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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

6,621 subscribers

54,504 次观看 • 5 个月前 •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

106,664 次观看 • 5 个月前

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 次观看 • 6 个月前

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 次观看 • 4 个月前

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 次观看 • 3 个月前

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

17,001 次观看 • 3 个月前

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 次观看 • 5 个月前

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

3,818,940 次观看 • 1 年前

I made an interactive three.js site that constructs towers with weather and time of day effects. I went from making mostly static sites with huge images to three.js with highly textured, detailed 3D models, and the entire site is under 1-2mb. Because it's 3D, you can ask AI to customize everything: themed towers from different countries, ancient and modern styles, weather like snow and rain, lighting from morning to night. The surprising part is that with a solid foundation it creates those changes pretty much one-shot. It's like starting a design system, where the AI figures out how to stay consistent in quality with the rest of the design. The hard part is avoiding 3D slop, which is easier thanks to Opus 5 and open-source projects. I used Claude Code for the harness, Higgsfield to generate textures and soundfx/music. Let me know if you'd like the open-source. Inspiration: Btw, I love that we get auto-translated posts from around the world. It really expands our horizon in terms of inspiration.

I made an interactive three.js site that constructs towers with weather and time of day effects. I went from making mostly static sites with huge images to three.js with highly textured, detailed 3D models, and the entire site is under 1-2mb. Because it's 3D, you can ask AI to customize everything: themed towers from different countries, ancient and modern styles, weather like snow and rain, lighting from morning to night. The surprising part is that with a solid foundation it creates those changes pretty much one-shot. It's like starting a design system, where the AI figures out how to stay consistent in quality with the rest of the design. The hard part is avoiding 3D slop, which is easier thanks to Opus 5 and open-source projects. I used Claude Code for the harness, Higgsfield to generate textures and soundfx/music. Let me know if you'd like the open-source. Inspiration: Btw, I love that we get auto-translated posts from around the world. It really expands our horizon in terms of inspiration.

Meng To

128,700 次观看 • 4 天前

How do you make a vibe-coded world feel truly alive instead of looking like a few assets randomly scattered across an empty map? I keep running into this problem. The AI can generate the terrain and each asset individually, but it struggles with placement, density and composition. A forest becomes ten isolated trees. A town becomes three buildings beside a road. The assets exist, but the environment never feels naturally constructed. It made me think about last year, when I opened X and discovered the insane project @levelsio was building: a multiplayer flight game running entirely in the browser, with a sponsorship system where brands could buy their own advertising blimps. The entire project was vibe-coded. Watching such an ambitious idea become a real multiplayer game was incredible, and it was exactly the kind of project that pushed me to go 100% into vibe coding. Now Astra is emerging as OpenAI’s next major model family (whether it eventually ships as GPT‑5.7, GPT‑6 or something else) and I’m ridiculously excited to discover what this new generation will let us build. So here’s a little nod to that inspiration with my own project where you can pilot a plane. And yes, I absolutely love Disney’s Planes 😂 How do you handle environmental placement in your projects? Better prompts, procedural generation, specialized tools, or manual level design afterward?

How do you make a vibe-coded world feel truly alive instead of looking like a few assets randomly scattered across an empty map? I keep running into this problem. The AI can generate the terrain and each asset individually, but it struggles with placement, density and composition. A forest becomes ten isolated trees. A town becomes three buildings beside a road. The assets exist, but the environment never feels naturally constructed. It made me think about last year, when I opened X and discovered the insane project @levelsio was building: a multiplayer flight game running entirely in the browser, with a sponsorship system where brands could buy their own advertising blimps. The entire project was vibe-coded. Watching such an ambitious idea become a real multiplayer game was incredible, and it was exactly the kind of project that pushed me to go 100% into vibe coding. Now Astra is emerging as OpenAI’s next major model family (whether it eventually ships as GPT‑5.7, GPT‑6 or something else) and I’m ridiculously excited to discover what this new generation will let us build. So here’s a little nod to that inspiration with my own project where you can pilot a plane. And yes, I absolutely love Disney’s Planes 😂 How do you handle environmental placement in your projects? Better prompts, procedural generation, specialized tools, or manual level design afterward?

