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At Open Sauce, a Meta engineer described how Yoga was built (flex layout for React Native) After a sweaty 24h, I've recreated CSS Grid using the same technique! bun add minicssgrid 14KB (no deps) MIT

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53,770 görüntüleme • 11 ay önce •via X (Twitter)

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Heyo exciting news! Just released my react-native-godot library to the world! Available on npm, just `yarn add react-native-godot` and you’re (almost) good to go :) Basically, you can embed any Godot projects on iOS (Android coming soon) into your React Native app, It supports multiple Godot views at the same time, full styling (you get flex box multiple Godot views and update their layout dynamically) Still pretty early but I think it’s already enough to hack around with it. If you have any questions or issues feel free to dm me. Alsoo, give us a star on Github if you support the initiative 😊
0:13

Heyo exciting news! Just released my react-native-godot library to the world! Available on npm, just `yarn add react-native-godot` and you’re (almost) good to go :) Basically, you can embed any Godot projects on iOS (Android coming soon) into your React Native app, It supports multiple Godot views at the same time, full styling (you get flex box multiple Godot views and update their layout dynamically) Still pretty early but I think it’s already enough to hack around with it. If you have any questions or issues feel free to dm me. Alsoo, give us a star on Github if you support the initiative 😊

𝒔𝒆𝒃 🔮

48,068 görüntüleme • 1 yıl önce

A Midjourney engineer just open-sourced a way to lay out entire web pages without CSS. Not a framework. Not a library that wraps the DOM. A pure TypeScript text measurement algorithm that bypasses browser reflow entirely. 600x faster. Why it matters: AI-generated interfaces need to lay out text dynamically. The browser wasn't built for that. CSS reflow was designed for humans writing static pages, not agents generating UIs on the fly. This exists because agents need rendering that doesn't depend on a 30-year-old pipeline. The AI-era stack is being rebuilt from scratch. One library at a time. P.S. Such a fun to play with that!
0:10

A Midjourney engineer just open-sourced a way to lay out entire web pages without CSS. Not a framework. Not a library that wraps the DOM. A pure TypeScript text measurement algorithm that bypasses browser reflow entirely. 600x faster. Why it matters: AI-generated interfaces need to lay out text dynamically. The browser wasn't built for that. CSS reflow was designed for humans writing static pages, not agents generating UIs on the fly. This exists because agents need rendering that doesn't depend on a 30-year-old pipeline. The AI-era stack is being rebuilt from scratch. One library at a time. P.S. Such a fun to play with that!

Paweł Huryn

238,834 görüntüleme • 3 ay önce

A guy in Vancouver built an entire operating system inside a Chrome tab. By himself. Over six years. His personal website is the OS. You open and a Windows-style desktop loads. File explorer. Start menu. Taskbar. You can drag in a zip and extract it. You can play DOOM. You can play Quake III Arena. You can boot Linux from an ISO. You can run Stable Diffusion locally for image generation. You can open a Python terminal. You can edit code in Monaco, the same engine that powers VS Code. All of it runs in your browser tab. Nothing installs. His name is Dustin Brett. Self-taught engineer. Father. Husband. 4,473 commits. All his. He had to swap the Windows icon for the π symbol because of legal pressure. The repo has 12,883 stars. MIT license. His hosting bill is one dollar a month. A single Cloudflare CDN does the rest. This is what the open web was built for. (Link in the comments)
0:18

A guy in Vancouver built an entire operating system inside a Chrome tab. By himself. Over six years. His personal website is the OS. You open and a Windows-style desktop loads. File explorer. Start menu. Taskbar. You can drag in a zip and extract it. You can play DOOM. You can play Quake III Arena. You can boot Linux from an ISO. You can run Stable Diffusion locally for image generation. You can open a Python terminal. You can edit code in Monaco, the same engine that powers VS Code. All of it runs in your browser tab. Nothing installs. His name is Dustin Brett. Self-taught engineer. Father. Husband. 4,473 commits. All his. He had to swap the Windows icon for the π symbol because of legal pressure. The repo has 12,883 stars. MIT license. His hosting bill is one dollar a month. A single Cloudflare CDN does the rest. This is what the open web was built for. (Link in the comments)

