFs from Unsloth via HuggingFace: # Qwen 3.5 9B (Dense): Base: [ Prompt: 626.7 t/s | Generation: 21.0 t/s ] With MTP: [ Prompt: 539.1 t/s | Generation: 24.8 t/s ] # Gemma 4 26B QAT (MoE): Base: [ Prompt: 634.2 t/s | Generation: 48.3 t/s ] With MTP: [ Prompt: 572.1 t/s | Generation: 64.9 t/s ] If you are paying attention, this single Colab notebook reveals 3 massive observations about the current state of local LLMs: # 1. The MTP Speedup (Software Overclocking) Standard autoregressive decoding guesses one token at a time. MTP acts like a highly optimized, built in speculative decoder. It predicts multiple future tokens at once and the main model verifies them in parallel. The result? Zero accuracy loss and a massive throughput increase. Gemma jumped from 48 to 65 t/s just by flipping a flag. # 2. The MoE Paradox (Bigger is Faster) How does a 26B parameter model absolutely destroy a 9B model in raw speed on the exact same hardware? Architecture. Qwen 3.5 9B is a dense model. it activates all 9 billion parameters for every single token. Gemma 4 26B is a Mixture of Experts (MoE) model. It routes data efficiently, activating only 4B parameters per token. You get the reasoning capabilities of a 26B model with the compute cost of a 4B model. 3. Thinking Efficiency When I ran the exact same complex prompt on both models, the larger MoE spent significantly fewer "thinking" tokens to arrive at the correct answer. A smarter model doesn't just give better answers; it gets to the point faster, saving you compute cycles and preserving your context window. # Want to run this yourself? Here are the exact llama.cpp CLI commands. For Qwen (MTP is baked into the main model): ./llama-cli -m Qwen3.5-9B-UD-Q4_K_XL.gguf -p "Explain quantum computing." -n 2000 -c 8000 -ngl 99 -fa on --spec-type draft-mtp --spec-draft-n-max 4 --spec-draft-p-min 0.7 For Gemma (Using a separate lightweight draft model): ./llama-cli -m gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf --model-draft mtp-gemma-4-26B-A4B-it.gguf -p "Explain quantum computing." -n 2000 -c 8000 -ngl 99 -fa on --spec-type draft-mtp --spec-draft-n-max 4 --spec-draft-p-min 0.7 Stop waiting for a $3,000 rig. Boot up Colab, pull these models, and start building your stack. I’ve put together a completely free, cell by cell Google Colab notebook that automates this entire workflow so you can test it yourself in 5 minutes and learn. Link to the notebook is in the comments below. Experiemt with different MTP parameters, context windows and post your results in the comments.
0:12

