gotcha

by yourself

[gotcha @chris3he]

@crypto_xiaobai_ 这个自助找回系统就应该说明需要哪些材料，用户才能做好双方沟通，钱都退回gate了才要用户补交资料，火币又要卡资料审核，这笔金额对用户来说挺多的，希望两个平台尽快解决下@HuobiGlobal @HTX_Molly @Gate @Godot_gate

[谁是藏镜人 @VedaAI00]

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。👇

[Elliott / Shangzhe Wu @elliottszwu]

Check out Ariticraft 🦾 - a highly efficient agentic system that generates articulated 3D assets fully automatically at scale!

🚀 https://t.co/anSM87Li49

[Marwa ElDiwiny @MarwaEldiwiny]

I'm curious what happened here..

[Correcting the Record with Guillotines ☭ @SetPixels]

@MarwaEldiwiny This is where they swapped out human workers controlling it.

[Generalist @GeneralistAI]

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 ↓

[Annie 所长 @web3annie]

20年后的 AI 世界，细看极恐啊！ 👇

[Yaoyao👾 @ynonestop]

好处是我最近频繁的听到身边很多人在回归内心最真实的追求

在国内最巨身大模型公司投资部门工作的发小最近意识到了自己对数学极致的热情，想辞职全职做研究

从小追名逐利想成为知名主持人的美女朋友爱上了哲学，放弃了从小学的声乐舞蹈专业成了东方哲学博士

北美名校毕业的好朋友毕业后开了一家宠物用品店 + 宠物咖啡，店里的狗狗都是救助基地的狗狗，把课堂上学的营销知识都用来做各类活动帮助狗狗们找领养

好朋友们都说，可能和赚钱无缘了，但是真的快乐，事实上现在的时代也许是最能够把热爱和赚钱分开的时代，只要愿意有无数的机会可以赚到钱，但是追求内心的热爱才有可能快乐，有成就。

如果和我一样不知道自己热爱什么也不要偷懒，多多不带目的性的去尝试，总有一天答案会显现….

[gotcha @chris3he]

@WhiteHouse what the fuck

[独立开发者William @DLKFZWilliam2]

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！

[gotcha @chris3he]

that is meaningful

[Neuralink @neuralink]

We are working to restore mobility that was lost due to disease or spinal cord injury by allowing participants to control robotic arms with their thoughts.

See how this is possible.

[Chris Paxton @chris_j_paxton]

An impressive collection of hands

[gotcha @chris3he]

再就业

[OpenAI @OpenAI]

This is not a screenshot.

[RoboPapers @RoboPapers]

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!

[Dwarkesh Patel @dwarkesh_sp]

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!

[Generalist @GeneralistAI]

GEN-1 still works with lights off, and generalizes under harsh lighting conditions.

The model uses raw video pixels to make decisions, so strong lighting changes can drastically alter its input distribution. Yet performance still holds.

Why? GEN-1 was pre-trained on a massive, diverse dataset of different lighting conditions—everywhere from outdoor farms, to warehouses, from grocery stores, to dimly lit homes—it's already seen it all, and transfers this knowledge to new tasks.

This is a glimpse of what we call Mastery, and is part of the reason these models can cross a new performance threshold.

Read more about it in our blog post in the comments below 👇

[The Humanoid Hub @TheHumanoidHub]

Skild showed something similar last year but with quadrupeds.

Skild brain, trained on 100,000 diverse simulated robot types enabled remarkable real-time adaptability.

In-context adaptation allows the brain to discern the robot form and adapt to extreme changes in its body.

[Sawyer Merritt @SawyerMerritt]

Tesla's Optimus V3 robot hand looks to have been revealed in a new international patent published today.

The patent describes a tendon/cable-driven hand:
• