Video yükleniyor...

Video Yüklenemedi

Bu video yüklenirken bir sorun oluştu. Bu geçici bir ağ sorunundan kaynaklanıyor olabilir veya video kullanılamıyor olabilir.

Ana Sayfaya Dön

🚀 Pixel Diffusion Decoder (PiD) v1.5 is out 🎨 No color-shifting problem, better 4K visual quality 📦 Undistilled checkpoint and full training code 🧩 Support FLUX, FLUX2, Qwen-Image, Z-Image, ... Feel free to use it, reproduce it, and build on top of it 💻 Code: 🔗 Demo & comparison:

Xuanchi Ren

1,516 subscribers

51,736 görüntüleme • 3 ay önce •via X (Twitter)

Bilim & Teknoloji

Anya Rossi• Live Now

Private livecam show

6 Yorum

Pausecatito profil fotoğrafı

Pausecatito3 ay önce

@kohya_tech No color shifting problem when there is massive color shift in every example🤔

Yarin Didi profil fotoğrafı

Yarin Didi3 ay önce

Thing is for references based image generation/editing, we will need conditional pid/upscaler or generating already at naitive resolution. Great work

franky profil fotoğrafı

franky3 ay önce

请问未来是否会支持Krea2？

Diego profil fotoğrafı

Diego3 ay önce

@bdsqlsz Amazing

Ares profil fotoğrafı

Ares3 ay önce

There is already Topaz for this...

AI Mastery Guide profil fotoğrafı

AI Mastery Guide3 ay önce

Fixing color shifting alone makes this worth trying

Benzer Videolar

Image-to-Pixel 3.0 is out 🎨 Turn images and video into pixel art right in your browser. Free, MIT open source. Try it: Or use it in code: npm i image-to-pixel

Image-to-Pixel 3.0 is out 🎨 Turn images and video into pixel art right in your browser. Free, MIT open source. Try it: Or use it in code: npm i image-to-pixel

Tezumie

163,459 görüntüleme • 4 gün önce

🎨 Qwen-Image-Layered is LIVE — native image decomposition, fully open-sourced! ✨ Why it stands out ✅ Photoshop-grade layering Physically isolated RGBA layers with true native editability ✅ Prompt-controlled structure Explicitly specify 3–10 layers — from coarse layouts to fine-grained details ✅ Infinite decomposition Keep drilling down: layers within layers, to any depth of detail 🤗 Hugging Face: 🧩 ModelScope: 💻 GitHub: 📝 Blog: 📄 Technical Report: 🚀 Demo (HF): 🚀 Demo (ModelScope):

🎨 Qwen-Image-Layered is LIVE — native image decomposition, fully open-sourced! ✨ Why it stands out ✅ Photoshop-grade layering Physically isolated RGBA layers with true native editability ✅ Prompt-controlled structure Explicitly specify 3–10 layers — from coarse layouts to fine-grained details ✅ Infinite decomposition Keep drilling down: layers within layers, to any depth of detail 🤗 Hugging Face: 🧩 ModelScope: 💻 GitHub: 📝 Blog: 📄 Technical Report: 🚀 Demo (HF): 🚀 Demo (ModelScope):

Qwen

1,212,750 görüntüleme • 10 ay önce

ACE-Step-1.5-xl is out now. We scaled the DiT decoder to 4B. And it shows better audio quality, better prompt following, and better musicality. It still fast -- 8 steps with turbo distillation. What didn't change: - Same generation API, same LoRA training code, same everything - All LM models (0.6B / 1.7B / 4B) fully compatible with all 3 variants - Your existing projects work with XL — no changes needed Try it for free at Weithgts and code: GitHub: Hugging Face: (3 variants: xl-base, xl-sft, xl-turbo) XL demos:

ACE-Step-1.5-xl is out now. We scaled the DiT decoder to 4B. And it shows better audio quality, better prompt following, and better musicality. It still fast -- 8 steps with turbo distillation. What didn't change: - Same generation API, same LoRA training code, same everything - All LM models (0.6B / 1.7B / 4B) fully compatible with all 3 variants - Your existing projects work with XL — no changes needed Try it for free at Weithgts and code: GitHub: Hugging Face: (3 variants: xl-base, xl-sft, xl-turbo) XL demos:

