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🚨 THIS IS INSANE... I built a custom Material Editor inside UEFN using nothing but Python. What it does: - Live preview color and surface changes - 15 built-in presets (Chrome, Gold, Neon, Hologram...) - Save your own custom presets - Randomize colors across objects - Paint gradients across... show more

Immature

22,169 subscribers

15,765 次观看 • 6 个月前 •via X (Twitter)

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I was always bad at creating cool mesh liquid gradients. When I had this style in mind for a client’s brand identity, I knew I needed a solution for their team. They had to create these visuals without friction. I knew how tricky those assets are. So I built a tool around it. Presets, color pickers, blur and grain controls, plus a liquify filter. This is how you deliver a brand toolkit in 2026. Do not blame AI for taking your job, blame yourself for not using it.

Alex Socoloff

93,776 次观看 • 7 个月前

NOT EXAGGERATING when i say this is the most useful AI video tool i've touched this year aleph 2.0 on Runway doesn't generate from scratch. it edits what you already have. bring your own footage. describe the change you want. it touches only that. everything else stays exactly as you shot it. season change. color swap. style update. across multi shot sequences. with a preview before you commit to the full render. i've been looking for something that fits into a real production workflow and this actually does go try it on your own footage → 22% off 2 months with code ALEPH2 — expires may 31 #MadeWithRunway #ad

NOT EXAGGERATING when i say this is the most useful AI video tool i've touched this year aleph 2.0 on Runway doesn't generate from scratch. it edits what you already have. bring your own footage. describe the change you want. it touches only that. everything else stays exactly as you shot it. season change. color swap. style update. across multi shot sequences. with a preview before you commit to the full render. i've been looking for something that fits into a real production workflow and this actually does go try it on your own footage → 22% off 2 months with code ALEPH2 — expires may 31 #MadeWithRunway #ad

Erina | AI Tools & News

25,555 次观看 • 4 个月前

SOMEONE BUILT AN ENTIRE COMPANY BRAIN INSIDE CLAUDE CODE IN 7 DAYS Not Obsidian. Fully custom. A living map of every employee, every agent, every SOP on one screen. Click any node and it opens up: - what department it's in - what SOPs are attached - what it can actually access that permission layer is the whole game an employee opens the chat and the AI already knows their access level, agents, SOPs and tools surface in the conversation like you tagged them by hand Obsidian cannot do this... no dev team. no six month build. no enterprise budget. just Claude Code and one week has everything to build one full guide on building your own team below

SOMEONE BUILT AN ENTIRE COMPANY BRAIN INSIDE CLAUDE CODE IN 7 DAYS Not Obsidian. Fully custom. A living map of every employee, every agent, every SOP on one screen. Click any node and it opens up: - what department it's in - what SOPs are attached - what it can actually access that permission layer is the whole game an employee opens the chat and the AI already knows their access level, agents, SOPs and tools surface in the conversation like you tagged them by hand Obsidian cannot do this... no dev team. no six month build. no enterprise budget. just Claude Code and one week has everything to build one full guide on building your own team below

Madni Aghadi

397,670 次观看 • 2 个月前

Everyone is using Fable 5 to build funny little demos so I decided to build something better. Vibe coded a 3D keyboard ecom platform in Three.js and GSAP. Every key. Every switch. Every color. Customizable in real time in 3D. It's really amusing that whole keyboard is built at runtime using Procedural geometry. To achieve that kind of realistic lighting on keyboard, it used a studio environment (gradient sky + softbox panels) rendered into a PMREM cubemap for realistic reflections. Opus helped a bit more here (as Fable was failing in loop in Claude chat) There is also custom GLSL shaders for RGB underflow/skirt and as well sRGB output encoding + explicit convertSRGBToLinear() on every material color so caps render at their exact swatch value. I don't know any of these techniques and concepts but I have had been reading how both Fable and Opus were trying to approach this by digging into their "thinking" phase. Go try it, there is a cool Claude theme keyboard you would want to purchase too :D Live: Code:

Everyone is using Fable 5 to build funny little demos so I decided to build something better. Vibe coded a 3D keyboard ecom platform in Three.js and GSAP. Every key. Every switch. Every color. Customizable in real time in 3D. It's really amusing that whole keyboard is built at runtime using Procedural geometry. To achieve that kind of realistic lighting on keyboard, it used a studio environment (gradient sky + softbox panels) rendered into a PMREM cubemap for realistic reflections. Opus helped a bit more here (as Fable was failing in loop in Claude chat) There is also custom GLSL shaders for RGB underflow/skirt and as well sRGB output encoding + explicit convertSRGBToLinear() on every material color so caps render at their exact swatch value. I don't know any of these techniques and concepts but I have had been reading how both Fable and Opus were trying to approach this by digging into their "thinking" phase. Go try it, there is a cool Claude theme keyboard you would want to purchase too :D Live: Code:

The Bugged Dev

98,323 次观看 • 2 个月前

xfinity Internet service launches xfinity wifi motion and it’s exactly what it sounds like Your WiFi will now be tracking you in your home and even discloses your movement to third parties “It tracks movement inside your home using nothing but your WiFi signals. No cameras, no sensors, just your router blasting invisible waves and reading how they bounce off your body. That's not WI fi anymore. That's a radar.” Comcast's terms of service specify that they'll use their WiFi Motion service to tell the police or any other third party about your movements at home “You walk across the room, it knows you open a door, it knows you sit still too long, it knows it's a motion detection through your Internet. And the craziest part? It's already built into the router, just waiting to be activated. Most people won't even realize they've installed live surveillance in their own home. Xfinity calls it a feature, but this isn't smart tech. It's a silent wireless tracking system. And once your router can map your movements, you're not just connected, you're monitored. Constantly. This is how the surveillance state gets in. Not with cameras, but with conveniences. Not with force, but with features. And by the time people figure it out, the cage is already built.”

xfinity Internet service launches xfinity wifi motion and it’s exactly what it sounds like Your WiFi will now be tracking you in your home and even discloses your movement to third parties “It tracks movement inside your home using nothing but your WiFi signals. No cameras, no sensors, just your router blasting invisible waves and reading how they bounce off your body. That's not WI fi anymore. That's a radar.” Comcast's terms of service specify that they'll use their WiFi Motion service to tell the police or any other third party about your movements at home “You walk across the room, it knows you open a door, it knows you sit still too long, it knows it's a motion detection through your Internet. And the craziest part? It's already built into the router, just waiting to be activated. Most people won't even realize they've installed live surveillance in their own home. Xfinity calls it a feature, but this isn't smart tech. It's a silent wireless tracking system. And once your router can map your movements, you're not just connected, you're monitored. Constantly. This is how the surveillance state gets in. Not with cameras, but with conveniences. Not with force, but with features. And by the time people figure it out, the cage is already built.”

Wall Street Apes

277,150 次观看 • 1 年前

In two years, every new tech company will run on a CRM you can vibe code to fit your business. This CRM will not be built from scratch on a coding platform though. It will be built on top of managed infrastructure with complete data capture, indices designed for LLMs to understand the whole picture, clean APIs, curated UI frameworks designed for selling, enterprise-grade security, and come with 24/7 support. You’ll instruct the agent using natural language and it will write the code + run it for you. That’s what we’re building at Lightfield and today we’re announcing step two of our plan - code execution. You can now ask your agent to build programs, artifacts, and run complex analysis instantly. It does this by writing and running Python in a high performance sandbox using full customer memory — including every email, meeting, and note that Lightfield has captured — and reasoning across every relationship to deliver high quality work. Ask your agent to build a competitive battle card before a call tomorrow. It pulls positioning, objections, and win/loss patterns from real conversations. Ask it to flag every open deal where your champion's engagement has dropped or sentiment has shifted. It reads across every conversation and tells you where to focus. Ask it to build a pipeline review with charts and graphs for your board. It produces the whole thing in minutes. Here’s what we did with it this week: → We asked our agent to grade our sales team on discovery, rapport, and closing. It gave a structured scorecard with specific examples from real conversations. → Our GTM team asked the agent to build a plan to expand one of our enterprise customers. It pulled competitive threats, upsell paths, stakeholder mapping, and a phased execution plan — in minutes. → We used it to find every feature request from the last quarter that our engineering team has since shipped, and draft a personalized follow-up to each customer using their original words. It closed loops across dozens of accounts that would have taken days to track down manually This is the first step towards building any custom GTM workflow in natural language on top of what Lightfield knows about your business - a world model built from every single interaction your team has had with customers.

In two years, every new tech company will run on a CRM you can vibe code to fit your business. This CRM will not be built from scratch on a coding platform though. It will be built on top of managed infrastructure with complete data capture, indices designed for LLMs to understand the whole picture, clean APIs, curated UI frameworks designed for selling, enterprise-grade security, and come with 24/7 support. You’ll instruct the agent using natural language and it will write the code + run it for you. That’s what we’re building at Lightfield and today we’re announcing step two of our plan - code execution. You can now ask your agent to build programs, artifacts, and run complex analysis instantly. It does this by writing and running Python in a high performance sandbox using full customer memory — including every email, meeting, and note that Lightfield has captured — and reasoning across every relationship to deliver high quality work. Ask your agent to build a competitive battle card before a call tomorrow. It pulls positioning, objections, and win/loss patterns from real conversations. Ask it to flag every open deal where your champion's engagement has dropped or sentiment has shifted. It reads across every conversation and tells you where to focus. Ask it to build a pipeline review with charts and graphs for your board. It produces the whole thing in minutes. Here’s what we did with it this week: → We asked our agent to grade our sales team on discovery, rapport, and closing. It gave a structured scorecard with specific examples from real conversations. → Our GTM team asked the agent to build a plan to expand one of our enterprise customers. It pulled competitive threats, upsell paths, stakeholder mapping, and a phased execution plan — in minutes. → We used it to find every feature request from the last quarter that our engineering team has since shipped, and draft a personalized follow-up to each customer using their original words. It closed loops across dozens of accounts that would have taken days to track down manually This is the first step towards building any custom GTM workflow in natural language on top of what Lightfield knows about your business - a world model built from every single interaction your team has had with customers.

