Loading video...

Video Failed to Load

There was a problem loading this video. This could be due to a temporary network issue or the video might be unavailable.

ChatGPT 5.1 vs. Claude Opus 4.5 vs. DeepSeek-V3.2 (left) (middle) (right) prompt I used: Create a single HTML file containing a fully functional 3D Rubik's Cube simulation using Three.js (via CDN). The cube must be able to automatically solve itself.

Alex Prompter

96,448 subscribers

14,891 views • 7 months ago •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

Gemini 3 Pro (left) vs ChatGPT 5.1 thinking vs Claude Opus 4.5 (right) Prompt: Multi-ball heptagon physics Write html, css, and javascript to display 20 balls bouncing inside a spinning heptagon. each ball should have a unique color, a number label, gravity, and collision detection with the walls. all code in one file.

Gemini 3 Pro (left) vs ChatGPT 5.1 thinking vs Claude Opus 4.5 (right) Prompt: Multi-ball heptagon physics Write html, css, and javascript to display 20 balls bouncing inside a spinning heptagon. each ball should have a unique color, a number label, gravity, and collision detection with the walls. all code in one file.

God of Prompt

105,823 views • 8 months ago

Claude vs Gemini vs DeepSeek vs Grok PROMPT: Build a production-ready 3D flight visualizer in one HTML file. Use Three.js to render a rotatable Earth and simulate real-time aircraft positions using smooth interpolation. Include polished lighting, a starfield background, and make it responsive for mobile and desktop. Which tool is the best?

Claude vs Gemini vs DeepSeek vs Grok PROMPT: Build a production-ready 3D flight visualizer in one HTML file. Use Three.js to render a rotatable Earth and simulate real-time aircraft positions using smooth interpolation. Include polished lighting, a starfield background, and make it responsive for mobile and desktop. Which tool is the best?

Oleg Bilozor

72,608 views • 4 months ago

Gemini (left) vs ChatGPT vs Claude (right) Prompt: Create an interactive HTML, CSS, and JavaScript simulation of a satellite system that transmits signals to ground receivers. The simulation should show a satellite orbiting the Earth and periodically sending signals that are received by multiple ground stations. Include realistic factors such as signal delay based on distance, occasional signal loss due to obstructions or interference, and visualization of the signal path as lines or waves. The satellites’ position should be updated using simplified orbital mechanics, and the ground receivers should display received signal strength and timestamps.

Gemini (left) vs ChatGPT vs Claude (right) Prompt: Create an interactive HTML, CSS, and JavaScript simulation of a satellite system that transmits signals to ground receivers. The simulation should show a satellite orbiting the Earth and periodically sending signals that are received by multiple ground stations. Include realistic factors such as signal delay based on distance, occasional signal loss due to obstructions or interference, and visualization of the signal path as lines or waves. The satellites’ position should be updated using simplified orbital mechanics, and the ground receivers should display received signal strength and timestamps.

Alex Prompter

229,940 views • 8 months ago

If you've tried getting AI to write TSL shaders, you know the pain: deprecated imports, phantom functions and compilation errors. In my latest blog post, I share a prompt to help you overcome these issues: Using the prompt, I was able to one-shot this particle simulation with Claude Opus 4.5. #threejs #webgl #webgpu #tsl #shaders

If you've tried getting AI to write TSL shaders, you know the pain: deprecated imports, phantom functions and compilation errors. In my latest blog post, I share a prompt to help you overcome these issues: Using the prompt, I was able to one-shot this particle simulation with Claude Opus 4.5. #threejs #webgl #webgpu #tsl #shaders

Dan Greenheck

61,631 views • 7 months ago

Opus 4.6 vs GPT 5.4 (High) (1/9) prompt: Build a single-file HTML/CSS/JS (no libs) demo that uses SVG to simulate a plant growing: stem extends, leaves sprout + unfurl with springy/windy “physics”, then seamlessly loops forever. For the initial impressions I'm really impressed by GPT, for speed they both felt about the same, but gpt was still half cheaper than opus. I also much prefer the design and animation that gpt produced, physics on the leaves are super cool and it also loops pretty nicely whilst opus just fades out the plant. Still got a bunch of tests to run but this is really exciting.