Nav Toor

71,836 görüntüleme • 23 gün önce

React Native now has its own shadcn/ui equivalent — introducing 𝗡𝗮𝘁𝗶𝘃𝗲𝗨𝗜. If you love the flexibility of copying customisable components directly into your project (avoiding heavy, dependency-laden packages), NativeUI is designed for you. 𝗡𝗮𝘁𝗶𝘃𝗲𝗨𝗜 offers beautifully crafted, accessible components tailored for React Native, following the same copy-paste philosophy as shadcn/ui. Built with 𝗡𝗮𝘁𝗶𝘃𝗲𝗪𝗶𝗻𝗱 for fast, declarative, and flexible styling optimised for React Native. ➡️ 𝗖𝗼𝗽𝘆 𝗰𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁 𝗰𝗼𝗱𝗲 𝗱𝗶𝗿𝗲𝗰𝘁𝗹𝘆 𝗶𝗻𝘁𝗼 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 — no black-box dependencies required. ➡️ 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 𝗮𝗿𝗲 𝗮𝗰𝗰𝗲𝘀𝘀𝗶𝗯𝗹𝗲 𝗯𝘆 𝗱𝗲𝗳𝗮𝘂𝗹𝘁, supporting screen readers and keyboard navigation, and designed to align with native iOS and Android UX patterns. ➡️ 𝗙𝘂𝗹𝗹 𝗰𝗼𝗻𝘁𝗿𝗼𝗹 𝗼𝘃𝗲𝗿 𝘆𝗼𝘂𝗿 𝗨𝗜 without rebuilding common elements like buttons, inputs, or sliders from scratch. ➡️ 𝗖𝗼𝗺𝗽𝗮𝘁𝗶𝗯𝗹𝗲 𝘄𝗶𝘁𝗵 𝗘𝘅𝗽𝗼 𝗮𝗻𝗱 𝘃𝗮𝗻𝗶𝗹𝗹𝗮 𝗥𝗲𝗮𝗰𝘁 𝗡𝗮𝘁𝗶𝘃𝗲 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀, but not yet integrated with Tamagui’s styling system (future support may be planned). ➡️ 𝗦𝘂𝗽𝗽𝗼𝗿𝘁𝘀 𝘁𝗵𝗲𝗺𝗶𝗻𝗴 𝘃𝗶𝗮 𝗡𝗮𝘁𝗶𝘃𝗲𝗪𝗶𝗻𝗱 — though you’ll need to wire it up manually using Tailwind variables, context providers, and config files. Note: The term “install” in the documentation refers to using the shadcn CLI (e.g., npx shadcn@latest add component) to fetch and copy component code into your project, not adding a package to your dependencies. NativeUI isn’t a plug-and-play library; it’s a lightweight toolbox that empowers you to shape your UI with precision and control. 𝗪𝗵𝗮𝘁’𝘀 𝘆𝗼𝘂𝗿 𝗽𝗿𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲: npm install a pre-built UI kit for speed, or copy/paste NativeUI components for ultimate customisation? #ReactNative #KeyboardUX #MobileDev #OpenSource #JSDev #Performance #iOSDev #KeyboardExtensions #ReactNativeKeyboard #UIUX #shadcn #nativeui
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React Native now has its own shadcn/ui equivalent — introducing 𝗡𝗮𝘁𝗶𝘃𝗲𝗨𝗜. If you love the flexibility of copying customisable components directly into your project (avoiding heavy, dependency-laden packages), NativeUI is designed for you. 𝗡𝗮𝘁𝗶𝘃𝗲𝗨𝗜 offers beautifully crafted, accessible components tailored for React Native, following the same copy-paste philosophy as shadcn/ui. Built with 𝗡𝗮𝘁𝗶𝘃𝗲𝗪𝗶𝗻𝗱 for fast, declarative, and flexible styling optimised for React Native. ➡️ 𝗖𝗼𝗽𝘆 𝗰𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁 𝗰𝗼𝗱𝗲 𝗱𝗶𝗿𝗲𝗰𝘁𝗹𝘆 𝗶𝗻𝘁𝗼 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 — no black-box dependencies required. ➡️ 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 𝗮𝗿𝗲 𝗮𝗰𝗰𝗲𝘀𝘀𝗶𝗯𝗹𝗲 𝗯𝘆 𝗱𝗲𝗳𝗮𝘂𝗹𝘁, supporting screen readers and keyboard navigation, and designed to align with native iOS and Android UX patterns. ➡️ 𝗙𝘂𝗹𝗹 𝗰𝗼𝗻𝘁𝗿𝗼𝗹 𝗼𝘃𝗲𝗿 𝘆𝗼𝘂𝗿 𝗨𝗜 without rebuilding common elements like buttons, inputs, or sliders from scratch. ➡️ 𝗖𝗼𝗺𝗽𝗮𝘁𝗶𝗯𝗹𝗲 𝘄𝗶𝘁𝗵 𝗘𝘅𝗽𝗼 𝗮𝗻𝗱 𝘃𝗮𝗻𝗶𝗹𝗹𝗮 𝗥𝗲𝗮𝗰𝘁 𝗡𝗮𝘁𝗶𝘃𝗲 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀, but not yet integrated with Tamagui’s styling system (future support may be planned). ➡️ 𝗦𝘂𝗽𝗽𝗼𝗿𝘁𝘀 𝘁𝗵𝗲𝗺𝗶𝗻𝗴 𝘃𝗶𝗮 𝗡𝗮𝘁𝗶𝘃𝗲𝗪𝗶𝗻𝗱 — though you’ll need to wire it up manually using Tailwind variables, context providers, and config files. Note: The term “install” in the documentation refers to using the shadcn CLI (e.g., npx shadcn@latest add component) to fetch and copy component code into your project, not adding a package to your dependencies. NativeUI isn’t a plug-and-play library; it’s a lightweight toolbox that empowers you to shape your UI with precision and control. 𝗪𝗵𝗮𝘁’𝘀 𝘆𝗼𝘂𝗿 𝗽𝗿𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲: npm install a pre-built UI kit for speed, or copy/paste NativeUI components for ultimate customisation? #ReactNative #KeyboardUX #MobileDev #OpenSource #JSDev #Performance #iOSDev #KeyboardExtensions #ReactNativeKeyboard #UIUX #shadcn #nativeui

The React Native Rewind

118,414 görüntüleme • 11 ay önce

Britney Spears spotted for the first time since restaurant incident. The pop star was photographed Thursday afternoon leaving a pet store in Thousand Oaks, CA. She looked carefree, hopping into the passenger seat of her black Mercedes G-Wagon with her driver. This comes one day after reports of a chaotic dinner at Blue Dog Tavern in Sherman Oaks, where witnesses described her yelling, barking, and walking with a knife. Britney’s rep called the story “ridiculous” and overblown — saying she was simply telling a story about her dog barking at neighbors and was only using the knife to cut her hamburger. No one was endangered. How do you think Britney is doing lately — and is the media treating her fairly? (Video: AI)
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Britney Spears spotted for the first time since restaurant incident. The pop star was photographed Thursday afternoon leaving a pet store in Thousand Oaks, CA. She looked carefree, hopping into the passenger seat of her black Mercedes G-Wagon with her driver. This comes one day after reports of a chaotic dinner at Blue Dog Tavern in Sherman Oaks, where witnesses described her yelling, barking, and walking with a knife. Britney’s rep called the story “ridiculous” and overblown — saying she was simply telling a story about her dog barking at neighbors and was only using the knife to cut her hamburger. No one was endangered. How do you think Britney is doing lately — and is the media treating her fairly? (Video: AI)

Paul A. Szypula 🇺🇸

26,794 görüntüleme • 1 ay önce

004/100 Buttons. A bit of the process on building an animation. When looking at a finished animation or in this example a finished button, it can look quite complex inside the CSS. But when building it, it’s more like a lot of simple steps, one after another. Here I had the idea to make some kind of text animation like the footer logo on the Osmo site. I try to add the base animation with no complex easing, for example transition: translate 0.4s ease. Starting with just moving the one text from bottom to top and the other text to top. Adding a stagger, play around with it. Searching for a way to make it more circular. On the research I found the sin() function inside CSS which can build a more smooth non linear curve for the stagger which creates this circular effect. And step by step adding more complexity like, different easing for hover/hover-out, opacity, 3D transform and more. I use also the sin() function to rotate the letters, so the middle ones are getting more rotated than the outer ones. Another thing which helps is to add a small delay on hover, for example 0.05s or 0.1s, you don’t really see the difference, but when you hover pretty fast on and out it doesn’t get that jumpy. I’m using here GSAP’s SplitText to split every char into spans. And then I’m adding a CSS index variable to every span, starting from the center. SplitText can provide CSS index variables, but you cannot tell it from which direction. For the sin() it’s also important to have a max length, so I add another CSS variable with the max char number on it. Crafting 100 Buttons with Osmo ⏳ Total time: 63h
0:17

004/100 Buttons. A bit of the process on building an animation. When looking at a finished animation or in this example a finished button, it can look quite complex inside the CSS. But when building it, it’s more like a lot of simple steps, one after another. Here I had the idea to make some kind of text animation like the footer logo on the Osmo site. I try to add the base animation with no complex easing, for example transition: translate 0.4s ease. Starting with just moving the one text from bottom to top and the other text to top. Adding a stagger, play around with it. Searching for a way to make it more circular. On the research I found the sin() function inside CSS which can build a more smooth non linear curve for the stagger which creates this circular effect. And step by step adding more complexity like, different easing for hover/hover-out, opacity, 3D transform and more. I use also the sin() function to rotate the letters, so the middle ones are getting more rotated than the outer ones. Another thing which helps is to add a small delay on hover, for example 0.05s or 0.1s, you don’t really see the difference, but when you hover pretty fast on and out it doesn’t get that jumpy. I’m using here GSAP’s SplitText to split every char into spans. And then I’m adding a CSS index variable to every span, starting from the center. SplitText can provide CSS index variables, but you cannot tell it from which direction. For the sin() it’s also important to have a max length, so I add another CSS variable with the max char number on it. Crafting 100 Buttons with Osmo ⏳ Total time: 63h

