Loading video...

Video Failed to Load

Go Home

I built a plugin to generate 3D assets directly from Codex, using the latest update that introduces image generation. It generates an image, calls the Tripo3D API, then starts an MCP server and a local viewer directly inside Codex. It uses the paid Tripo3D API (12 free generations, 600...

DFintelligence's profile picture

Defend Intelligence (Anis Ayari)

75,970 subscribers

12,151 views • 6 months ago •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

A reference sheet can now become a complete 3D asset and an interactive website. I generated the multi-view temple concept with GPT Image 2.0, then gave it to Codex + GPT-5.6 Ultra through Blender MCP. Blender was controlled entirely from the terminal. The model, materials, cameras, renders and exports were all generated through Python scripts. Codex then created a Three.js viewer where I can rotate the temple, switch between every angle and hide each architectural layer. The result still needs refinement, especially around the roofs and ornaments, but the complete image → Blender → web workflow is working. All from one prompt 👇
0:18

A reference sheet can now become a complete 3D asset and an interactive website. I generated the multi-view temple concept with GPT Image 2.0, then gave it to Codex + GPT-5.6 Ultra through Blender MCP. Blender was controlled entirely from the terminal. The model, materials, cameras, renders and exports were all generated through Python scripts. Codex then created a Three.js viewer where I can rotate the temple, switch between every angle and hide each architectural layer. The result still needs refinement, especially around the roofs and ornaments, but the complete image → Blender → web workflow is working. All from one prompt 👇

Givros

26,024 views • 3 months ago

You can now run Grok inside Claude Code and Codex, using your existing subscription. it's called grok-plugin. Grok’s genuinely good right now, but if your setup lives in Claude Code and Codex, switching to use it is a pain. this solves that exact issue: > Grok in Claude Code and Codex > directly auths through X Premium or Supergrok, no api key > your workflow and subagents stay untouched free and open source. the low-friction way to test Grok in the tools you already use.
0:16

You can now run Grok inside Claude Code and Codex, using your existing subscription. it's called grok-plugin. Grok’s genuinely good right now, but if your setup lives in Claude Code and Codex, switching to use it is a pain. this solves that exact issue: > Grok in Claude Code and Codex > directly auths through X Premium or Supergrok, no api key > your workflow and subagents stay untouched free and open source. the low-friction way to test Grok in the tools you already use.

Alvaro Cintas

26,018 views • 2 months ago

🚨 one of the sharpest image to 3D models just went open source Hi3D V1.0 , this is Sparc3D from last year scaled up , code + weights now on GitHub / HF and it’s completely free to download and use , honestly the most interesting open 3D drop i’ve seen in a while i threw 4 of my own images at it , goku / charizard / the new roadster / the pelican on a bike Try it free Hi3D
0:12

🚨 one of the sharpest image to 3D models just went open source Hi3D V1.0 , this is Sparc3D from last year scaled up , code + weights now on GitHub / HF and it’s completely free to download and use , honestly the most interesting open 3D drop i’ve seen in a while i threw 4 of my own images at it , goku / charizard / the new roadster / the pelican on a bike Try it free Hi3D

Chetaslua

26,448 views • 11 hours ago

Working on a “Hand-Off to Mac” way to load the chats from the Codex Remote Control for iOS app to the Codex App Codex app-server doesn’t quite support a live update So I decided to find a workaround A button that forces the closing of the Codex App and reopening it in the chat created from iPhone Would you use this button?
0:13

Working on a “Hand-Off to Mac” way to load the chats from the Codex Remote Control for iOS app to the Codex App Codex app-server doesn’t quite support a live update So I decided to find a workaround A button that forces the closing of the Codex App and reopening it in the chat created from iPhone Would you use this button?

