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Say hello to text-to-cad, an open source tool for generating 3D models in Codex! Prompt and edit complex 3D models. Export STEP, STL, GLB, DXF and URDF files. Built for CAD newbies. Link to github below. CAD is hard. As a software engineer getting back into robotics, I’ve been...

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Jake Fitzgerald

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52,094 görüntüleme • 1 ay önce •via X (Twitter)

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To generate 3D models in Codex with text-to-cad: Start a new project, write your prompt for a 3D model, and include a link to clone the text-to-cad repo. Codex handles the rest. The repo is open source and all scripts / ui run locally. Link below.
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To generate 3D models in Codex with text-to-cad: Start a new project, write your prompt for a 3D model, and include a link to clone the text-to-cad repo. Codex handles the rest. The repo is open source and all scripts / ui run locally. Link below.

Jake Fitzgerald

22,113 görüntüleme • 1 ay önce

You can build your 3D models directly from a prompt into Claude code. All thanks to this beautiful python project, build123 a parametric modeling framework for 2D and 3D CAD. It is built on the Open Cascade geometric kernel and it is suitable for 3D printing.
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You can build your 3D models directly from a prompt into Claude code. All thanks to this beautiful python project, build123 a parametric modeling framework for 2D and 3D CAD. It is built on the Open Cascade geometric kernel and it is suitable for 3D printing.

Marco Franzon

20,743 görüntüleme • 2 ay önce

CAD AI is getting crazier.. this new text to CAD AI lets you generate 3D models with precise control of the dimensions and.. you can even do it on your phone. 3D printing is so much easier now.
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CAD AI is getting crazier.. this new text to CAD AI lets you generate 3D models with precise control of the dimensions and.. you can even do it on your phone. 3D printing is so much easier now.

el.cine

173,918 görüntüleme • 1 yıl önce

Over the weekend, I added multiplayer "paint ball" dogfight to Tiny Skies, so players can fly around pelting each other with colourful paint balls, just for fun. I tried meshy and tripo to generate some 3D models for the game and the mesh vertices count are really high and lags the game a lot (probably because my game is a globe with thousands of objects - albeit mostly instanced). To my surprise, I found Gemini 3.1 Pro to be really quite good at generating good looking 3D models that are low in poly count. It generated 3D models at a much higher quality than Opus 4.6, Sonnet 4.6 and GPT 5.4. You can see the upgraded biplane models and some objects in the video below. #vibejam
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Over the weekend, I added multiplayer "paint ball" dogfight to Tiny Skies, so players can fly around pelting each other with colourful paint balls, just for fun. I tried meshy and tripo to generate some 3D models for the game and the mesh vertices count are really high and lags the game a lot (probably because my game is a globe with thousands of objects - albeit mostly instanced). To my surprise, I found Gemini 3.1 Pro to be really quite good at generating good looking 3D models that are low in poly count. It generated 3D models at a much higher quality than Opus 4.6, Sonnet 4.6 and GPT 5.4. You can see the upgraded biplane models and some objects in the video below. #vibejam

Danny Limanseta

35,955 görüntüleme • 2 ay önce

weekend hack: implicit.js an open source library that lets agents generate CAD with code + mathematical functions representing surfaces no CAD kernels, no STEP files just agents using math + code to directly generate 3D models details below!
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weekend hack: implicit.js an open source library that lets agents generate CAD with code + mathematical functions representing surfaces no CAD kernels, no STEP files just agents using math + code to directly generate 3D models details below!

Jake Fitzgerald

34,472 görüntüleme • 18 gün önce

New open-source 3D world-generation model. I'm rendering a couple of worlds in the video, so check it out. You'll find the GitHub and the Hugging Face links to the model below. This is a multi-modal world model that you can use for a bunch of things: • To generate new worlds • To reconstruct worlds • To simulate 3D interactive worlds from a prompt, images, or a video You can edit the 3D outputs in Unity and Unreal Engine (they export as meshes, 3DGS files, and point clouds). You can also generate 3D characters in the world and walk around. Pretty fun stuff!
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New open-source 3D world-generation model. I'm rendering a couple of worlds in the video, so check it out. You'll find the GitHub and the Hugging Face links to the model below. This is a multi-modal world model that you can use for a bunch of things: • To generate new worlds • To reconstruct worlds • To simulate 3D interactive worlds from a prompt, images, or a video You can edit the 3D outputs in Unity and Unreal Engine (they export as meshes, 3DGS files, and point clouds). You can also generate 3D characters in the world and walk around. Pretty fun stuff!