ACE Music

36,190 görüntüleme • 6 ay önce

Video understanding isn't just recognizing —it demands reasoning across thousands of frames. Meet Long-RL🚀 Highlights: 🧠 Dataset: LongVideo-Reason — 52K QAs with reasoning. ⚡ System: MR-SP - 2.1× faster RL for long videos. 📈 Scalability: Hour-long videos (3,600 frames) RL on a single node (8×A100s). 🖼️📝🎵 RL training for video, text, audio — works with VILA, Qwen series, and image/video generation models 🎨🎬 📄 Paper: 🎥 Demo: 💻 Code:

Video understanding isn't just recognizing —it demands reasoning across thousands of frames. Meet Long-RL🚀 Highlights: 🧠 Dataset: LongVideo-Reason — 52K QAs with reasoning. ⚡ System: MR-SP - 2.1× faster RL for long videos. 📈 Scalability: Hour-long videos (3,600 frames) RL on a single node (8×A100s). 🖼️📝🎵 RL training for video, text, audio — works with VILA, Qwen series, and image/video generation models 🎨🎬 📄 Paper: 🎥 Demo: 💻 Code:

Yukang Chen

31,876 görüntüleme • 1 yıl önce

FREE UNLIMITED GPT-6 LUNA. NO CARD. NO LOGIN. > DuckDuckGo just opened Luna to everyone on DuckAi, with no limits. What you get: - chat with Luna, nothing to sign up for - image generation - full voice conversation, works great on mobile How to start: open and type. No account, no payment details. Use it for drafts, code review and image ideas, so your paid quota is saved for the heavy stuff.

FREE UNLIMITED GPT-6 LUNA. NO CARD. NO LOGIN. > DuckDuckGo just opened Luna to everyone on DuckAi, with no limits. What you get: - chat with Luna, nothing to sign up for - image generation - full voice conversation, works great on mobile How to start: open and type. No account, no payment details. Use it for drafts, code review and image ideas, so your paid quota is saved for the heavy stuff.

Atenov int.

106,335 görüntüleme • 6 gün önce

Some food for thought when designing benchmarks... So, here's a little computer-use (visual) comparison between GPT-5.6 Astra and Qwen3.8 Max. The task here was to recreate the image in the center using the Paint UI. Super interesting how the two different LLMs+Harnesses approached this totally differently by default. I.e., Astra tried to approach this by drawing and layering geometric shapes. Qwen approached this pixel by pixel. (Of course, the pixel-by-pixel result looks closer to the original, it's essentially a low-res version of that by nature.) So, the Qwen-generated image would surely score higher in the sense that it's closer to the original. But I wouldn’t conclude from this example that one LLM generalizes better than the other on other tasks. Also, I wouldn't say Qwen has better compute-use capabilities or better visual understanding than Astra. But it highlights an interesting point about how slippery benchmarks are when they only compare final results.

Some food for thought when designing benchmarks... So, here's a little computer-use (visual) comparison between GPT-5.6 Astra and Qwen3.8 Max. The task here was to recreate the image in the center using the Paint UI. Super interesting how the two different LLMs+Harnesses approached this totally differently by default. I.e., Astra tried to approach this by drawing and layering geometric shapes. Qwen approached this pixel by pixel. (Of course, the pixel-by-pixel result looks closer to the original, it's essentially a low-res version of that by nature.) So, the Qwen-generated image would surely score higher in the sense that it's closer to the original. But I wouldn’t conclude from this example that one LLM generalizes better than the other on other tasks. Also, I wouldn't say Qwen has better compute-use capabilities or better visual understanding than Astra. But it highlights an interesting point about how slippery benchmarks are when they only compare final results.

Sebastian Raschka

24,404 görüntüleme • 1 ay önce

Fable 5.1 is an absolute beast for game dev. I took my Opus 5 space game and told Fable to "just make it look better". It overhauled everything with a TON of detail, AND made it 2x faster 🤯 Code: Demo: Every pixel you see is made by AI. Fable modeled everything in Blender, generated textures with code, and in a few cases ran image models via CLI. In mere months we'll be vibe-coding AAA graphics at this rate. Just insane. Of course, Fable recorded and edited this comparison video too (with a little help from Opus at the end, because of the Fable outage)

Fable 5.1 is an absolute beast for game dev. I took my Opus 5 space game and told Fable to "just make it look better". It overhauled everything with a TON of detail, AND made it 2x faster 🤯 Code: Demo: Every pixel you see is made by AI. Fable modeled everything in Blender, generated textures with code, and in a few cases ran image models via CLI. In mere months we'll be vibe-coding AAA graphics at this rate. Just insane. Of course, Fable recorded and edited this comparison video too (with a little help from Opus at the end, because of the Fable outage)