I told you to claim your free 16GB NVIDIA GPU for learning Local LLMs. Now I’m going to show you how to double its inference speed without touching the hardware. Google Colab gives you an enterprise grade NVIDIA Tesla T4 GPU for free, roughly 4 hours every single day. It is the absolute perfect sandbox for learning AI engineering, testing inference flags, and pushing massive context windows. The local AI timeline is moving way too fast. If you aren't using Multi Token Prediction (MTP) yet, you are leaving massive performance on the table. I just pushed DeepMind’s Gemma 4 26B to 64.9 t/s on this exact free tier. Let's look at the raw benchmark data running on an Ubuntu Linux environment with the latest compiled llama.cpp binaries and quantized GGUFs from Unsloth via HuggingFace: # Qwen 3.5 9B (Dense): Base: [ Prompt: 626.7 t/s | Generation: 21.0 t/s ] With MTP: [ Prompt: 539.1 t/s | Generation: 24.8 t/s ] # Gemma 4 26B QAT (MoE): Base: [ Prompt: 634.2 t/s | Generation: 48.3 t/s ] With MTP: [ Prompt: 572.1 t/s | Generation: 64.9 t/s ] If you are paying attention, this single Colab notebook reveals 3 massive observations about the current state of local LLMs: # 1. The MTP Speedup (Software Overclocking) Standard autoregressive decoding guesses one token at a time. MTP acts like a highly optimized, built in speculative decoder. It predicts multiple future tokens at once and the main model verifies them in parallel. The result? Zero accuracy loss and a massive throughput increase. Gemma jumped from 48 to 65 t/s just by flipping a flag. # 2. The MoE Paradox (Bigger is Faster) How does a 26B parameter model absolutely destroy a 9B model in raw speed on the exact same hardware? Architecture. Qwen 3.5 9B is a dense model. it activates all 9 billion parameters for every single token. Gemma 4 26B is a Mixture of Experts (MoE) model. It routes data efficiently, activating only 4B parameters per token. You get the reasoning capabilities of a 26B model with the compute cost of a 4B model. 3. Thinking Efficiency When I ran the exact same complex prompt on both models, the larger MoE spent significantly fewer "thinking" tokens to arrive at the correct answer. A smarter model doesn't just give better answers; it gets to the point faster, saving you compute cycles and preserving your context window. # Want to run this yourself? Here are the exact llama.cpp CLI commands. For Qwen (MTP is baked into the main model): ./llama-cli -m Qwen3.5-9B-UD-Q4_K_XL.gguf -p "Explain quantum computing." -n 2000 -c 8000 -ngl 99 -fa on --spec-type draft-mtp --spec-draft-n-max 4 --spec-draft-p-min 0.7 For Gemma (Using a separate lightweight draft model): ./llama-cli -m gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf --model-draft mtp-gemma-4-26B-A4B-it.gguf -p "Explain quantum computing." -n 2000 -c 8000 -ngl 99 -fa on --spec-type draft-mtp --spec-draft-n-max 4 --spec-draft-p-min 0.7 Stop waiting for a $3,000 rig. Boot up Colab, pull these models, and start building your stack. I’ve put together a completely free, cell by cell Google Colab notebook that automates this entire workflow so you can test it yourself in 5 minutes and learn. Link to the notebook is in the comments below. Experiemt with different MTP parameters, context windows and post your results in the comments.

Alok

170,442 次观看 • 1 个月前

after consistently booking 100 b2b calls/mo from linkedin inbound funnels... i sat down with Lara Acosta and gave away our ENTIRE playbook in a 1-hour interview → every system → every framework → every DM script for context, a post with 40 comments booked me 4 qualified calls last week a single question in the DMs booked a client 72 calls in 26 days and one lead magnet batch generated 7,000 comments across 4 variations in this interview i break down exactly how all of it works... here's what's inside: → why i think content pillars are dead and what replaced them (awareness stage positioning: problem unaware, pain aware, solution aware) → the lead magnet system we run at 100 per week across all client accounts (what makes a good one, why 50 pages of text is a waste, and why the best lead magnets right now are all Claude/AI related) → the DM conversion framework that turns a free resource into a booked call in 3-5 messages ("hey, what caught your attention?" → discover the pain → drop a case study → assume the yes with a specific date and time) → why engagement doesn't equal revenue and what actually does (30k followers with low engagement booking 10x more calls than before because the conversion infrastructure changed) → the trend jacking strategy that makes dead accounts go viral overnight (spot it on X in the morning, build the resource by noon, post on linkedin by evening) → how to get your first case study using pay-for-results instead of free trials → the competitor analysis shortcut for never running out of content ideas → why the linkedin algorithm conversation is 90% excuse and 10% useful like + comment "POD" and i'll send you the full interview (must be following + RT for priority access)
0:15