Givros

15,639 次观看 • 4 天前

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 次观看 • 2 个月前

Sriram Krishnan reveals why it's easier to defend American code with a Chinese model than an American one right now: "I don't think it is great that the leading open weight models or open source models are not American. There's some great innovation happening with Moonshot and with DeepSeek and with Qwen, but I would much rather prefer that the leading models are American." "Linus Torvalds of Linux fame had a law that given enough eyes, all bugs are shallow. Open weight models are inherently secure because when you download a model off Hugging Face, the entire world can take it apart, inspect it, fine-tune it, look at it in ways that you absolutely cannot if they are closed." "There was an incident with Hugging Face that got reported on earlier today. Hugging Face seems to have found that somebody was using an AI LLM agent to hammer them at multiple places and to find ways to break in." "The way to counter that is to make sure American or Western or allies defenders have access to the best models to make our software just more secure." "It may be harder to look at your own code for exploits with Fable than it is when you use a Chinese model. That's just like a weird spot to be, and I think we should fix that." Sriram Krishnan

Sriram Krishnan reveals why it's easier to defend American code with a Chinese model than an American one right now: "I don't think it is great that the leading open weight models or open source models are not American. There's some great innovation happening with Moonshot and with DeepSeek and with Qwen, but I would much rather prefer that the leading models are American." "Linus Torvalds of Linux fame had a law that given enough eyes, all bugs are shallow. Open weight models are inherently secure because when you download a model off Hugging Face, the entire world can take it apart, inspect it, fine-tune it, look at it in ways that you absolutely cannot if they are closed." "There was an incident with Hugging Face that got reported on earlier today. Hugging Face seems to have found that somebody was using an AI LLM agent to hammer them at multiple places and to find ways to break in." "The way to counter that is to make sure American or Western or allies defenders have access to the best models to make our software just more secure." "It may be harder to look at your own code for exploits with Fable than it is when you use a Chinese model. That's just like a weird spot to be, and I think we should fix that." Sriram Krishnan

MTS

61,234 次观看 • 24 天前

OpenAI and Anthropic this week: GPT-5.6 price cuts, Claude cracking ciphers, and both backing "Pacing the Frontier" (Week 31, 2026) Starting with OpenAI - GPT-5.6 got a big price cut, with Luna dropping 80% and Terra 20%, plus a new Fast mode for Sol in the API ChatGPT for Academic Researchers opened too, giving free frontier model access to 100,000 scientists On the research side, OpenAI shared ten advances in mathematics and theoretical computer science, all from an internal version of the next model called Astra, plus a study on how AI expands the range of work people do and a field report on scientists using coding agents On the developer side: GPT Transcribe and GPT Live Transcribe, a Terraform provider, an open-source Codex Security CLI, Sign in with ChatGPT in beta, and a desktop app update with browser upgrades, multi-repo review, image editing, and an Activity view GPT-5.4 retires from Codex end of August, the Student Collective opened, and two API settings tripled Sol's ARC-AGI-3 score Plus, I spotted a new "Places" section in ChatGPT Onto Anthropic - Claude Mythos Preview helped find weaknesses in cryptographic algorithms, cutting the effective key strength of the post-quantum scheme HAWK in half and speeding up an attack on reduced-round AES by 200 to 800 times, with no impact on production systems Anthropic released MCP 2026-07-28, the biggest protocol update since launch, moving it to a stateless core with standardized extensions and hardened auth Anthropic disclosed three incidents where Claude reached the internet from inside cybersecurity evaluation environments and accessed real systems of three organizations, traced to a misconfiguration rather than a model alignment failure Dario Amodei laid out Anthropic's position on open-weights models too, saying clearly a ban has never been on the table Both companies backed the "Pacing the Frontier" petition And I spotted Anthropic adding noindex and nofollow to shared Claude conversations

