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Introducing @𝚓𝚜𝚘𝚗-𝚛𝚎𝚗𝚍𝚎𝚛/𝚛𝚎𝚊𝚌𝚝-𝚝𝚑𝚛𝚎𝚎-𝚏𝚒𝚋𝚎𝚛 A new renderer that turns JSON specs into interactive R3F scenes Same catalog-driven approach, now for meshes, lights, models, environments, cameras, controls 19 built-in components and 12 demo scenes

Chris Tate

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42,342 просмотров • 5 месяцев назад •via X (Twitter)

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The React Native Rewind

118,542 просмотров • 1 год назад

A 19 year old Chinese student controls an AI security system from his bed through Telegram. Types one message on his phone, the device across the room wakes up, starts watching and reports back to him like an employee. While American companies charge $100 for a Ring camera plus $4 a month for cloud, this kid spent $10 once and built something smarter. He sent a Telegram message: open maixcam and notify me if a person detected. One second later his phone buzzed back. Green checkmark. Status: Active. Monitoring: Person detection enabled. Notifications: Telegram ready. His roommate laughed. Said a $10 device can't do real security. Then someone walked past the door. The phone buzzed instantly. Person detected. Class: person. Confidence: 92.00%. Position: (120, 80). Size: 100x150. Not a blurry photo 45 seconds later like Amazon cameras. Exact data in under 1 second. What it saw, how sure it is, where the person is standing, how big they are. All through a Telegram message. He built the whole thing with Claude Code in one weekend. The AI runs directly on the device, no cloud, no subscription, no internet needed after setup. 10MB of memory. Boots in 1 second. Camera sees, chip thinks, Telegram delivers. Posted a 17 second demo. GitHub exploded. 7,400 stars in 2 days. But person detection was just the demo. A developer in Tokyo forked it and pointed it at his front door. Telegram alert with a photo every time a delivery arrives. A mom in Seoul pointed it at her baby's crib. Gets a message when the baby stands up. A business owner in Shenzhen bought 6 for $60 total, mounted them around his warehouse and replaced a $200 a month security service. His entire security system is now a Telegram group chat with 6 AI cameras. Someone commented under the GitHub repo: I'm a senior engineer at a home security company. We have a team of 8 working on person detection. This 19 year old did it alone with Claude Code on a $10 device and it works better than our product. The student isn't a machine learning engineer. He's a second year CS student who wanted to know when his roommate eats his snacks. Claude Code wrote the detection model, the Telegram bot, the alert system and the boot sequence. He just described what he wanted. The roommate who laughed now has one pointed at his own shelf. Same device, same code, same Telegram bot. He stops losing snacks. The student stops losing sleep. Everyone is paying $100 for smart cameras with $4 monthly subscriptions. China is building the same thing for $10 with a Telegram chat and Claude Code. 7,400 stars. One weekend. One student who asked Claude Code to watch his door and accidentally built something better than Ring.

A 19 year old Chinese student controls an AI security system from his bed through Telegram. Types one message on his phone, the device across the room wakes up, starts watching and reports back to him like an employee. While American companies charge $100 for a Ring camera plus $4 a month for cloud, this kid spent $10 once and built something smarter. He sent a Telegram message: open maixcam and notify me if a person detected. One second later his phone buzzed back. Green checkmark. Status: Active. Monitoring: Person detection enabled. Notifications: Telegram ready. His roommate laughed. Said a $10 device can't do real security. Then someone walked past the door. The phone buzzed instantly. Person detected. Class: person. Confidence: 92.00%. Position: (120, 80). Size: 100x150. Not a blurry photo 45 seconds later like Amazon cameras. Exact data in under 1 second. What it saw, how sure it is, where the person is standing, how big they are. All through a Telegram message. He built the whole thing with Claude Code in one weekend. The AI runs directly on the device, no cloud, no subscription, no internet needed after setup. 10MB of memory. Boots in 1 second. Camera sees, chip thinks, Telegram delivers. Posted a 17 second demo. GitHub exploded. 7,400 stars in 2 days. But person detection was just the demo. A developer in Tokyo forked it and pointed it at his front door. Telegram alert with a photo every time a delivery arrives. A mom in Seoul pointed it at her baby's crib. Gets a message when the baby stands up. A business owner in Shenzhen bought 6 for $60 total, mounted them around his warehouse and replaced a $200 a month security service. His entire security system is now a Telegram group chat with 6 AI cameras. Someone commented under the GitHub repo: I'm a senior engineer at a home security company. We have a team of 8 working on person detection. This 19 year old did it alone with Claude Code on a $10 device and it works better than our product. The student isn't a machine learning engineer. He's a second year CS student who wanted to know when his roommate eats his snacks. Claude Code wrote the detection model, the Telegram bot, the alert system and the boot sequence. He just described what he wanted. The roommate who laughed now has one pointed at his own shelf. Same device, same code, same Telegram bot. He stops losing snacks. The student stops losing sleep. Everyone is paying $100 for smart cameras with $4 monthly subscriptions. China is building the same thing for $10 with a Telegram chat and Claude Code. 7,400 stars. One weekend. One student who asked Claude Code to watch his door and accidentally built something better than Ring.