Santiago

65,411 görüntüleme • 2 ay önce

This is Text-to-CAD. A new open-source prompt interface for generating CAD files through text prompts. It looks insane 🤯
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This is Text-to-CAD. A new open-source prompt interface for generating CAD files through text prompts. It looks insane 🤯

Alvaro Cintas

111,304 görüntüleme • 1 yıl önce

Here's a step-by-step video tutorial of how I vibe coded my point & click adventure game! → 1. Nano Banana Pro → 2. Meshy → 3. Codex 5.3 → 4. Capacitor This part 1 covers: → Generate 3D models → ThreeJS integration → Building for iOS Agent skills + prompts in reply! --- 00:00 Intro 01:40 What We'll Be Building 02:08 Step 1: Nano Banana Pro for Character Art 04:36 Step 2: Meshy for Image to 3D 09:51 Step 3: GLB Export 12:14 Step 4: ThreeJS Skill for GLB Index 15:56 Step 5: ThreeJS Skill for Scene 18:07 Step 6: CapacitorJS Skill for Mobile 19:41 Step 7: Build for iOS 21:06 Wrap Up
22:05

Here's a step-by-step video tutorial of how I vibe coded my point & click adventure game! → 1. Nano Banana Pro → 2. Meshy → 3. Codex 5.3 → 4. Capacitor This part 1 covers: → Generate 3D models → ThreeJS integration → Building for iOS Agent skills + prompts in reply! --- 00:00 Intro 01:40 What We'll Be Building 02:08 Step 1: Nano Banana Pro for Character Art 04:36 Step 2: Meshy for Image to 3D 09:51 Step 3: GLB Export 12:14 Step 4: ThreeJS Skill for GLB Index 15:56 Step 5: ThreeJS Skill for Scene 18:07 Step 6: CapacitorJS Skill for Mobile 19:41 Step 7: Build for iOS 21:06 Wrap Up

Chong-U

105,101 görüntüleme • 4 ay önce

The first AI design agent just dropped. It combines major models for image, video, and 3D asset generation all in one place. You can also edit the images, generate music, and voiceovers! Step-by-step tutorial 👇
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The first AI design agent just dropped. It combines major models for image, video, and 3D asset generation all in one place. You can also edit the images, generate music, and voiceovers! Step-by-step tutorial 👇

Alvaro Cintas

120,917 görüntüleme • 1 yıl önce

Introducing CADGenBench: measure how well AI systems produce engineering-grade 3D parts! While current models can generate 3D parts, they are far from precise enough to build functional parts. We built a benchmark to systematically measure their capabilities on two tasks: 1. Generation from an engineering drawing of a part 2. Editing: given an existing STEP file and a requested change The benchmark is tool-agnostic. It makes no assumptions about how you build the model. You can vary the LLM, and you can vary the environment. Use build123d, Onshape, Autodesk, or a model without an LLM entirely. We open sourced the scoring engine and a reference baseline on top of build123d. A collaboration between Hugging Face and Mecado! Submission space: Code repository:
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Introducing CADGenBench: measure how well AI systems produce engineering-grade 3D parts! While current models can generate 3D parts, they are far from precise enough to build functional parts. We built a benchmark to systematically measure their capabilities on two tasks: 1. Generation from an engineering drawing of a part 2. Editing: given an existing STEP file and a requested change The benchmark is tool-agnostic. It makes no assumptions about how you build the model. You can vary the LLM, and you can vary the environment. Use build123d, Onshape, Autodesk, or a model without an LLM entirely. We open sourced the scoring engine and a reference baseline on top of build123d. A collaboration between Hugging Face and Mecado! Submission space: Code repository:

Michael Rabinovich

61,185 görüntüleme • 11 gün önce

GPT Image 2 + Codex: or how to make Codex not suck at UI. Step 1: Generate a UI image (native in Codex) Step 2: Get Codex to implement the UI based on it Step 3: Get Codex to iterate until it aligns with the image as much as possible Codex is bad at initial UI, but very good at implementing a reference design, so this is your way out - iterate with the image model first and then Codex will do a good job.
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GPT Image 2 + Codex: or how to make Codex not suck at UI. Step 1: Generate a UI image (native in Codex) Step 2: Get Codex to implement the UI based on it Step 3: Get Codex to iterate until it aligns with the image as much as possible Codex is bad at initial UI, but very good at implementing a reference design, so this is your way out - iterate with the image model first and then Codex will do a good job.

