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Minimax H3 Max has generates video faster than you can watch it so I hooked it to a twitch livestream! Now you can watch infinite interdimensional cable - link to the stream below
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It's crazy how newer VLA models are up to 2x better on the robodojo benchmark when evaluated in sim, but π0.5 (by now 16 months old) is still leading in real-world evals.
It would be great for the field if we could distinguish how much of the difference is down to model quality vs data quantity (& quality).
Great work by @MarioChan2002.
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What do you do with 16TB of pure robotic manipulation data? 🤖
You open source it on @huggingface
Simple World Lab just dropped HiFi-UMI-2K - an absolutely massive dataset pushing the limits of high-fidelity manipulation.
The entire 16TB is already available in the LeRobot format, meaning you can plug this massive scale straight into your pipelines with zero friction.
Dataset: huggingface.co/datasets/simp…
Paper: huggingface.co/papers/2607.2…
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Every single startup selling AI Training Data (July 2026)
>50 cos sell data and RL environments to big AI labs and drive AI progress behind the scenes.
They total ~$8.5B in rev and ~$100B in valuation, >75% of which are just 4 players: Scale, Surge, Mercor and Handshake.
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New Anthropic research: A global workspace in language models.
Of everything happening in your brain right now, only a tiny fraction is consciously accessible—thoughts you can describe, hold in mind, and reason with.
We found a strikingly similar divide inside Claude.
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这可能是自动驾驶研发领域「数据工程」最硬核的一次暴力提效。
看到日本 XR 专家 @tokufxug 推荐的开源库 **123D**，我第一反应是：那些还在为了 KITTI、Waymo 等不同数据集格式反复写解析脚本的工程师，真的该看看什么叫「物理级的格式对齐」。
这个库的工程逻辑极其「残暴」：
- **全量数据集协议平移**：它将 heterogeneous（异构）的传感器数据，暴力统一到了 Apache Arrow 格式。这意味着无论原始数据是 Protobuf 还是 ROS bag，在你的代码里都变成了标准化的 Columnar（列式）流。
- **Zero-copy 暴力吞吐**：基于 Arrow 的内存映射机制，数据读取几乎没有序列化开销。这种对 I/O 瓶颈的极致压榨，让训练 Foundation Models 的吞吐量直接原地起飞。
- **存储语义对齐**：它采用引用原始路径的策略，避免了 TB 级传感器数据的物理重复存储。这是在用最聪明的架构管理最暴力的数据。
这就是我一直盯着的：**具身智能的门槛，往往不在算法，而在于你处理「物理现实数据」的基建带宽。**
关注 @VedaAI00，同步全球最炸裂的自动驾驶与工程数据实践。👇
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Check out Ariticraft 🦾 - a highly efficient agentic system that generates articulated 3D assets fully automatically at scale!
🚀 articraft3d.github.io/
🚀 Introducing Articraft, a coding agent for articulated 3D asset creation.
Articraft writes code, executes it, receives validation feedback, and refines the result into simulation-ready 3D assets with parts, joints, and motion.
We’re also releasing Articraft-10K: 10,000+ articulated objects across 250 categories, unlocking large-scale interactive scenes for robotics simulation and physical AI.
🔗 Project page: articraft3d.github.io/
💻 Code: github.com/mattzh72/articraf…
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GEN-1 delicately arranges potato chips, and lifts a heavy bag of potatoes — from a gentle touch to a strong grip.
Read more about Gen-1 in our blog posts in the comments below ↓
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我宣布！我拉垮了那么多年的blender技术，
就在今天，将得到飞跃！
因为今天！Claude 宣布推出连接器（Connectors），允许通过对话直接控制 Blender！
还有Autodesk Fusion以及 Adobe Creative Cloud、Ableton、Splice、Affinity、SketchUp 和 Resolume 等软件！
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Teaching robots to perform dexterous manipulation tasks currently requires teleoperation, which limits demonstration quality, speed, and scalability. Instead, why not use human videos? The problem is that a human hand isn’t a robot hand, so data must be retargeted using simulation to resolve issues like collisions and interpenetration when controlling the hand.
In VideoManip, @chen_hongyi_ and co-authors built a system to solve this problem, taking in RGB videos of humans performing manipulation tasks and using them to create accurate simulations with which to learn robot policies.
Watch episode #73 of RoboPapers, hosted by @micoolcho and @chris_j_paxton, now to learn more!
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The Jensen Huang episode.
0:00:00 – Is Nvidia’s biggest moat its grip on scarce supply chains?
0:16:25 – Will TPUs break Nvidia’s hold on AI compute?
0:41:06 – Why doesn’t Nvidia become a hyperscaler?
0:57:36 – Should we be selling AI chips to China?
1:35:06 – Why doesn’t Nvidia make multiple different chip architectures?
Look up Dwarkesh Podcast on YouTube, Apple Podcasts, Spotify, etc. Enjoy!