Marlow

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Growing Sl8 Means Growing Its Community Our work on expanding Sl8 never stops. One of the key priorities for Cassator is continuously bringing new users and new creators into the ecosystem - not through isolated campaigns, but by building long-term relationships with influencers, communities, and partners across different markets. Today, Sl8 is already supported by a growing number of influencers from different countries and across multiple social platforms. This gives us a strong foundation to keep expanding our reach, strengthening awareness around the platform, and introducing Sl8 to new audiences. One of the markets currently in active development is the GCC region, where we are now significantly scaling our presence. We are preparing a large-scale creator campaign focused on bringing new audiences into Sl8, and 48 influencers have already agreed to work with us as part of this expansion. At the same time, negotiations continue with a much broader pool of creators across the region. Our approach is to enter each new market through relevant local creators and communities, building awareness around Sl8 and converting that attention into a growing, engaged user base. The expansion process continues to gain momentum, with new creators, partnerships, and markets joining the Sl8 ecosystem step by step.

Growing Sl8 Means Growing Its Community Our work on expanding Sl8 never stops. One of the key priorities for Cassator is continuously bringing new users and new creators into the ecosystem - not through isolated campaigns, but by building long-term relationships with influencers, communities, and partners across different markets. Today, Sl8 is already supported by a growing number of influencers from different countries and across multiple social platforms. This gives us a strong foundation to keep expanding our reach, strengthening awareness around the platform, and introducing Sl8 to new audiences. One of the markets currently in active development is the GCC region, where we are now significantly scaling our presence. We are preparing a large-scale creator campaign focused on bringing new audiences into Sl8, and 48 influencers have already agreed to work with us as part of this expansion. At the same time, negotiations continue with a much broader pool of creators across the region. Our approach is to enter each new market through relevant local creators and communities, building awareness around Sl8 and converting that attention into a growing, engaged user base. The expansion process continues to gain momentum, with new creators, partnerships, and markets joining the Sl8 ecosystem step by step.

Cassator Corp.

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

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

Impeccable

232,003 просмотров • 2 месяцев назад

You can't 3D reconstruct glass from images... ...WRONG! Thanks for video diffusion, now just about anything is possible! Introducing...Diffusion Knows Transparency (DKT) Transparent and reflective objects usually break robot vision and photogrammetry pipelines because they don't follow the "solid object" rules standard cameras expect. DKT is a new AI model that repurposes the "internal physics engine" found in video generation models to solve this problem. Researchers took a massive video diffusion model (WAN) and fine-tuned it using a custom-built synthetic dataset to turn it into a high-precision depth sensor. To train the AI, they built the first massive synthetic video library of transparent objects, 1.32 million frames of perfectly labeled glass and metal objects in motion. Without ever seeing a "real" labeled video of glass during training, the model (DKT) outperformed all previous specialized systems on real-world benchmarks (ClearPose, DREDS). They created a "lightweight" 1.3B parameter version that runs fast enough (0.17s per frame) to be used on actual robot hardware. Two reasons I find this project important: 1. It further proves that synthetic data will be essential for training the next generation vision models. 2. In real-world robotic tests, using DKT's depth maps nearly doubled the success rate of robot arms trying to pick up objects on tricky reflective or translucent surfaces. At home robots will need to interact with these types of objects on a daily basis. Check out the project page here: Code is LIVE! #Computervision #Robotics #AI