Anshu

54,586 görüntüleme • 1 ay önce

"People assume prototyping requires writing code, but Image Gen is simply much faster." Rohan (Codex PM) showed me exactly how he does it: "I took a screenshot of the Codex composer where you can type in a new message. Then I asked Codex to use Image Gen to prototype 4-5 ideas to improve project selection. I can then ask it to take the first image and make a prototype of it on a Codex site. We usually think of Image Gen as useful for things like turning my hair blue. But it can create amazing digital mocks too." 📌 Full episode:

"People assume prototyping requires writing code, but Image Gen is simply much faster." Rohan (Codex PM) showed me exactly how he does it: "I took a screenshot of the Codex composer where you can type in a new message. Then I asked Codex to use Image Gen to prototype 4-5 ideas to improve project selection. I can then ask it to take the first image and make a prototype of it on a Codex site. We usually think of Image Gen as useful for things like turning my hair blue. But it can create amazing digital mocks too." 📌 Full episode:

Peter Yang

17,210 görüntüleme • 3 ay önce

🚀 Excited to introduce StreamRender-H3 — bringing real-time interactive worlds to life with diffusion models! Built on Sol-Engine, we post-trained MiniMax (official) H3 Ref2VA into a real-time streaming neural renderer. 🎮 Code drives the world. A game engine handles physics, object states, and player interactions, while H3 generates the visuals. ⚡ Real-time streaming: 2 denoising steps, 24 FPS, and ~273 ms per chunk on 8×GB200 GPUs. 🎨 One world, endless appearances: Change the reference image and prompt to render the same interactive world in entirely different styles. 🔥 Powered by streaming post-training, distillation, a fast causal H3 VAE, and full-stack inference acceleration. Check out our demo and code! 👇 demo: code:

🚀 Excited to introduce StreamRender-H3 — bringing real-time interactive worlds to life with diffusion models! Built on Sol-Engine, we post-trained MiniMax (official) H3 Ref2VA into a real-time streaming neural renderer. 🎮 Code drives the world. A game engine handles physics, object states, and player interactions, while H3 generates the visuals. ⚡ Real-time streaming: 2 denoising steps, 24 FPS, and ~273 ms per chunk on 8×GB200 GPUs. 🎨 One world, endless appearances: Change the reference image and prompt to render the same interactive world in entirely different styles. 🔥 Powered by streaming post-training, distillation, a fast causal H3 VAE, and full-stack inference acceleration. Check out our demo and code! 👇 demo: code:

Enze Xie

70,483 görüntüleme • 7 gün önce

I installed a new Visual Studio Code extension to help me write code. But this extension doesn't get in your way. It doesn't try to write code for you. It only gives you hints and recommendations about your code. I like that. Like a second pair of eyes, looking over my shoulder and telling me how I can do things better. The extension is Traycer. You can install it directly from inside Visual Studio Code. It's free forever for anyone working on open-source projects (and if you aren't, you can use it for free until the end of June.) I recorded a super-duper-quick video to show you how this extension works. I know code generation is a huge thing, but I really appreciate tools that help me do what I do better. Thanks to the Traycer team for helping me with the extension and collaborating with me on this post!

I installed a new Visual Studio Code extension to help me write code. But this extension doesn't get in your way. It doesn't try to write code for you. It only gives you hints and recommendations about your code. I like that. Like a second pair of eyes, looking over my shoulder and telling me how I can do things better. The extension is Traycer. You can install it directly from inside Visual Studio Code. It's free forever for anyone working on open-source projects (and if you aren't, you can use it for free until the end of June.) I recorded a super-duper-quick video to show you how this extension works. I know code generation is a huge thing, but I really appreciate tools that help me do what I do better. Thanks to the Traycer team for helping me with the extension and collaborating with me on this post!

Santiago

101,068 görüntüleme • 2 yıl önce

We have brands wanting to work with us every week on this or that. Kris and I are super picky. It has to be a product that we would use, is really high quality, and solves a problem. (Like the raincoats, Beaker’s SNOWPANTS and Brix’ backpack) We are happy to promote the Cheerble Pet Fountain D1 Pro. It has a massive capacity, easy to fill and clean system, and neato reachable base! Best of all, every pet in our house LOVES it. Here is the link to product: Use this code: 05BRIX You can enjoy a total of 30% off when using the code! Feel free to ask us any questions about it!

