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I vibe coded a ThreeJS world editor with GPT 5.4 in 48 hours from scratch Open Source, MIT Features: - Integrated AI texture generation - AI model generator - Vertex/Edge/Face Editing - Physics and a Player Controller to test the world - Export (glb) - Pretty much everything essential... show more

robot

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54,504 Aufrufe • vor 4 Monaten •via X (Twitter)

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fine, i'll build it myself, open source ofc Launching Three-Maps, a rapid blockout editor for building game maps for threejs games: Create a floor plan in 2D, automatically generate 3D levels from it, edit them, export to .glb and use in your games (see end of video) Features: - Floorplan 2D: A 2D canvas to map out the floor - Edit Mode for Objects: Edit Vertices/Edges/Faces, extrude, bevel, fillet and chamfer - Brush Mode: Rapidly build map elements with a RealtimeCSG like experience. Cubes, Stairs, Doors, Arch, Cylinder, Custom polygons. Yes, you can even curve the stairs - Modifiers: Yes, you read that right, slap that array modifier on your pillar and call it a day - Terrain Generator: Perlin, Voronoi, you name it. Oh yes, you can rasterize it and edit every single vertex in edit mode. Keyboard shortcuts are mostly same as blender to activate that muscle memory (G/R/S, axis lock XYZ) Soon: - AI integration. Boot that MCP and directly connect your fav AI agent to build the floorplan, generate 3D maps and more (you will love how this movie ends) It's a little rough and in early, very early alpha so come and join as a contributor instead of calling it slop It's all vibe coded btw, i have no idea what the code does I need this to build my game, as i'm a total blender noob and threejs is great, so why not? Outshipping every CAD and 3D startup just because i want to build a game, that's how you accelerate

fine, i'll build it myself, open source ofc Launching Three-Maps, a rapid blockout editor for building game maps for threejs games: Create a floor plan in 2D, automatically generate 3D levels from it, edit them, export to .glb and use in your games (see end of video) Features: - Floorplan 2D: A 2D canvas to map out the floor - Edit Mode for Objects: Edit Vertices/Edges/Faces, extrude, bevel, fillet and chamfer - Brush Mode: Rapidly build map elements with a RealtimeCSG like experience. Cubes, Stairs, Doors, Arch, Cylinder, Custom polygons. Yes, you can even curve the stairs - Modifiers: Yes, you read that right, slap that array modifier on your pillar and call it a day - Terrain Generator: Perlin, Voronoi, you name it. Oh yes, you can rasterize it and edit every single vertex in edit mode. Keyboard shortcuts are mostly same as blender to activate that muscle memory (G/R/S, axis lock XYZ) Soon: - AI integration. Boot that MCP and directly connect your fav AI agent to build the floorplan, generate 3D maps and more (you will love how this movie ends) It's a little rough and in early, very early alpha so come and join as a contributor instead of calling it slop It's all vibe coded btw, i have no idea what the code does I need this to build my game, as i'm a total blender noob and threejs is great, so why not? Outshipping every CAD and 3D startup just because i want to build a game, that's how you accelerate

robot 2.0

106,304 Aufrufe • vor 4 Monaten

this is vibe coded threejs in the browser btw the level editor is good enough to actually build some maps (with prefab and export support, so i don't have to model from scratch all the time). Also finally found some dedication to implement some ledge detection + climb for some cinematic jumps in certain maps! The first hour of playtime will be on a large space station, where you will have to escape from a secretive organization which has kept you in a cryo capsule for 555 years! What you're seeing in the video is a very rough draft (greybox) of the first map, where you'll have to escape the medical unit of the station (very rough) Yes i know it's slop, it's soo much AI slop but hey, it's my AAA slop okay?

this is vibe coded threejs in the browser btw the level editor is good enough to actually build some maps (with prefab and export support, so i don't have to model from scratch all the time). Also finally found some dedication to implement some ledge detection + climb for some cinematic jumps in certain maps! The first hour of playtime will be on a large space station, where you will have to escape from a secretive organization which has kept you in a cryo capsule for 555 years! What you're seeing in the video is a very rough draft (greybox) of the first map, where you'll have to escape the medical unit of the station (very rough) Yes i know it's slop, it's soo much AI slop but hey, it's my AAA slop okay?

robot

12,197 Aufrufe • vor 5 Monaten

Introducing GGEZ: The Nextjs for ThreeJS Games It's an open source framework which adds all the missing pieces to vibe code better ThreeJS games It has full codex integration, so a $20 ChatGPT sub is enough to build games! Literally "bun run start" and you have the full development environment on localhost 0. GGEZ Runtime - Abstraction layer over physics libraries - Character Controllers - It's just ThreeJS, no magic - Load ggez scenes and animations automatically 1. Trident - a World Editor - Codex World agent - A full editor to build scenes - Including Mesh editing, vertex, edge, face - Terrain sculpting - Physics and Player Controller settings - It's just exporting json files and glb assets, no magic 2. Animation Studio - The best you can find on the web - Codex Animation agent - Build state machines and animation graphs - Multi dimensional blend trees - Clip Editor: Create new animations with codex or edit keyframes - Equipment Editor: Never miss the placement of your rifle anymore - ROOT MOTION SUPPORT 3. GGEZ CLI Yea relax, it works fully headless and you can just create new games with bunx create-ggez new-game But at this point just use vanilla threejs?? Anyways if you are like me and you can't guess with code where objects should be placed and you are fifty prompts deep into figuring out where that box should be placed, this is for you If you are an anti AI game developer who insists that this is slop, then just leave a raging comment below please it's good for the algo 🙏 The whole thing is absolutely experimental and things will break as i move very fast, but I will be building my game with it so i will make sure it becomes stable asap! Link to repo below

Introducing GGEZ: The Nextjs for ThreeJS Games It's an open source framework which adds all the missing pieces to vibe code better ThreeJS games It has full codex integration, so a $20 ChatGPT sub is enough to build games! Literally "bun run start" and you have the full development environment on localhost 0. GGEZ Runtime - Abstraction layer over physics libraries - Character Controllers - It's just ThreeJS, no magic - Load ggez scenes and animations automatically 1. Trident - a World Editor - Codex World agent - A full editor to build scenes - Including Mesh editing, vertex, edge, face - Terrain sculpting - Physics and Player Controller settings - It's just exporting json files and glb assets, no magic 2. Animation Studio - The best you can find on the web - Codex Animation agent - Build state machines and animation graphs - Multi dimensional blend trees - Clip Editor: Create new animations with codex or edit keyframes - Equipment Editor: Never miss the placement of your rifle anymore - ROOT MOTION SUPPORT 3. GGEZ CLI Yea relax, it works fully headless and you can just create new games with bunx create-ggez new-game But at this point just use vanilla threejs?? Anyways if you are like me and you can't guess with code where objects should be placed and you are fifty prompts deep into figuring out where that box should be placed, this is for you If you are an anti AI game developer who insists that this is slop, then just leave a raging comment below please it's good for the algo 🙏 The whole thing is absolutely experimental and things will break as i move very fast, but I will be building my game with it so i will make sure it becomes stable asap! Link to repo below

robot

44,009 Aufrufe • vor 3 Monaten

America has the edge in the world’s most powerful AI models, no question. But China is playing a different game: efficiency, scale, and global deployment. Kyle Chan from Brookings China explains: "Does winning mean developing the world's most powerful AI models? If yes, then the us has the decisive lead. American ai models are widely recognized as the best in the world on virtually every metric you can think of, from math and reasoning to coding and AI agents, American private companies continue to set the pace at the technological frontier supported by deep capital markets, a dynamic innovation ecosystem, and the best talent from around the world. China's AI models are improving quickly but continue to lag behind. Chinese AI companies face real constraints on compute due to our export controls and their own limited capital resources, so instead they're prioritizing other goals. They're focused on efficiency, building models that are cheaper to train and run. They're focused on adoption using an open source strategy to win users around the world, and they're focused on integration with other platforms and services. On sheer performance, I would expect the gap between our models and theirs to persist or even widen over time. But model performance is not everything."