You can't 3D reconstruct glass from images... ...WRONG! Thanks for video diffusion, now just about anything is possible! Introducing...Diffusion Knows Transparency (DKT) Transparent and reflective objects usually break robot vision and photogrammetry pipelines because they don't follow the "solid object" rules standard cameras expect. DKT is a new AI model that repurposes the "internal physics engine" found in video generation models to solve this problem. Researchers took a massive video diffusion model (WAN) and fine-tuned it using a custom-built synthetic dataset to turn it into a high-precision depth sensor. To train the AI, they built the first massive synthetic video library of transparent objects, 1.32 million frames of perfectly labeled glass and metal objects in motion. Without ever seeing a "real" labeled video of glass during training, the model (DKT) outperformed all previous specialized systems on real-world benchmarks (ClearPose, DREDS). They created a "lightweight" 1.3B parameter version that runs fast enough (0.17s per frame) to be used on actual robot hardware. Two reasons I find this project important: 1. It further proves that synthetic data will be essential for training the next generation vision models. 2. In real-world robotic tests, using DKT's depth maps nearly doubled the success rate of robot arms trying to pick up objects on tricky reflective or translucent surfaces. At home robots will need to interact with these types of objects on a daily basis. Check out the project page here: Code is LIVE! #Computervision #Robotics #AI

Jonathan Stephens

17,712 просмотров • 7 месяцев назад

THIS SITE COST AROUND $12 IN CREDITS TO BUILD. STUDIOS QUOTE $35,000 FOR THE SAME THING. What's on screen isn't a basic landing page. It's a fully animated, scroll-driven site, generated end to end in one agentic session with Claude Code + Higgsfield. What's actually on the page: → Cinematic motion clips pulled from 30+ generative models → Scroll animations written automatically - zero hand-coded keyframes → 6 cinematic effects baked in with no config: film grain, particles, vignette, glass cards, color tints, scroll pacing Scroll the demo and one question won't go away: did Claude really assemble all of this in a single pass? For boutique studios billing $100-149/hr, that question lands like a verdict. What it normally takes: → A designer, a motion artist, and a developer → Weeks of handoffs between them → 6 systems wired by hand - GSAP ScrollTrigger, Lenis smooth-scroll, frame extraction, asset optimization, layout, copy That pipeline was the moat. It's what justified the invoice. Here's the part studios and their clients won't enjoy hearing. The price gap: → Boutique agency build: $6,000-$35,000+ → Industry average project: ~$5,280 → Delivery cost: a Claude subscription + a few dollars of Higgsfield credits → Timeline: weeks of production → a single session One operator can now run all six systems in one pass and ship a working site - without touching a frame extractor or writing a CSS keyframe by hand. Full breakdown of how it's built in the article below. Save it & read today 👇

THIS SITE COST AROUND $12 IN CREDITS TO BUILD. STUDIOS QUOTE $35,000 FOR THE SAME THING. What's on screen isn't a basic landing page. It's a fully animated, scroll-driven site, generated end to end in one agentic session with Claude Code + Higgsfield. What's actually on the page: → Cinematic motion clips pulled from 30+ generative models → Scroll animations written automatically - zero hand-coded keyframes → 6 cinematic effects baked in with no config: film grain, particles, vignette, glass cards, color tints, scroll pacing Scroll the demo and one question won't go away: did Claude really assemble all of this in a single pass? For boutique studios billing $100-149/hr, that question lands like a verdict. What it normally takes: → A designer, a motion artist, and a developer → Weeks of handoffs between them → 6 systems wired by hand - GSAP ScrollTrigger, Lenis smooth-scroll, frame extraction, asset optimization, layout, copy That pipeline was the moat. It's what justified the invoice. Here's the part studios and their clients won't enjoy hearing. The price gap: → Boutique agency build: $6,000-$35,000+ → Industry average project: ~$5,280 → Delivery cost: a Claude subscription + a few dollars of Higgsfield credits → Timeline: weeks of production → a single session One operator can now run all six systems in one pass and ship a working site - without touching a frame extractor or writing a CSS keyframe by hand. Full breakdown of how it's built in the article below. Save it & read today 👇