Keith Peiris

27,634 次观看 • 7 个月前

Anthropic just got outplayed again. Devs built the multiplayer assistant Anthropic couldn't, and open-sourced it. Claude Cowork is a solo desktop agent. You point it at a folder, give it a task, and it works through your local files on your own machine. The moment a teammate enters the picture, it has nothing to offer. Most real work does not happen alone. A teammate asks for a status update on something you own. The context they need is scattered across your meetings, your notes, and decisions made last week. Typing all of that out takes time you do not have. This is the gap Claude Cowork was never designed to cross. Rowboat Spaces is built on a different model entirely. Each person brings their own assistant into a shared channel. Your assistant is your second brain. It knows your meetings, your notes, and your open decisions. That personal context stays yours. When a teammate asks a question in the channel, you ask your assistant to brief them. It pulls from everything you know and delivers the answer on your behalf, attributed to you. Your teammate's assistant does the same, from their own context. Teams can draft specs, track decisions, and update shared files from plain conversation. Each assistant reads the full channel history, cross references it against what exists, and flags what is missing. The whole thing is open-source, and each assistant acts as the person it belongs to, not as a shared bot pulling from a common pool. The video below shows this in action. I joined a shared space and asked my team member for a status update. My team member asked their second brain to answer. A spec got built from that conversation, versioned, with every change tracked back to the message that triggered it. Rowboat GitHub: (don't forget to star 🌟) My co-founder also wrote a great article on building your second brain with Rowboat, and I highly recommend reading it as well. The article is quoted below.

Anthropic just got outplayed again. Devs built the multiplayer assistant Anthropic couldn't, and open-sourced it. Claude Cowork is a solo desktop agent. You point it at a folder, give it a task, and it works through your local files on your own machine. The moment a teammate enters the picture, it has nothing to offer. Most real work does not happen alone. A teammate asks for a status update on something you own. The context they need is scattered across your meetings, your notes, and decisions made last week. Typing all of that out takes time you do not have. This is the gap Claude Cowork was never designed to cross. Rowboat Spaces is built on a different model entirely. Each person brings their own assistant into a shared channel. Your assistant is your second brain. It knows your meetings, your notes, and your open decisions. That personal context stays yours. When a teammate asks a question in the channel, you ask your assistant to brief them. It pulls from everything you know and delivers the answer on your behalf, attributed to you. Your teammate's assistant does the same, from their own context. Teams can draft specs, track decisions, and update shared files from plain conversation. Each assistant reads the full channel history, cross references it against what exists, and flags what is missing. The whole thing is open-source, and each assistant acts as the person it belongs to, not as a shared bot pulling from a common pool. The video below shows this in action. I joined a shared space and asked my team member for a status update. My team member asked their second brain to answer. A spec got built from that conversation, versioned, with every change tracked back to the message that triggered it. Rowboat GitHub: (don't forget to star 🌟) My co-founder also wrote a great article on building your second brain with Rowboat, and I highly recommend reading it as well. The article is quoted below.

Akshay 🚀

117,616 次观看 • 15 天前

🚨 Claude Code costs $200/month. GitHub Copilot costs $19/month. Jack Dorsey's company built a free alternative. 35,000 GitHub stars. It's called Goose. An open source AI agent built by Block that goes beyond code suggestions. It installs, executes, edits, and tests. With any LLM you choose. Not autocomplete. Not suggestions. A full autonomous agent that takes actions on your computer. No vendor lock-in. No monthly subscription. Bring your own model. Here's what Goose does: → Works with ANY LLM. Claude, GPT, Gemini, Llama, DeepSeek, Ollama. Your choice. → Reads and understands your entire codebase → Writes, edits, and refactors code across multiple files → Runs shell commands and installs dependencies → Executes and debugs your code automatically → Extensible through MCP. Connect it to any external tool. → Desktop app, CLI, and web interface. Pick your workflow. → Written in Rust. Fast. Lightweight. No bloat. Here's the wildest part: Block is a $40 billion company. They built Cash App, Square, and TIDAL. They use Goose internally. Then they open sourced the entire thing. This isn't a side project from a random developer. This is production-grade tooling from a company that processes billions in payments. Built for their own engineers. Given to everyone. Claude Code: $200/month. Locked to Claude. GitHub Copilot: $19/month. Locked to GitHub. Cursor: $20/month. Locked to their editor. Goose: Free. Any LLM. Any editor. Any workflow. Forever. 35.3K GitHub stars. 3.3K forks. 4,078 commits. Built by Block. 100% Open Source. Apache 2.0 License.