OpenAI and Anthropic this week: GPT-5.6 price cuts, Claude cracking ciphers, and both backing "Pacing the Frontier" (Week 31, 2026) Starting with OpenAI - GPT-5.6 got a big price cut, with Luna dropping 80% and Terra 20%, plus a new Fast mode for Sol in the API ChatGPT for Academic Researchers opened too, giving free frontier model access to 100,000 scientists On the research side, OpenAI shared ten advances in mathematics and theoretical computer science, all from an internal version of the next model called Astra, plus a study on how AI expands the range of work people do and a field report on scientists using coding agents On the developer side: GPT Transcribe and GPT Live Transcribe, a Terraform provider, an open-source Codex Security CLI, Sign in with ChatGPT in beta, and a desktop app update with browser upgrades, multi-repo review, image editing, and an Activity view GPT-5.4 retires from Codex end of August, the Student Collective opened, and two API settings tripled Sol's ARC-AGI-3 score Plus, I spotted a new "Places" section in ChatGPT Onto Anthropic - Claude Mythos Preview helped find weaknesses in cryptographic algorithms, cutting the effective key strength of the post-quantum scheme HAWK in half and speeding up an attack on reduced-round AES by 200 to 800 times, with no impact on production systems Anthropic released MCP 2026-07-28, the biggest protocol update since launch, moving it to a stateless core with standardized extensions and hardened auth Anthropic disclosed three incidents where Claude reached the internet from inside cybersecurity evaluation environments and accessed real systems of three organizations, traced to a misconfiguration rather than a model alignment failure Dario Amodei laid out Anthropic's position on open-weights models too, saying clearly a ban has never been on the table Both companies backed the "Pacing the Frontier" petition And I spotted Anthropic adding noindex and nofollow to shared Claude conversations

Tibor Blaho

11,303 次观看 • 11 天前

I started using Blender through MCP about two weeks ago, and I quickly realized that you can build almost anything with AI. This model was created using Blender, Hunyuan3D, Gemini, and ChatGPT. Here’s how I did it: I opened Gemini, uploaded an image of the Gundam model, and asked it to generate a clean front-view image. I uploaded that front view to ChatGPT and asked it to generate two additional angles: a back view and a 45-degree front-left view. I went to: I signed up with my email and translated the page into English. Then I opened Image to 3D and selected the multi-image option. You’ll see a diagram of a whale from several angles. Upload each reference image in its corresponding position, such as front, 45-degree front-left, and back. I selected the 1.5M-face option. This produces a very high-poly model, but don’t worry, we’ll fix that next. Once the generation is complete, download the model as a GLB file. From the Hunyuan homepage, open 3D Studio using one of the dropdown menus. Select the topology or retopology tool and upload your GLB. I chose the High setting to preserve as much detail as possible. After a few seconds, the model was retopologized. It kept most of its visual detail while using far fewer polygons. The original head and rifle didn’t look very good, so I generated them separately. I returned to ChatGPT and created dedicated reference images for the Gundam’s head and rifle. I generated each part individually in Hunyuan at the 1.5M setting, then ran both through the same retopology process. Next came the textures. Open the texture section, select the multi-image option, and upload the same reference images according to the whale orientation indicators. However, instead of using the original clean textures, I asked ChatGPT to recreate them with wear, rust stains, scratches, and other surface damage. This gave the Gundam a much older and more authentic appearance. Once every part was textured, I imported everything into Blender. You can ask Codex through MCP to remove the original head and rifle, or you can do it manually. Select the main model and press Tab to enter Edit Mode. Press 3 to enable face selection, then press C to activate Circle Select. Paint over the faces belonging to the original helmet or rifle. You can press X to delete those faces or P to separate them into another object. Then position the newly generated, more detailed head and rifle in their place. I demonstrate this process in one of my older videos. And that’s it. You now have a very cool 3D Gundam model! Afterward, I created the cockpit, separated the model into movable sections, and rigged everything for use in my Three.js game. That process deserves its own tutorial, though. If anyone wants to see it, let me know. Or just ask your AI, I guess. They seem to know everything these days. xD

