Nice! We no longer need a depth sensor, a... Kinect, or even Windows to build amazing interactive installations. Just an Apple device, ARKit, and AI. Rendered with Three.js in Electron, using its new SharedTexture API for direct GPU-to-GPU texture sharing. 🚀show more

In what ways are you using #AI on your... RTX GPU in your personal or professional life? 🤔 Reply with your answer and use #AIonRTX for a chance to win a MSI Gaming GeForce RTX 4080 GAMING X TRIO GPU. 👇show more

"Asym games need to stop using stamina to balance... their games, its a poor mechanic and its tiring. We should prioritize interactive gameplay like obstacles (eg. doors, pallets, windows), that allow for more interactions between survivors and killers."show more

this is the worst local AI will ever be.... tomorrow it gets faster. next month the models get smarter. next year your GPU runs what a data center runs today. Qwen3.5-35B-A3B on a single 3090. told it to visualize its own expert routing. 256 experts, 8 active per token, rendered in 3D on the same GPU running inference. no API key. no subscription. no permission needed. closed AI isn't losing ground. it's losing the argument.show more

Just wrapped up a local meetup teaching people how... to build their MVP in an hour using Replit ⠕. Participants walked out with their first web/mobile app and a new appreciation for what’s possible with AI.show more

A preview Into the future of #quantum-GPU computing: At... GTC Washington, D.C., we announced NVIDIA NVQLink, an open system architecture for tightly coupling the extreme performance of GPU computing with quantum processors to build accelerated quantum supercomputers. In the expo hall, accelerated quantum computing was a centerpiece of the NVIDIA booth. See our live blog:show more

#FameCoin is on a mission to transform #crypto using... #AI influencers who share the wealth with their followers. Get ready for a new era of empowerment and profit sharing!🚀show more

Learn How To Build a Gemini-Powered Robotics Simulator in... the Browser with MuJoCo WASM. MuJoCo (WebAssembly) + Three.js + Gemini ER Thanks for sharing, Omar Sanseviero! 📍Tutorial: Demo: —— if it matters in AI or Robotics you'll see it here first:show more

AuraJs via ◥◣ A U R A M A... X X ◥◣ alpha build is out 🗿🖤 > open-source game engine. MIT. no fee no lock in > write one Javascript, compile to Mac, Windows, Linux > 2D & 3D w/ native access to GPU > play, publish, & vibecode straight from your terminal with any AI > binary size ~2MB vs Unity's 80MB > multiplayer out of the boxshow more

I'm excited to share Code Layers are available today... in Figma Sites! I'm excited for people to build rich, interactive ideas for their sites either with Code or using AI. Huge shoutout to the team, it was an incredible amount of work and there's more to come!show more

Loading DeFAI infrastructure ▓▓▓▓▓▓▓▓▓░ io.net, a decentralized GPU compute... network has expanded to power the future of DeFAI for Injective builders. The integration aims to support the growing trend of developers building AI Agents, DeFAI, and gaming initiatives in web3. io.net provides Web3 builders with tools to train, fine-tune, and deploy ML models using decentralized resources. The collaboration combines Injective's iAgent framework with io.net’s extensive GPU network, setting the stage for more accessible and innovative development across use cases that require compute resources. Key features include: ✅ Access to over 10,000 cluster-ready GPUs and CPUs, ✅ AI-driven blockchain activities using Injective's iAgent SDK, and ✅ Potential for new on-chain financial products leveraging GPU pricing and data feeds. 👀 io.net’s DePIN network deploys on-demand, decentralized GPU resources globally, designed for low latency and high-throughput processing. Benefits include reduced barriers for AI/ML projects, enhanced integration of AI with on-chain activities, and democratized access to high-performance computing resources. Together, this marks a significant milestone in decentralized AI infrastructure, addressing key challenges and paving the way for unprecedented innovation in AI development and on-chain finance.show more

Big Marigold update! Last year, we showed how to... turn Stable Diffusion 2 into a SOTA depth estimator with a few synthetic samples and 2–3 days on just 1 GPU. Today's release features: 🏎️ 1-step inference 🔢 New modalities 🫣 High resolution 🧨 Diffusers support 🕹️ New demos 🧶👇show more

A real-time AI motion capture model with 1B parameters.... Connects to a 1080P camera or processes uploaded video to generate 3D motion data in real time. Supports Unreal Engine, Unity, and Blender, requiring 8GB of GPU VRAM for real-time processing.show more