America has the edge in the world’s most powerful AI models, no question. But China is playing a different game: efficiency, scale, and global deployment. Kyle Chan from Brookings China explains: "Does winning mean developing the world's most powerful AI models? If yes, then the us has the decisive lead. American ai models are widely recognized as the best in the world on virtually every metric you can think of, from math and reasoning to coding and AI agents, American private companies continue to set the pace at the technological frontier supported by deep capital markets, a dynamic innovation ecosystem, and the best talent from around the world. China's AI models are improving quickly but continue to lag behind. Chinese AI companies face real constraints on compute due to our export controls and their own limited capital resources, so instead they're prioritizing other goals. They're focused on efficiency, building models that are cheaper to train and run. They're focused on adoption using an open source strategy to win users around the world, and they're focused on integration with other platforms and services. On sheer performance, I would expect the gap between our models and theirs to persist or even widen over time. But model performance is not everything."

Select Committee on China

60,135 Aufrufe • vor 2 Monaten

Introducing WorldForge: testable world-model workflows for physical AI systems. You can think of it, loosely, as “LangChain for world models”. The problem is that “world model” has become an overloaded label. Depending on context, it can mean a video generator, a cost model, a robot policy, a JEPA-style latent predictor, etc. They share almost nothing, different inputs, runtimes, failure modes. I built WorldForge to stop pretending they're interchangeable. Front-door demo: a real Hugging Face LeRobot (LeRobot) diffusion_pusht policy combined with LeWorldModel by Lucas Maes checkpoint for scoring. Both run locally on my MacBook in the demo video. LeWM is extremely efficient (~15M params), can plan up to 48× faster, and runs on commodity hardware. WorldForge wires the loop: policy → candidates → score → select Replay happens in a local TUI today, but the same loop could drive a real robot in the world. Would love feedback from people working on world models, physical AI, robotics, ML infra, and adjacent tooling. Fully open source. Contributions very welcome. Plan in the dream, replay in real world.

Introducing WorldForge: testable world-model workflows for physical AI systems. You can think of it, loosely, as “LangChain for world models”. The problem is that “world model” has become an overloaded label. Depending on context, it can mean a video generator, a cost model, a robot policy, a JEPA-style latent predictor, etc. They share almost nothing, different inputs, runtimes, failure modes. I built WorldForge to stop pretending they're interchangeable. Front-door demo: a real Hugging Face LeRobot (LeRobot) diffusion_pusht policy combined with LeWorldModel by Lucas Maes checkpoint for scoring. Both run locally on my MacBook in the demo video. LeWM is extremely efficient (~15M params), can plan up to 48× faster, and runs on commodity hardware. WorldForge wires the loop: policy → candidates → score → select Replay happens in a local TUI today, but the same loop could drive a real robot in the world. Would love feedback from people working on world models, physical AI, robotics, ML infra, and adjacent tooling. Fully open source. Contributions very welcome. Plan in the dream, replay in real world.

abdel

16,916 Aufrufe • vor 2 Monaten

Wow! AI ASSISTED GARAGE MANUFACTURING IS ABOUT TO EXPLODE! CAD Drawings From Just A Picture! MIT just released something profound for creators and engineers alike. Picture this. You take a photo of an object, upload it, and an AI delivers a fully parametric CAD model, complete with editable code and construction history. This is open source GenCAD, from MIT's Decode Lab. It uses autoregressive transformers and diffusion models, trained on hundreds of thousands of images and CAD files. Input a 2D photo or sketch. Output valid CadQuery Python code that beats models like GPT-4.5 in accuracy. Why does this matter? It speeds up reverse engineering, prototyping, and part searches in vast databases. No more hours spent modeling from scratch. Field repairs, custom designs, education, all transformed. It even retrieves similar parts from libraries of thousands. For industries like manufacturing and aerospace, it cuts costs and boosts innovation. Hobbyists gain pro tools without the steep curve. I am testing it now on random objects and can not believe how much of a super power this is. I can start dozens of companies just on this AI model. This open-source gem is here: The future of building stuff arrives in a snapshot.

Wow! AI ASSISTED GARAGE MANUFACTURING IS ABOUT TO EXPLODE! CAD Drawings From Just A Picture! MIT just released something profound for creators and engineers alike. Picture this. You take a photo of an object, upload it, and an AI delivers a fully parametric CAD model, complete with editable code and construction history. This is open source GenCAD, from MIT's Decode Lab. It uses autoregressive transformers and diffusion models, trained on hundreds of thousands of images and CAD files. Input a 2D photo or sketch. Output valid CadQuery Python code that beats models like GPT-4.5 in accuracy. Why does this matter? It speeds up reverse engineering, prototyping, and part searches in vast databases. No more hours spent modeling from scratch. Field repairs, custom designs, education, all transformed. It even retrieves similar parts from libraries of thousands. For industries like manufacturing and aerospace, it cuts costs and boosts innovation. Hobbyists gain pro tools without the steep curve. I am testing it now on random objects and can not believe how much of a super power this is. I can start dozens of companies just on this AI model. This open-source gem is here: The future of building stuff arrives in a snapshot.

Brian Roemmele

121,948 Aufrufe • vor 4 Monaten

Everything you love about generative models — now powered by real physics! Announcing the Genesis project — after a 24-month large-scale research collaboration involving over 20 research labs — a generative physics engine able to generate 4D dynamical worlds powered by a physics simulation platform designed for general-purpose robotics and physical AI applications. Genesis's physics engine is developed in pure Python, while being 10-80x faster than existing GPU-accelerated stacks like Isaac Gym and MJX. It delivers a simulation speed ~430,000 faster than in real-time, and takes only 26 seconds to train a robotic locomotion policy transferrable to the real world on a single RTX4090 (see tutorial: The Genesis physics engine and simulation platform is fully open source at We'll gradually roll out access to our generative framework in the near future. Genesis implements a unified simulation framework all from scratch, integrating a wide spectrum of state-of-the-art physics solvers, allowing simulation of the whole physical world in a virtual realm with the highest realism. We aim to build a universal data engine that leverages an upper-level generative framework to autonomously create physical worlds, together with various modes of data, including environments, camera motions, robotic task proposals, reward functions, robot policies, character motions, fully interactive 3D scenes, open-world articulated assets, and more, aiming towards fully automated data generation for robotics, physical AI and other applications. Open Source Code: Project webpage: Documentation: 1/n