Emanuele Di Pietro

24,432 views • 7 months ago

.Metaplex Genesis is the ignition key for the Internet Capital Markets. We have just handed that key to AI agents. Introducing metaplex-genesis-mcp: an open-source Model Context Protocol server connecting agents directly to the Genesis protocol on Solana. Special thanks to Blockiosaurus🦾🥖 and Tony Boyle for the support. Link below.
0:15

.Metaplex Genesis is the ignition key for the Internet Capital Markets. We have just handed that key to AI agents. Introducing metaplex-genesis-mcp: an open-source Model Context Protocol server connecting agents directly to the Genesis protocol on Solana. Special thanks to Blockiosaurus🦾🥖 and Tony Boyle for the support. Link below.

maikers

10,198 views • 10 months ago

is it legal? NVIDIA and Microsoft just open-sourced the pieces of a full 3D studio for Opus 5.5 15 github repos, sorted by what you want to make: GENERATE 3D ASSETS 01. TRELLIS.2 by Microsoft: -> one image in, a fully textured 3D asset out, about 3 seconds on an H100 02. Hunyuan3D-2.1 by Tencent: -> production-ready 3D assets with PBR textures 03. stable-fast-3d by Stability AI: -> one photo to a clean UV-unwrapped mesh, built for speed 04. meshy-mcp-server by Meshy: -> text or image to 3D, then remesh, retexture, rig and animate from your agent BUILD SCENES AND WORLDS 05. mcp-for-blender: -> prompt-driven modeling, scene building and edits in a live Blender 06. HY-World-2.0 by Tencent: -> text or a single image to a navigable 3D world 07. supersplat by PlayCanvas: -> inspect, edit, optimize and publish 3D Gaussian splats in the browser 08. kit-usd-agents by NVIDIA: -> edit Omniverse USD scenes in plain English, plus MCP servers for Kit and Isaac Sim MAKE A GAME 09. unity-mcp: -> run the Unity Editor in plain English: scenes, C# scripts, assets, tests, builds 10. godot-ai: -> 46 tools for a live Godot editor: scenes, scripts, signals, UI, particles 11. unreal-engine-mcp: -> drive a live Unreal Editor: actors, world building, Blueprints 3D ON THE WEB 12. editor-mcp-server by PlayCanvas: -> your agent builds PlayCanvas scenes and checks they actually run 13. three.js: -> the JavaScript 3D library itself, 115k stars on GitHub 14. react-three-fiber: -> build three.js scenes as reusable React components 15. threejs-skills: -> 10 three.js skills for Claude Code, from shaders to post-processing a full 3D pipeline, and every piece of it lives on github
0:20

is it legal? NVIDIA and Microsoft just open-sourced the pieces of a full 3D studio for Opus 5.5 15 github repos, sorted by what you want to make: GENERATE 3D ASSETS 01. TRELLIS.2 by Microsoft: -> one image in, a fully textured 3D asset out, about 3 seconds on an H100 02. Hunyuan3D-2.1 by Tencent: -> production-ready 3D assets with PBR textures 03. stable-fast-3d by Stability AI: -> one photo to a clean UV-unwrapped mesh, built for speed 04. meshy-mcp-server by Meshy: -> text or image to 3D, then remesh, retexture, rig and animate from your agent BUILD SCENES AND WORLDS 05. mcp-for-blender: -> prompt-driven modeling, scene building and edits in a live Blender 06. HY-World-2.0 by Tencent: -> text or a single image to a navigable 3D world 07. supersplat by PlayCanvas: -> inspect, edit, optimize and publish 3D Gaussian splats in the browser 08. kit-usd-agents by NVIDIA: -> edit Omniverse USD scenes in plain English, plus MCP servers for Kit and Isaac Sim MAKE A GAME 09. unity-mcp: -> run the Unity Editor in plain English: scenes, C# scripts, assets, tests, builds 10. godot-ai: -> 46 tools for a live Godot editor: scenes, scripts, signals, UI, particles 11. unreal-engine-mcp: -> drive a live Unreal Editor: actors, world building, Blueprints 3D ON THE WEB 12. editor-mcp-server by PlayCanvas: -> your agent builds PlayCanvas scenes and checks they actually run 13. three.js: -> the JavaScript 3D library itself, 115k stars on GitHub 14. react-three-fiber: -> build three.js scenes as reusable React components 15. threejs-skills: -> 10 three.js skills for Claude Code, from shaders to post-processing a full 3D pipeline, and every piece of it lives on github