🚨 Claude Code costs $200/month. GitHub Copilot costs $19/month. Jack Dorsey's company built a free alternative. 35,000 GitHub stars. It's called Goose. An open source AI agent built by Block that goes beyond code suggestions. It installs, executes, edits, and tests. With any LLM you choose. Not autocomplete. Not suggestions. A full autonomous agent that takes actions on your computer. No vendor lock-in. No monthly subscription. Bring your own model. Here's what Goose does: → Works with ANY LLM. Claude, GPT, Gemini, Llama, DeepSeek, Ollama. Your choice. → Reads and understands your entire codebase → Writes, edits, and refactors code across multiple files → Runs shell commands and installs dependencies → Executes and debugs your code automatically → Extensible through MCP. Connect it to any external tool. → Desktop app, CLI, and web interface. Pick your workflow. → Written in Rust. Fast. Lightweight. No bloat. Here's the wildest part: Block is a $40 billion company. They built Cash App, Square, and TIDAL. They use Goose internally. Then they open sourced the entire thing. This isn't a side project from a random developer. This is production-grade tooling from a company that processes billions in payments. Built for their own engineers. Given to everyone. Claude Code: $200/month. Locked to Claude. GitHub Copilot: $19/month. Locked to GitHub. Cursor: $20/month. Locked to their editor. Goose: Free. Any LLM. Any editor. Any workflow. Forever. 35.3K GitHub stars. 3.3K forks. 4,078 commits. Built by Block. 100% Open Source. Apache 2.0 License.

Nav Toor

394,897 次观看 • 5 个月前

Developers, this isn’t just a badge; it’s an ESP32-S3 waiting for your next project. 👀 It's basically a customizable, always-on color e-paper display that can move between your bag, desk, fridge, or workspace. ✔ 3.7-inch color e-paper display with 48+ native colors ✔ ESP32-S3 inside with open-source support for custom projects ✔ Clip ring, built-in magnet, and desk stand ✔ Upload and edit designs through the mobile app ✔ Share your portfolio, profile, or links instantly through NFC ✔ Over two months of battery life with daily use ✔ Clear, paper-like visibility—even in direct sunlight The biggest advantage isn’t just the display—it’s how many different roles one small device can take on. You can use it as a digital bag charm, anime badge, task board, event pass, portfolio card, fridge note, or even build your own integrations with the ESP32-S3. Because it’s e-paper, it uses no power to keep an image visible after refreshing. That makes it feel less like another distracting screen and more like a piece of calm, programmable technology. For developers, makers, artists, and anyone who enjoys customizing their setup, TICKEY is more than a digital accessory. Check it out on Kickstarter:

• nanou •

32,883 次观看 • 24 天前

I genuinely never thought this would be possible 😱 AI just built a full embroidery editor that runs entirely in the browser. Freaking crazy. And no, it’s not GPT 6 Astra. It’s Claude Fable 5.1. Also, No Three.js. No WebGL. No shaders. Just Canvas 2D and math. You literally draw using a needle cursor and the app converts your path into actual embroidery stitches like running, back, chain, cross, satin, French knots and lazy daisy. Even the fabric is generated pixel by pixel. Two overlapping sine waves create a height field for the horizontal and vertical threads. Then a fake normal is calculated and lit from one direction. Basically a bump map with zero GPU rendering. The embroidery thread itself is rendered in 6 passes. Shadow, core color, twisted ply lines, dark edge and two highlight layers based on the light angle. That’s why it actually looks like embroidery floss instead of a simple marker stroke. And trust me, I've zero sense or knowledge of any of this. ZERO. All built. All working. All by AI. And somehow it’s all inside one HTML file. Nothing bigg. Absolutely insane what’s possible now. You can really genuinely take a random idea and bring it to life. And that too in < 1 hour. And who knows, this could actually be pretty useful for fashion designers and embroidery artists maybe? Live:

I genuinely never thought this would be possible 😱 AI just built a full embroidery editor that runs entirely in the browser. Freaking crazy. And no, it’s not GPT 6 Astra. It’s Claude Fable 5.1. Also, No Three.js. No WebGL. No shaders. Just Canvas 2D and math. You literally draw using a needle cursor and the app converts your path into actual embroidery stitches like running, back, chain, cross, satin, French knots and lazy daisy. Even the fabric is generated pixel by pixel. Two overlapping sine waves create a height field for the horizontal and vertical threads. Then a fake normal is calculated and lit from one direction. Basically a bump map with zero GPU rendering. The embroidery thread itself is rendered in 6 passes. Shadow, core color, twisted ply lines, dark edge and two highlight layers based on the light angle. That’s why it actually looks like embroidery floss instead of a simple marker stroke. And trust me, I've zero sense or knowledge of any of this. ZERO. All built. All working. All by AI. And somehow it’s all inside one HTML file. Nothing bigg. Absolutely insane what’s possible now. You can really genuinely take a random idea and bring it to life. And that too in < 1 hour. And who knows, this could actually be pretty useful for fashion designers and embroidery artists maybe? Live:

The Bugged Dev

65,903 次观看 • 14 天前

I just built a branded IG carousel generator in Claude Code 🤯 One brand URL + one product name = 6 finished carousel slides. The kind an agency charges $2K to produce. All inside Claude Code. Perfect for DTC brands, agencies, and mobile app operators who need on-brand social content without briefing a designer or waiting a week for revisions. If you're building IG carousels manually — writing copy in Notion, designing in Canva, exporting slides one by one, going back and forth with your creative team for days... This tool eliminates the entire loop: → Drop in a brand URL and product name → Claude scrapes the site and extracts colors, fonts, voice, and positioning → Generates 6 slide concepts across proven carousel frameworks → Writes a detailed image prompt for each slide → Fires all 6 to ChatGPT Images 2.0 via FAL in parallel → Downloads finished slides + opens an HTML gallery No Canva. No designer back-and-forth. No generic AI slop. What you get: -> Finished 1080x1350px slides ready to post directly to Instagram -> Wavy color-blocked backgrounds, bold typography, product hero — all baked into the image -> Brand-accurate colors and copy pulled from the live site automatically -> A reusable pipeline — new brand, new folder, same 3-minute workflow Built 100% in Claude Code. I put together the full step-by-step playbook so you can build this yourself. Want it for free? > Like this post > Comment "SLIDES" And I'll send it over (must be following so I can DM)

Mike Futia

93,686 次观看 • 4 个月前

I built an entire content team in 21 minutes. It writes LinkedIn posts, YouTube scripts, Newsletters, and Tweets. All in my voice. Content used to be the bottleneck. Writing for multiple platforms meant either spending hours on it or hiring someone who never quite sounded like me. I spent weeks refining this system so I could show you exactly how to set it up. What's in the guide: - Setting up your personal AI writer from scratch - The folder structure that makes AI writing actually work - Using successful examples to train your AI's voice - Building and refining custom writing skills - Advanced techniques for scaling across platforms This is for creators, founders, and marketers who want to produce more content, faster, without losing their voice or hiring a team. I recorded the full walkthrough. Every step. Live. Comment "WRITER" and I'll DM you the free GitHub repo with the complete folder structure and skills. (Make sure we're connected so I can DM you)

I built an entire content team in 21 minutes. It writes LinkedIn posts, YouTube scripts, Newsletters, and Tweets. All in my voice. Content used to be the bottleneck. Writing for multiple platforms meant either spending hours on it or hiring someone who never quite sounded like me. I spent weeks refining this system so I could show you exactly how to set it up. What's in the guide: - Setting up your personal AI writer from scratch - The folder structure that makes AI writing actually work - Using successful examples to train your AI's voice - Building and refining custom writing skills - Advanced techniques for scaling across platforms This is for creators, founders, and marketers who want to produce more content, faster, without losing their voice or hiring a team. I recorded the full walkthrough. Every step. Live. Comment "WRITER" and I'll DM you the free GitHub repo with the complete folder structure and skills. (Make sure we're connected so I can DM you)

JJ Englert

77,013 次观看 • 7 个月前

🔥 Your X Agent just got eyes. Watch it think. Built for strict X API compliance. No automated bulk actions. Every engagement is a suggestion you act on manually. Your account stays safe. 💥 New: Live Reasoning Terminal — see every search, thread read, memory recall, and engagement decision across 21 tools as your autonomous agent works. No black box. Here's the full loop in 60 seconds: ↓🎥 1️⃣ Configure Topics, accounts, model tier, run interval, action toggles — full control over what your agent does and how often. 2️⃣ Run Trigger manually or set it on autopilot. Every 30 min to once a day. 3️⃣ Watch it think Live terminal streams every tool call, search, thread read, and decision in real-time. You see exactly what the agent sees. 4️⃣ Review suggestions Replies, likes, follows land in your inbox. Nothing goes live without you. Human-in-the-loop — built for strict X API compliance. 5️⃣ Restyle Don't like the tone? Rewrite any reply — concise, insightful, casual, or your own custom style. One click. 6️⃣ Feedback Hit "Replied" after you post. The agent learns what you approve and adapts next run. The more you use it, the sharper it gets. 🆓 X API costs on us for early users. No extra keys needed. 10 latest models. 7 providers. (OpenAI, Anthropic, Kimi.ai, and more) Try now →