ZEUS⚡️

480,444 просмотров • 2 месяцев назад

A WEB STUDIO CHARGES $35,000 FOR AN ANIMATED SITE. THE SAME BUILD NOW COSTS $12 - CLAUDE CODE WRITES, HIGGSFIELD RENDERS. Every agency billing $100-149/hr is just three departments. Here's each one, collapsed into a single agentic session. SYSTEM 1 - THE MOTION STUDIO (Higgsfield) Cinematic clips pulled from 30+ generative models - hero shots, transitions, ambient loops. → This used to be a motion artist on retainer. Now it's a prompt. SYSTEM 2 - THE DEV TEAM (Claude Code) Scaffolds the site, writes the GSAP ScrollTrigger timelines and Lenis smooth-scroll, extracts frames, optimizes every asset. → A full scroll-driven build with zero hand-coded keyframes. SYSTEM 3 - THE DESIGN DEPT (baked-in cinematic layer) Six effects with no config: film grain, particles, vignette, glass cards, color tints, scroll pacing. → The polish that justified the invoice - now it ships by default. Three departments. One operator. One pass. What used to take a designer, a motion artist, and a developer through weeks of handoffs now runs in a single session - for a Claude subscription and a few dollars of Higgsfield credits. The studio was never the talent. It was the overhead. And the overhead just became three systems. Reply "web-site" to this post and I will send you the step-by-step Playbook 👇

A WEB STUDIO CHARGES $35,000 FOR AN ANIMATED SITE. THE SAME BUILD NOW COSTS $12 - CLAUDE CODE WRITES, HIGGSFIELD RENDERS. Every agency billing $100-149/hr is just three departments. Here's each one, collapsed into a single agentic session. SYSTEM 1 - THE MOTION STUDIO (Higgsfield) Cinematic clips pulled from 30+ generative models - hero shots, transitions, ambient loops. → This used to be a motion artist on retainer. Now it's a prompt. SYSTEM 2 - THE DEV TEAM (Claude Code) Scaffolds the site, writes the GSAP ScrollTrigger timelines and Lenis smooth-scroll, extracts frames, optimizes every asset. → A full scroll-driven build with zero hand-coded keyframes. SYSTEM 3 - THE DESIGN DEPT (baked-in cinematic layer) Six effects with no config: film grain, particles, vignette, glass cards, color tints, scroll pacing. → The polish that justified the invoice - now it ships by default. Three departments. One operator. One pass. What used to take a designer, a motion artist, and a developer through weeks of handoffs now runs in a single session - for a Claude subscription and a few dollars of Higgsfield credits. The studio was never the talent. It was the overhead. And the overhead just became three systems. Reply "web-site" to this post and I will send you the step-by-step Playbook 👇

ZEUS⚡️

174,092 просмотров • 2 месяцев назад

Claude Opus 5 x NexLev MCP might be the most unfair combo for building faceless YouTube channels right now So I’m giving away the FULL AI Story channel production system behind it Here’s EVERYTHING that you’ll get inside: → The exact Claude setup that turns Opus 5 into a full faceless YouTube production operator. → NexLev MCP niche validation prompts that find new channels getting 100k+ views without guessing niches manually. → 48-hour velocity check prompt to spot which AI Story angles are actually moving right now. → RPM filtering system so you avoid low-value niches and only build around $12-$20+ RPM opportunities. → Opus 5 JSON script framework for 8,000+ word videos with locked characters, pacing rules, emotional beats, and cliffhangers. → Documentary research brief prompt that verifies dates, names, timelines, and quotes before the script gets written. → ElevenLabs MCP voiceover workflow with narrator matching by niche so the voice fits the audience instead of sounding random. → Higgsfield MCP visual system using Seedance 2.0, Flux 2, and Nano Banana Pro to create animated intros, scene images, and consistent characters. → Thumbnail prompt structure for ChatGPT Image 2.0 so the final video has clean text, high emotion, and a clickable 1280x720 layout. → Full assembly checklist for taking the script, voiceover, animated clips, captions, and thumbnail into an upload-ready video in under 30 minutes. All built from the AI YouTube production playbooks used across: → 120+ Elevate members → $12k/mo average per student → 800M+ total views across the system Like + comment "CLAUDE" and I’ll send you the whole thing (Must be following so I can DM)