after consistently booking 100 b2b calls/mo from linkedin inbound funnels... i sat down with Lara Acosta and gave away our ENTIRE playbook in a 1-hour interview → every system → every framework → every DM script for context, a post with 40 comments booked me 4 qualified calls last week a single question in the DMs booked a client 72 calls in 26 days and one lead magnet batch generated 7,000 comments across 4 variations in this interview i break down exactly how all of it works... here's what's inside: → why i think content pillars are dead and what replaced them (awareness stage positioning: problem unaware, pain aware, solution aware) → the lead magnet system we run at 100 per week across all client accounts (what makes a good one, why 50 pages of text is a waste, and why the best lead magnets right now are all Claude/AI related) → the DM conversion framework that turns a free resource into a booked call in 3-5 messages ("hey, what caught your attention?" → discover the pain → drop a case study → assume the yes with a specific date and time) → why engagement doesn't equal revenue and what actually does (30k followers with low engagement booking 10x more calls than before because the conversion infrastructure changed) → the trend jacking strategy that makes dead accounts go viral overnight (spot it on X in the morning, build the resource by noon, post on linkedin by evening) → how to get your first case study using pay-for-results instead of free trials → the competitor analysis shortcut for never running out of content ideas → why the linkedin algorithm conversation is 90% excuse and 10% useful like + comment "POD" and i'll send you the full interview (must be following + RT for priority access)

paolo trivellato

23,266 次观看 • 2 个月前

Monthly WINR Protocol Development Update: To begin with, the WINR Protocol has distributed $900,000 to token holders, generated more than $500,000 in pure profit for liquidity providers on WLP, acquired more than 5,000 users, and has almost 9% of the supply burned. In the upcoming months, the WINR Protocol, which has been in production for years, will introduce a range of new products and deployments. These developments will represent the practical and technical evolution to V2 of the protocol. Here are the latest updates and further details as they progress: Progress on WINR Bonanza, Casino Hold'em, and Blackjack is nearing completion. These games are in the final stages of testing. Additional games, including a new type of crash game, have been finalized and are set to debut with the JustBet v2 launch. Take a look at the gameplay videos for a preview. These games achieve the long-term goal of providing a full-suite WINR Game Engine SDK, which can be used to build games with complex logic on-chain with modular smart contract infrastructure. For example, any grid slot game that dominates the iGaming industry could easily be developed on-chain using the WINR Bonanza SDK. - Permissionless Frontend Operator SDK Dashboard Release: March is poised to be a milestone month with the launch of the Frontend Operator SDK dashboards. These dashboards will enable any WINR Labs game to be seamlessly deployed on frontends, marking a significant advancement in protocol accessibility and integration for future games developed by independent iGaming developers. The frontend operator can deploy any game they choose through a few simple steps while utilizing 10,000 WINR per game via the WINR Game Factory smart contract. Each operator is assigned a unique smart contract address(es) for every game they deploy, allowing their revenue to be tracked independently. The deployment process includes instructions on integrating the game as a package into the operator's frontend. In subsequent phases, the games will transition through WINR Chain, streamlining user onboarding steps like wallet connection and token bridging to Arbitrum. This abstraction will make it easy for any web2/web3 platform on any chain to seamlessly integrate WINR-based games with just a few clicks. The new budget system changes how the revenue is calculated on