Opus 4.6 vs GPT 5.4 (High) (1/9) prompt: Build a single-file HTML/CSS/JS (no libs) demo that uses SVG to simulate a plant growing: stem extends, leaves sprout + unfurl with springy/windy “physics”, then seamlessly loops forever. For the initial impressions I'm really impressed by GPT, for speed they both felt about the same, but gpt was still half cheaper than opus. I also much prefer the design and animation that gpt produced, physics on the leaves are super cool and it also loops pretty nicely whilst opus just fades out the plant. Still got a bunch of tests to run but this is really exciting.

Dev Ed

660,154 views • 4 months ago

NEW HTML VIDEO SKILL: Claude can now make photo-grid promo video ads like these. These are very popular offer-style ads that work well for bottom-of-funnel conversions. Brands will often run these when they're doing seasonal sales, for example. It's pure HTML, so it's very cheap to make because there's no video generation cost. You can create dozens of variants very fast and then test them in Meta. All you have to do is install the skills and give Claude your brand website. This skill is insane - it'll make the entire Ad and give you a link where you can download the mp4 file. > If you want more variants, just mention in the prompt > If you have a specific concept in mind, just mention in the prompt > If you want any edits on the generated ad, just mention in the prompt Comment Goose and I'll DM you the skill (must be following so I can DM you)

Shiv

91,654 views • 12 days ago

THREE 3090s ON ONE BOARD GIVE YOU 72GB OF VRAM AND KILL YOUR $200 CLAUDE CODE AND $200 OPENAI BILL people are pulling three used 3090s off ebay for around $2,100 total and stacking them in one tower to build a dedicated ai rig. that pools 72gb of vram for less than what a single rtx 5090 retails for alibaba shipped qwen 3.6 27b in april under apache 2.0. on realworldqa vision it scores 84.1 against claude 4.5 opus at 77.0. on ifbench instructions it lands at 76.5 against claude's 58.0 a single 3090 already runs qwen 3.6 27b with eight gigs of headroom. three of them in parallel handle larger models like deepseek r1 70b and qwen 235b without breaking a sweat a heavy ai user pays $200 claude code, $200 chatgpt pro plus $40 cursor and gemini. that's $5,280 a year and the rig pays itself off before month nine on $8 a month in electricity setup is one shell command for ollama, one to pull the model, one environment variable to point claude code at localhost. cli stays identical, nothing leaves the network, requests stop costing money bookmark this and read the article below

THREE 3090s ON ONE BOARD GIVE YOU 72GB OF VRAM AND KILL YOUR $200 CLAUDE CODE AND $200 OPENAI BILL people are pulling three used 3090s off ebay for around $2,100 total and stacking them in one tower to build a dedicated ai rig. that pools 72gb of vram for less than what a single rtx 5090 retails for alibaba shipped qwen 3.6 27b in april under apache 2.0. on realworldqa vision it scores 84.1 against claude 4.5 opus at 77.0. on ifbench instructions it lands at 76.5 against claude's 58.0 a single 3090 already runs qwen 3.6 27b with eight gigs of headroom. three of them in parallel handle larger models like deepseek r1 70b and qwen 235b without breaking a sweat a heavy ai user pays $200 claude code, $200 chatgpt pro plus $40 cursor and gemini. that's $5,280 a year and the rig pays itself off before month nine on $8 a month in electricity setup is one shell command for ollama, one to pull the model, one environment variable to point claude code at localhost. cli stays identical, nothing leaves the network, requests stop costing money bookmark this and read the article below

starmex

16,683 views • 1 month ago

GLM 5.1 just went open-weight on Hugging Face, but how does it compare to GLM 5? I have tested both with the canvas tree challenge. 5.1 thinks longer, but delivers wind animation, sun, clouds, and way more detail. Prompt attached: Write a single HTML file with a full-page canvas, no libraries. Animate a tree that grows from the bottom center of the screen in real time. The trunk grows upward first, then branches split off recursively with slight randomness in angle and length. Each generation of branches should be thinner and slightly lighter in color. When branches reach their final size, add small leaves as soft green circles at the tips. The tree should take about 15 seconds to fully grow. Use warm brown for wood and varied greens for leaves against a soft sky-blue gradient background.