Eduard Bodak

166,023 görüntüleme • 2 ay önce

SOMEONE JUST VIBECODED A PROGRAM THAT DOWNLOADS ANY WEBSITE ON THE INTERNET every $35,000 agency site is now yours to rip clean for $0 fully open source, save it before it's gone you paste one url and it does the rest: → crawls the entire site → grabs every file: html, css, js, images, even the fonts → rewrites every link to run fully offline → packs it all into a downloadable zip runs entirely in the browser, nothing to install or sign up for point it at any site you admire and the whole front-end is yours: → clone it → archive it before it dies → crack open exactly how it was built the thing agencies gatekeep for five figures is now a 10-second download bookmark this one
0:14

SOMEONE JUST VIBECODED A PROGRAM THAT DOWNLOADS ANY WEBSITE ON THE INTERNET every $35,000 agency site is now yours to rip clean for $0 fully open source, save it before it's gone you paste one url and it does the rest: → crawls the entire site → grabs every file: html, css, js, images, even the fonts → rewrites every link to run fully offline → packs it all into a downloadable zip runs entirely in the browser, nothing to install or sign up for point it at any site you admire and the whole front-end is yours: → clone it → archive it before it dies → crack open exactly how it was built the thing agencies gatekeep for five figures is now a 10-second download bookmark this one

Argona

121,402 görüntüleme • 9 gün önce

Contact sheet prompting is the hottest AI video technique right now 🤯 If you've seen this technique blowing up, here's why it works: You feed AI one image, and it generates a grid of consistent shots—same face, same outfit, different angles and poses. Instant storyboarding. Full creative control. No reshoots. But doing it manually is brutal: → Write the prompt from scratch → Generate the contact sheet → Crop each frame by hand → Feed frames into a video model one at a time → Repeat for every single product That's hours of work per campaign. This n8n automation handles everything: → Upload a character image + product image → AI analyzes both and writes the contact sheet prompt → Nano Banana Pro generates a 6-frame grid → System extracts each frame automatically → Kling 2.5 creates smooth transitions between frames → 5 video clips land in Airtable ready to use No manual cropping. No frame-by-frame prompting. No tedious busywork. What you get in Airtable: - AI-generated creative prompt - Hero image (model + product) - Full 6-frame contact sheet - 5 cinematic video clips - Approval gates before each step All inside n8n + Airtable. Contact sheet prompting on complete autopilot. I recorded a 20-minute Loom showing exactly how I built this. Want the walkthrough + the full n8n workflow + Airtable base? > Like this post > Comment "CONTACT" And I'll send it over (must be following so I can DM)
0:13

Contact sheet prompting is the hottest AI video technique right now 🤯 If you've seen this technique blowing up, here's why it works: You feed AI one image, and it generates a grid of consistent shots—same face, same outfit, different angles and poses. Instant storyboarding. Full creative control. No reshoots. But doing it manually is brutal: → Write the prompt from scratch → Generate the contact sheet → Crop each frame by hand → Feed frames into a video model one at a time → Repeat for every single product That's hours of work per campaign. This n8n automation handles everything: → Upload a character image + product image → AI analyzes both and writes the contact sheet prompt → Nano Banana Pro generates a 6-frame grid → System extracts each frame automatically → Kling 2.5 creates smooth transitions between frames → 5 video clips land in Airtable ready to use No manual cropping. No frame-by-frame prompting. No tedious busywork. What you get in Airtable: - AI-generated creative prompt - Hero image (model + product) - Full 6-frame contact sheet - 5 cinematic video clips - Approval gates before each step All inside n8n + Airtable. Contact sheet prompting on complete autopilot. I recorded a 20-minute Loom showing exactly how I built this. Want the walkthrough + the full n8n workflow + Airtable base? > Like this post > Comment "CONTACT" And I'll send it over (must be following so I can DM)

Mike Futia

24,559 görüntüleme • 5 ay önce

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.
0:17

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

23,390 görüntüleme • 2 ay önce

🚨 SCIENTISTS JUST REPROGRAMMED MATTER AT THE ATOMIC LEVEL MIT researchers have shattered a 40-year-old record by moving tens of thousands of atoms inside a material using a focused electron beam. Not in simulations. In real physical matter. The breakthrough allowed scientists to generate over 40,000 quantum defects in just 40 minutes compared to the famous IBM experiment in the 1980s that moved only 35 atoms. But this changes far more than speed. For the first time, researchers can precisely reposition atoms: • In 3 dimensions • At room temperature • Inside solid materials themselves Using a beam controlled with picometer precision, they literally “push” columns of atoms through a crystal like swiping icons on a phone screen. Why this matters: • It could enable programmable matter • Build stable quantum devices • Create entirely new material properties • Unlock exotic quantum states on demand • Rewrite how future computers and sensors are built The wildest part? The defects themselves become the technology. Scientists are no longer just discovering materials. They are beginning to engineer reality atom by atom. Follow for more future physics and quantum breakthroughs.
0:10

🚨 SCIENTISTS JUST REPROGRAMMED MATTER AT THE ATOMIC LEVEL MIT researchers have shattered a 40-year-old record by moving tens of thousands of atoms inside a material using a focused electron beam. Not in simulations. In real physical matter. The breakthrough allowed scientists to generate over 40,000 quantum defects in just 40 minutes compared to the famous IBM experiment in the 1980s that moved only 35 atoms. But this changes far more than speed. For the first time, researchers can precisely reposition atoms: • In 3 dimensions • At room temperature • Inside solid materials themselves Using a beam controlled with picometer precision, they literally “push” columns of atoms through a crystal like swiping icons on a phone screen. Why this matters: • It could enable programmable matter • Build stable quantum devices • Create entirely new material properties • Unlock exotic quantum states on demand • Rewrite how future computers and sensors are built The wildest part? The defects themselves become the technology. Scientists are no longer just discovering materials. They are beginning to engineer reality atom by atom. Follow for more future physics and quantum breakthroughs.