the protocol and will see daylight with frontend operator, Solana, and Fantom deployments. This model was first tested with a lightweight version on and gave a lot of actionable feedback. Shifting from the existing bribe model, which in practice distributes almost half of the edge of the games in volume to WINR holders, the brand-new budget system checks the profitability of WLP. It distributes a larger part of the profit to game providers, frontend operators, and, most importantly, WINR holders. Any time a game's budget is in profit, a part of every loss is distributed to stakeholders. Here is an example: 1. The WINR Bonanza Game has a 10,000 budget for a frontend operator. 2. Let's assume the bet amount was $50, and Bonanza paid back $10 on that spin. That leaves $40 of pure profit. 3. This is distributed amongst 50% to WLP, 20% to frontend operators, 20% to WINR holders, and 10% to game providers. 4. To achieve this, V2 of WINR Liquidity Engine (WLP) will have a buffer for purchasing and selling, working in epochs to determine the above distribution and math. - Solana Deployment and Expansions: Solana audits are in their final phase, and frontend tests are ongoing. Solana launch will be alongside the V2 launch of @JustBetOfficial, with a chain switch available on the top bar. The VRF system WINR developed already is seeing requests from builders around the Solana ecosystem, and this will over time add an extra layer of income to the protocol. Solana's bankroll, at first, will be a lighter version of WLP but will inherit the above-mentioned budgeting system to generate income immediately upon launch. - Fantom Deployment and Expansions: Fantom's upcoming Sonic upgrade, with its 200ms finality and fast block production is a perfect chain for WINR Protocol to expand. Through the partnership with WINR Protocol will tap into a brand new user base on Fantom. The bankroll on Fantom will consist of FTM and stables. The launch is planned for late March or early April. This expansion aims to open up new markets and collaborations with fresh teams. which operates independently, will integrate a broad spectrum of WINR technologies, including WINR Account Abstraction, WINR VRF, and WINR games, marking a pivotal step in WINR Protocol’s journey of horizontal expansion. - XAI VRF Deployment Progress Update: The WINR Account Abstraction Wallet and WINR Verifiable Random Function (VRF) deployment on the XAI 🎮⛓️ is well underway, with the majority of the work completed. This step forward showcases WINR's expansion beyond gambling and trading, highlighting the protocol's adaptability and commitment to broadening decentralized services. The process to automize and permissionlessly let game developers start using WINR VRF by paying (and burning) fees in WINR will launch alongside WINR VRF deployment on XAI and expand to further chains to help DApp builders with the tooling they need. This process will work very similarly to frontend operators, where DApp builders will be able to easily deploy their VRF contract, pay the WINR fees, and enjoy the fastest random number generation transaction, as showcased on @JustBetOfficial for some time. - JustBet v2 Development Update: Set to launch in March, the last testing phase with long-time community members of JustBet V2 is underway. Boasting a completely refreshed look, JustBet v2 aims to captivate more users with its modern features, a significant upgrade from the classic JustBet designed in 2019. Expect dynamic animations and a user experience that rivals traditional web2 casinos, setting a new standard for online gambling platforms and serving once again as proof of concept for all the new WINR features and infrastructure set to launch over the coming months. As always, WINR Labs simultaneously develops protocol infrastructure and platform to best address the needs of one and only goal: horizontal expansion. Easter eggs: Upcoming Gitbook update with all the technical information of WINR V2. CEX listing. Detailed product pages on WINR web. Pyth competitions. WIP-4 and WIP-5 are ready to deploy. And an 🪂
0:18