We have brands wanting to work with us every week on this or that. Kris and I are super picky. It has to be a product that we would use, is really high quality, and solves a problem. (Like the raincoats, Beaker’s SNOWPANTS and Brix’ backpack) We are happy to promote the Cheerble Pet Fountain D1 Pro. It has a massive capacity, easy to fill and clean system, and neato reachable base! Best of all, every pet in our house LOVES it. Here is the link to product: Use this code: 05BRIX You can enjoy a total of 30% off when using the code! Feel free to ask us any questions about it!

Bunsen, Beaker, Bernoulli & Brix

14,011 görüntüleme • 4 ay önce

🚀 New Paper: Pixel Reasoner 🧠🖼️ How can Vision-Language Models (VLMs) perform chain-of-thought reasoning within the image itself? We introduce Pixel Reasoner, the first open-source framework that enables VLMs to “think in pixel space” through curiosity-driven reinforcement learning. Current VLMs reason only in text — even when grounded in rich images or videos, their logical steps are verbalized in natural language. This restricts their ability to interrogate visual evidence and demonstrate how conclusions are drawn. 🔍 So we ask: What if we could make VLMs "show their work" by reasoning directly in the pixel space? Inspired by GPT-o3’s "think-in-image" ability, we propose a framework where VLMs use interactive visual operations — zoom, select-frame, highlight — to reason through complex visual inputs. To do this, we design a two-stage training process: Instruction tuning with synthesized visual reasoning traces. Reinforcement learning with curiosity-driven reward to balance exploration between pixel and text reasoning ✨ With this, Pixel Reasoner achieves near-SoTA performance on many information-rich multimodal benchmarks: 📊 84% on InfographicsVQA 🧠 84% on V* benchmark 🧩 74% on TallyQA-Complex It also achieves strong accuracy of 68% on MVBench (a video benchmark). Website: Paper: Code: Demo: (coming soon)

🚀 New Paper: Pixel Reasoner 🧠🖼️ How can Vision-Language Models (VLMs) perform chain-of-thought reasoning within the image itself? We introduce Pixel Reasoner, the first open-source framework that enables VLMs to “think in pixel space” through curiosity-driven reinforcement learning. Current VLMs reason only in text — even when grounded in rich images or videos, their logical steps are verbalized in natural language. This restricts their ability to interrogate visual evidence and demonstrate how conclusions are drawn. 🔍 So we ask: What if we could make VLMs "show their work" by reasoning directly in the pixel space? Inspired by GPT-o3’s "think-in-image" ability, we propose a framework where VLMs use interactive visual operations — zoom, select-frame, highlight — to reason through complex visual inputs. To do this, we design a two-stage training process: Instruction tuning with synthesized visual reasoning traces. Reinforcement learning with curiosity-driven reward to balance exploration between pixel and text reasoning ✨ With this, Pixel Reasoner achieves near-SoTA performance on many information-rich multimodal benchmarks: 📊 84% on InfographicsVQA 🧠 84% on V* benchmark 🧩 74% on TallyQA-Complex It also achieves strong accuracy of 68% on MVBench (a video benchmark). Website: Paper: Code: Demo: (coming soon)

Wenhu Chen

82,829 görüntüleme • 1 yıl önce

Today, we introduce HunyuanImage 3.0-Instruct, a native multimodal model focusing on image-editing by integrating visual understanding with precise image synthesis! 🚀 It understands input images and reasons before generating images. Built on an 80B-parameter MoE architecture (13B activated), it natively unifies deep multimodal comprehension and high-fidelity generation. 🧠 A "Thinking" Model with Native CoT & MixGRPO: The model doesn’t just execute commands, it processes them through a Native Chain-of-Thought (CoT) schema. Enhanced by our self-developed MixGRPO algorithm, it reasons through complex instructions to achieve flawless intent alignment and human-preference consistency. 🎨 Precise Editing & Multi-Image Fusion: The model enables accurate image editing by adding, removing, or modifying elements while keeping non-target areas perfectly intact. It also excels at seamless multi-image fusion, synthesizing complex scenes by extracting and blending elements from multiple sources into a unified, consistent output. 🏆 SOTA Performance: HunyuanImage 3.0-Instruct sets a new benchmark in visual quality and alignment, delivering performance that matches leading proprietary models. We aim to enable the community to explore new ideas with a state-of-the-art foundation model, fostering a dynamic and vibrant image generation ecosystem. 🛠️🎨 💻Try it at (PC only):