🔥 Your X Agent just got eyes. Watch it think. Built for strict X API compliance. No automated bulk actions. Every engagement is a suggestion you act on manually. Your account stays safe. 💥 New: Live Reasoning Terminal — see every search, thread read, memory recall, and engagement decision across 21 tools as your autonomous agent works. No black box. Here's the full loop in 60 seconds: ↓🎥 1️⃣ Configure Topics, accounts, model tier, run interval, action toggles — full control over what your agent does and how often. 2️⃣ Run Trigger manually or set it on autopilot. Every 30 min to once a day. 3️⃣ Watch it think Live terminal streams every tool call, search, thread read, and decision in real-time. You see exactly what the agent sees. 4️⃣ Review suggestions Replies, likes, follows land in your inbox. Nothing goes live without you. Human-in-the-loop — built for strict X API compliance. 5️⃣ Restyle Don't like the tone? Rewrite any reply — concise, insightful, casual, or your own custom style. One click. 6️⃣ Feedback Hit "Replied" after you post. The agent learns what you approve and adapts next run. The more you use it, the sharper it gets. 🆓 X API costs on us for early users. No extra keys needed. 10 latest models. 7 providers. (OpenAI, Anthropic, Kimi.ai, and more) Try now →

Solaris AI

16,671 次观看 • 7 个月前

I just built a branded IG carousel generator in Claude Code 🤯 One brand URL + one product name = 6 finished carousel slides. The kind an agency charges $2K to produce. All inside Claude Code. Perfect for DTC brands and agencies who need to post carousels every week but can't turn each one into a two-day design project. If every carousel is the same mini-project — writing the copy in Notion, rebuilding the template in Canva, exporting slides one at a time, then three rounds of revisions with your designer before it's even live... This tool eliminates the entire loop: → Drop in a brand URL and product name → Claude scrapes the site and extracts colors, fonts, voice, and positioning → Generates 6 slide concepts across proven carousel frameworks → Writes a detailed image prompt for each slide → Fires all 6 to ChatGPT Images 2.0 via FAL in parallel → Downloads finished slides + opens an HTML gallery No Canva. No designer back-and-forth. No generic AI slop. What you get: → Finished 1080x1350px slides ready to post directly to Instagram → Wavy color-blocked backgrounds, bold typography, product hero — all baked into the image → Brand-accurate colors and copy pulled from the live site automatically → A reusable pipeline — new brand, new folder, same 3-minute workflow Built 100% in Claude Code. I put together the full step-by-step playbook so you can build this yourself. Want it for free? Like this post Comment "SLIDES" And I'll send it over (must be following so I can DM)

Mike Futia

36,432 次观看 • 3 个月前

166,700 fruit fly neurons are walking a robot cat across a desk right now. In September 2026, Google Research and HHMI Janelia mapped every neuron and every connection in an adult male fruit fly's full nervous system, brain, optic lobes, and ventral nerve cord together, and published it in Cell. The number is staggering on its own: 166,700 neurons, roughly 125 million synapses, the most complete nervous system map of any animal ever built. Now that exact wiring is running live, simulated neuron for neuron, inside a small robotic quadruped equipped with a camera and motion sensors. Movement comes in through the sensors, the biological circuitry fires the way it always did, and the output drives the robot's legs directly. No one wrote the balance logic. No one coded the gait. The insect's own nervous system does that job, just on legs it never had. To be clear about what this is and isn't: nothing biological is alive in this loop. It's a full computational reconstruction of real neural wiring, controlling hardware instead of an insect body. The brain is real. The body is borrowed. Remarkable proof that a mapped nervous system can drive any body you give it, or a preview of something people aren't ready to think through yet?

166,700 fruit fly neurons are walking a robot cat across a desk right now. In September 2026, Google Research and HHMI Janelia mapped every neuron and every connection in an adult male fruit fly's full nervous system, brain, optic lobes, and ventral nerve cord together, and published it in Cell. The number is staggering on its own: 166,700 neurons, roughly 125 million synapses, the most complete nervous system map of any animal ever built. Now that exact wiring is running live, simulated neuron for neuron, inside a small robotic quadruped equipped with a camera and motion sensors. Movement comes in through the sensors, the biological circuitry fires the way it always did, and the output drives the robot's legs directly. No one wrote the balance logic. No one coded the gait. The insect's own nervous system does that job, just on legs it never had. To be clear about what this is and isn't: nothing biological is alive in this loop. It's a full computational reconstruction of real neural wiring, controlling hardware instead of an insect body. The brain is real. The body is borrowed. Remarkable proof that a mapped nervous system can drive any body you give it, or a preview of something people aren't ready to think through yet?