GLM 5.1 just went open-weight on Hugging Face, but how does it compare to GLM 5? I have tested both with the canvas tree challenge. 5.1 thinks longer, but delivers wind animation, sun, clouds, and way more detail. Prompt attached: Write a single HTML file with a full-page canvas, no libraries. Animate a tree that grows from the bottom center of the screen in real time. The trunk grows upward first, then branches split off recursively with slight randomness in angle and length. Each generation of branches should be thinner and slightly lighter in color. When branches reach their final size, add small leaves as soft green circles at the tips. The tree should take about 15 seconds to fully grow. Use warm brown for wood and varied greens for leaves against a soft sky-blue gradient background.

stevibe

46,657 views • 3 months ago

Opus 4.6 vs GPT-5.4 (4/9) prompt: Build a production-quality 3D flight-tracking web app using React + Vite + Three.js (react-three-fiber + drei) that visualizes live OpenSky aircraft data on a rotatable 3D Earth, with real-time plane motion, smooth interpolation, altitude-accurate positioning, and polished lighting/post-processing. Both models did really well on this one and honestly I’m impressed with both. GPT-5.4 had the nicer post-processing out of the box. I really liked the subtle light shimmer on the airplanes when rotating the planet, and the camera work when clicking a plane felt better overall. Opus 4.6 though had a few details I liked more. It automatically went and found a much nicer Earth texture on GitHub, while with GPT-5.4 I had to reprompt it to go look for a better one. I also preferred Opus’s plane model overall, it just looked more polished, whereas GPT-5.4’s plane asset looked a bit funny. One thing I noticed with GPT-5.4 is that when you click into the plane, the camera sometimes clips through the planet, which breaks the effect a bit. Opus handled that part more cleanly. Overall this felt like a strong result from both, just with different strengths. GPT-5.4 felt better on presentation and post-processing, while Opus had better asset choices and a more premium-looking Earth/plane combo.

Opus 4.6 vs GPT-5.4 (4/9) prompt: Build a production-quality 3D flight-tracking web app using React + Vite + Three.js (react-three-fiber + drei) that visualizes live OpenSky aircraft data on a rotatable 3D Earth, with real-time plane motion, smooth interpolation, altitude-accurate positioning, and polished lighting/post-processing. Both models did really well on this one and honestly I’m impressed with both. GPT-5.4 had the nicer post-processing out of the box. I really liked the subtle light shimmer on the airplanes when rotating the planet, and the camera work when clicking a plane felt better overall. Opus 4.6 though had a few details I liked more. It automatically went and found a much nicer Earth texture on GitHub, while with GPT-5.4 I had to reprompt it to go look for a better one. I also preferred Opus’s plane model overall, it just looked more polished, whereas GPT-5.4’s plane asset looked a bit funny. One thing I noticed with GPT-5.4 is that when you click into the plane, the camera sometimes clips through the planet, which breaks the effect a bit. Opus handled that part more cleanly. Overall this felt like a strong result from both, just with different strengths. GPT-5.4 felt better on presentation and post-processing, while Opus had better asset choices and a more premium-looking Earth/plane combo.

Dev Ed

279,565 views • 4 months ago

Kimi K3 vs GPT-5.6 vs Claude Fable 5 A game is one of the hardest places to fake working code. A web app can hide bugs behind a polished screenshot. A game can't. Bad collision, clipping, jitter, broken physics, awkward controls, or a snowboard sinking into the terrain become obvious within seconds. That's why I like using games to evaluate coding models. I gave Kimi K3 a single prompt to build a snowboard game in Godot, and just 20 seconds of gameplay told me more than a page of benchmark scores ever could. Benchmarks matter, but watching a model build something interactive reveals a different side of its capabilities. If the gameplay feels right, the systems work together, and the experience holds up under real input, that's a much more convincing demonstration than any leaderboard.

Kimi K3 vs GPT-5.6 vs Claude Fable 5 A game is one of the hardest places to fake working code. A web app can hide bugs behind a polished screenshot. A game can't. Bad collision, clipping, jitter, broken physics, awkward controls, or a snowboard sinking into the terrain become obvious within seconds. That's why I like using games to evaluate coding models. I gave Kimi K3 a single prompt to build a snowboard game in Godot, and just 20 seconds of gameplay told me more than a page of benchmark scores ever could. Benchmarks matter, but watching a model build something interactive reveals a different side of its capabilities. If the gameplay feels right, the systems work together, and the experience holds up under real input, that's a much more convincing demonstration than any leaderboard.

FHILY👑

35,933 views • 6 days ago

I just built a Claude Cowork skill that turns your Google Ads data into a visual performance dashboard in 60 seconds 🤯 One prompt → campaign breakdowns, CPA trends, spend vs conversions charts, and hourly conversion patterns, all rendered as an interactive HTML dashboard you open in Chrome. All inside Claude Cowork. Perfect for DTC brands and agencies who are pulling Google Ads data into spreadsheets every week, manually building charts, and spending an hour formatting a report that's outdated by the time you send it. If you're managing Google Ads and your weekly reporting workflow looks like this — export a CSV, open Google Sheets, build a pivot table, copy the numbers into a slide deck, manually create charts, format everything, realize you forgot a campaign, start over ... This skill does the whole thing in one prompt: → Connects to your live Google Ads data via MCP → Pulls spend, conversions, CPA, ROAS, CTR across every campaign → Builds an interactive HTML dashboard → Summary cards at the top: total spend, total conversions, avg CPA, avg ROAS → Bar chart comparing spend vs conversions by campaign → CPA trend line over the last 30 days → Campaign table ranked by performance, color-coded green/yellow/red → Opens in Chrome: hover over charts, compare campaigns, screenshot for your team No spreadsheets. No manual chart building. No hour-long formatting sessions. What you get: → A visual dashboard from live data in under 60 seconds → Campaign performance you can actually see, not just read in a table → CPA trends that show you where things are heading, not just where they are → A dashboard you can screenshot and drop into Slack, a client report, or a team standup → Reusable — run it weekly and the data updates automatically One prompt. Live data. A finished dashboard you open in your browser. I put together a playbook with the full skill file, the setup, and the exact prompts to customize the dashboard for your account. Want it for free? > Like this post > Comment "DASH" And I'll send it over (must be following so I can DM)

I just built a Claude Cowork skill that turns your Google Ads data into a visual performance dashboard in 60 seconds 🤯 One prompt → campaign breakdowns, CPA trends, spend vs conversions charts, and hourly conversion patterns, all rendered as an interactive HTML dashboard you open in Chrome. All inside Claude Cowork. Perfect for DTC brands and agencies who are pulling Google Ads data into spreadsheets every week, manually building charts, and spending an hour formatting a report that's outdated by the time you send it. If you're managing Google Ads and your weekly reporting workflow looks like this — export a CSV, open Google Sheets, build a pivot table, copy the numbers into a slide deck, manually create charts, format everything, realize you forgot a campaign, start over ... This skill does the whole thing in one prompt: → Connects to your live Google Ads data via MCP → Pulls spend, conversions, CPA, ROAS, CTR across every campaign → Builds an interactive HTML dashboard → Summary cards at the top: total spend, total conversions, avg CPA, avg ROAS → Bar chart comparing spend vs conversions by campaign → CPA trend line over the last 30 days → Campaign table ranked by performance, color-coded green/yellow/red → Opens in Chrome: hover over charts, compare campaigns, screenshot for your team No spreadsheets. No manual chart building. No hour-long formatting sessions. What you get: → A visual dashboard from live data in under 60 seconds → Campaign performance you can actually see, not just read in a table → CPA trends that show you where things are heading, not just where they are → A dashboard you can screenshot and drop into Slack, a client report, or a team standup → Reusable — run it weekly and the data updates automatically One prompt. Live data. A finished dashboard you open in your browser. I put together a playbook with the full skill file, the setup, and the exact prompts to customize the dashboard for your account. Want it for free? > Like this post > Comment "DASH" And I'll send it over (must be following so I can DM)

