绝了!NVIDIA最新开源了2,575 个高规格 3D 资产,整整472GB!!
从日常用品到工业设备,里面收录了不少现实环境里的物体。
一揽子,全部放进了 Hugging Face。
NVIDIA SimReady Assets。
绝不单纯是一个“免费建模素材库”!!
NVIDIA给每个3D 资产都带上了物理属性和语义信息,比如有多重,什么材质,重心在哪,用在什么场景等等,全都写进了参数里。
明摆着,就是用来让AI学习的!
学习怎么和真实的物理世界互动!
传统的3D资产在AI眼中,就是个有贴图的“视觉资产”,NVIDIA想做的是,把“视觉资产”变成AI可以理解的“物理资产”!
所以,这批资产的价值可想而知!
至少能够用到这些场景上!
机器人抓取和搬运训练,先在虚拟环境里练习拿取、摆放物体;
搭建工厂、仓库的数字孪生,测试设备和物体的布局;
配合合成数据工具,改变光照、视角、遮挡和物体位置,生成带标注的训练数据。
大家都在卷文字、代码与图像生成时,NVIDIA 开始为Physical AI(物理 AI) 铺设地基了。
不过,对大部分人来说,也是一个相当全面的3D资产素材库!
链接在下面,必须收藏了!
Nigel Hungerford-Symes retweeted
Turns out there's a livestream straight from the JLCPCB factory where you can watch PCBs being manufactured in real time:
Prompt t/s: 2,170 -> 2,653 (+22%)
Output t/s: 64.5 -> 76.4 (+18%)
Enjoy new performance boost! 👀
github.com/Niko1221/Strata/r…
Qwen3.8-Flash-Next first 32K tokens are 4%-10% faster, LOW RAM mode added (for low hardware).
New update here
github.com/Niko1221/Strata/r…
Nigel Hungerford-Symes retweeted
Replying to @itslueul
Grok 4.7 should be roughly on par with Opus 5.0, not 5.1. Better in some ways, worse in others. We need to fix multimodal performance.
Grok 4.8 will be a noticeable improvement.
Grok 4.9 is probably Astra/Fable class.
Grok 5 maybe better than anything. We shall see.
WARNING: Omarchy dot net is not legitimate. It looks like a scam/malware/phishing operation. I alerted their hosting service @spaceship, which acknowledged receipt, but refused to act. Wild that they want to be part of that, but ok!
Nigel Hungerford-Symes retweeted

zerohedge@zerohedgeAug 17
🇺🇸
United StatesConnected via United States App StoreAccount-level information, not a live location or per-post device.
Diesel crack spread $100 any second.
This is the real Hormuz hedge, diesel prices around the world exploding and industrial economy relies entirely on diesel not gasoline
Nigel Hungerford-Symes retweeted
If the “watermark” is embedded in the statistical sequencing of text ie it can’t be stripped out without rewriting the text - doesn’t that necessarily make LLM text generation less useful?
The model is no longer performing purely for the user’s request. Some fraction of its freedom in choosing words, phrasing, and structure is now being spent on preserving the watermark.
You get an inherent tradeoff between watermark robustness and model flexibility/compliance. Ergo, the stronger & more robust the watermark, the greater the constraint.
In other words: LLMs will suck more and be harder to work with.
Nigel Hungerford-Symes retweeted
Minimax H3, same 4x3090 rig, same clip. Yesterday it rendered in 11:21. Today it renders in 3:45 🤯
The hardware did not change. Two things happened overnight. An AI agent rewrote the attention CUDA kernel and hit a half speed trap inside GeForce tensor cores that most people never heard of. Then a stranger on HuggingFace dropped a LoRA that cuts 20 sampling steps down to 4.
One of these two mattered way more than the other. Breakdown below.
I packed all of it into ready ComfyUI workflows for my rig. If you want them, ask in the replies and I'll share.
Just sent Prime Agent with Kimi K3 to optimize the SageAttention Kernel for more efficient Minimax H3 inference on RTX 3090.
The issue is that the handwritten Cuda Kernel for Ampere loses to the Triton version from the same library. on my shapes it gets 85 ms versus 74 ms with identical accuracy. Triton autogenerator beats human code.
Looks like a tile sizing problem. the 128/64/32/64 dimensions are hardcoded constants tuned for A100. 3090 has 100 kb shared memory instead of 164 kb so the resource balance is completely different.
lets see what it finds.
Nigel Hungerford-Symes retweeted
All that needs to be said about pit bulls was said by S-tier dog trainer Stonnie Dennis in 3 minutes
Nigel Hungerford-Symes retweeted
🚨 Elon Musk says SpaceX will attempt its first Starship upper stage tower catch on Flight 14.
Nigel Hungerford-Symes retweeted
📢Meet Qwen3.8-Max — our most capable model to date.
Next week, the open weights of Qwen3.8-Max will be released, and Qwen3.8-27B is also going open-weights to meet you all!🎉
Qwen3.8-Max, a new bar for coding and cowork at 2.4T parameters:
- Autonomous coding: 10+ days of self-evolving development, from empty folder to production without hand-holding, complete project trace in the GitHub:github.com/qwen-code-dev-bot…
- Real work, real results: Production-quality deliverables across hundreds of professions.
- Long-horizon mastery: System-level autonomous planning with closed-loop adaptive learning, driving 500+ turns of chip design optimization and 365 days of e-commerce strategy.
- Native multimodal intelligence: Vision isn't just input — it's a continuous feedback loop for planning, execution, and self-correction.
💰Pricing:
Input: $2.0 / M tokens
Output: $6.0 / M tokens
Implicit Caching: $0.25 / M tokens
Start building with Qwen3.8-Max! 🚀
📖 Blog: qwen.ai/blog?id=qwen3.8
✅ Qwen Studio: chat.qwen.ai/?models=qwen3.8…
⚡ API: qwencloud.com/models/qwen3.8…





