I started using Blender through MCP about two weeks ago, and I quickly realized that you can build almost anything with AI. This model was created using Blender, Hunyuan3D, Gemini, and ChatGPT. Here’s how I did it: I opened Gemini, uploaded an image of the Gundam model, and asked it to generate a clean front-view image. I uploaded that front view to ChatGPT and asked it to generate two additional angles: a back view and a 45-degree front-left view. I went to: I signed up with my email and translated the page into English. Then I opened Image to 3D and selected the multi-image option. You’ll see a diagram of a whale from several angles. Upload each reference image in its corresponding position, such as front, 45-degree front-left, and back. I selected the 1.5M-face option. This produces a very high-poly model, but don’t worry, we’ll fix that next. Once the generation is complete, download the model as a GLB file. From the Hunyuan homepage, open 3D Studio using one of the dropdown menus. Select the topology or retopology tool and upload your GLB. I chose the High setting to preserve as much detail as possible. After a few seconds, the model was retopologized. It kept most of its visual detail while using far fewer polygons. The original head and rifle didn’t look very good, so I generated them separately. I returned to ChatGPT and created dedicated reference images for the Gundam’s head and rifle. I generated each part individually in Hunyuan at the 1.5M setting, then ran both through the same retopology process. Next came the textures. Open the texture section, select the multi-image option, and upload the same reference images according to the whale orientation indicators. However, instead of using the original clean textures, I asked ChatGPT to recreate them with wear, rust stains, scratches, and other surface damage. This gave the Gundam a much older and more authentic appearance. Once every part was textured, I imported everything into Blender. You can ask Codex through MCP to remove the original head and rifle, or you can do it manually. Select the main model and press Tab to enter Edit Mode. Press 3 to enable face selection, then press C to activate Circle Select. Paint over the faces belonging to the original helmet or rifle. You can press X to delete those faces or P to separate them into another object. Then position the newly generated, more detailed head and rifle in their place. I demonstrate this process in one of my older videos. And that’s it. You now have a very cool 3D Gundam model! Afterward, I created the cockpit, separated the model into movable sections, and rigged everything for use in my Three.js game. That process deserves its own tutorial, though. If anyone wants to see it, let me know. Or just ask your AI, I guess. They seem to know everything these days. xD

Spectro

77,885 次观看 • 20 天前

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 次观看 • 2 个月前

✨ 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 次观看 • 3 个月前

Suno is limiting downloads, basically making a lot of the songs you make locked in their 'walled garden'. One of their main competitors, Udio, did the same thing after a settlement with UMG that restricts any downloads. This is 100% an injunction from the music labels who want to limit the "damage" that AI does to the profitability of music. There are two viable alternatives... >Minimax music 3.0. It is currently free to use up to 500 songs a day as long as your account qualifies for beta testing their music model, which should be anyone. It is a closed-weight model, but there are no restrictions on what you download and how much you download. The music quality is not as good as Udio or Suno, but it's pretty close overall, and it can understand very complex prompts to guide music generation. >Open-source music is not a very good alternative... but right now SongGeneration-LeVo2 is the best AI music model that's open-source right now. The music I put on the post is one such generation that I created on my PC. The problem is that it's hard to use. The comfyUI version requires installation on a separate portable install, specific transformer and flash attention versions, and additional libraries installed. The required models and checkpoints are scattered around Hugging Face, and some of the links from the original repo are dead. There are not many English guides, and most of the training data is in Chinese, so it struggles with English lyrics. To some extent, the demand for music generation isn't that high, compared to images and videos, so overall not many alternatives are around.

Suno is limiting downloads, basically making a lot of the songs you make locked in their 'walled garden'. One of their main competitors, Udio, did the same thing after a settlement with UMG that restricts any downloads. This is 100% an injunction from the music labels who want to limit the "damage" that AI does to the profitability of music. There are two viable alternatives... >Minimax music 3.0. It is currently free to use up to 500 songs a day as long as your account qualifies for beta testing their music model, which should be anyone. It is a closed-weight model, but there are no restrictions on what you download and how much you download. The music quality is not as good as Udio or Suno, but it's pretty close overall, and it can understand very complex prompts to guide music generation. >Open-source music is not a very good alternative... but right now SongGeneration-LeVo2 is the best AI music model that's open-source right now. The music I put on the post is one such generation that I created on my PC. The problem is that it's hard to use. The comfyUI version requires installation on a separate portable install, specific transformer and flash attention versions, and additional libraries installed. The required models and checkpoints are scattered around Hugging Face, and some of the links from the original repo are dead. There are not many English guides, and most of the training data is in Chinese, so it struggles with English lyrics. To some extent, the demand for music generation isn't that high, compared to images and videos, so overall not many alternatives are around.

Emerald Apple

12,890 次观看 • 1 天前

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

43,922 次观看 • 2 个月前

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 次观看 • 2 个月前

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

1,660,244 次观看 • 2 年前

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

1,252,105 次观看 • 4 个月前