TheNewPhysics

74,652 görüntüleme • 2 ay önce

Hold up, here is the prompt: works with almost any model. enjoy :) Role & Objective: Act as an Elite UI/UX Front-End Engineer specializing in Apple-tier micro-interactions and advanced CSS. Your task is to program a perfectly centered navigation bar in a strictly SINGLE HTML file containing all HTML, vanilla CSS, and vanilla JavaScript. No external libraries or frameworks (No Tailwind, React, etc.). Design Concept - "True Liquid Glass": CRITICAL INSTRUCTION: Do NOT generate standard, flat "glassmorphism" or basic frosted glass. I require a physically accurate "Liquid Glass" aesthetic. It must look like wet, poured clear resin, combining the high-gloss specular highlights of classic macOS Aqua with the volumetric spatial depth of modern Apple VisionOS. 1. The Liquid Glass Material & Lighting (CSS): - Deep Refraction: Use `backdrop-filter` with extreme blur (e.g., 50px) and over-saturation (200%). - Specular Highlight: Create a curved, semi-transparent white gradient on the top half using a pseudo-element (`::before`) to simulate a hard light reflection on a wet, rounded 3D surface. - Caustics & Volume: Use multi-layered inner and outer `box-shadow` properties to simulate light refracting at the bottom edge and casting a realistic ambient drop shadow. - Interactive Glare: Implement a soft radial-gradient spotlight inside the glass that dynamically tracks the user's mouse cursor (X/Y coordinates) using JavaScript and CSS variables (`mix-blend-mode: overlay`). 2. Navigation Layout & Elements: - Center the pill-shaped navigation bar perfectly in the middle of the viewport. - Include 3 main navigation items with minimalist, inline SVG stroke icons and text labels: "Home", "Call", and "List". - Add a subtle vertical divider line after the main buttons. - Next to the divider, add a Dark/Light Mode toggle button containing inline SVG Sun and Moon icons. 3. Animations & "Apple Magic": - Sliding Active Pill: Create a solid background "pill" that sits *behind* the active navigation item's text/icon. When a different item is clicked, this pill must dynamically recalculate its width and slide to the new position. - Spring Physics: The sliding transition MUST use an exact Apple-style bouncy spring easing curve (e.g., `transition: all 0.5s cubic-bezier(0.34, 1.2, 0.64, 1)`). - Tactile Feedback: Buttons and icons must physically press down slightly (`transform: scale(0.92)`) when clicked (`:active`). - Theme Switch: The Sun and Moon icons must smoothly rotate, scale, and cross-fade during the transition. 4. Background Environment (Crucial): - Glass needs light and color to refract! Create a full-viewport, smoothly animated mesh gradient background using 3 large, heavily blurred, floating color blobs. - Implement full Dark/Light mode logic using CSS variables (`:root` and `[data-theme="dark"]`). Toggling the theme must seamlessly transition the background blob colors, glass opacity, shadow intensity, and text colors. Output ONLY the pristine, production-ready code. Prioritize maximum visual fidelity and silky-smooth 60fps animations.
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Hold up, here is the prompt: works with almost any model. enjoy :) Role & Objective: Act as an Elite UI/UX Front-End Engineer specializing in Apple-tier micro-interactions and advanced CSS. Your task is to program a perfectly centered navigation bar in a strictly SINGLE HTML file containing all HTML, vanilla CSS, and vanilla JavaScript. No external libraries or frameworks (No Tailwind, React, etc.). Design Concept - "True Liquid Glass": CRITICAL INSTRUCTION: Do NOT generate standard, flat "glassmorphism" or basic frosted glass. I require a physically accurate "Liquid Glass" aesthetic. It must look like wet, poured clear resin, combining the high-gloss specular highlights of classic macOS Aqua with the volumetric spatial depth of modern Apple VisionOS. 1. The Liquid Glass Material & Lighting (CSS): - Deep Refraction: Use `backdrop-filter` with extreme blur (e.g., 50px) and over-saturation (200%). - Specular Highlight: Create a curved, semi-transparent white gradient on the top half using a pseudo-element (`::before`) to simulate a hard light reflection on a wet, rounded 3D surface. - Caustics & Volume: Use multi-layered inner and outer `box-shadow` properties to simulate light refracting at the bottom edge and casting a realistic ambient drop shadow. - Interactive Glare: Implement a soft radial-gradient spotlight inside the glass that dynamically tracks the user's mouse cursor (X/Y coordinates) using JavaScript and CSS variables (`mix-blend-mode: overlay`). 2. Navigation Layout & Elements: - Center the pill-shaped navigation bar perfectly in the middle of the viewport. - Include 3 main navigation items with minimalist, inline SVG stroke icons and text labels: "Home", "Call", and "List". - Add a subtle vertical divider line after the main buttons. - Next to the divider, add a Dark/Light Mode toggle button containing inline SVG Sun and Moon icons. 3. Animations & "Apple Magic": - Sliding Active Pill: Create a solid background "pill" that sits *behind* the active navigation item's text/icon. When a different item is clicked, this pill must dynamically recalculate its width and slide to the new position. - Spring Physics: The sliding transition MUST use an exact Apple-style bouncy spring easing curve (e.g., `transition: all 0.5s cubic-bezier(0.34, 1.2, 0.64, 1)`). - Tactile Feedback: Buttons and icons must physically press down slightly (`transform: scale(0.92)`) when clicked (`:active`). - Theme Switch: The Sun and Moon icons must smoothly rotate, scale, and cross-fade during the transition. 4. Background Environment (Crucial): - Glass needs light and color to refract! Create a full-viewport, smoothly animated mesh gradient background using 3 large, heavily blurred, floating color blobs. - Implement full Dark/Light mode logic using CSS variables (`:root` and `[data-theme="dark"]`). Toggling the theme must seamlessly transition the background blob colors, glass opacity, shadow intensity, and text colors. Output ONLY the pristine, production-ready code. Prioritize maximum visual fidelity and silky-smooth 60fps animations.