Everything you love about generative models — now powered by real physics! Announcing the Genesis project — after a 24-month large-scale research collaboration involving over 20 research labs — a generative physics engine able to generate 4D dynamical worlds powered by a physics simulation platform designed for general-purpose robotics and physical AI applications. Genesis's physics engine is developed in pure Python, while being 10-80x faster than existing GPU-accelerated stacks like Isaac Gym and MJX. It delivers a simulation speed ~430,000 faster than in real-time, and takes only 26 seconds to train a robotic locomotion policy transferrable to the real world on a single RTX4090 (see tutorial: The Genesis physics engine and simulation platform is fully open source at We'll gradually roll out access to our generative framework in the near future. Genesis implements a unified simulation framework all from scratch, integrating a wide spectrum of state-of-the-art physics solvers, allowing simulation of the whole physical world in a virtual realm with the highest realism. We aim to build a universal data engine that leverages an upper-level generative framework to autonomously create physical worlds, together with various modes of data, including environments, camera motions, robotic task proposals, reward functions, robot policies, character motions, fully interactive 3D scenes, open-world articulated assets, and more, aiming towards fully automated data generation for robotics, physical AI and other applications. Open Source Code: Project webpage: Documentation: 1/n

Zhou Xian

3,816,831 Aufrufe • vor 1 Jahr

This game was vibe coded in 2 days - INSANE how far we’ve come Model used: muranyi-3: - Prompts: 39 - Token usage: $90 (so far) - Starter prompt: First world prompt for the game: “Create the Foundation for a third-person 3D high medieval fantasy set in a mountainous open plain with a distant castle landmark.” First prompt to make the character: "Okay, I wanna start building a new game and just figuring out a really awesome character to start. A hooded purple wizard with a world-class third-person player and movement system. Decoupled camera versus walking direction, with jogging and walking in all directions with the camera behind the player.” Tip from our best builders: Start with 3 to 4 prompts just planning. Tell the AI what your game is, the core loop, the vibe, the genre. Don't rush into building yet. Let the model scope it out with you first. Once that first output lands, that's when you start iterating. Tweak the animations, shape the world, adjust the UI, dial in the details. The best games on Tesana aren't built in one prompt. They're built in one conversation.

This game was vibe coded in 2 days - INSANE how far we’ve come Model used: muranyi-3: - Prompts: 39 - Token usage: $90 (so far) - Starter prompt: First world prompt for the game: “Create the Foundation for a third-person 3D high medieval fantasy set in a mountainous open plain with a distant castle landmark.” First prompt to make the character: "Okay, I wanna start building a new game and just figuring out a really awesome character to start. A hooded purple wizard with a world-class third-person player and movement system. Decoupled camera versus walking direction, with jogging and walking in all directions with the camera behind the player.” Tip from our best builders: Start with 3 to 4 prompts just planning. Tell the AI what your game is, the core loop, the vibe, the genre. Don't rush into building yet. Let the model scope it out with you first. Once that first output lands, that's when you start iterating. Tweak the animations, shape the world, adjust the UI, dial in the details. The best games on Tesana aren't built in one prompt. They're built in one conversation.

Tesana - Make games with AI

551,866 Aufrufe • vor 1 Monat

GeoLibre v1.3.0 is here! GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. One application that runs everywhere: in your web browser, as a native desktop app, on your phone, and inside a Jupyter notebook. No account, no server, no cost. Everything runs locally and your data stays private. This release packs in 50+ pull requests of new capabilities. A few highlights: - GIS in your pocket. A native Android build with offline tile caching and download-a-region support, so you can take your maps into the field with no signal. - AI, built in. A natural-language GIS assistant that turns plain-English requests into real geoprocessing, plus an AI segmentation toolbox powered by SamGeo and SAM 3 for extracting features from imagery. - Automate everything with Python. A full scripting API and an in-app Python Console, with new helpers for local rasters, choropleths, marker clusters, split-map comparisons, legends, and colorbars. - Map together, live. Real-time multi-user collaboration so you can open a project and edit the map with others at the same time. - Tell stories with maps. A scroll-driven story map builder and presenter that exports interactive narrative maps to standalone HTML. - A much bigger analysis toolbox. Reproject, explode, and aggregate tools, IDW and kriging interpolation, zonal statistics, a raster calculator, a Spatial Statistics toolbox, and network analysis with isochrones, service areas, and OD cost matrices, plus batch runs and model/pipeline chaining. - Smarter raster and SQL. Single-band pseudocolor classification, RGB band combinations, a no-backend client-side raster fallback, Apache Sedona as a SQL Workspace engine, and transparent S3, GCS, and Azure URL support in queries. - More ways to add, view, and share. New Shapefile and GeoPackage export, glTF/GLB 3D model layers, multi-provider batch and reverse geocoding, collapsible layer groups, and a macOS Homebrew cask. Try the live demo: Star it on GitHub: Docs and roadmap: Release notes: #GIS #OpenSource #Geospatial #MapLibre #WebGIS #Android #GeoLibre

GeoLibre v1.3.0 is here! GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. One application that runs everywhere: in your web browser, as a native desktop app, on your phone, and inside a Jupyter notebook. No account, no server, no cost. Everything runs locally and your data stays private. This release packs in 50+ pull requests of new capabilities. A few highlights: - GIS in your pocket. A native Android build with offline tile caching and download-a-region support, so you can take your maps into the field with no signal. - AI, built in. A natural-language GIS assistant that turns plain-English requests into real geoprocessing, plus an AI segmentation toolbox powered by SamGeo and SAM 3 for extracting features from imagery. - Automate everything with Python. A full scripting API and an in-app Python Console, with new helpers for local rasters, choropleths, marker clusters, split-map comparisons, legends, and colorbars. - Map together, live. Real-time multi-user collaboration so you can open a project and edit the map with others at the same time. - Tell stories with maps. A scroll-driven story map builder and presenter that exports interactive narrative maps to standalone HTML. - A much bigger analysis toolbox. Reproject, explode, and aggregate tools, IDW and kriging interpolation, zonal statistics, a raster calculator, a Spatial Statistics toolbox, and network analysis with isochrones, service areas, and OD cost matrices, plus batch runs and model/pipeline chaining. - Smarter raster and SQL. Single-band pseudocolor classification, RGB band combinations, a no-backend client-side raster fallback, Apache Sedona as a SQL Workspace engine, and transparent S3, GCS, and Azure URL support in queries. - More ways to add, view, and share. New Shapefile and GeoPackage export, glTF/GLB 3D model layers, multi-provider batch and reverse geocoding, collapsible layer groups, and a macOS Homebrew cask. Try the live demo: Star it on GitHub: Docs and roadmap: Release notes: #GIS #OpenSource #Geospatial #MapLibre #WebGIS #Android #GeoLibre

Qiusheng Wu

18,075 Aufrufe • vor 29 Tagen

✨ I made a little demo of #vibejam sponsor Tripo and how you can use it to generate assets for your games In this case I'm generating an apartment flat that's half destroyed and open for my drone sim to fly around, take cover and shoot at enemies from First I write the prompt and select Nano Banana to generate an image first, I get a few different ones, and I select the one I like Then that one is converted into a 3d object called a Smart Mesh Smart Mesh is different than regular image-to-3d models because usually you get issues with uneven polygon distribution, too dense geometry and poor edge flow, so while you think you save time with generating it with AI you end up doing lots of editing after to fix the topology which kinda defeats the point Smart Mesh doesn't have any of this, it's fast and clean low-poly models you can drop into your game Anyway, after generating the model I can then see it in 3d, and if I don't like it, regenerate it, or if I like it start texturing it Once that's done, I export it as a GLB, FBX or OBJ and drop it in (usually I use GLB as ThreeJS seems to prefer that), in my case I just drop the URL into Cursor and let it figure out the placement of the asset into my game!