Mnimiy

205,109 views • 11 days ago

Litter iOS just got a beta (TestFlight) release so now you can install it and try it out. It's a remote for codex! - Finds all the computers on your local network (or tailscale) that run codex app-server or ssh - Lets you continue any session or start a new one - Experimental local codex with oauth support Free, fully open-source, fully native, with android release coming soon! Thanks to Maky drumroll.dev ☄️ PincentΞ for the contributions! TestFlight ⬇️
0:17

Litter iOS just got a beta (TestFlight) release so now you can install it and try it out. It's a remote for codex! - Finds all the computers on your local network (or tailscale) that run codex app-server or ssh - Lets you continue any session or start a new one - Experimental local codex with oauth support Free, fully open-source, fully native, with android release coming soon! Thanks to Maky drumroll.dev ☄️ PincentΞ for the contributions! TestFlight ⬇️

SIGKITTEN

68,308 views • 7 months ago

Nike would pay you $3000 for this. Took me some 15 minutes to set up a Claude skill using Seedance 2.0 and Arcads. This is how it works: → I send a product image to Claude → Claude analyzes the product → Writes an ad script → Triggers arcads api with image and prompt → Arcads generates a product ad → Claude updates my Notion database RT + comment "AGENT" and I'll send you the skills .md, full setup, and a guide for free.
0:17

Nike would pay you $3000 for this. Took me some 15 minutes to set up a Claude skill using Seedance 2.0 and Arcads. This is how it works: → I send a product image to Claude → Claude analyzes the product → Writes an ad script → Triggers arcads api with image and prompt → Arcads generates a product ad → Claude updates my Notion database RT + comment "AGENT" and I'll send you the skills .md, full setup, and a guide for free.

Kritarth Mittal | Soshals

12,317 views • 6 months ago

Every app eventually needs an image viewer. Somehow, most of them still feel janky on the web. I’ve been working on a composable Lightbox primitive - Base UI / Radix by WorkOS / shadcn style API, native-feeling transitions, touch gestures, pinch-to-zoom, keyboard support, and all the little details that make it feel real. Is this something you’d reach for?
0:15

Every app eventually needs an image viewer. Somehow, most of them still feel janky on the web. I’ve been working on a composable Lightbox primitive - Base UI / Radix by WorkOS / shadcn style API, native-feeling transitions, touch gestures, pinch-to-zoom, keyboard support, and all the little details that make it feel real. Is this something you’d reach for?

Artur Bień

35,936 views • 3 months ago

ChatGPT Web is now inside Codex 😲 this open-source project has already crossed 2.7k stars instead of using a separate workflow, it lets you use ChatGPT Web models directly from Codex's model picker what you get: - GPT-5.6 Pro for eligible accounts - free Luna access - ChatGPT Web quota - Codex tools + context - images, streaming and reasoning - open-source + MIT licensed getting started: 1. go to 2. install the launcher 3. sign in with your ChatGPT account 4. run the browser checks 5. install the models 6. restart Codex and select ChatGPT Web the interesting part? you can keep using Codex normally while routing the selected model through ChatGPT Web no separate API key for the ChatGPT model 2.7k+ stars and still actively updated worth checking if you already use Codex and want to experiment with ChatGPT Web models
0:17

ChatGPT Web is now inside Codex 😲 this open-source project has already crossed 2.7k stars instead of using a separate workflow, it lets you use ChatGPT Web models directly from Codex's model picker what you get: - GPT-5.6 Pro for eligible accounts - free Luna access - ChatGPT Web quota - Codex tools + context - images, streaming and reasoning - open-source + MIT licensed getting started: 1. go to 2. install the launcher 3. sign in with your ChatGPT account 4. run the browser checks 5. install the models 6. restart Codex and select ChatGPT Web the interesting part? you can keep using Codex normally while routing the selected model through ChatGPT Web no separate API key for the ChatGPT model 2.7k+ stars and still actively updated worth checking if you already use Codex and want to experiment with ChatGPT Web models

K2S

104,873 views • 1 month ago

Alrighty, everything is ready 😎 here’s an unofficial “2x Codex limits” promo from my side for you all. meet DevSpace — an MCP connector app that turns ChatGPT into Codex. npm install -g @waishnav/devspace After installing, tunnel the MCP server over the internet and enjoy 2x limits. You can use GPT-5.5 Pro, xHigh, or High for planning, then hand off the task to your local Codex/pi/opencode/cursor/claude code instance. Or you can just use it for reviewing code written by other local coding agents Go ahead, experiment with different workflows, and keep the feedback coming on GitHub Issues or in my DMs And let’s thank OpenAI for being so generous by giving us separate ChatGPT and Codex limits and by being so chill around this MCP :) Please use it sparingly, only when you run out of limits. Don’t overuse it — in the end, they do have a button to stop it 🙂
0:18

Alrighty, everything is ready 😎 here’s an unofficial “2x Codex limits” promo from my side for you all. meet DevSpace — an MCP connector app that turns ChatGPT into Codex. npm install -g @waishnav/devspace After installing, tunnel the MCP server over the internet and enjoy 2x limits. You can use GPT-5.5 Pro, xHigh, or High for planning, then hand off the task to your local Codex/pi/opencode/cursor/claude code instance. Or you can just use it for reviewing code written by other local coding agents Go ahead, experiment with different workflows, and keep the feedback coming on GitHub Issues or in my DMs And let’s thank OpenAI for being so generous by giving us separate ChatGPT and Codex limits and by being so chill around this MCP :) Please use it sparingly, only when you run out of limits. Don’t overuse it — in the end, they do have a button to stop it 🙂

waishnav

541,801 views • 4 months ago

Qwen just released Qwen-Image-2.1, and this is exactly why open models matter. I loaded it onto my NVIDIA DGX Spark and built Spark Image Lab, a local image generation and editing workspace designed for the DGX Spark ecosystem. QWEN-IMAGE-2.1 • Text-to-image generation • Image editing • Native transparent/RGBA workflows • Up to 10 reference images • Strong identity and product preservation • Improved typography, lighting, textures and detail SPARK IMAGE LAB • Clean Gradio interface • Width and height controls • Steps, seed and batch controls • Persistent generation history • Prompts and settings saved with every result • Reference images saved and restored • Docker setup for DGX Spark • Measured DGX Spark performance benchmarks Everything runs locally. Spark Image Lab is now open source under the MIT License. This is the first public alpha, so clone it, test it on your DGX Spark, open an issue and show me what you create. MODEL REPO
0:11

Qwen just released Qwen-Image-2.1, and this is exactly why open models matter. I loaded it onto my NVIDIA DGX Spark and built Spark Image Lab, a local image generation and editing workspace designed for the DGX Spark ecosystem. QWEN-IMAGE-2.1 • Text-to-image generation • Image editing • Native transparent/RGBA workflows • Up to 10 reference images • Strong identity and product preservation • Improved typography, lighting, textures and detail SPARK IMAGE LAB • Clean Gradio interface • Width and height controls • Steps, seed and batch controls • Persistent generation history • Prompts and settings saved with every result • Reference images saved and restored • Docker setup for DGX Spark • Measured DGX Spark performance benchmarks Everything runs locally. Spark Image Lab is now open source under the MIT License. This is the first public alpha, so clone it, test it on your DGX Spark, open an issue and show me what you create. MODEL REPO