Claude Opus 5 x NexLev MCP might be the most unfair combo for building faceless YouTube channels right now So I’m giving away the FULL AI Story channel production system behind it Here’s EVERYTHING that you’ll get inside: → The exact Claude setup that turns Opus 5 into a full faceless YouTube production operator. → NexLev MCP niche validation prompts that find new channels getting 100k+ views without guessing niches manually. → 48-hour velocity check prompt to spot which AI Story angles are actually moving right now. → RPM filtering system so you avoid low-value niches and only build around $12-$20+ RPM opportunities. → Opus 5 JSON script framework for 8,000+ word videos with locked characters, pacing rules, emotional beats, and cliffhangers. → Documentary research brief prompt that verifies dates, names, timelines, and quotes before the script gets written. → ElevenLabs MCP voiceover workflow with narrator matching by niche so the voice fits the audience instead of sounding random. → Higgsfield MCP visual system using Seedance 2.0, Flux 2, and Nano Banana Pro to create animated intros, scene images, and consistent characters. → Thumbnail prompt structure for ChatGPT Image 2.0 so the final video has clean text, high emotion, and a clickable 1280x720 layout. → Full assembly checklist for taking the script, voiceover, animated clips, captions, and thumbnail into an upload-ready video in under 30 minutes. All built from the AI YouTube production playbooks used across: → 120+ Elevate members → $12k/mo average per student → 800M+ total views across the system Like + comment "CLAUDE" and I’ll send you the whole thing (Must be following so I can DM)

gold.

22,021 просмотров • 1 месяц назад

Two weeks ago I fixed one of my teeth with algorithms I wrote a couple of years ago! I got hooked by 3D scanning when I started to work for a software shop in Zurich that was programming 3D computational geometry algorithms for denture scanning to produce crowns (and more). Back then, a typical reconstruction pipeline was like: scan the patient’s teeth using an intraoral scanner, reconstruct the surface mesh, design the restoration digitally, and finally mill the crown out of ceramic. We were working mostly with point clouds and meshes, but it wasn’t just math, it was craftsmanship translated into a digital process. Every micron mattered. You could literally see how a good algorithm meant a better fit in someone’s mouth. Gaussian Splatting isn’t about surface reconstruction, it’s about appearance reconstruction. It doesn’t care about explicit topology, it captures how light interacts with the scene. In a sense, it’s the opposite philosophy of the dental world: instead of modeling what the object is, it models how the object looks. 3D Gaussian Splatting enables applications like training self driving cars, teaching robots to understand their environment, creating virtual worlds, or monitoring real sites. It represents scenes as millions of small Gaussians rendered in real time without the need for meshes or textures. Coming from a world where precision geometry was everything, this shift felt natural. It’s still about reconstruction, but with a different goal: not manufacturing a perfect object, but reproducing how the world actually looks. Two weeks ago I got my first dental crown, made with the same software, reconstruction algorithms, and Swiss precision I once helped develop. I haven’t worked there in two years, but sitting in that chair and seeing the process from the other side was a proud moment. It reminded me why I love this field.

Two weeks ago I fixed one of my teeth with algorithms I wrote a couple of years ago! I got hooked by 3D scanning when I started to work for a software shop in Zurich that was programming 3D computational geometry algorithms for denture scanning to produce crowns (and more). Back then, a typical reconstruction pipeline was like: scan the patient’s teeth using an intraoral scanner, reconstruct the surface mesh, design the restoration digitally, and finally mill the crown out of ceramic. We were working mostly with point clouds and meshes, but it wasn’t just math, it was craftsmanship translated into a digital process. Every micron mattered. You could literally see how a good algorithm meant a better fit in someone’s mouth. Gaussian Splatting isn’t about surface reconstruction, it’s about appearance reconstruction. It doesn’t care about explicit topology, it captures how light interacts with the scene. In a sense, it’s the opposite philosophy of the dental world: instead of modeling what the object is, it models how the object looks. 3D Gaussian Splatting enables applications like training self driving cars, teaching robots to understand their environment, creating virtual worlds, or monitoring real sites. It represents scenes as millions of small Gaussians rendered in real time without the need for meshes or textures. Coming from a world where precision geometry was everything, this shift felt natural. It’s still about reconstruction, but with a different goal: not manufacturing a perfect object, but reproducing how the world actually looks. Two weeks ago I got my first dental crown, made with the same software, reconstruction algorithms, and Swiss precision I once helped develop. I haven’t worked there in two years, but sitting in that chair and seeing the process from the other side was a proud moment. It reminded me why I love this field.