✨ I made a little demo of #vibejam sponsor Tripo and how you can use it to generate assets for your games In this case I'm generating an apartment flat that's half destroyed and open for my drone sim to fly around, take cover and shoot at enemies from First I write the prompt and select Nano Banana to generate an image first, I get a few different ones, and I select the one I like Then that one is converted into a 3d object called a Smart Mesh Smart Mesh is different than regular image-to-3d models because usually you get issues with uneven polygon distribution, too dense geometry and poor edge flow, so while you think you save time with generating it with AI you end up doing lots of editing after to fix the topology which kinda defeats the point Smart Mesh doesn't have any of this, it's fast and clean low-poly models you can drop into your game Anyway, after generating the model I can then see it in 3d, and if I don't like it, regenerate it, or if I like it start texturing it Once that's done, I export it as a GLB, FBX or OBJ and drop it in (usually I use GLB as ThreeJS seems to prefer that), in my case I just drop the URL into Cursor and let it figure out the placement of the asset into my game!

@levelsio

72,338 Aufrufe • vor 2 Monaten

I built a mobile app to check Paddle revenue (because they don't have one): 👉 - Use your Paddle API key (read-only and scoped) - Live data with beautiful and useful graphs built with native Swift UI. - Multi-account supported, unified revenue metrics. - Data stay on device, no server (api requests are sent directly from your phone) - Home widgets - I made it free to download on App Store (once it's approved) - Buy the source code for $19 and customize it however you want (save 5hrs of prompting if you try to do it yourself). Some interesting facts about this side project: - I vibe coded with 100% claude code remotely on my Mac Mini (with my AI assistant setup) in less than 24 hours. - I have read 0 line of code in this project and never opened Xcode myself. - My AI assistant designed the app with GPT Image 2, built the app with Swift UI, test it on simulator (via screenshots), send the test build to TestFlight for me to test, and invited me to the app store connect account so I can test on my phone, then the AI submitted the app to App Store and currently waiting for approval. - For the website, I ask it to come up with a domain name, I bought it via manually and give it access via Cloudflare API, the AI design and create a static website with GitHub, test it with lighthouse CLI, deploy via GitHub pages, config the domain DNS, deploy the website. - Then I sign up an account with Polar payment, create an API key and ask the AI to setup a store, add payment, link with the account, and add the payment to the website. The entire process happened in the last 24 hours with me only talking to the AI via Telegram. This is such a fun side project not only to create an app that I wish exists, but also to push the limit of what I can use AI for, and so far I'm very impressed. I'll create so much more apps! It feels like I have unlocked a super power.

I built a mobile app to check Paddle revenue (because they don't have one): 👉 - Use your Paddle API key (read-only and scoped) - Live data with beautiful and useful graphs built with native Swift UI. - Multi-account supported, unified revenue metrics. - Data stay on device, no server (api requests are sent directly from your phone) - Home widgets - I made it free to download on App Store (once it's approved) - Buy the source code for $19 and customize it however you want (save 5hrs of prompting if you try to do it yourself). Some interesting facts about this side project: - I vibe coded with 100% claude code remotely on my Mac Mini (with my AI assistant setup) in less than 24 hours. - I have read 0 line of code in this project and never opened Xcode myself. - My AI assistant designed the app with GPT Image 2, built the app with Swift UI, test it on simulator (via screenshots), send the test build to TestFlight for me to test, and invited me to the app store connect account so I can test on my phone, then the AI submitted the app to App Store and currently waiting for approval. - For the website, I ask it to come up with a domain name, I bought it via manually and give it access via Cloudflare API, the AI design and create a static website with GitHub, test it with lighthouse CLI, deploy via GitHub pages, config the domain DNS, deploy the website. - Then I sign up an account with Polar payment, create an API key and ask the AI to setup a store, add payment, link with the account, and add the payment to the website. The entire process happened in the last 24 hours with me only talking to the AI via Telegram. This is such a fun side project not only to create an app that I wish exists, but also to push the limit of what I can use AI for, and so far I'm very impressed. I'll create so much more apps! It feels like I have unlocked a super power.

Tony Dinh

43,787 Aufrufe • vor 1 Monat

Vibe Coding 3D Garment Software with ThreeJS : A Small Step For Me So, after modeling the human i did what any reasonable vibe coder would do, i asked codex how to get clothes for my models After it was done running subliminal ad campaigns for Marvelous Designer and CLO 3D, i asked it to explain their architecture to me and adapt it to my threejs app. Guess what it did? You damn right, it built the most basic shit interpretation you can think of. And this is the average interaction the Anti-AI coders have until they conclude that AI is slop and/or it can only work if you micro manage it on every line of code. Well, eons of humanities knowledge are now packaged in tiny silicon and transferred across the globe in realtime, available on tap. So anyways i just iterated quite a lot over it, told it repeatedly why it was bad (the initial one used rapier physics and a naive cloth simulation) We found out together that: 1. A ground truth document model is needed 2. The visual mesh in 3D should be triangulated from the 2D shape 3. The physical object is running independently through different solvers: - A fast proxy which is generated by reading all the bones in runtime and just inflating these areas with spheres and capsules - A medium quality proxy which resamples the human model and creates a lower-poly mesh for simulations - Full mesh simulation (can't run it, every simulation tick takes about 5 minutes on my machine) It ended the session by telling me that this is still crap because it runs everything on CPU (thanks, not that i care, but i guess we'll be fixing that?) Oh yea also built a 2D canvas editor with boolean operations so i can build cool stuff like ponchos. It also allows me to mark stitches between two objects, which is how the shirt in the video pulls towards the other half. The garment's properties and materials are not yet exposed, yes i know it looks very stiff like a poncho made from a persian rug, we're working on it, okay? So, yea, tbh this is another endless rabbit hole, let's go i guess

Vibe Coding 3D Garment Software with ThreeJS : A Small Step For Me So, after modeling the human i did what any reasonable vibe coder would do, i asked codex how to get clothes for my models After it was done running subliminal ad campaigns for Marvelous Designer and CLO 3D, i asked it to explain their architecture to me and adapt it to my threejs app. Guess what it did? You damn right, it built the most basic shit interpretation you can think of. And this is the average interaction the Anti-AI coders have until they conclude that AI is slop and/or it can only work if you micro manage it on every line of code. Well, eons of humanities knowledge are now packaged in tiny silicon and transferred across the globe in realtime, available on tap. So anyways i just iterated quite a lot over it, told it repeatedly why it was bad (the initial one used rapier physics and a naive cloth simulation) We found out together that: 1. A ground truth document model is needed 2. The visual mesh in 3D should be triangulated from the 2D shape 3. The physical object is running independently through different solvers: - A fast proxy which is generated by reading all the bones in runtime and just inflating these areas with spheres and capsules - A medium quality proxy which resamples the human model and creates a lower-poly mesh for simulations - Full mesh simulation (can't run it, every simulation tick takes about 5 minutes on my machine) It ended the session by telling me that this is still crap because it runs everything on CPU (thanks, not that i care, but i guess we'll be fixing that?) Oh yea also built a 2D canvas editor with boolean operations so i can build cool stuff like ponchos. It also allows me to mark stitches between two objects, which is how the shirt in the video pulls towards the other half. The garment's properties and materials are not yet exposed, yes i know it looks very stiff like a poncho made from a persian rug, we're working on it, okay? So, yea, tbh this is another endless rabbit hole, let's go i guess