Joey

23,944 views • 1 month ago

OpenClaw, but built for normal people. Sim is an open-source platform that lets you build AI agent workflows on a drag-and-drop canvas. Connect them to channels like Telegram and WhatsApp and deploy without writing a single line of code. They also have a built-in Copilot that generates entire workflows from plain English, which you can then tweak and customize in the UI. Key features: - Free and open-source (Apache 2.0) - Vector store integration for RAG-grounded agents - Self-host with one command (`npx simstudio`) - Run fully local with Ollama, no API keys needed - Supports vLLM for production-grade self-hosted inference The thing I really like about Sim is the level of control you get. You can add conditional branching, parallel execution, human-in-the-loop approval gates, and even nest workflows inside other workflows. Everything is visible on the canvas, so you know exactly what your agent is doing at every step. And you can build a workflow in Sim, deploy it as an MCP server, and plug it into any agent, including OpenClaw. I've shared the link to Sim's GitHub repo in the next tweet.
0:14

OpenClaw, but built for normal people. Sim is an open-source platform that lets you build AI agent workflows on a drag-and-drop canvas. Connect them to channels like Telegram and WhatsApp and deploy without writing a single line of code. They also have a built-in Copilot that generates entire workflows from plain English, which you can then tweak and customize in the UI. Key features: - Free and open-source (Apache 2.0) - Vector store integration for RAG-grounded agents - Self-host with one command (`npx simstudio`) - Run fully local with Ollama, no API keys needed - Supports vLLM for production-grade self-hosted inference The thing I really like about Sim is the level of control you get. You can add conditional branching, parallel execution, human-in-the-loop approval gates, and even nest workflows inside other workflows. Everything is visible on the canvas, so you know exactly what your agent is doing at every step. And you can build a workflow in Sim, deploy it as an MCP server, and plug it into any agent, including OpenClaw. I've shared the link to Sim's GitHub repo in the next tweet.

Akshay 🚀

52,426 views • 7 months ago

Your PC can now run an entire AI studio for free. Locally Uncensored puts chat, image generation, video generation, and a coding agent in one desktop app for Windows and Linux. - Chat with local models and your own documents - Generate images and videos on your GPU - Write code and review changes before applying them - Run agents with tools and MCP connections Install it, pick a model, and start. It shows which models fit your hardware before you download. No Docker. No terminal. No cloud required for the local workflows. 100% Free. Open Source.
0:15

Your PC can now run an entire AI studio for free. Locally Uncensored puts chat, image generation, video generation, and a coding agent in one desktop app for Windows and Linux. - Chat with local models and your own documents - Generate images and videos on your GPU - Write code and review changes before applying them - Run agents with tools and MCP connections Install it, pick a model, and start. It shows which models fit your hardware before you download. No Docker. No terminal. No cloud required for the local workflows. 100% Free. Open Source.

Oliver Prompts

21,751 views • 16 days ago

✨ I can now generate 3d assets for my drone sim at directly from Cursor (sponsor of #vibejam) I need buildings that you'd see in a war torn city, like warehouses in ruins, broken down abandoned houses, bombed out bridges etc. Nano Banana Pro or 2 can generate them really well and then you can put them in an image-to-3d model and you get a GLB or FBX That one you can then import into your Three.js game, the models might be big though, in my case like 16MB, so I ask it to compress it and make it more low poly so it loads fast ThreeJS then loads the individual GLBs on page load and puts them in my drone sim somewhere randomly, I think I should remove some of the grass and match the sandy color of the ruins though to make it fit in more
0:20

✨ I can now generate 3d assets for my drone sim at directly from Cursor (sponsor of #vibejam) I need buildings that you'd see in a war torn city, like warehouses in ruins, broken down abandoned houses, bombed out bridges etc. Nano Banana Pro or 2 can generate them really well and then you can put them in an image-to-3d model and you get a GLB or FBX That one you can then import into your Three.js game, the models might be big though, in my case like 16MB, so I ask it to compress it and make it more low poly so it loads fast ThreeJS then loads the individual GLBs on page load and puts them in my drone sim somewhere randomly, I think I should remove some of the grass and match the sandy color of the ruins though to make it fit in more