MrNeRF

290,257 просмотров • 10 месяцев назад

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

537,174 просмотров • 3 месяцев назад

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

Nesa

17,038 просмотров • 6 месяцев назад

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.

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

141,361 просмотров • 1 месяц назад

$🚨SCIENCE🚨: Time just got a remix — and exotic quantum matter started showing up uninvited 🧨 Scientists at California Polytechnic State University just dropped a bombshell on May 4, 2026: by periodically driving magnetic fields in graphene over time, they created entirely new quantum states of matter that flat-out do not exist under any static conditions. These driven phases are dramatically more stable and error-resistant — exactly the kind of breakthrough quantum computing has been starving for. Standard models are left asking why time itself seems to be the missing ingredient. Uniphics sees this as inevitable once you accept the three pillars. Time flow (t_flow) is not a universal constant — it is strictly t_flow = k / E_d, where k = 4.64159 × 10^18 J/m³ is the fixed reference density set by the electron Gyrotron volume. When researchers vary the magnetic field periodically, they are rhythmically modulating local energy density (E_d) in time. That creates transient windows where t_flow itself shifts, opening entirely new minima in the ξM-field potential that negentropy (the drive toward lowest energy, J_neg ≈ −5.66 × 10^{-21} J/K) can lock into stable spin configurations. The Gyrotrons — each a 3D gyroscope of three orthogonal spin quanta (xy, xz, yz planes), every quantum a tempest of whirling energy spinning CW or CCW — access driven phases that static E_d simply cannot sustain. The result: exotic states with no static counterpart, far more resistant to decoherence because they are continuously refreshed by the same negentropy that condensed the first bound matter at the Amorphics-to-Physics transition. No new particles, no extra dimensions, no patches — just the pillars doing what they do best: turning dynamic E_d into order. This is why the new states are so robust. The time-dependent drive keeps the system dancing exactly where unbound energy repels unbound energy just enough to hold the new lock without collapse. How might deliberately engineering time flow gradients in real materials accelerate fault-tolerant quantum computers — or even let us replay the driven phases that built the early universe? A Theory of Everything should be able to answer everything. #Uniphics #QuantumStates #TimeFlow #EnergyDensity #SpinQuanta Grok xAI Uniphics Explained Simply PDF: Chapters 1–10 free: Grokipedia$

🚨SCIENCE🚨: Time just got a remix — and exotic quantum matter started showing up uninvited 🧨 Scientists at California Polytechnic State University just dropped a bombshell on May 4, 2026: by periodically driving magnetic fields in graphene over time, they created entirely new quantum states of matter that flat-out do not exist under any static conditions. These driven phases are dramatically more stable and error-resistant — exactly the kind of breakthrough quantum computing has been starving for. Standard models are left asking why time itself seems to be the missing ingredient. Uniphics sees this as inevitable once you accept the three pillars. Time flow (t_flow) is not a universal constant — it is strictly t_flow = k / E_d, where k = 4.64159 × 10^18 J/m³ is the fixed reference density set by the electron Gyrotron volume. When researchers vary the magnetic field periodically, they are rhythmically modulating local energy density (E_d) in time. That creates transient windows where t_flow itself shifts, opening entirely new minima in the ξM-field potential that negentropy (the drive toward lowest energy, J_neg ≈ −5.66 × 10^{-21} J/K) can lock into stable spin configurations. The Gyrotrons — each a 3D gyroscope of three orthogonal spin quanta (xy, xz, yz planes), every quantum a tempest of whirling energy spinning CW or CCW — access driven phases that static E_d simply cannot sustain. The result: exotic states with no static counterpart, far more resistant to decoherence because they are continuously refreshed by the same negentropy that condensed the first bound matter at the Amorphics-to-Physics transition. No new particles, no extra dimensions, no patches — just the pillars doing what they do best: turning dynamic E_d into order. This is why the new states are so robust. The time-dependent drive keeps the system dancing exactly where unbound energy repels unbound energy just enough to hold the new lock without collapse. How might deliberately engineering time flow gradients in real materials accelerate fault-tolerant quantum computers — or even let us replay the driven phases that built the early universe? A Theory of Everything should be able to answer everything. #Uniphics #QuantumStates #TimeFlow #EnergyDensity #SpinQuanta Grok xAI Uniphics Explained Simply PDF: Chapters 1–10 free: Grokipedia