robot

38,935 Aufrufe • vor 1 Monat

For generative AI to become an interesting art tool, we need much more control over the output. The slot-machine-like nature of pure text-to-image leaves too much to chance. Using the "Real-time Latent Consistency Model" that I'm using in the example here, is the first time I truly got a glimpse of a future where we'll be able to use our artistic skills and sensibility, to get control over AI image gen. Systems like these will never be able to match the quality or originality of a skilled artist, it won't surprise us in the same way an artist can. Things are a mess in terms of the training data these models are based on, and the questions about copyright concerns and about a time when everything will look the same are very valid. At some point capabilities like these will be embedded in photoshop, and anyone will be able to generate a pretty picture. But to create interesting designs, to tell original stories and to surprise us, we need creatives and artists with something on their mind. We'll be able to create immersive worlds, by making brush-strokes and sculpt marks, without needing to worry about all the dials, plugins, wires of our 3d and 2d tools today. I love to sculpt, I love to draw, and I love to explore new mediums and new ways to create. The Gen AI tools we have today are far from perfect, and things need to be steered in a better direction. For that we need artists to help point the way. Gen AI isn't going away.. it's too powerful and has the potential to allow us to tell stories like never before. Like all other big technological shifts, tech like this will come at a cost, but it will also open up new opportunities and empower a new generation of storytellers. I might be naive, but I believe that human ingenuity and creativity will persevere in this new world ♥️ #art #ai

For generative AI to become an interesting art tool, we need much more control over the output. The slot-machine-like nature of pure text-to-image leaves too much to chance. Using the "Real-time Latent Consistency Model" that I'm using in the example here, is the first time I truly got a glimpse of a future where we'll be able to use our artistic skills and sensibility, to get control over AI image gen. Systems like these will never be able to match the quality or originality of a skilled artist, it won't surprise us in the same way an artist can. Things are a mess in terms of the training data these models are based on, and the questions about copyright concerns and about a time when everything will look the same are very valid. At some point capabilities like these will be embedded in photoshop, and anyone will be able to generate a pretty picture. But to create interesting designs, to tell original stories and to surprise us, we need creatives and artists with something on their mind. We'll be able to create immersive worlds, by making brush-strokes and sculpt marks, without needing to worry about all the dials, plugins, wires of our 3d and 2d tools today. I love to sculpt, I love to draw, and I love to explore new mediums and new ways to create. The Gen AI tools we have today are far from perfect, and things need to be steered in a better direction. For that we need artists to help point the way. Gen AI isn't going away.. it's too powerful and has the potential to allow us to tell stories like never before. Like all other big technological shifts, tech like this will come at a cost, but it will also open up new opportunities and empower a new generation of storytellers. I might be naive, but I believe that human ingenuity and creativity will persevere in this new world ♥️ #art #ai

Martin Nebelong

1,660,017 Aufrufe • vor 2 Jahren

Distilled recap of the back-and-forth with Jensen on export controls: Dwarkesh: Wouldn’t selling Nvidia chips to China enable them to train models like Claude Mythos with cyber offensive capabilities that would be threats to American companies and national security? Jensen: First of all, Mythos was trained on fairly mundane capacity and a fairly mundane amount of it by an extraordinary company. The amount of capacity and the type of compute it was trained on is abundantly available in China. Dwarkesh: With that, could they eventually train a model like Mythos? Yes. But the question is, because we have more FLOPs, American labs are able to get to this level of capabilities first. Furthermore, even if they trained a model like this, the ability to deploy it at scale matters. If you had a cyber hacker, it's much more dangerous if they have a million of them versus a thousand of them. Jensen: Your premise is just wrong. The fact of the matter is their AI development is going just fine. The best AI researchers in the world, because they are limited in compute, also come up with extremely smart algorithms. DeepSeek is not an inconsequential advance. The day that DeepSeek comes out on Huawei first, that is a horrible outcome for our nation. Dwarkesh: Currently, you can have a model like DeepSeek that can run on any accelerator if it's open source. Why would that stop being the case in the future? Jensen: Suppose it optimizes for Huawei. Suppose it optimizes for their architecture. It would put others at a disadvantage. As AI diffuses out into the rest of the world, their standards and their tech stack will become superior to ours because their models are open. Dwarkesh: Tesla sold extremely good electric vehicles to China for a long time. iPhones are sold in China. They didn't cause some lock-in. China will still make their version of EVs, and they're dominating, or smartphones, they're dominating. Jensen: We are not a car. The fact that I can buy this car brand one day and use another car brand another day is easy. Computing is not like that. There's a reason why x86 still exists. There's a reason why Arm is so sticky. These ecosystems are hard to replace. Dwarkesh: It's just hard to imagine that there's a long-term lock-in to the Chinese ecosystem, even if they have this slightly better open-source model for a while. American labs port across accelerators constantly. Anthropic's models are run on GPUs, they're run on Trainium, they're run on TPUs. There are so many things you can do, from distilling to a model that's well fit for your chips. Jensen: China is the largest contributor to open source software in the world. China's the largest contributor to open models in the world. Today it's built on the American tech stack, Nvidia’s. Fact. All five layers of the tech stack for AI are important. The United States ought to go win all five of them. in a few years time, I'm making you the prediction that when we want American technology to be diffused around the world—out to India, out to the Middle East, out to Africa, out to Southeast Asia—on that day, I will tell you exactly about today's conversation, about how your policy ... caused the United States to concede the second largest market in the world for no good reason at all.

Dwarkesh Patel

1,250,513 Aufrufe • vor 3 Monaten

This AI Training Includes $3M in Perks, Live Build Sessions, Enterprise Systems and More My mission: Turn AI from overwhelming hype into actual freedom for 1 billion+ people worldwide. I've spent 7+ years building AI systems across 10+ industries. Entered when GPT was just a next-word prediction model. Built over 100 automations that replaced teams and bought back time. Now I'm sharing everything inside AIC+. Here's what you're unlocking: → $3M+ in software perks: Perplexity, Loom, Notion, Make, Airtable, AWS, DigitalOcean, Google Cloud + 950 more → 50+ revenue-generating automation templates you can deploy immediately → Complete AI mastery curriculum from foundations to enterprise solutions → Lead generation machines that run themselves → Sales systems that close deals automatically → Live weekly build sessions where we create automations together → Live Q&A calls to get unstuck immediately → Direct mentorship from someone who's been in the trenches for 7+ years This is for entrepreneurs drowning in manual work, agency owners scaling without hiring, and anyone tired of AI tutorials that go nowhere. The promise: While others stay stuck in tutorial hell, you'll deploy systems that generate leads, close deals, and buy back your time. Founding members get lifetime pricing for 24 hours only. Like, RT + reply with "FREEDOM" and I'll DM you the founding member details (Must be following so I can DM) This is your moment to master practical AI before the world catches up.