Today, we introduce HunyuanImage 3.0-Instruct, a native multimodal model focusing on image-editing by integrating visual understanding with precise image synthesis! 🚀 It understands input images and reasons before generating images. Built on an 80B-parameter MoE architecture (13B activated), it natively unifies deep multimodal comprehension and high-fidelity generation. 🧠 A "Thinking" Model with Native CoT & MixGRPO: The model doesn’t just execute commands, it processes them through a Native Chain-of-Thought (CoT) schema. Enhanced by our self-developed MixGRPO algorithm, it reasons through complex instructions to achieve flawless intent alignment and human-preference consistency. 🎨 Precise Editing & Multi-Image Fusion: The model enables accurate image editing by adding, removing, or modifying elements while keeping non-target areas perfectly intact. It also excels at seamless multi-image fusion, synthesizing complex scenes by extracting and blending elements from multiple sources into a unified, consistent output. 🏆 SOTA Performance: HunyuanImage 3.0-Instruct sets a new benchmark in visual quality and alignment, delivering performance that matches leading proprietary models. We aim to enable the community to explore new ideas with a state-of-the-art foundation model, fostering a dynamic and vibrant image generation ecosystem. 🛠️🎨 💻Try it at (PC only):

Tencent Hy

126,668 görüntüleme • 8 ay önce

🚀 Excited to release LongLive 2.0! 🎬 An end-to-end infrastructure for long video generation, with FP4 and parallelism at the core of both training and inference. ⚡45.7 FPS generation speed on 5B model⚡ ✨ LongLive 2.0 supports real-video training, few-step distillation, multi-shot training/inference, sequence-parallel acceleration, NVFP4 KV cache, and async VAE decoding deployment. 🧩 To our knowledge, this is the first open-source 4-bit long video generation infra that covers both training and inference. 🙌 Welcome to check it out, try it, and share feedback! 🔗 Code: 📰 Paper: 🎥 Demo: #LongVideoGeneration #VideoGeneration #Realtime #AIInfra #EfficientAI #FP4 #Parallel #NVIDIA

🚀 Excited to release LongLive 2.0! 🎬 An end-to-end infrastructure for long video generation, with FP4 and parallelism at the core of both training and inference. ⚡45.7 FPS generation speed on 5B model⚡ ✨ LongLive 2.0 supports real-video training, few-step distillation, multi-shot training/inference, sequence-parallel acceleration, NVFP4 KV cache, and async VAE decoding deployment. 🧩 To our knowledge, this is the first open-source 4-bit long video generation infra that covers both training and inference. 🙌 Welcome to check it out, try it, and share feedback! 🔗 Code: 📰 Paper: 🎥 Demo: #LongVideoGeneration #VideoGeneration #Realtime #AIInfra #EfficientAI #FP4 #Parallel #NVIDIA

Yukang Chen

60,053 görüntüleme • 5 ay önce

🚀 Exciting News! We're thrilled to announce the launch of Qwen Chat ( ) – your new go-to Web UI for interacting with Qwen models! 🌟 💬 Chat effortlessly with our flagship model Qwen2.5-Plus , explore vision-language capabilities with Qwen2-VL-Max , and dive into reasoning models like QwQ and QVQ, code with coding expert Qwen2.5-Coder-32B-Instruct, etc. ✨ Key Features: * Choose & compare multiple models in one interface * Upload documents & get answers based on them * Preview mode with HTML support * Upload images for visual understanding 🌟 Coming Soon: Web search, image generation, voice mode, and more! 🔗 Ready to experience the future? Start to chat with Qwen today 💬 Feel free to send us your feedback and suggestions!

🚀 Exciting News! We're thrilled to announce the launch of Qwen Chat ( ) – your new go-to Web UI for interacting with Qwen models! 🌟 💬 Chat effortlessly with our flagship model Qwen2.5-Plus , explore vision-language capabilities with Qwen2-VL-Max , and dive into reasoning models like QwQ and QVQ, code with coding expert Qwen2.5-Coder-32B-Instruct, etc. ✨ Key Features: * Choose & compare multiple models in one interface * Upload documents & get answers based on them * Preview mode with HTML support * Upload images for visual understanding 🌟 Coming Soon: Web search, image generation, voice mode, and more! 🔗 Ready to experience the future? Start to chat with Qwen today 💬 Feel free to send us your feedback and suggestions!