@levelsio

134,777 views • 6 months ago

Wonderland: Navigating 3D Scenes from a Single Image Contributions: • First, we introduce a representation for controllable 3D generation by leveraging the generative priors from camera-guided video diffusion models. Unlike image models, video diffusion models are trained on extensive video datasets. This enables them to capture comprehensive spatial relationships within scenes across multiple views and embed a form of "3D awareness" in their latent space, which allows us to maintain 3D consistency in novel view synthesis. • Second, to achieve controllable novel view generation, we empower video models with precise control over specified camera motions. We introduce a novel dual-branch conditioning mechanism that effectively incorporates desired diverse camera trajectories into the video diffusion model. This enables expansion of a single image into a multi-view consistent capture of a 3D scene with precise pose control. • Third, to achieve efficient 3D reconstruction, we directly transform video latents into 3DGS. We propose a novel latent-based large reconstruction model (LaLRM) that lifts video latents to 3D in a feed-forward manner. With this design, during inference, our model directly predicts 3DGS from a single input image, effectively aligning the generation and reconstruction tasks—and bridging image space and 3D space—through the video latent space. Compared with reconstructing scenes from images, the video latent space offers a 256× spatial-temporal reduction while retaining essential and consistent 3D structural details. Such a high degree of compression is crucial, as it allows the LaLRM to handle a wider range of 3D scenes within the reconstruction framework, with the same memory constraints.
0:12

Wonderland: Navigating 3D Scenes from a Single Image Contributions: • First, we introduce a representation for controllable 3D generation by leveraging the generative priors from camera-guided video diffusion models. Unlike image models, video diffusion models are trained on extensive video datasets. This enables them to capture comprehensive spatial relationships within scenes across multiple views and embed a form of "3D awareness" in their latent space, which allows us to maintain 3D consistency in novel view synthesis. • Second, to achieve controllable novel view generation, we empower video models with precise control over specified camera motions. We introduce a novel dual-branch conditioning mechanism that effectively incorporates desired diverse camera trajectories into the video diffusion model. This enables expansion of a single image into a multi-view consistent capture of a 3D scene with precise pose control. • Third, to achieve efficient 3D reconstruction, we directly transform video latents into 3DGS. We propose a novel latent-based large reconstruction model (LaLRM) that lifts video latents to 3D in a feed-forward manner. With this design, during inference, our model directly predicts 3DGS from a single input image, effectively aligning the generation and reconstruction tasks—and bridging image space and 3D space—through the video latent space. Compared with reconstructing scenes from images, the video latent space offers a 256× spatial-temporal reduction while retaining essential and consistent 3D structural details. Such a high degree of compression is crucial, as it allows the LaLRM to handle a wider range of 3D scenes within the reconstruction framework, with the same memory constraints.

MrNeRF

52,849 views • 1 year ago

Everyone's sleeping on image-to-3D AI models. They can make your app look incredibly unique, with just a little effort. Here's how. This is my calorie tracker, built in a week with nothing but prompting. Just Claude Code + a couple APIs. The visuals are all AI-generated. I'll be sharing the full workflow + all the crazy technical stuff Claude and I did to make this work, so nobody has to struggle through it like me. Deep dive coming soon! Till then, this is the high-level idea: 1. Get a clean image of the food (or whatever your asset is) - In my app, the user describes foods via text, or attaches images (or both) - If text, an LLM extracts the food description and formats it into a specific prompt I tuned for this design, and we generate an image using Z-Image Turbo through fal - If image, we do the same thing but with FLUX.2 [dev] to edit the user image into our reference design - Originally, both used Google Nano Banana, but switching to open models cut costs and latency a ton 2. Gaussian splatting (2D image → 3D model) - I tried various 2D-to-3D options on fal and ended up with TripoSplat as my preferred balance of speed, cost, latency; this turns an image into a 3D model that looks super high quality (link below) - The app displays the 2D image while our backend generates the 3D splat - We "groom" the splat to reduce size and load time by culling low-opacity/scale points 3. Render efficiently on device Originally, it looked great but ran at 10 FPS. Getting to 120 FPS was a crazy journey. TL;DR: - SwiftUI had to go; it forced us to render each asset in independent MTKViews, which wasn't workable - Instead, we composite every dish into one full-bleed CAMetalLayer using MetalSplatter (link below) - We had to make some optimizations within MetalSplatter's code too, to reduce the overhead of sorting points per render Then I added some finishing touches like the subtle rotation and parallax as they move around. I think it turned out pretty cool :) Overall, this took some effort, but we still got it done in less than a day. Hopefully your agent can follow in the footsteps of mine and do it much faster. Keep an eye out for the bigger writeup, which'll give your agent everything it needs. If you have any questions, drop em below!
0:14

Everyone's sleeping on image-to-3D AI models. They can make your app look incredibly unique, with just a little effort. Here's how. This is my calorie tracker, built in a week with nothing but prompting. Just Claude Code + a couple APIs. The visuals are all AI-generated. I'll be sharing the full workflow + all the crazy technical stuff Claude and I did to make this work, so nobody has to struggle through it like me. Deep dive coming soon! Till then, this is the high-level idea: 1. Get a clean image of the food (or whatever your asset is) - In my app, the user describes foods via text, or attaches images (or both) - If text, an LLM extracts the food description and formats it into a specific prompt I tuned for this design, and we generate an image using Z-Image Turbo through fal - If image, we do the same thing but with FLUX.2 [dev] to edit the user image into our reference design - Originally, both used Google Nano Banana, but switching to open models cut costs and latency a ton 2. Gaussian splatting (2D image → 3D model) - I tried various 2D-to-3D options on fal and ended up with TripoSplat as my preferred balance of speed, cost, latency; this turns an image into a 3D model that looks super high quality (link below) - The app displays the 2D image while our backend generates the 3D splat - We "groom" the splat to reduce size and load time by culling low-opacity/scale points 3. Render efficiently on device Originally, it looked great but ran at 10 FPS. Getting to 120 FPS was a crazy journey. TL;DR: - SwiftUI had to go; it forced us to render each asset in independent MTKViews, which wasn't workable - Instead, we composite every dish into one full-bleed CAMetalLayer using MetalSplatter (link below) - We had to make some optimizations within MetalSplatter's code too, to reduce the overhead of sorting points per render Then I added some finishing touches like the subtle rotation and parallax as they move around. I think it turned out pretty cool :) Overall, this took some effort, but we still got it done in less than a day. Hopefully your agent can follow in the footsteps of mine and do it much faster. Keep an eye out for the bigger writeup, which'll give your agent everything it needs. If you have any questions, drop em below!

Anshu

29,342 views • 4 months ago

Someone used Claude Code with an open-source Blender MCP addon to create most of the 3D assets for a fishing game. → A single Claude Code session controlled both Blender and Unity through the Model Context Protocol → The developer's case study describes a 40-part, 42-bone crab model created in 53 minutes → Claude rendered images from multiple angles so the developer could review and give feedback → The MCP for Blender addon is MIT-licensed and also works with Codex, Cursor and other AI tools Repo:
0:15

Someone used Claude Code with an open-source Blender MCP addon to create most of the 3D assets for a fishing game. → A single Claude Code session controlled both Blender and Unity through the Model Context Protocol → The developer's case study describes a 40-part, 42-bone crab model created in 53 minutes → Claude rendered images from multiple angles so the developer could review and give feedback → The MCP for Blender addon is MIT-licensed and also works with Codex, Cursor and other AI tools Repo:

0xMarioNawfal - The Roundtable Show

54,879 views • 5 days ago