Mike Futia

38,793 views • 3 months ago

Claude Code + ChatGPT Images 2.0 is f*cking cracked 🤯 I rebuilt my static ad system inside Claude Code on the new ChatGPT Images 2.0 model. One brand name + one URL = 40 production-ready static ads. All inside Claude Code. Perfect for DTC brands and agencies who need high-volume ad creative without briefing a designer or spending hours in Canva. If you're finding winning ad concepts on Meta and manually recreating them one at a time — copying prompts, pasting product details, tweaking aspect ratios, downloading, organizing... This system eliminates the entire loop: → Give Claude a brand name and URL → It researches the brand's fonts, colors, packaging, and photography style → Builds a Brand DNA document from scratch → Fills in 40 proven ad templates (headline, us vs them, testimonial, UGC, review cards, stat callouts) with brand-specific details → Fires every prompt to ChatGPT Images 2.0 with your product photos as reference → Downloads finished ads into organized folders with an HTML gallery No manual prompt filling. No Canva templates. No copy-pasting between tools. What you get: → 40 ad formats filled with your exact brand colors, fonts, and copy → Text that actually renders correctly (the new model handles dense copy, logos, and multi-language callouts cleanly) → Product photos passed as reference so the model matches your real packaging → A reusable system — new brand, new folder, same pipeline Built 100% in Claude Code with ChatGPT Images 2.0. I put together a DIY playbook showing the exact architecture so you can build this yourself in Claude Code. Want it for free? > Like this post > Comment "CHAT" And I'll send it over (must be following so I can DM)

Mike Futia

188,915 views • 3 months ago

Claude Code + ChatGPT Images 2.0 is f*cking cracked 🤯 I rebuilt my static ad system inside Claude Code on the new ChatGPT Images 2.0 model. One brand name + one URL = 40 production-ready static ads. All inside Claude Code. Perfect for DTC brands and agencies who need high-volume ad creative without briefing a designer or spending hours in Canva. If you're finding winning ad concepts on Meta and manually recreating them one at a time — copying prompts, pasting product details, tweaking aspect ratios, downloading, organizing... This system eliminates the entire loop: → Give Claude a brand name and URL → It researches the brand's fonts, colors, packaging, and photography style → Builds a Brand DNA document from scratch → Fills in 40 proven ad templates (headline, us vs them, testimonial, UGC, review cards, stat callouts) with brand-specific details → Fires every prompt to ChatGPT Images 2.0 with your product photos as reference → Downloads finished ads into organized folders with an HTML gallery No manual prompt filling. No Canva templates. No copy-pasting between tools. What you get: → 40 ad formats filled with your exact brand colors, fonts, and copy → Text that actually renders correctly (the new model handles dense copy, logos, and multi-language callouts cleanly) → Product photos passed as reference so the model matches your real packaging → A reusable system — new brand, new folder, same pipeline Built 100% in Claude Code with ChatGPT Images 2.0. I put together a DIY playbook showing the exact architecture so you can build this yourself in Claude Code. Want it for free? > Like this post > Comment "CHAT" And I'll send it over (must be following @learnwithella so I can DM)

Ismail Khan

19,750 views • 3 months ago

Claude + Facebook Ads MCP is absolutely WILD 🤯 This AI system turns Claude into your personal ads analyst using MCP integration. And generates full client reports in seconds. Perfect for agencies & ecomm operators who hate wrestling with Ads Manager. But instead of manually pulling data, building charts, and formatting reports... Just give Claude one natural language prompt and get a complete visual storytelling report with insights. Each report includes account performance, campaign breakdowns, demographics, and actionable recommendations. All with a single prompt. Here's what it does: → Connects directly to your Facebook Ads account → Pulls all performance data automatically → Calculates ROAS, CPA, conversion rates on demand → Creates interactive charts and visualizations → Generates beautiful reports → Builds everything from scratch in real-time Built with Claude MCP. Zero manual work. Want the complete MCP setup? Comment "MCP" + like and I'll send it over (must be following so I can DM)