Monthly WINR Protocol Development Update: To begin with, the WINR Protocol has distributed $900,000 to token holders, generated more than $500,000 in pure profit for liquidity providers on WLP, acquired more than 5,000 users, and has almost 9% of the supply burned. In the upcoming months, the WINR Protocol, which has been in production for years, will introduce a range of new products and deployments. These developments will represent the practical and technical evolution to V2 of the protocol. Here are the latest updates and further details as they progress: Progress on WINR Bonanza, Casino Hold'em, and Blackjack is nearing completion. These games are in the final stages of testing. Additional games, including a new type of crash game, have been finalized and are set to debut with the JustBet v2 launch. Take a look at the gameplay videos for a preview. These games achieve the long-term goal of providing a full-suite WINR Game Engine SDK, which can be used to build games with complex logic on-chain with modular smart contract infrastructure. For example, any grid slot game that dominates the iGaming industry could easily be developed on-chain using the WINR Bonanza SDK. - Permissionless Frontend Operator SDK Dashboard Release: March is poised to be a milestone month with the launch of the Frontend Operator SDK dashboards. These dashboards will enable any WINR Labs game to be seamlessly deployed on frontends, marking a significant advancement in protocol accessibility and integration for future games developed by independent iGaming developers. The frontend operator can deploy any game they choose through a few simple steps while utilizing 10,000 WINR per game via the WINR Game Factory smart contract. Each operator is assigned a unique smart contract address(es) for every game they deploy, allowing their revenue to be tracked independently. The deployment process includes instructions on integrating the game as a package into the operator's frontend. In subsequent phases, the games will transition through WINR Chain, streamlining user onboarding steps like wallet connection and token bridging to Arbitrum. This abstraction will make it easy for any web2/web3 platform on any chain to seamlessly integrate WINR-based games with just a few clicks. The new budget system changes how the revenue is calculated on the protocol and will see daylight with frontend operator, Solana, and Fantom deployments. This model was first tested with a lightweight version on and gave a lot of actionable feedback. Shifting from the existing bribe model, which in practice distributes almost half of the edge of the games in volume to WINR holders, the brand-new budget system checks the profitability of WLP. It distributes a larger part of the profit to game providers, frontend operators, and, most importantly, WINR holders. Any time a game's budget is in profit, a part of every loss is distributed to stakeholders. Here is an example: 1. The WINR Bonanza Game has a 10,000 budget for a frontend operator. 2. Let's assume the bet amount was $50, and Bonanza paid back $10 on that spin. That leaves $40 of pure profit. 3. This is distributed amongst 50% to WLP, 20% to frontend operators, 20% to WINR holders, and 10% to game providers. 4. To achieve this, V2 of WINR Liquidity Engine (WLP) will have a buffer for purchasing and selling, working in epochs to determine the above distribution and math. - Solana Deployment and Expansions: Solana audits are in their final phase, and frontend tests are ongoing. Solana launch will be alongside the V2 launch of @JustBetOfficial, with a chain switch available on the top bar. The VRF system WINR developed already is seeing requests from builders around the Solana ecosystem, and this will over time add an extra layer of income to the protocol. Solana's bankroll, at first, will be a lighter version of WLP but will inherit the above-mentioned budgeting system to generate income immediately upon launch. - Fantom Deployment and Expansions: Fantom's upcoming Sonic upgrade, with its 200ms finality and fast block production is a perfect chain for WINR Protocol to expand. Through the partnership with WINR Protocol will tap into a brand new user base on Fantom. The bankroll on Fantom will consist of FTM and stables. The launch is planned for late March or early April. This expansion aims to open up new markets and collaborations with fresh teams. which operates independently, will integrate a broad spectrum of WINR technologies, including WINR Account Abstraction, WINR VRF, and WINR games, marking a pivotal step in WINR Protocol’s journey of horizontal expansion. - XAI VRF Deployment Progress Update: The WINR Account Abstraction Wallet and WINR Verifiable Random Function (VRF) deployment on the XAI 🎮⛓️ is well underway, with the majority of the work completed. This step forward showcases WINR's expansion beyond gambling and trading, highlighting the protocol's adaptability and commitment to broadening decentralized services. The process to automize and permissionlessly let game developers start using WINR VRF by paying (and burning) fees in WINR will launch alongside WINR VRF deployment on XAI and expand to further chains to help DApp builders with the tooling they need. This process will work very similarly to frontend operators, where DApp builders will be able to easily deploy their VRF contract, pay the WINR fees, and enjoy the fastest random number generation transaction, as showcased on @JustBetOfficial for some time. - JustBet v2 Development Update: Set to launch in March, the last testing phase with long-time community members of JustBet V2 is underway. Boasting a completely refreshed look, JustBet v2 aims to captivate more users with its modern features, a significant upgrade from the classic JustBet designed in 2019. Expect dynamic animations and a user experience that rivals traditional web2 casinos, setting a new standard for online gambling platforms and serving once again as proof of concept for all the new WINR features and infrastructure set to launch over the coming months. As always, WINR Labs simultaneously develops protocol infrastructure and platform to best address the needs of one and only goal: horizontal expansion. Easter eggs: Upcoming Gitbook update with all the technical information of WINR V2. CEX listing. Detailed product pages on WINR web. Pyth competitions. WIP-4 and WIP-5 are ready to deploy. And an 🪂