Zentrix⌚️

231,536 次观看 • 16 天前

Will Astra kill us in the future? 😱 Maybe Maybe not. But it absolutely killed this by recreating Barcelona’s Camp Nou in 3D with 86,964 individually selectable seats ❤️‍🔥 You can literally pick any seat and see what your view would look like from that exact position inside the stadium. You can also switch between daylight, golden hour, and floodlit night modes to get a much more realistic feel. I very much loved how it optimized the website’s performance by reducing the overview seat geometry from 2,087,136 triangles to 695,712, which is around a 67% reduction, without reducing the number of seats. The stadium itself is built using procedural geometry and custom BufferGeometry, while Astra used InstancedMesh, spatial batching, and custom distance based LOD to efficiently render and manage nearly 87K selectable seats without creating tens of thousands of separate scene objects. For interaction and realism, it also uses optimized raycasting with broad phase batch rejection, TSL and WebGPU materials, shader driven surface detail, and proper seat level camera transitions. A month ago, I built an imaginary stadium using Claude Fable 5. It was great too, but this feels much more realistic. Astra went further by adding proper doorways, staircases, concourses, tier structures, tunnels, railings, and other architectural details you would expect in an actual stadium. Really classy. We should do this for more stadiums and eventually rethink the ticketing experience altogether. Live:

Will Astra kill us in the future? 😱 Maybe Maybe not. But it absolutely killed this by recreating Barcelona’s Camp Nou in 3D with 86,964 individually selectable seats ❤️‍🔥 You can literally pick any seat and see what your view would look like from that exact position inside the stadium. You can also switch between daylight, golden hour, and floodlit night modes to get a much more realistic feel. I very much loved how it optimized the website’s performance by reducing the overview seat geometry from 2,087,136 triangles to 695,712, which is around a 67% reduction, without reducing the number of seats. The stadium itself is built using procedural geometry and custom BufferGeometry, while Astra used InstancedMesh, spatial batching, and custom distance based LOD to efficiently render and manage nearly 87K selectable seats without creating tens of thousands of separate scene objects. For interaction and realism, it also uses optimized raycasting with broad phase batch rejection, TSL and WebGPU materials, shader driven surface detail, and proper seat level camera transitions. A month ago, I built an imaginary stadium using Claude Fable 5. It was great too, but this feels much more realistic. Astra went further by adding proper doorways, staircases, concourses, tier structures, tunnels, railings, and other architectural details you would expect in an actual stadium. Really classy. We should do this for more stadiums and eventually rethink the ticketing experience altogether. Live:

The Bugged Dev

226,976 次观看 • 18 天前

Video editing has nowadays become a conversation.. you drop raw footage in a folder, tell Claude Code what you want, and get final.mp4 back. its free, open source, 17,000 stars on Github.. it's called video-use. and the reason it's different from every "AI video editor" is how it actually works under the hood.. it doesn't guess. it transcribes every word you said first, then edits off the actual transcript. what it does once your footage is in the folder: - cuts every umm, uh, and false start automatically - kills the dead space between takes - color grades each segment.. warm cinematic, neutral punch, or a custom ffmpeg chain - builds the cut with editor sub-agents, runs the animations in parallel - renders a preview, grades its own work, and iterates until it's clean and yeah.. it asks before it cuts. it shows you the plan in plain english and waits for your yes. it's not a black box that spits out a mystery edit.. you stay the director, it does the labor. ▫️ how to start install the skill in claude code (repo below) drop your raw clips in a folder tell it "cut the filler, tighten the pauses, warm cinematic grade" and let it work And yes, it's a tool, not a taste replacement. it'll hand you a clean, tight cut, but the creative calls, the hook, the pacing, the story.. that's still you. it does the 80% that's grunt work so you spend your time on the 20% that matters. editing stopped being a skill you grind for years. it's a prompt now. repo:

Video editing has nowadays become a conversation.. you drop raw footage in a folder, tell Claude Code what you want, and get final.mp4 back. its free, open source, 17,000 stars on Github.. it's called video-use. and the reason it's different from every "AI video editor" is how it actually works under the hood.. it doesn't guess. it transcribes every word you said first, then edits off the actual transcript. what it does once your footage is in the folder: - cuts every umm, uh, and false start automatically - kills the dead space between takes - color grades each segment.. warm cinematic, neutral punch, or a custom ffmpeg chain - builds the cut with editor sub-agents, runs the animations in parallel - renders a preview, grades its own work, and iterates until it's clean and yeah.. it asks before it cuts. it shows you the plan in plain english and waits for your yes. it's not a black box that spits out a mystery edit.. you stay the director, it does the labor. ▫️ how to start install the skill in claude code (repo below) drop your raw clips in a folder tell it "cut the filler, tighten the pauses, warm cinematic grade" and let it work And yes, it's a tool, not a taste replacement. it'll hand you a clean, tight cut, but the creative calls, the hook, the pacing, the story.. that's still you. it does the 80% that's grunt work so you spend your time on the 20% that matters. editing stopped being a skill you grind for years. it's a prompt now. repo:

Axel Bitblaze 🪓

116,254 次观看 • 2 个月前

After Effects costs $22.99/month. CapCut Pro costs $9.99/month. A guy named Clement built the free alternative that runs entirely in your browser. It is called Pikimov. Open a competitor's editor and you're locked into a download, an account, and a monthly bill before you touch a single clip. Open Pikimov and you just start. No sign-up, no install, no upload. Load it in Chrome or Edge and you're compositing 4K in seconds, with the editing running locally on your own machine. It packs keyframe animation on nearly every property, particles, motion tracking, rotoscoping, a one-click background remover, transitions, and full 3D model support. Then it goes one step further. It reads After Effects javascript expressions, so the animation code you already know runs straight inside the tab instead of forcing you to relearn anything. Your files never leave your computer. Nothing uploads to a server. Your projects are never fed into AI training, so your privacy isn't the price of admission. No, it does not magically reproduce every plugin and decade-deep feature Adobe bolted onto After Effects. But it breaks the most important rule of the old motion design market: You no longer need a subscription to own the tool.

After Effects costs $22.99/month. CapCut Pro costs $9.99/month. A guy named Clement built the free alternative that runs entirely in your browser. It is called Pikimov. Open a competitor's editor and you're locked into a download, an account, and a monthly bill before you touch a single clip. Open Pikimov and you just start. No sign-up, no install, no upload. Load it in Chrome or Edge and you're compositing 4K in seconds, with the editing running locally on your own machine. It packs keyframe animation on nearly every property, particles, motion tracking, rotoscoping, a one-click background remover, transitions, and full 3D model support. Then it goes one step further. It reads After Effects javascript expressions, so the animation code you already know runs straight inside the tab instead of forcing you to relearn anything. Your files never leave your computer. Nothing uploads to a server. Your projects are never fed into AI training, so your privacy isn't the price of admission. No, it does not magically reproduce every plugin and decade-deep feature Adobe bolted onto After Effects. But it breaks the most important rule of the old motion design market: You no longer need a subscription to own the tool.

Ihtesham Ali

19,708 次观看 • 29 天前

Everything far away in a game engine can feel small, flat, and washed out. That is correct perspective, but it can make for some poor scenery sometimes. Two features I've added to my engine try to fight against that: Spatial Recession magnifies the distance. After a vertex is projected I push its screen position outward from center. Near geometry is untouched. It only moves x and y, never depth, so nothing sorts differently and nothing z-fights. The result is a long lens bolted onto the far half of a wide one: the tower and the ridgeline loom instead of receding, while I keep the wide field of view I actually play in (FOV is identical in both of these examples). Dimensional Falloff is four distance ramps on the surface itself. Lighting contrast flattens so distant hills stop having a hard lit side and a hard dark side, which is what haze does in real life. Color then desaturates toward its own brightness to fake more distance. Then that desaturated color is pushed, and that is the one doing most of the visible work here. Last is texture detail, with a custom MIP value based on distances. All of it keys off real radial distance from the camera rather than depth This is so it stays put when you turn your head. Performance cost is next to nothing. The recession is two lines in the vertex shader. The falloff is a few lerps at the end of the pixel shader, which was already computing that distance for the mip bias anyway. Quite happy with the results, what do you think? #gamedev

Everything far away in a game engine can feel small, flat, and washed out. That is correct perspective, but it can make for some poor scenery sometimes. Two features I've added to my engine try to fight against that: Spatial Recession magnifies the distance. After a vertex is projected I push its screen position outward from center. Near geometry is untouched. It only moves x and y, never depth, so nothing sorts differently and nothing z-fights. The result is a long lens bolted onto the far half of a wide one: the tower and the ridgeline loom instead of receding, while I keep the wide field of view I actually play in (FOV is identical in both of these examples). Dimensional Falloff is four distance ramps on the surface itself. Lighting contrast flattens so distant hills stop having a hard lit side and a hard dark side, which is what haze does in real life. Color then desaturates toward its own brightness to fake more distance. Then that desaturated color is pushed, and that is the one doing most of the visible work here. Last is texture detail, with a custom MIP value based on distances. All of it keys off real radial distance from the camera rather than depth This is so it stays put when you turn your head. Performance cost is next to nothing. The recession is two lines in the vertex shader. The falloff is a few lerps at the end of the pixel shader, which was already computing that distance for the mip bias anyway. Quite happy with the results, what do you think? #gamedev

Analog Dream Dev

85,894 次观看 • 26 天前