Claude + Facebook Ads MCP is absolutely WILD 🤯 This AI system turns Claude into your personal ads analyst using MCP integration. And generates full client reports in seconds. Perfect for agencies & ecomm operators who hate wrestling with Ads Manager. But instead of manually pulling data, building charts, and formatting reports... Just give Claude one natural language prompt and get a complete visual storytelling report with insights. Each report includes account performance, campaign breakdowns, demographics, and actionable recommendations. All with a single prompt. Here's what it does: → Connects directly to your Facebook Ads account → Pulls all performance data automatically → Calculates ROAS, CPA, conversion rates on demand → Creates interactive charts and visualizations → Generates beautiful reports → Builds everything from scratch in real-time Built with Claude MCP. Zero manual work. Want the complete MCP setup? Comment "MCP" + like and I'll send it over (must be following so I can DM)

Mike Futia

270,651 views • 10 months ago

single RTX 3090. 24 GB VRAM. Qwen3.5-35B-A3B. 4-bit quant, 113 tokens per second at full 262K context harnessing Claude Code locally with no API, no subscription, no proxy. told it what it is. 30 Mamba2 layers, 10 attention, 256 experts, 8 active per token. said "build something that shows off what you can do." it visualized its own architecture. interactive. tokens flowing through layers. 256 experts lighting up on routing. served in the browser from the same GPU running inference. single prompt. then i said level up. 3D. Three.js. separate files. flythrough camera. clickable layers. it planned first, scaffolded 6 files, hit one API bug, fixed it itself, then optimized for smooth framerate. two iterations to a working 3D neural network explorer. llama.cpp just merged a native Anthropic endpoint. Claude Code points at localhost. the whole setup is two commands. no LiteLLM. no proxy config. the open source models coming out of china right now are genuinely changing what's possible on consumer hardware. respect to the Qwen team. this is acceleration.

single RTX 3090. 24 GB VRAM. Qwen3.5-35B-A3B. 4-bit quant, 113 tokens per second at full 262K context harnessing Claude Code locally with no API, no subscription, no proxy. told it what it is. 30 Mamba2 layers, 10 attention, 256 experts, 8 active per token. said "build something that shows off what you can do." it visualized its own architecture. interactive. tokens flowing through layers. 256 experts lighting up on routing. served in the browser from the same GPU running inference. single prompt. then i said level up. 3D. Three.js. separate files. flythrough camera. clickable layers. it planned first, scaffolded 6 files, hit one API bug, fixed it itself, then optimized for smooth framerate. two iterations to a working 3D neural network explorer. llama.cpp just merged a native Anthropic endpoint. Claude Code points at localhost. the whole setup is two commands. no LiteLLM. no proxy config. the open source models coming out of china right now are genuinely changing what's possible on consumer hardware. respect to the Qwen team. this is acceleration.

Sudo su

110,206 views • 5 months ago

$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.$

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 views • 4 months ago

I just vibe coded a full DTC advertorial page builder in Claude Code 🤯 One prompt --> complete presell page. Product images + editorial copy + testimonials + pricing —> all generated automatically. Built 100% in Claude Code. Perfect for DTC brands and agencies who need fresh advertorial pages for Meta but can't keep paying $1,500 per page. If you're running advertorials, you need variation & volume. 5-10 different advertorials per month. Different hooks, different audiences, different products. That means briefing a copywriter, waiting days, getting back a draft, giving notes, waiting again. Rinse repeat. This system solves it: → Enter your brand, product, target customer, and unique mechanism → Pick a style preset (clinical editorial, news exposé, lifestyle magazine, warm and trustworthy) → Claude writes the full page — every section from urgency banner to guarantee to final CTA → Nano Banana Pro generates all product images and mechanism diagrams inline No copywriter back-and-forth. No designing from scratch. No starting from a blank page every time. What you get: → A complete HTML advertorial page ready to paste into Shopify → DR copy structure extracted from real pages scaling on Meta → AI-generated product photography and diagrams → 4 style presets that automatically shift tone, colors, and authority framing per niche → Fully customizable system prompt — swap in your own advertorial templates, formulas, or page structures and it follows those instead I built 3 complete advertorial pages for 3 different brands in under 5 minutes. Skincare, supplements, and pet products. All different styles, all production-ready. I put the full Claude Code system prompt into a free Google Doc so you can get this up & running asap. Want access for free? > Like this post > Comment "CLAUDE" And I'll send it over (must be following so I can DM)