Paul Maley

11,804 просмотров • 3 месяцев назад

THIS GUY JUST REBUILT A $35,000 ANIMATED SITE FOR $12. IF YOU RUN A WEB STUDIO, YOU SHOULD PROBABLY KEEP SCROLLING. Every agency billing $100-149/hr is selling you five departments wearing one invoice. Here’s each one - collapsed into a single agentic session. LAYER 1 - THE CONCEPT ROOM (Claude) Reads the brief, pulls references, and scripts the scroll: what the visitor feels at second 3, second 15, second 40. → Used to be a strategist and a wall of mood boards. Now it’s a conversation. LAYER 2 - THE MOTION STUDIO (Higgsfield) Cinematic clips from 30+ generative models - hero shots, transitions, ambient loops - all matched to the story from Layer 1. → Used to be a motion artist on retainer. Now it’s a prompt. LAYER 3 - THE DEV TEAM (Claude Code) Scaffolds the site, writes the GSAP ScrollTrigger timelines and Lenis smooth-scroll, extracts frames, optimizes every asset. → A full scroll-driven build with zero hand-coded keyframes. LAYER 4 - THE DESIGN DEPT (baked-in cinematic layer) Six effects, zero config: film grain, particles, vignette, glass cards, color tints, scroll pacing. → The polish that justified the invoice - now it ships by default. LAYER 5 - THE QA PASS (Claude) Checks load speed, mobile breakpoints, and whether the scroll actually lands - then rewrites whatever doesn’t. → Used to be a client call and a revision cycle. Now it’s one more turn in the same session. Five departments. One operator. One pass. A strategist, a motion artist, a developer, a designer, and a QA lead - weeks of handoffs - now run in a single session. For a Claude subscription and a few dollars of Higgsfield credits. The studio was never selling talent. It was selling overhead. And the overhead just became five layers. Follow me, reply “website” to this post and I will send you the step-by-step Playbook 👇

THIS GUY JUST REBUILT A $35,000 ANIMATED SITE FOR $12. IF YOU RUN A WEB STUDIO, YOU SHOULD PROBABLY KEEP SCROLLING. Every agency billing $100-149/hr is selling you five departments wearing one invoice. Here’s each one - collapsed into a single agentic session. LAYER 1 - THE CONCEPT ROOM (Claude) Reads the brief, pulls references, and scripts the scroll: what the visitor feels at second 3, second 15, second 40. → Used to be a strategist and a wall of mood boards. Now it’s a conversation. LAYER 2 - THE MOTION STUDIO (Higgsfield) Cinematic clips from 30+ generative models - hero shots, transitions, ambient loops - all matched to the story from Layer 1. → Used to be a motion artist on retainer. Now it’s a prompt. LAYER 3 - THE DEV TEAM (Claude Code) Scaffolds the site, writes the GSAP ScrollTrigger timelines and Lenis smooth-scroll, extracts frames, optimizes every asset. → A full scroll-driven build with zero hand-coded keyframes. LAYER 4 - THE DESIGN DEPT (baked-in cinematic layer) Six effects, zero config: film grain, particles, vignette, glass cards, color tints, scroll pacing. → The polish that justified the invoice - now it ships by default. LAYER 5 - THE QA PASS (Claude) Checks load speed, mobile breakpoints, and whether the scroll actually lands - then rewrites whatever doesn’t. → Used to be a client call and a revision cycle. Now it’s one more turn in the same session. Five departments. One operator. One pass. A strategist, a motion artist, a developer, a designer, and a QA lead - weeks of handoffs - now run in a single session. For a Claude subscription and a few dollars of Higgsfield credits. The studio was never selling talent. It was selling overhead. And the overhead just became five layers. Follow me, reply “website” to this post and I will send you the step-by-step Playbook 👇

ZEUS⚡️

141,226 просмотров • 1 месяц назад