This AI Training Includes $3M in Perks, Live Build Sessions, Enterprise Systems and More My mission: Turn AI from overwhelming hype into actual freedom for 1 billion+ people worldwide. I've spent 7+ years building AI systems across 10+ industries. Entered when GPT was just a next-word prediction model. Built over 100 automations that replaced teams and bought back time. Now I'm sharing everything inside AIC+. Here's what you're unlocking: → $3M+ in software perks: Perplexity, Loom, Notion, Make, Airtable, AWS, DigitalOcean, Google Cloud + 950 more → 50+ revenue-generating automation templates you can deploy immediately → Complete AI mastery curriculum from foundations to enterprise solutions → Lead generation machines that run themselves → Sales systems that close deals automatically → Live weekly build sessions where we create automations together → Live Q&A calls to get unstuck immediately → Direct mentorship from someone who's been in the trenches for 7+ years This is for entrepreneurs drowning in manual work, agency owners scaling without hiring, and anyone tired of AI tutorials that go nowhere. The promise: While others stay stuck in tutorial hell, you'll deploy systems that generate leads, close deals, and buy back your time. Founding members get lifetime pricing for 24 hours only. Like, RT + reply with "FREEDOM" and I'll DM you the founding member details (Must be following so I can DM) This is your moment to master practical AI before the world catches up.

Samruddhi Mokal

12,423 Aufrufe • vor 8 Monaten

how to use Google's NEW open source Design.md + AI Skills to make your startup look like a $100 million company in 1 hour: 1. Design.md is an open source file from Google that captures the soul of a design. Typography, colors, spacing, all in one markdown file. You attach it to your prompt and your agent builds beautiful things every time. 2. Think of it this way. The HTML is the finished dish. The design.md is the recipe. The skills are the ingredients. Put them together and everything you build looks consistent and professional. 3. Don't create a design system from scratch. Find a brand you love. Linear, Stripe, Vercel, whatever resonates. Study it. Use ChatGPT or Claude to help you extract the design language into your own design.md file. 4. Build skills on top of your design.md. A landing page skill. A mobile app skill. A motion design skill. A slide deck skill. Each one references the same design.md so everything looks like it came from the same designer. 5. The biggest mistake people make: they nail one screen and then everything else looks generic. Design.md solves this. One file keeps every page, every format, every medium consistent. 6. Use it across everything. Your landing page. Your app. Your pitch deck. Your promo videos. Same DNA. Same taste. Same system. That's what separates a startup that looks real from one that looks vibe-coded. 7. Build a second brain for design inspiration. When you see something beautiful in the real world or online, capture it. Save it. When you're building something new, reference it. Taste is developed, not downloaded. 8. It's obvious but the difference between a product people trust and a product people bounce from is how it looks and feels. Design.md gives you that edge. you can watch below shoutout to Meng To for coming on The Startup Ideas Podcast (SIP) 🧃 and walking through his full workflow. if you want to use AI to actually build gorgeous designs, you'll want to use see this. watch

GREG ISENBERG

503,527 Aufrufe • vor 2 Monaten

Woot woot! THE BIG DAY IS HERE. Eddie AI v2! Download it: It’s been a sprint pace to cover a marathon distance. We launched Eddie AI v1 in October last year. Thousands of new users. Lots of feature requests. Learned a ton. (Yes, Paul Graham is right: ship early and often!) Eddie AI v2 releases three big advancements. And underpinning those decisions are the learnings baked in. 1. Eddie now runs locally on Macs and Windows. Our native apps are out of beta and available to everyone. Video is large. The video files used by video professionals are even larger. No one wants to wait around uploading. Native applications make it MUCH MUCH faster to import and thus much faster to get on with editing. One of the challenges with Eddie is how do we *quickly* get the user to aha – that moment where they can see the magic of this assistant AI video editor – if it takes awhile to upload files. These native applications are a big step towards getting to aha, swiftly, and help deliver on our promise to save customers time. 2. Analyze raw footage, aka logging interviews and B-rolls. Eddie v1 only created edits. But users were asking for a step before the edit too. They want their footage logged – what happened in this shot, who said what, etc – and grouped logically with this metadata back in their video editing application. We released a test of this a month ago. Users were blown away by the accuracy of the AI, the rich descriptions, and how the metadata seamlessly is sent to their video editing application where they can find the right shot when they need. We were blown away by the usage of this feature. Logging is now updated and released to all in v2. 3. Rough cut mode. This is the big dream: Eddie is the AI assistant video editor. There’s a lot of tedious work in video editing. Eddie takes it on. And helps you get to a great story faster. Sorting through hours of interviews and finding great soundbites that can be cut together to form a coherent story is a big step toward this dream. We attempted to do this in v1. But the prompt box was too open ended. People were typing in things like “Cut me a great story” and the subjectivity of the statement led to variable results. So we re-thought the story crafting process. We added friction. YES you read that correctly. We added friction into the product that slows the customer down. BUT they get much better results, reliably. And that trade-off is well worth it. Eddie now takes a step-by-step approach with you on crafting your story. It aligns with you on the story framework and then cuts chapter by chapter with your feedback. What used to take days, now takes minutes. We spent months hacking at LLMs to make them able to provide coherent edits over a longer duration and handle a large amount of source material (aka context-window problem). It’s ready for release. Version 2.0 is here. Download Eddie at

Woot woot! THE BIG DAY IS HERE. Eddie AI v2! Download it: It’s been a sprint pace to cover a marathon distance. We launched Eddie AI v1 in October last year. Thousands of new users. Lots of feature requests. Learned a ton. (Yes, Paul Graham is right: ship early and often!) Eddie AI v2 releases three big advancements. And underpinning those decisions are the learnings baked in. 1. Eddie now runs locally on Macs and Windows. Our native apps are out of beta and available to everyone. Video is large. The video files used by video professionals are even larger. No one wants to wait around uploading. Native applications make it MUCH MUCH faster to import and thus much faster to get on with editing. One of the challenges with Eddie is how do we quickly get the user to aha – that moment where they can see the magic of this assistant AI video editor – if it takes awhile to upload files. These native applications are a big step towards getting to aha, swiftly, and help deliver on our promise to save customers time. 2. Analyze raw footage, aka logging interviews and B-rolls. Eddie v1 only created edits. But users were asking for a step before the edit too. They want their footage logged – what happened in this shot, who said what, etc – and grouped logically with this metadata back in their video editing application. We released a test of this a month ago. Users were blown away by the accuracy of the AI, the rich descriptions, and how the metadata seamlessly is sent to their video editing application where they can find the right shot when they need. We were blown away by the usage of this feature. Logging is now updated and released to all in v2. 3. Rough cut mode. This is the big dream: Eddie is the AI assistant video editor. There’s a lot of tedious work in video editing. Eddie takes it on. And helps you get to a great story faster. Sorting through hours of interviews and finding great soundbites that can be cut together to form a coherent story is a big step toward this dream. We attempted to do this in v1. But the prompt box was too open ended. People were typing in things like “Cut me a great story” and the subjectivity of the statement led to variable results. So we re-thought the story crafting process. We added friction. YES you read that correctly. We added friction into the product that slows the customer down. BUT they get much better results, reliably. And that trade-off is well worth it. Eddie now takes a step-by-step approach with you on crafting your story. It aligns with you on the story framework and then cuts chapter by chapter with your feedback. What used to take days, now takes minutes. We spent months hacking at LLMs to make them able to provide coherent edits over a longer duration and handle a large amount of source material (aka context-window problem). It’s ready for release. Version 2.0 is here. Download Eddie at