Leon Lin

127,732 görüntüleme • 4 ay önce

Created this by using a movement sheet as a reference image to animate the dance using Seedance 2.0 + ChatGPT image 2.0 on Yapper GPT Image 2.0 Prompt: Dance Sequence Instruction Sheet [VISUAL STYLE] A composition featuring a highly detailed 3D-rendered female dancer. Designed like a professional choreography guide with a technical, diagram-inspired layout. Clean white background, soft studio lighting, and strong contrast to highlight body movement and posture. [GRID LAYOUT] Structured 4×4 panel grid (16 frames total), evenly spaced with thin black divider lines. Each panel is identical in size and clearly numbered from 1 to 16 to show a continuous dance progression. [CHARACTER] Use image1 as the base character. The same female dancer appears consistently across all panels with accurate likeness and proportions. [WARDROBE] The dancer wears a stylish, performance-ready outfit: a well-fitted top paired with a short, flowy skirt. The look should feel modern and visually appealing while still practical for dance movement. Fabric should subtly respond to motion (slight flow and folds), even in grayscale. [PANEL STRUCTURE – EACH FRAME] Top-left: Step number + short dance move title (e.g., “Step 5 – Spin Transition”) Center: Full-body pose capturing a precise moment in the choreography Bottom-left: 3–4 lines of concise instruction describing the move Overlay: Motion arrows and directional guides illustrating how the dancer transitions [MOTION INDICATORS] Incorporate curved arrows for fluid motion, straight arrows for directional steps, and circular indicators for spins or turns. Emphasize rhythm, weight shifts, and body isolation. [RENDER QUALITY] High-detail sculpted 3D style with smooth grayscale shading, subtle shadows, and clean linework. Maintain a polished, concept-art level finish with clarity in every pose. [RESTRICTIONS] No color, no background scenery, no extra characters, no visual clutter, only the dancer and instructional elements.
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Created this by using a movement sheet as a reference image to animate the dance using Seedance 2.0 + ChatGPT image 2.0 on Yapper GPT Image 2.0 Prompt: Dance Sequence Instruction Sheet [VISUAL STYLE] A composition featuring a highly detailed 3D-rendered female dancer. Designed like a professional choreography guide with a technical, diagram-inspired layout. Clean white background, soft studio lighting, and strong contrast to highlight body movement and posture. [GRID LAYOUT] Structured 4×4 panel grid (16 frames total), evenly spaced with thin black divider lines. Each panel is identical in size and clearly numbered from 1 to 16 to show a continuous dance progression. [CHARACTER] Use image1 as the base character. The same female dancer appears consistently across all panels with accurate likeness and proportions. [WARDROBE] The dancer wears a stylish, performance-ready outfit: a well-fitted top paired with a short, flowy skirt. The look should feel modern and visually appealing while still practical for dance movement. Fabric should subtly respond to motion (slight flow and folds), even in grayscale. [PANEL STRUCTURE – EACH FRAME] Top-left: Step number + short dance move title (e.g., “Step 5 – Spin Transition”) Center: Full-body pose capturing a precise moment in the choreography Bottom-left: 3–4 lines of concise instruction describing the move Overlay: Motion arrows and directional guides illustrating how the dancer transitions [MOTION INDICATORS] Incorporate curved arrows for fluid motion, straight arrows for directional steps, and circular indicators for spins or turns. Emphasize rhythm, weight shifts, and body isolation. [RENDER QUALITY] High-detail sculpted 3D style with smooth grayscale shading, subtle shadows, and clean linework. Maintain a polished, concept-art level finish with clarity in every pose. [RESTRICTIONS] No color, no background scenery, no extra characters, no visual clutter, only the dancer and instructional elements.

Johnn

54,611 görüntüleme • 2 ay önce

GPT Image 2 + Seedance 2.0 on OpenArt Prompt: Create a professional production storyboard sheet for a cinematic Japanese cooking video. Format: A wide horizontal storyboard layout, 16:9 aspect ratio, arranged in a clean 3x3 grid with 9 panels total. Thin black borders separating each frame. Warm parchment/off-white background like a printed production board. At the top center, add the title: JAPANESE BEEF BOWL — Production Storyboard Each panel should have a small white label box in the top-left corner with the panel number in brackets: [1], [2], [3], etc. Under each panel, add a short italic serif caption starting with a two-digit number and an em dash. Visual style: High-end cinematic food photography, warm rustic Japanese kitchen, moody amber lighting, shallow depth of field, steam, dark wooden table, handmade ceramic bowls, old kitchen tools in the background, realistic textures, glossy sauce, appetizing food details, premium commercial cooking video look. Storyboard panels: [1] Close-up of hands slicing raw beef into thick bite-sized pieces on a worn wooden cutting board with a large chef's knife. Caption: 01 — Slice beef into bite-sized pieces. [2] Close-up of beef pieces searing in a black cast-iron pan, caramelized golden-brown edges, sizzling oil, steam rising, chopsticks turning one piece. Caption: 02 — Sear beef until browned. [3] Close-up of soy sauce being poured from a small ceramic cup into the hot pan, dark sauce bubbling around the beef, steam filling the frame. Caption: 03 — Add soy sauce to the pan. [4] Beef pieces simmering in a thick glossy sauce, chopsticks lifting a glazed piece, sauce bubbling heavily in the pan. Caption: 04 — Simmer beef in the sauce. [5] A wooden rice paddle scooping freshly steamed white rice into a large handmade ceramic bowl. Caption: 05 — Scoop steamed rice into bowl. [6] Thick Japanese-style savory sauce being poured over the mound of white rice in the ceramic bowl, glossy dark sauce spreading over the rice. Caption: 06 — Drizzle sauce over rice. [7] Glazed beef slices being placed carefully over the rice with chopsticks, close-up, rich caramelized shine. Caption: 07 — Arrange glazed beef over rice. [8] Finished bowl being assembled with green onions, soft-boiled egg halves, pickled ginger, sesame seeds, and fresh vegetables around the beef. Caption: 08 — Add vegetables, egg, and garnish. [9] Final hero shot of the completed Japanese beef bowl on a rustic wooden table, glossy beef, golden egg yolks, steam rising, hands resting near the bowl, cinematic food commercial look. Caption: 09 — Completed beef bowl, hero shot. Make the entire storyboard feel like a polished production planning sheet for a premium Japanese beef bowl commercial. Keep every panel realistic, cinematic, warm, detailed, and consistent in lighting, color palette, kitchen environment, and bowl design. Video Prompt: CRITICAL INSTRUCTION: The reference image contains a 9-step chronological cooking storyboard for a Japanese Beef Bowl. Animate the chef seamlessly through these exact 9 steps in order. Start at Step 1 (Slice raw beef into bite-sized pieces), flow into Step 2 (Sear beef until browned), then Step 3 (Add soy sauce to the pan). Continue through Step 4 (Simmer beef in the sauce), Step 5 (Scoop steamed rice into bowl), Step 6 (Drizzle sauce over rice), Step 7 (Arrange glazed beef over rice), Step 8 (Add vegetables, egg, and garnish), finishing on Step 9 (Completed beef bowl hero shot). Prioritize the strict sequence of actions. No music. No subtitles. Location: Traditional Japanese kitchen. 15 seconds, 16:9, realistic, cinematic, appetizing, natural camera movement. Warm moody lighting, shallow depth of field, rich steam, glossy sauce, premium food commercial quality.
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GPT Image 2 + Seedance 2.0 on OpenArt Prompt: Create a professional production storyboard sheet for a cinematic Japanese cooking video. Format: A wide horizontal storyboard layout, 16:9 aspect ratio, arranged in a clean 3x3 grid with 9 panels total. Thin black borders separating each frame. Warm parchment/off-white background like a printed production board. At the top center, add the title: JAPANESE BEEF BOWL — Production Storyboard Each panel should have a small white label box in the top-left corner with the panel number in brackets: [1], [2], [3], etc. Under each panel, add a short italic serif caption starting with a two-digit number and an em dash. Visual style: High-end cinematic food photography, warm rustic Japanese kitchen, moody amber lighting, shallow depth of field, steam, dark wooden table, handmade ceramic bowls, old kitchen tools in the background, realistic textures, glossy sauce, appetizing food details, premium commercial cooking video look. Storyboard panels: [1] Close-up of hands slicing raw beef into thick bite-sized pieces on a worn wooden cutting board with a large chef's knife. Caption: 01 — Slice beef into bite-sized pieces. [2] Close-up of beef pieces searing in a black cast-iron pan, caramelized golden-brown edges, sizzling oil, steam rising, chopsticks turning one piece. Caption: 02 — Sear beef until browned. [3] Close-up of soy sauce being poured from a small ceramic cup into the hot pan, dark sauce bubbling around the beef, steam filling the frame. Caption: 03 — Add soy sauce to the pan. [4] Beef pieces simmering in a thick glossy sauce, chopsticks lifting a glazed piece, sauce bubbling heavily in the pan. Caption: 04 — Simmer beef in the sauce. [5] A wooden rice paddle scooping freshly steamed white rice into a large handmade ceramic bowl. Caption: 05 — Scoop steamed rice into bowl. [6] Thick Japanese-style savory sauce being poured over the mound of white rice in the ceramic bowl, glossy dark sauce spreading over the rice. Caption: 06 — Drizzle sauce over rice. [7] Glazed beef slices being placed carefully over the rice with chopsticks, close-up, rich caramelized shine. Caption: 07 — Arrange glazed beef over rice. [8] Finished bowl being assembled with green onions, soft-boiled egg halves, pickled ginger, sesame seeds, and fresh vegetables around the beef. Caption: 08 — Add vegetables, egg, and garnish. [9] Final hero shot of the completed Japanese beef bowl on a rustic wooden table, glossy beef, golden egg yolks, steam rising, hands resting near the bowl, cinematic food commercial look. Caption: 09 — Completed beef bowl, hero shot. Make the entire storyboard feel like a polished production planning sheet for a premium Japanese beef bowl commercial. Keep every panel realistic, cinematic, warm, detailed, and consistent in lighting, color palette, kitchen environment, and bowl design. Video Prompt: CRITICAL INSTRUCTION: The reference image contains a 9-step chronological cooking storyboard for a Japanese Beef Bowl. Animate the chef seamlessly through these exact 9 steps in order. Start at Step 1 (Slice raw beef into bite-sized pieces), flow into Step 2 (Sear beef until browned), then Step 3 (Add soy sauce to the pan). Continue through Step 4 (Simmer beef in the sauce), Step 5 (Scoop steamed rice into bowl), Step 6 (Drizzle sauce over rice), Step 7 (Arrange glazed beef over rice), Step 8 (Add vegetables, egg, and garnish), finishing on Step 9 (Completed beef bowl hero shot). Prioritize the strict sequence of actions. No music. No subtitles. Location: Traditional Japanese kitchen. 15 seconds, 16:9, realistic, cinematic, appetizing, natural camera movement. Warm moody lighting, shallow depth of field, rich steam, glossy sauce, premium food commercial quality.