WINR

37,891 次观看 • 2 年前

How to build a viral Web3 app in an afternoon using the ChainGPT AI skill for Claude Code. No coding experience required. I built Roast My Wallet. Paste any Ethereum wallet address, get a savage AI-generated roast of your trading history, a Degen Score out of 100, an on-chain report card, and three AI-generated NFT portraits. Here's exactly how it came together. Setup (3 minutes): 🔸Install Claude Code at 🔸Run /plugin install ChainGPT-org/chaingpt-claude-skill 🔸Get an API key at 🔸Type /chaingpt and describe what you want to build What the skill actually does: The ChainGPT skill doesn't just give you starter code. It knows the entire API. Every endpoint, every parameter, every credit cost, every error code. When I asked it to build the roast feature, it knew to call the LLM endpoint, how to stream the response back to the browser in real time, and how to handle errors automatically. I didn't look up a single thing. How it works under the hood: 1. Pulls real ETH balance and transaction count from the Ethereum blockchain 2. Feeds those numbers into ChainGPT's LLM and streams the roast back live 3. Calculates a Degen Score from your tx count vs balance ratio 4. Generates a report card with letter grades across Trading, Patience, Risk, Diamond Hands, and NGMI 5. Uses the roast text to generate three custom NFT portraits in parallel via VeloGen 6. Packages everything into a downloadable PNG card ready to post 7. Every feature came from describing what I wanted: 8. "Make the API key server-side." Done. 9. "Add an animated arc gauge for the degen score." Done. 10. "Generate NFT portraits using the roast text as context." Done. I never wrote a function or debugged an API response. I described outcomes. The ChainGPT skill handled the rest. If you can describe what you want to build, you can build it. Get your API key. Install the skill. /plugin install ChainGPT-org/chaingpt-claude-skill Anyone can build with ChainGPT AI!
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How to build a viral Web3 app in an afternoon using the ChainGPT AI skill for Claude Code. No coding experience required. I built Roast My Wallet. Paste any Ethereum wallet address, get a savage AI-generated roast of your trading history, a Degen Score out of 100, an on-chain report card, and three AI-generated NFT portraits. Here's exactly how it came together. Setup (3 minutes): 🔸Install Claude Code at 🔸Run /plugin install ChainGPT-org/chaingpt-claude-skill 🔸Get an API key at 🔸Type /chaingpt and describe what you want to build What the skill actually does: The ChainGPT skill doesn't just give you starter code. It knows the entire API. Every endpoint, every parameter, every credit cost, every error code. When I asked it to build the roast feature, it knew to call the LLM endpoint, how to stream the response back to the browser in real time, and how to handle errors automatically. I didn't look up a single thing. How it works under the hood: 1. Pulls real ETH balance and transaction count from the Ethereum blockchain 2. Feeds those numbers into ChainGPT's LLM and streams the roast back live 3. Calculates a Degen Score from your tx count vs balance ratio 4. Generates a report card with letter grades across Trading, Patience, Risk, Diamond Hands, and NGMI 5. Uses the roast text to generate three custom NFT portraits in parallel via VeloGen 6. Packages everything into a downloadable PNG card ready to post 7. Every feature came from describing what I wanted: 8. "Make the API key server-side." Done. 9. "Add an animated arc gauge for the degen score." Done. 10. "Generate NFT portraits using the roast text as context." Done. I never wrote a function or debugged an API response. I described outcomes. The ChainGPT skill handled the rest. If you can describe what you want to build, you can build it. Get your API key. Install the skill. /plugin install ChainGPT-org/chaingpt-claude-skill Anyone can build with ChainGPT AI!

ChainGPT

29,303 次观看 • 3 个月前