Shamir Allibhai

57,672 Aufrufe • vor 1 Jahr

What's the Big Deal with DeepSeek in AI? Here's why DeepSeek is making everyone take notice: 1. Super Smart on a Budget: DeepSeek showed you can make awesome AI without breaking the bank. Their latest model, DeepSeek-V3, was trained for only about $10 million, which is a lot less than the usual big bucks spent on AI, like the rumored $78 million for some of OpenAI's models. They did this in just two months with fewer fancy computers. 2. Open for Everyone: DeepSeek isn't keeping their tech a secret. They've made it open-source, meaning anyone can use, tweak, and learn from it. It's like they're saying, "Come join the party!" 3. Beating the Big Names: DeepSeek-V3 has done better than some top dogs from companies like OpenAI and Google in solving puzzles, math, and coding. This proves you can get great AI results without spending a fortune. 4. Challenging NVIDIA: NVIDIA's chips are usually the choice for AI because they're really powerful. But since DeepSeek did so well with less expensive chips, it might make people think twice about always going for NVIDIA's priciest options. 5. The DeepSeek Crew: The team at DeepSeek is young and smart, mostly from top Chinese schools, with brains in physics, math, and computer science. They learned AI in about six months by themselves! They use first principle thinking, which means they break down problems to the basics and build from there. This has helped them come up with cool new ways to do AI. 6. Changing AI for Good: DeepSeek is showing that AI can be cheaper and more open to everyone. They're changing how we think AI should be made and shared, which could shake up the whole AI world. So, as we watch DeepSeek, it's clear they're not just another player; they're changing the rules of the game. I predicted that this would be a make or break year for all the massive investments made in AI by American VC's. A few weeks later, DeepSeek happens! Watch the rest of my predictions in my 2025 outlook video . Link in replies #AIInnovation #DeepSeek #NVIDIA #OpenAI #TechDisruption

What's the Big Deal with DeepSeek in AI? Here's why DeepSeek is making everyone take notice: 1. Super Smart on a Budget: DeepSeek showed you can make awesome AI without breaking the bank. Their latest model, DeepSeek-V3, was trained for only about $10 million, which is a lot less than the usual big bucks spent on AI, like the rumored $78 million for some of OpenAI's models. They did this in just two months with fewer fancy computers. 2. Open for Everyone: DeepSeek isn't keeping their tech a secret. They've made it open-source, meaning anyone can use, tweak, and learn from it. It's like they're saying, "Come join the party!" 3. Beating the Big Names: DeepSeek-V3 has done better than some top dogs from companies like OpenAI and Google in solving puzzles, math, and coding. This proves you can get great AI results without spending a fortune. 4. Challenging NVIDIA: NVIDIA's chips are usually the choice for AI because they're really powerful. But since DeepSeek did so well with less expensive chips, it might make people think twice about always going for NVIDIA's priciest options. 5. The DeepSeek Crew: The team at DeepSeek is young and smart, mostly from top Chinese schools, with brains in physics, math, and computer science. They learned AI in about six months by themselves! They use first principle thinking, which means they break down problems to the basics and build from there. This has helped them come up with cool new ways to do AI. 6. Changing AI for Good: DeepSeek is showing that AI can be cheaper and more open to everyone. They're changing how we think AI should be made and shared, which could shake up the whole AI world. So, as we watch DeepSeek, it's clear they're not just another player; they're changing the rules of the game. I predicted that this would be a make or break year for all the massive investments made in AI by American VC's. A few weeks later, DeepSeek happens! Watch the rest of my predictions in my 2025 outlook video . Link in replies #AIInnovation #DeepSeek #NVIDIA #OpenAI #TechDisruption

Dr Ola Brown

83,394 Aufrufe • vor 1 Jahr

Announcing DreamDojo: our open-source, interactive world model that takes robot motor controls and generates the future in pixels. No engine, no meshes, no hand-authored dynamics. It's Simulation 2.0. Time for robotics to take the bitter lesson pill. Real-world robot learning is bottlenecked by time, wear, safety, and resets. If we want Physical AI to move at pretraining speed, we need a simulator that adapts to pretraining scale with as little human engineering as possible. Our key insights: (1) human egocentric videos are a scalable source of first-person physics; (2) latent actions make them "robot-readable" across different hardware; (3) real-time inference unlocks live teleop, policy eval, and test-time planning *inside* a dream. We pre-train on 44K hours of human videos: cheap, abundant, and collected with zero robot-in-the-loop. Humans have already explored the combinatorics: we grasp, pour, fold, assemble, fail, retry—across cluttered scenes, shifting viewpoints, changing light, and hour-long task chains—at a scale no robot fleet could match. The missing piece: these videos have no action labels. So we introduce latent actions: a unified representation inferred directly from videos that captures "what changed between world states" without knowing the underlying hardware. This lets us train on any first-person video as if it came with motor commands attached. As a result, DreamDojo generalizes zero-shot to objects and environments never seen in any robot training set, because humans saw them first. Next, we post-train onto each robot to fit its specific hardware. Think of it as separating "how the world looks and behaves" from "how this particular robot actuates." The base model follows the general physical rules, then "snaps onto" the robot's unique mechanics. It's kind of like loading a new character and scene assets into Unreal Engine, but done through gradient descent and generalizes far beyond the post-training dataset. A world simulator is only useful if it runs fast enough to close the loop. We train a real-time version of DreamDojo that runs at 10 FPS, stable for over a minute of continuous rollout. This unlocks exciting possibilities: - Live teleoperation *inside* a dream. Connect a VR controller, stream actions into DreamDojo, and teleop a virtual robot in real time. We demo this on Unitree G1 with a PICO headset and one RTX 5090. - Policy evaluation. You can benchmark a policy checkpoint in DreamDojo instead of the real world. The simulated success rates strongly correlate with real-world results - accurate enough to rank checkpoints without burning a single motor. - Model-based planning. Sample multiple action proposals → simulate them all in parallel → pick the best future. Gains +17% real-world success out of the box on a fruit packing task. We open-source everything!! Weights, code, post-training dataset, eval set, and whitepaper with tons of details to reproduce. DreamDojo is based on NVIDIA Cosmos, which is open-weight too. 2026 is the year of World Models for physical AI. We want you to build with us. Happy scaling! Links in thread:

Announcing DreamDojo: our open-source, interactive world model that takes robot motor controls and generates the future in pixels. No engine, no meshes, no hand-authored dynamics. It's Simulation 2.0. Time for robotics to take the bitter lesson pill. Real-world robot learning is bottlenecked by time, wear, safety, and resets. If we want Physical AI to move at pretraining speed, we need a simulator that adapts to pretraining scale with as little human engineering as possible. Our key insights: (1) human egocentric videos are a scalable source of first-person physics; (2) latent actions make them "robot-readable" across different hardware; (3) real-time inference unlocks live teleop, policy eval, and test-time planning inside a dream. We pre-train on 44K hours of human videos: cheap, abundant, and collected with zero robot-in-the-loop. Humans have already explored the combinatorics: we grasp, pour, fold, assemble, fail, retry—across cluttered scenes, shifting viewpoints, changing light, and hour-long task chains—at a scale no robot fleet could match. The missing piece: these videos have no action labels. So we introduce latent actions: a unified representation inferred directly from videos that captures "what changed between world states" without knowing the underlying hardware. This lets us train on any first-person video as if it came with motor commands attached. As a result, DreamDojo generalizes zero-shot to objects and environments never seen in any robot training set, because humans saw them first. Next, we post-train onto each robot to fit its specific hardware. Think of it as separating "how the world looks and behaves" from "how this particular robot actuates." The base model follows the general physical rules, then "snaps onto" the robot's unique mechanics. It's kind of like loading a new character and scene assets into Unreal Engine, but done through gradient descent and generalizes far beyond the post-training dataset. A world simulator is only useful if it runs fast enough to close the loop. We train a real-time version of DreamDojo that runs at 10 FPS, stable for over a minute of continuous rollout. This unlocks exciting possibilities: - Live teleoperation inside a dream. Connect a VR controller, stream actions into DreamDojo, and teleop a virtual robot in real time. We demo this on Unitree G1 with a PICO headset and one RTX 5090. - Policy evaluation. You can benchmark a policy checkpoint in DreamDojo instead of the real world. The simulated success rates strongly correlate with real-world results - accurate enough to rank checkpoints without burning a single motor. - Model-based planning. Sample multiple action proposals → simulate them all in parallel → pick the best future. Gains +17% real-world success out of the box on a fruit packing task. We open-source everything!! Weights, code, post-training dataset, eval set, and whitepaper with tons of details to reproduce. DreamDojo is based on NVIDIA Cosmos, which is open-weight too. 2026 is the year of World Models for physical AI. We want you to build with us. Happy scaling! Links in thread:

Jim Fan

221,486 Aufrufe • vor 4 Monaten

$China just made Silicon Valley's entire AI industry look like a scam. The US government spent 3 years trying to stop China from building competitive AI. But this backfired HORRIBLY. Here's what happened: Yesterday, a Chinese startup called DeepSeek released a new AI model called V4. It matches the performance of OpenAI and Anthropic's best models. At 1/7th the price. And for the first time ever, it was built on Chinese chips. NOT American ones. That last part is the one that terrifies the west. For context: Since 2022, the US has banned the export of advanced AI chips to China. The entire strategy was built on the assumption that if China can't access Nvidia's best hardware, they can't build frontier AI. But DeepSeek just proved that assumption wrong. Their V4 model was trained and runs on Huawei's Ascend chips. Huawei spent months working directly with DeepSeek to make sure V4 runs across their entire line of AI processors. Jensen Huang even predicted this on a recent podcast: "The day that DeepSeek comes out on Huawei first, that is a horrible outcome for our nation." That day was yesterday. And the numbers are crazy: DeepSeek V4 costs $3.48 per million output tokens. OpenAI's latest model GPT-5.5 costs $30. Anthropic's Claude charges $25. Same ballpark performance. 7x cheaper. Uber's CTO just admitted they burned through their ENTIRE 2026 AI budget in 4 months using Anthropic's tools. If Uber had used DeepSeek instead, that same budget would have lasted 7 YEARS. 4 months vs 7 years. Same work getting done. But the pricing isn't even the big thing here. The real story is what DeepSeek did with their technical report: They published the benchmarks where they LOSE. Every AI company cherry-picks the tests where their model wins. DeepSeek ran the full comparison against GPT-5.4 and Google's Gemini, found they trail frontier models by 3 to 6 months, and printed it anyway. They literally don't care because the price gap makes the performance gap irrelevant for 90% of use cases. So the US export controls didn't slow China down. They ACCELERATED China's independence. Because Chinese developers were FORCED to train models with limited resources, they had to figure out how to make AI radically more efficient. That constraint became their competitive advantage. Every generation of DeepSeek has gotten dramatically cheaper to train. V4 continues the trend. Meanwhile US companies are going the OPPOSITE direction: OpenAI's GPT-5.5 Pro costs $180 per million output tokens. That's 51x more expensive than DeepSeek V4 for comparable work. The Commerce Secretary confirmed this week that ZERO Nvidia advanced chip shipments have actually gone through to China despite being approved in January. So China built frontier AI anyway. Without American chips. At a fraction of the cost. And the market response tells you everything: Chinese chipmaker SMIC surged 10%. Huahong Semiconductor jumped 15%. DeepSeek's Chinese AI competitors Zhipu AI and MiniMax dropped 9% because V4 is destroying them too. DeepSeek is making Silicon Valley's pricing model look like a scam. US tech companies spent $650 billion on AI infrastructure this year. DeepSeek just showed the world you can match their output for pennies. The export controls were supposed to be America's ace card. Instead they taught China how to win without American chips, at American prices nobody can compete with. Jensen Huang was right. This is a horrible outcome. But it's the outcome America built for itself.$

China just made Silicon Valley's entire AI industry look like a scam. The US government spent 3 years trying to stop China from building competitive AI. But this backfired HORRIBLY. Here's what happened: Yesterday, a Chinese startup called DeepSeek released a new AI model called V4. It matches the performance of OpenAI and Anthropic's best models. At 1/7th the price. And for the first time ever, it was built on Chinese chips. NOT American ones. That last part is the one that terrifies the west. For context: Since 2022, the US has banned the export of advanced AI chips to China. The entire strategy was built on the assumption that if China can't access Nvidia's best hardware, they can't build frontier AI. But DeepSeek just proved that assumption wrong. Their V4 model was trained and runs on Huawei's Ascend chips. Huawei spent months working directly with DeepSeek to make sure V4 runs across their entire line of AI processors. Jensen Huang even predicted this on a recent podcast: "The day that DeepSeek comes out on Huawei first, that is a horrible outcome for our nation." That day was yesterday. And the numbers are crazy: DeepSeek V4 costs $3.48 per million output tokens. OpenAI's latest model GPT-5.5 costs $30. Anthropic's Claude charges $25. Same ballpark performance. 7x cheaper. Uber's CTO just admitted they burned through their ENTIRE 2026 AI budget in 4 months using Anthropic's tools. If Uber had used DeepSeek instead, that same budget would have lasted 7 YEARS. 4 months vs 7 years. Same work getting done. But the pricing isn't even the big thing here. The real story is what DeepSeek did with their technical report: They published the benchmarks where they LOSE. Every AI company cherry-picks the tests where their model wins. DeepSeek ran the full comparison against GPT-5.4 and Google's Gemini, found they trail frontier models by 3 to 6 months, and printed it anyway. They literally don't care because the price gap makes the performance gap irrelevant for 90% of use cases. So the US export controls didn't slow China down. They ACCELERATED China's independence. Because Chinese developers were FORCED to train models with limited resources, they had to figure out how to make AI radically more efficient. That constraint became their competitive advantage. Every generation of DeepSeek has gotten dramatically cheaper to train. V4 continues the trend. Meanwhile US companies are going the OPPOSITE direction: OpenAI's GPT-5.5 Pro costs $180 per million output tokens. That's 51x more expensive than DeepSeek V4 for comparable work. The Commerce Secretary confirmed this week that ZERO Nvidia advanced chip shipments have actually gone through to China despite being approved in January. So China built frontier AI anyway. Without American chips. At a fraction of the cost. And the market response tells you everything: Chinese chipmaker SMIC surged 10%. Huahong Semiconductor jumped 15%. DeepSeek's Chinese AI competitors Zhipu AI and MiniMax dropped 9% because V4 is destroying them too. DeepSeek is making Silicon Valley's pricing model look like a scam. US tech companies spent $650 billion on AI infrastructure this year. DeepSeek just showed the world you can match their output for pennies. The export controls were supposed to be America's ace card. Instead they taught China how to win without American chips, at American prices nobody can compete with. Jensen Huang was right. This is a horrible outcome. But it's the outcome America built for itself.

Ricardo

279,980 Aufrufe • vor 2 Monaten