Zara

19,727 görüntüleme • 10 gün önce

After half a decade of creating templates for the Webflow marketplace, I'm incredibly grateful for this program, which has allowed me to support my family and pursue my dream job for a significant part of my career. I've seen many companies using my templates to speed up their development process. My work has inspired some designers and I've got good and loyal clients. However, not sure why, the marketplace team has suddenly delisted all my main templates, which has decreased my monthly income by 80%. It was a shock. I remain confused about the reason, as I have been a strong supporter of the company since day one, which has given me many negative thoughts, feelings of disappointment, frustration, and anger. I stopped creating templates for them for a while. In the meantime, I created my own marketplace and learn something powerful: If something can go wrong, everything will go wrong. Lost clients, no money, no team, no income, but nothing to lose, since I've lost almost everything already. Even so, I managed to launch an honest MVP, and after reviewing all my templates, those that were living in a limbo, I've decided to offer full access at a huge discount. Now, anyone can get everything I've created all at once, from the top sellers to my favorite out-of-the-box templates. Buy once, build forever. I'm back to Webflow and the Framer templates. Stronger than never.
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After half a decade of creating templates for the Webflow marketplace, I'm incredibly grateful for this program, which has allowed me to support my family and pursue my dream job for a significant part of my career. I've seen many companies using my templates to speed up their development process. My work has inspired some designers and I've got good and loyal clients. However, not sure why, the marketplace team has suddenly delisted all my main templates, which has decreased my monthly income by 80%. It was a shock. I remain confused about the reason, as I have been a strong supporter of the company since day one, which has given me many negative thoughts, feelings of disappointment, frustration, and anger. I stopped creating templates for them for a while. In the meantime, I created my own marketplace and learn something powerful: If something can go wrong, everything will go wrong. Lost clients, no money, no team, no income, but nothing to lose, since I've lost almost everything already. Even so, I managed to launch an honest MVP, and after reviewing all my templates, those that were living in a limbo, I've decided to offer full access at a huge discount. Now, anyone can get everything I've created all at once, from the top sellers to my favorite out-of-the-box templates. Buy once, build forever. I'm back to Webflow and the Framer templates. Stronger than never.

JP

25,525 görüntüleme • 8 ay önce

the 24gb vram tier is enough for most builder work in 2026. gemma 4 31b dense on my rog scar 18 just autonomously built a production hero section in one prompt, one html file and 5 minutes end to end. hardware: rog scar 18, rtx 5090 laptop 24gb vram. model: google gemma 4 31b dense at q4_k_m quant, using 22.8 of 24gb. engine: llama.cpp built for blackwell (sm_120). harness: hermes agent with native tool parsing. speed: 15 tok/s sustained, 94 watts, 50c. flags i used: ./build/bin/llama-server -m ~/models/gemma4-31b/google_gemma-4-31B-it-Q4_K_M.gguf -ngl 99 -c 131072 -np 1 -fa on --cache-type-k q4_0 --cache-type-v q4_0 --jinja --host 127.0.0.1 --port 8080 if you own 24gb vram in 2026, you have enough for most ui work, most agentic coding, most autonomous builds. no subscription, no one logging your prompts. a dense open model on consumer hardware shipping real software on your desk. this was the warmup. full page next on same hardware, then the octopus invaders final multifile autonomous challenge.
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the 24gb vram tier is enough for most builder work in 2026. gemma 4 31b dense on my rog scar 18 just autonomously built a production hero section in one prompt, one html file and 5 minutes end to end. hardware: rog scar 18, rtx 5090 laptop 24gb vram. model: google gemma 4 31b dense at q4_k_m quant, using 22.8 of 24gb. engine: llama.cpp built for blackwell (sm_120). harness: hermes agent with native tool parsing. speed: 15 tok/s sustained, 94 watts, 50c. flags i used: ./build/bin/llama-server -m ~/models/gemma4-31b/google_gemma-4-31B-it-Q4_K_M.gguf -ngl 99 -c 131072 -np 1 -fa on --cache-type-k q4_0 --cache-type-v q4_0 --jinja --host 127.0.0.1 --port 8080 if you own 24gb vram in 2026, you have enough for most ui work, most agentic coding, most autonomous builds. no subscription, no one logging your prompts. a dense open model on consumer hardware shipping real software on your desk. this was the warmup. full page next on same hardware, then the octopus invaders final multifile autonomous challenge.