Mike Futia

41,470 views • 5 months ago

We ranked a B2B SaaS brand #1 on ChatGPT for their category in 7 days. (And it's being used by marketing teams at Webflow, Chime, and Deepgram) This platform tracks AI visibility + generates cited content automatically across ChatGPT, Perplexity, Claude, and Gemini... → No more 6-12 months waiting for Google rankings to move → No more $60K agency dashboards that only show problems → No more 10 different tools to track, create, and publish content → No more manual content gap analysis taking 20+ hours weekly → No more AI slop that ChatGPT refuses to cite Just connect your data sources → autonomous visibility tracking + content generation system. Here's how it works: → AI Citation Scanner (tracks mentions across ChatGPT, Perplexity, Claude, Gemini) → Competitive Gap Analysis (identifies where competitors get cited and you don't) → First-Party Data Integration (connects Zendesk, HubSpot, Drive, product docs) → AI Content Generator (creates authoritative content with human review checkpoints) → Direct CMS Publishing (publishes to Webflow, Contentful automatically) → Performance Measurement (tracks results across traditional + AI search) Companies using this infrastructure: • Webflow: 40% traffic lift + 5X content velocity • Chime: 3X AI citations in 30 days • Deepgram: 24X organic traffic (37K → 1.5M visitors in 60 days) Built with AI-assisted workflows. Runs on human + AI collaboration. 30-day results vs 6-month SEO cycles. Want to see how you rank in AI search? Like + comment "SEO" + repost, and I'll DM you the free scanner. (must be following)

Aryan Mahajan

28,451 views • 8 months ago

I just ran Gemma 4 31B on @CerebrasSystems at 1,800+ tokens/sec and it's multimodal. For context: that's 35x faster than a typical GPU endpoint, and the first token (reasoning included) lands in 1.5 seconds. This isn't a benchmark slide, I recorded the inference live. Prompt I used: "Create a simulation of an iPhone. Include at least one working dummy note taking app, a functional notification pulldown, high quality graphics, single HTML file, any libs via CDN." - Generation time: 3 seconds. - Notes app worked. - Notification panel worked. - Rendered first try. This is what wafer-scale inference unlocks, not just "faster," but a different category of product. When generation is this fast, you stop waiting and start iterating in real time. Why this matters: Gemma 4 31B is Google DeepMind's flagship open weight model, Apache 2.0 licensed, dense (not MoE), and built for efficiency over raw parameter count. It scores close to Claude Haiku 4.5 on the Artificial Analysis Intelligence Index (30 vs 29) but runs ~18x faster on Cerebras. It's also the first multimodal model on Cerebras's platform, meaning you can now feed it screenshots, documents, charts, and UI states at wafer scale speed. # Applications I'm most excited about: - Screenshot → Insight: Drop in a dashboard or document screenshot, get structured findings back instantly. no waiting, no batching. - Live UI generation: Full interactive interfaces (like my iPhone sim) generated and rendered in under 2 seconds. - Screenshot -> Patch: Feed it a broken UI + console error, get a minimal code fix and verification steps back. - Computer use & agentic loops: See -> reason -> act - verify, fast enough to keep a human in the loop instead of waiting on the model. - Long context summarization: Full research reports condensed into decision ready summaries you can read and requery in one sitting. The bigger unlock isn't the speed number itself, it's that agentic and multimodal loops (see -> reason -> output -> tool call -> verify -> retry) finally run in real time instead of feeling sluggish. As Logan Kilpatrick (Logan Kilpatrick) put it: "If every model was doing 2,000 tokens per second, you wouldn't build the same product and just have it be faster, you'd build different products." Gemma 4 31B is live now on Cerebras Inference Cloud in public preview. If you're building multimodal, agentic, or real time apps, this is worth testing today. What would you build with such insane inference throughput?