Peter Gostev

66,675 görüntüleme • 1 ay önce

Can frontier language models turn a technical drawing into real CAD? We ran the experiment. Gemini 3.1 Pro, Claude Opus 4.7, and GPT-5.4 got a set of technical drawings and were asked to reproduce a part as a valid 3D CAD model. Still a far way to go, but clearly a sign of life.
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Can frontier language models turn a technical drawing into real CAD? We ran the experiment. Gemini 3.1 Pro, Claude Opus 4.7, and GPT-5.4 got a set of technical drawings and were asked to reproduce a part as a valid 3D CAD model. Still a far way to go, but clearly a sign of life.

Michael Rabinovich

171,709 görüntüleme • 1 ay önce

Before Claude Fable 5 got banned, I turned all my fine-tuning research and experiments into a product: (A CLI for data generation to fine-tune AI models). Finetuner uses Codex 5.5 or Opus 4.8 as the orchestrator and Chinese models (DeepSeek v4 Pro, Kimi K2.7, MiMo v2.5, etc.) to generate dataset rows. This system is unique because these are not Python-script-paraphrased datasets. Each and every row is handcrafted by Chinese models based on the orchestrator model's instructions. Now anyone can generate datasets to fine-tune small language models (1B to 30B models). I achieved 10x lower costs and 5x better dataset quality. Releasing the product in a few days. (I'll open-source the skills.)
1:58

Before Claude Fable 5 got banned, I turned all my fine-tuning research and experiments into a product: (A CLI for data generation to fine-tune AI models). Finetuner uses Codex 5.5 or Opus 4.8 as the orchestrator and Chinese models (DeepSeek v4 Pro, Kimi K2.7, MiMo v2.5, etc.) to generate dataset rows. This system is unique because these are not Python-script-paraphrased datasets. Each and every row is handcrafted by Chinese models based on the orchestrator model's instructions. Now anyone can generate datasets to fine-tune small language models (1B to 30B models). I achieved 10x lower costs and 5x better dataset quality. Releasing the product in a few days. (I'll open-source the skills.)

CJ Zafir

37,175 görüntüleme • 4 gün önce

In Prompt Engineering for Vision Models, taught by Abby Jacques Verre and Caleb Kaiser of Comet , you’ll learn how to prompt and fine-tune vision models for personalized image generation, image editing, object detection and segmentation. The prompts you'll use for vision models could be text, point coordinates, or bounding boxes, depending on the model. You'll also learn to tune hyperparameters to shape the output. Models you'll use include Segment-Anything Model (SAM), OWL-ViT, and Stable Diffusion. You'll also learn to fine-tune Stable Diffusion to generate personalized images (say, an image of a specific person), using a handful of images for training. As an example of a multi-step workflow, you'll use OWL-ViT to detect an object based on a text prompt, then pass the bounding box to SAM to create a segmentation mask, and input that mask into Stable Diffusion to replace the original object with a new one based on a text prompt. Controlling vision models can be tricky; this course will teach prompting and fine-tuning techniques to get precise control over their output. Get started here:
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In Prompt Engineering for Vision Models, taught by Abby Jacques Verre and Caleb Kaiser of Comet , you’ll learn how to prompt and fine-tune vision models for personalized image generation, image editing, object detection and segmentation. The prompts you'll use for vision models could be text, point coordinates, or bounding boxes, depending on the model. You'll also learn to tune hyperparameters to shape the output. Models you'll use include Segment-Anything Model (SAM), OWL-ViT, and Stable Diffusion. You'll also learn to fine-tune Stable Diffusion to generate personalized images (say, an image of a specific person), using a handful of images for training. As an example of a multi-step workflow, you'll use OWL-ViT to detect an object based on a text prompt, then pass the bounding box to SAM to create a segmentation mask, and input that mask into Stable Diffusion to replace the original object with a new one based on a text prompt. Controlling vision models can be tricky; this course will teach prompting and fine-tuning techniques to get precise control over their output. Get started here:

Andrew Ng

151,198 görüntüleme • 2 yıl önce

The first AI design agent with Nano Banana has dropped. It combines major models for image, video, and 3D asset generation all in one place. You can also edit the images, generate music, and even voiceovers! Step-by-step tutorial 👇
0:37