Sudo su

19,576 görüntüleme • 2 ay önce

If you’ve ever managed custom icons in React Native, you know the frustration of choosing between performance and developer experience. Libraries like 𝗿𝗲𝗮𝗰𝘁-𝗻𝗮𝘁𝗶𝘃𝗲-𝘀𝘃𝗴 are flexible but heavy. Every icon creates its own React subtree. The alternative? Manual icon fonts that force you into a constant back-and-forth of using web tools like IcoMoon and syncing font assets every time a design changes. 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗠𝗮𝗻𝘀𝗶𝗼𝗻 𝗟𝗮𝗯𝘀 just released 𝗿𝗲𝗮𝗰𝘁-𝗻𝗮𝘁𝗶𝘃𝗲-𝗻𝗮𝗻𝗼-𝗶𝗰𝗼𝗻𝘀 to solve exactly this. It’s a build-time icon font generator that gives you the performance of native fonts with the flexibility of simple SVG files. 𝗪𝗵𝗮𝘁’𝘀 𝗵𝗮𝗽𝗽𝗲𝗻𝗶𝗻𝗴? Instead of rendering complex vector paths at runtime, this library automatically converts your folder of SVGs into an optimized icon font during the build process. It essentially teaches your app to treat icons like standard text characters, which allows it to bypass React’s component tree and layout engine entirely. ➡️ 𝗕𝘂𝗶𝗹𝗱-𝘁𝗶𝗺𝗲 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻: It handles the entire pipeline—watching your icon folder, converting SVGs to .ttf files, and linking them to your native project automatically. ➡️ 𝗡𝗮𝘁𝗶𝘃𝗲 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲: Because icons render as native text glyphs, it is significantly faster than traditional SVG rendering, making it the ideal choice for long, scrollable lists or icon-heavy dashboards. ➡️ 𝗘𝘅𝗽𝗼 𝗖𝗼𝗻𝗳𝗶𝗴 𝗣𝗹𝘂𝗴𝗶𝗻: It features first-class support for Expo, automating the native plumbing like Info.plist updates and asset linking during the prebuild phase. ➡️ 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗰 𝗧𝘆𝗽𝗲 𝗦𝗮𝗳𝗲𝘁𝘆: The library generates TypeScript definitions for your icon set, providing full IDE autocomplete and ensuring you never break the UI with a misspelled icon name. 𝗪𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀? In high-performance applications, small overheads add up. By shifting the heavy lifting from the mobile device to your build machine, 𝗿𝗲𝗮𝗰𝘁-𝗻𝗮𝘁𝗶𝘃𝗲-𝗻𝗮𝗻𝗼-𝗶𝗰𝗼𝗻𝘀 ensures your UI stays buttery smooth while keeping your developer workflow modern. It’s another great example of how 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗠𝗮𝗻𝘀𝗶𝗼𝗻 continues to solve the "last mile" of performance friction in the ecosystem. Before you migrate your entire library, keep in mind: this is designed for the 𝗡𝗲𝘄 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 (Fabric) and requires React Native 0.74 or higher. #ReactNative #Expo #SoftwareMansion #Icons #MobileDev #Performance #DeveloperExperience #OpenSource #JavaScript #TypeScript #SVG #Fabric
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If you’ve ever managed custom icons in React Native, you know the frustration of choosing between performance and developer experience. Libraries like 𝗿𝗲𝗮𝗰𝘁-𝗻𝗮𝘁𝗶𝘃𝗲-𝘀𝘃𝗴 are flexible but heavy. Every icon creates its own React subtree. The alternative? Manual icon fonts that force you into a constant back-and-forth of using web tools like IcoMoon and syncing font assets every time a design changes. 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗠𝗮𝗻𝘀𝗶𝗼𝗻 𝗟𝗮𝗯𝘀 just released 𝗿𝗲𝗮𝗰𝘁-𝗻𝗮𝘁𝗶𝘃𝗲-𝗻𝗮𝗻𝗼-𝗶𝗰𝗼𝗻𝘀 to solve exactly this. It’s a build-time icon font generator that gives you the performance of native fonts with the flexibility of simple SVG files. 𝗪𝗵𝗮𝘁’𝘀 𝗵𝗮𝗽𝗽𝗲𝗻𝗶𝗻𝗴? Instead of rendering complex vector paths at runtime, this library automatically converts your folder of SVGs into an optimized icon font during the build process. It essentially teaches your app to treat icons like standard text characters, which allows it to bypass React’s component tree and layout engine entirely. ➡️ 𝗕𝘂𝗶𝗹𝗱-𝘁𝗶𝗺𝗲 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻: It handles the entire pipeline—watching your icon folder, converting SVGs to .ttf files, and linking them to your native project automatically. ➡️ 𝗡𝗮𝘁𝗶𝘃𝗲 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲: Because icons render as native text glyphs, it is significantly faster than traditional SVG rendering, making it the ideal choice for long, scrollable lists or icon-heavy dashboards. ➡️ 𝗘𝘅𝗽𝗼 𝗖𝗼𝗻𝗳𝗶𝗴 𝗣𝗹𝘂𝗴𝗶𝗻: It features first-class support for Expo, automating the native plumbing like Info.plist updates and asset linking during the prebuild phase. ➡️ 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗰 𝗧𝘆𝗽𝗲 𝗦𝗮𝗳𝗲𝘁𝘆: The library generates TypeScript definitions for your icon set, providing full IDE autocomplete and ensuring you never break the UI with a misspelled icon name. 𝗪𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀? In high-performance applications, small overheads add up. By shifting the heavy lifting from the mobile device to your build machine, 𝗿𝗲𝗮𝗰𝘁-𝗻𝗮𝘁𝗶𝘃𝗲-𝗻𝗮𝗻𝗼-𝗶𝗰𝗼𝗻𝘀 ensures your UI stays buttery smooth while keeping your developer workflow modern. It’s another great example of how 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗠𝗮𝗻𝘀𝗶𝗼𝗻 continues to solve the "last mile" of performance friction in the ecosystem. Before you migrate your entire library, keep in mind: this is designed for the 𝗡𝗲𝘄 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 (Fabric) and requires React Native 0.74 or higher. #ReactNative #Expo #SoftwareMansion #Icons #MobileDev #Performance #DeveloperExperience #OpenSource #JavaScript #TypeScript #SVG #Fabric