I just ran Gemma 4 31B on @CerebrasSystems at 1,800+ tokens/sec and it's multimodal. For context: that's 35x faster than a typical GPU endpoint, and the first token (reasoning included) lands in 1.5 seconds. This isn't a benchmark slide, I recorded the inference live. Prompt I used: "Create a simulation of an iPhone. Include at least one working dummy note taking app, a functional notification pulldown, high quality graphics, single HTML file, any libs via CDN." - Generation time: 3 seconds. - Notes app worked. - Notification panel worked. - Rendered first try. This is what wafer-scale inference unlocks, not just "faster," but a different category of product. When generation is this fast, you stop waiting and start iterating in real time. Why this matters: Gemma 4 31B is Google DeepMind's flagship open weight model, Apache 2.0 licensed, dense (not MoE), and built for efficiency over raw parameter count. It scores close to Claude Haiku 4.5 on the Artificial Analysis Intelligence Index (30 vs 29) but runs ~18x faster on Cerebras. It's also the first multimodal model on Cerebras's platform, meaning you can now feed it screenshots, documents, charts, and UI states at wafer scale speed. # Applications I'm most excited about: - Screenshot → Insight: Drop in a dashboard or document screenshot, get structured findings back instantly. no waiting, no batching. - Live UI generation: Full interactive interfaces (like my iPhone sim) generated and rendered in under 2 seconds. - Screenshot -> Patch: Feed it a broken UI + console error, get a minimal code fix and verification steps back. - Computer use & agentic loops: See -> reason -> act - verify, fast enough to keep a human in the loop instead of waiting on the model. - Long context summarization: Full research reports condensed into decision ready summaries you can read and requery in one sitting. The bigger unlock isn't the speed number itself, it's that agentic and multimodal loops (see -> reason -> output -> tool call -> verify -> retry) finally run in real time instead of feeling sluggish. As Logan Kilpatrick (Logan Kilpatrick) put it: "If every model was doing 2,000 tokens per second, you wouldn't build the same product and just have it be faster, you'd build different products." Gemma 4 31B is live now on Cerebras Inference Cloud in public preview. If you're building multimodal, agentic, or real time apps, this is worth testing today. What would you build with such insane inference throughput?

Alok

12,962 views • 28 days ago

Claude Code + Nano Banana 2 is f*cking cracked for advertorials 🤯 One prompt → a complete presell page with editorial copy, AI product photography, testimonials, and pricing — ready to paste into Shopify. Built 100% in Claude Code. Perfect for DTC brands and agencies running advertorials on Meta who need 5-10 different pages per month but can't keep paying $1,500 each. If you're briefing copywriters, waiting days for a draft, giving notes, waiting again, and still only getting one or two new pages per month... This system eliminates the entire loop: → Enter your brand, product, target customer, and unique mechanism → Pick a style preset (clinical editorial, news exposé, lifestyle magazine, warm and trustworthy) → Claude writes the full page — urgency banner to guarantee to final CTA → Nano Banana 2 generates product images and mechanism diagrams inline → Get back a complete HTML page following the same DR structure that's already scaling on Meta No copywriter back-and-forth. No designing from scratch. No starting from a blank page every time. What you get: → A production-ready HTML advertorial page you paste into Shopify → DR copy structure extracted from real pages scaling on Meta right now → AI-generated product photography and diagrams matched to your brand → 4 style presets that shift tone, colors, and authority framing per niche → A fully customizable system prompt — swap in your own templates and it follows those instead I built 3 complete advertorial pages for 3 different brands in under 5 minutes. Skincare, supplements, and pet products. All different styles, all production-ready. I put together a full playbook with the exact system prompt so you can get this running yourself. Want access for free? > Like this post > Comment "CLAUDE" And I'll send it over (must be following so I can DM)

Mike Futia

34,276 views • 4 months ago