The first AI design agent with Nano Banana has dropped. It combines major models for image, video, and 3D asset generation all in one place. You can also edit the images, generate music, and even voiceovers! Step-by-step tutorial 👇

Alvaro Cintas

158,854 görüntüleme • 9 ay önce

Adobe.. has released a tool that combines Generative Gaussian Splats with a Diffusion layer and it's not all over the internet? WHAT IS GOING ON :D I had to test this out ofc! Substance 3D viewer, the new 3d viewer just released by Adobe, not only supports viewing of a large, large list of 3d formats, but it also supports generating of 3d models as Gaussian Splats. Combine that with the built-in 3d to image functionality and you basically have at least a few of the steps I've been doing for the last year or so using multiple tools. Exciting stuff! The new Photoshop Beta also supports the 3D viewer and you can import 3d files directly in photoshop as smart objects linked to the viewer. The elements you prompt in based on your models, can be easily exported to your clipboard without backgrounds making it very easy to use this in your normal image editing workflows. I have a suspicion that we'll see much more of this in other Adobe tools and I'm very curious to see this used for things like this in more 3d tools than "just" Adobe Neo (which is fantastic btw). #adobe #art #gaussiansplatting
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Adobe.. has released a tool that combines Generative Gaussian Splats with a Diffusion layer and it's not all over the internet? WHAT IS GOING ON :D I had to test this out ofc! Substance 3D viewer, the new 3d viewer just released by Adobe, not only supports viewing of a large, large list of 3d formats, but it also supports generating of 3d models as Gaussian Splats. Combine that with the built-in 3d to image functionality and you basically have at least a few of the steps I've been doing for the last year or so using multiple tools. Exciting stuff! The new Photoshop Beta also supports the 3D viewer and you can import 3d files directly in photoshop as smart objects linked to the viewer. The elements you prompt in based on your models, can be easily exported to your clipboard without backgrounds making it very easy to use this in your normal image editing workflows. I have a suspicion that we'll see much more of this in other Adobe tools and I'm very curious to see this used for things like this in more 3d tools than "just" Adobe Neo (which is fantastic btw). #adobe #art #gaussiansplatting

Martin Nebelong

279,947 görüntüleme • 1 yıl önce

So this game is real now, fully built with GPT-5.5. I told Codex to use the imagegen skill to generate a reference UI and sprites, and used the Build macOS Apps plugin to build the app for a native retro fantasy labyrinth game! Adjusted the UI in a few prompts and here we are!
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So this game is real now, fully built with GPT-5.5. I told Codex to use the imagegen skill to generate a reference UI and sprites, and used the Build macOS Apps plugin to build the app for a native retro fantasy labyrinth game! Adjusted the UI in a few prompts and here we are!

Thomas Ricouard

144,154 görüntüleme • 1 ay önce

Tutorial: how to make equirectangular panorama with GPT Image 2 + Codex Step 1: generate an image with GPT Image 2 prompt: make equirectangular panorama of [PLACE] Step 2: feed your panorama to Codex as a reference and prompt: make mouse-controlled 3D view I'd love to see your experiments!
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Tutorial: how to make equirectangular panorama with GPT Image 2 + Codex Step 1: generate an image with GPT Image 2 prompt: make equirectangular panorama of [PLACE] Step 2: feed your panorama to Codex as a reference and prompt: make mouse-controlled 3D view I'd love to see your experiments!

Kris Kashtanova

70,989 görüntüleme • 1 ay önce

Here is a step-by-step introduction to building a workflow with a custom AI agent that uses MCP. I explain every component in the video: 1. Building the MCP server 2. Building the agent and an MCP client 3. Building a workflow that uses the agent The goal is simple: Generate a dialogue between two people and make one yell and the other answer with sarcasm. Kestra is sponsoring this video. They are an open-source orchestration platform (repo link below), and I used them to build the workflow that connects every component.
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Here is a step-by-step introduction to building a workflow with a custom AI agent that uses MCP. I explain every component in the video: 1. Building the MCP server 2. Building the agent and an MCP client 3. Building a workflow that uses the agent The goal is simple: Generate a dialogue between two people and make one yell and the other answer with sarcasm. Kestra is sponsoring this video. They are an open-source orchestration platform (repo link below), and I used them to build the workflow that connects every component.

Santiago

51,527 görüntüleme • 1 yıl önce