The React Native Rewind

24,992 görüntüleme • 2 ay önce

Holy shit... Microsoft open sourced an inference framework that runs a 100B parameter LLM on a single CPU. It's called BitNet. And it does what was supposed to be impossible. No GPU. No cloud. No $10K hardware setup. Just your laptop running a 100-billion parameter model at human reading speed. Here's how it works: Every other LLM stores weights in 32-bit or 16-bit floats. BitNet uses 1.58 bits. Weights are ternary just -1, 0, or +1. That's it. No floats. No expensive matrix math. Pure integer operations your CPU was already built for. The result: - 100B model runs on a single CPU at 5-7 tokens/second - 2.37x to 6.17x faster than llama.cpp on x86 - 82% lower energy consumption on x86 CPUs - 1.37x to 5.07x speedup on ARM (your MacBook) - Memory drops by 16-32x vs full-precision models The wildest part: Accuracy barely moves. BitNet b1.58 2B4T their flagship model was trained on 4 trillion tokens and benchmarks competitively against full-precision models of the same size. The quantization isn't destroying quality. It's just removing the bloat. What this actually means: - Run AI completely offline. Your data never leaves your machine - Deploy LLMs on phones, IoT devices, edge hardware - No more cloud API bills for inference - AI in regions with no reliable internet The model supports ARM and x86. Works on your MacBook, your Linux box, your Windows machine. 27.4K GitHub stars. 2.2K forks. Built by Microsoft Research. 100% Open Source. MIT License.
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Holy shit... Microsoft open sourced an inference framework that runs a 100B parameter LLM on a single CPU. It's called BitNet. And it does what was supposed to be impossible. No GPU. No cloud. No $10K hardware setup. Just your laptop running a 100-billion parameter model at human reading speed. Here's how it works: Every other LLM stores weights in 32-bit or 16-bit floats. BitNet uses 1.58 bits. Weights are ternary just -1, 0, or +1. That's it. No floats. No expensive matrix math. Pure integer operations your CPU was already built for. The result: - 100B model runs on a single CPU at 5-7 tokens/second - 2.37x to 6.17x faster than llama.cpp on x86 - 82% lower energy consumption on x86 CPUs - 1.37x to 5.07x speedup on ARM (your MacBook) - Memory drops by 16-32x vs full-precision models The wildest part: Accuracy barely moves. BitNet b1.58 2B4T their flagship model was trained on 4 trillion tokens and benchmarks competitively against full-precision models of the same size. The quantization isn't destroying quality. It's just removing the bloat. What this actually means: - Run AI completely offline. Your data never leaves your machine - Deploy LLMs on phones, IoT devices, edge hardware - No more cloud API bills for inference - AI in regions with no reliable internet The model supports ARM and x86. Works on your MacBook, your Linux box, your Windows machine. 27.4K GitHub stars. 2.2K forks. Built by Microsoft Research. 100% Open Source. MIT License.

Guri Singh

2,180,357 görüntüleme • 4 ay önce

I just built a skill that lets Claude Code watch & analyze ANY video 🤯 Drop in any video file — UGC ads, competitor Meta ads, organic TikToks, screen recordings — and Claude hands you back a full creative teardown. All inside Claude Code. Perfect for media buyers and creative strategists who reverse-engineer competitor ads every week — and lose half a day doing it by hand. If your creative process starts with studying what's already working, you're scrubbing through competitor ads frame by frame, pausing to write down every hook, screenshotting the on-screen text, and by the tenth video you can't remember what made the first one land... This skill solves it: → Drop any video file into Claude Code → Skill routes it through the Gemini API for native video understanding → Returns a full creative teardown — hook breakdown, target audience, angle, beat-by-beat, on-screen text verbatim → Surfaces the steal-worthy patterns you can apply to your own creative → Same skill works on UGC ads, produced video ads, organic TikToks, and Loom recordings No manual scrubbing. No pausing every 5 seconds. No $200/mo ad intelligence platform. What you get: → Native video understanding via Gemini (not just transcripts) → Structured analysis — hook, angle, audience, pain point, CTA → Verbatim on-screen text and dialogue with timestamps → Hook variations generated directly from competitor ads → About 27 cents per 30-minute video Built 100% in Claude Code with the Gemini API. I recorded a full breakdown showing exactly how I built this, and I'm giving away the skill for free. Want the skill? > Like this post > Comment "CLAUDE" And I'll send it over (must be following so I can DM)
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I just built a skill that lets Claude Code watch & analyze ANY video 🤯 Drop in any video file — UGC ads, competitor Meta ads, organic TikToks, screen recordings — and Claude hands you back a full creative teardown. All inside Claude Code. Perfect for media buyers and creative strategists who reverse-engineer competitor ads every week — and lose half a day doing it by hand. If your creative process starts with studying what's already working, you're scrubbing through competitor ads frame by frame, pausing to write down every hook, screenshotting the on-screen text, and by the tenth video you can't remember what made the first one land... This skill solves it: → Drop any video file into Claude Code → Skill routes it through the Gemini API for native video understanding → Returns a full creative teardown — hook breakdown, target audience, angle, beat-by-beat, on-screen text verbatim → Surfaces the steal-worthy patterns you can apply to your own creative → Same skill works on UGC ads, produced video ads, organic TikToks, and Loom recordings No manual scrubbing. No pausing every 5 seconds. No $200/mo ad intelligence platform. What you get: → Native video understanding via Gemini (not just transcripts) → Structured analysis — hook, angle, audience, pain point, CTA → Verbatim on-screen text and dialogue with timestamps → Hook variations generated directly from competitor ads → About 27 cents per 30-minute video Built 100% in Claude Code with the Gemini API. I recorded a full breakdown showing exactly how I built this, and I'm giving away the skill for free. Want the skill? > Like this post > Comment "CLAUDE" And I'll send it over (must be following so I can DM)

Mike Futia

40,962 görüntüleme • 21 gün önce

Expo + OpenAI + RevenueCat = $25K MRR AI product. Most AI apps fail because: → No monetization strategy → No structured UX → No reason for users to pay → No distribution loop Just structured input → AI orchestration → media → monetization → distribution. Mosaic fixes that: → Mobile-first AI experience → Subscription-based value delivery → Audio + personalization moat → Shareable links that drive installs Here’s how Mosaic works: → Mobile app (React Native + Expo) captures structured story inputs → Backend (Node + TypeScript) orchestrates OpenAI + Gemini + TTS → Audio pipeline (ElevenLabs) narrates stories at scale → Subscription system (Revenuecat) enforces credits & usage → Share URL layer (Next.js) converts stories into viral links Built as a system, not a hackathon app. Full breakdown in the video ↓ Like + comment “MVP” + repost, and I’ll DM it to you ( must be following )
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Expo + OpenAI + RevenueCat = $25K MRR AI product. Most AI apps fail because: → No monetization strategy → No structured UX → No reason for users to pay → No distribution loop Just structured input → AI orchestration → media → monetization → distribution. Mosaic fixes that: → Mobile-first AI experience → Subscription-based value delivery → Audio + personalization moat → Shareable links that drive installs Here’s how Mosaic works: → Mobile app (React Native + Expo) captures structured story inputs → Backend (Node + TypeScript) orchestrates OpenAI + Gemini + TTS → Audio pipeline (ElevenLabs) narrates stories at scale → Subscription system (Revenuecat) enforces credits & usage → Share URL layer (Next.js) converts stories into viral links Built as a system, not a hackathon app. Full breakdown in the video ↓ Like + comment “MVP” + repost, and I’ll DM it to you ( must be following )

Harshil Tomar

23,351 görüntüleme • 6 ay önce