🔥Nexera & Aethir: Unleashing AI’s Next Frontier Through Tokenized... GPU Power 🤝 Nexera is proud to join forces with Aethir in a strategic partnership to make cutting-edge AI infrastructure globally accessible. By tokenizing fractional GPU ownership, we’re enabling developers, enterprises, and investors everywhere to harness the explosive growth of deep learning and generative AI without being limited by geography, scale, or cost. With transparent tokenization, innovators can access powerful GPUs for faster model training and more advanced applications. GPU providers gain streamlined funding for expansion and upgrades, and investors tap into a high-growth market with secure, compliant opportunities that can provide higher yields than other RWA products. It’s an entirely new ecosystem where everyone can thrive, fueling AI’s evolution at an unprecedented pace. By 2030, the global GPU market is projected to exceed hundreds of billions of dollars, driven by the explosive demand for AI-powered applications, deep learning, and increasingly sophisticated generative models, ensuring that tokenizing these invaluable resources is poised to tap into a massive, rapidly expanding opportunity. $NXRAshow more

Free NVIDIA GPU with 16 GB VRAM GPU for... Running Local LLMs! If you want to master local LLMs but you're waiting until you can afford a $1,500 GPU, you're honestly not going to make it. The open source AI ecosystem is moving way too fast for you to wait on your budget to catch up. Especially when you can build a bleeding edge inference engine from scratch right now, completely for free. You don't need a heavy local rig to start. Google is literally letting you use an enterprise grade NVIDIA Tesla T4 GPU for $0/hour. At standard cloud computing rates (~$0.20/hr), Google Colab’s 4 hour daily free tier hands you roughly $24 worth of data center tier GPU compute every single month. And most people just waste it. Let’s talk about the hardware you get access to for free. The NVIDIA Tesla T4 is an absolute workhorse: - Architecture: NVIDIA Turing (TU104) - VRAM: 16GB GDDR6 (320 GB/s bandwidth) - Compute: 320 Tensor Cores | 2560 CUDA Cores - Performance: 130 TOPS INT8 | 8.1 TFLOPS FP32 - Power: Sipping energy at a max 70W TDP This is the exact same hardware I used to run DeepMind's Gemma 4 26B A4B QAT MoE at a 250,000 context window without a single Out Of Memory (OOM) crash. If you have a web browser and 10 minutes, you have everything you need. I’ve put together a fully documented, cell by cell Google Colab notebook that teaches you exactly how to do this. Here is what the notebook actually teaches you: - How to provision an Ubuntu Linux environment with CUDA 13.0 and verify your driver stack. - How to pull the source code and compile the latest llama.cpp C++ binaries from scratch, specifically optimizing the build for your exact GPU using the -DCMAKE_CUDA_ARCHITECTURES=native flag. - How to directly download quantized local LLMs (GGUF format) straight from HuggingFace using the CLI. - How to manage 16GB VRAM limits, offload neural network layers to the GPU, and push massive context windows. Compile raw llama.cpp, ollama run a model, or spin up the LM Studio CLI. Pick whatever stack you are comfortable with. just start building. No hardware. No credit card. No excuses. Bookmark this post right now so you don't lose the tutorial. Even if you don't have time to run it today, you are going to want this workflow in your engineering toolkit. The link to the free Colab Notebook is in the comments below. Lemme know if you need more tutorials like this.show more

You don't need Claude Fable 5 or GPT-5.6 I... built with Claude 👇 Drifting particles + pulsing sound rings, the techniques: 1. Flow field = each particle reads a Simplex noise field for which way to drift, organic not random. 2. GPGPU = thousands of particles updated on the GPU in a texture, 60fps on a phone. 3. Audio-reactive shader = the rings pulse to the track frequency data. 4. Selective bloom = only the bright parts glow. Lenis + GSAP + Three.js. Save this 🔊show more

i'm writing my own gui framework in c++ and... rewriting my image editor using it, which is going good so far, i wanted to move from swiftui, qt, tauri, electron, react native, and flutter so i can use metal and directx gpu apis on macos and windows as much as possible,so i think going full metal/directx is the way to go i know i have a very hard and long way to go though as there are many many flaws,bugs in this very very early version and doesnt utilize the gpu as much as i want to but i hope we will get there soon ,i also need to make the ui look somewhat decent instead of whatever mess it is right nowshow more