Qwen

215,564 görüntüleme • 1 yıl önce

✨ To inspire more people to go build something now that we have AI to help us (especially non-tech people, cause I still know so many who are scared of building something): I added a [ BUILD IT ] button to It's like a mini-Lovable/Replit/v0: Any idea you see you like, you can click [ BUILD IT ], and it will use Opus 4.6 to build a landing page for it And then you can download the code it generated It's not a full startup of course, but a nice preview of what it can be, to give you an idea and inspire you to build it out further The code is live streamed also so you can see it being built 😊 Ironically this itself took me 1 hour to build with AI too Completely free and I pay for the tokens (please don't abuse it :D)

✨ To inspire more people to go build something now that we have AI to help us (especially non-tech people, cause I still know so many who are scared of building something): I added a [ BUILD IT ] button to It's like a mini-Lovable/Replit/v0: Any idea you see you like, you can click [ BUILD IT ], and it will use Opus 4.6 to build a landing page for it And then you can download the code it generated It's not a full startup of course, but a nice preview of what it can be, to give you an idea and inspire you to build it out further The code is live streamed also so you can see it being built 😊 Ironically this itself took me 1 hour to build with AI too Completely free and I pay for the tokens (please don't abuse it :D)

@levelsio

277,832 görüntüleme • 6 ay önce

StyleDrop: Text-to-Image Generation in Any Style introduce StyleDrop, a method that enables the synthesis of images that faithfully follow a specific style using a text-to-image model. The proposed method is extremely versatile and captures nuances and details of a user-provided style, such as color schemes, shading, design patterns, and local and global effects. It efficiently learns a new style by fine-tuning very few trainable parameters (less than 1% of total model parameters) and improving the quality via iterative training with either human or automated feedback. Better yet, StyleDrop is able to deliver impressive results even when the user supplies only a single image that specifies the desired style. An extensive study shows that, for the task of style tuning text-to-image models, StyleDrop implemented on Muse convincingly outperforms other methods, including DreamBooth and textual inversion on Imagen or Stable Diffusion. paper page:

StyleDrop: Text-to-Image Generation in Any Style introduce StyleDrop, a method that enables the synthesis of images that faithfully follow a specific style using a text-to-image model. The proposed method is extremely versatile and captures nuances and details of a user-provided style, such as color schemes, shading, design patterns, and local and global effects. It efficiently learns a new style by fine-tuning very few trainable parameters (less than 1% of total model parameters) and improving the quality via iterative training with either human or automated feedback. Better yet, StyleDrop is able to deliver impressive results even when the user supplies only a single image that specifies the desired style. An extensive study shows that, for the task of style tuning text-to-image models, StyleDrop implemented on Muse convincingly outperforms other methods, including DreamBooth and textual inversion on Imagen or Stable Diffusion. paper page:

AK

56,408 görüntüleme • 3 yıl önce

I feel like we haven't seen enough of what Gemini 4 Argon can actually do ❤️‍🔥 I gave it one image of a Gothic Victorian carriage and asked it to build the whole thing as an interactive 3D experience. Just one image and this is what you get. A 174,000 face model from scratch, complete with opening doors, hinged windows, working wheel physics, suspension, flickering lanterns and even custom sculpted skulls. All of this generated through code. No blender. Argon absolutely delivered with this one too. Many more such experiments are on the way :D

I feel like we haven't seen enough of what Gemini 4 Argon can actually do ❤️‍🔥 I gave it one image of a Gothic Victorian carriage and asked it to build the whole thing as an interactive 3D experience. Just one image and this is what you get. A 174,000 face model from scratch, complete with opening doors, hinged windows, working wheel physics, suspension, flickering lanterns and even custom sculpted skulls. All of this generated through code. No blender. Argon absolutely delivered with this one too. Many more such experiments are on the way :D

The Bugged Dev

126,670 görüntüleme • 4 gün önce

Your data is only as valuable as what you build on top of it. We now integrate with Databricks to let enterprise teams vibe code living applications and deploy them directly into Databricks Apps: with governance, security, and observability built in. 👷🏼‍♂️ More builders. 🛠️ Better tools. 🎮 No compromises on control.

Your data is only as valuable as what you build on top of it. We now integrate with Databricks to let enterprise teams vibe code living applications and deploy them directly into Databricks Apps: with governance, security, and observability built in. 👷🏼‍♂️ More builders. 🛠️ Better tools. 🎮 No compromises on control.

Replit ⠕

42,008 görüntüleme • 8 ay önce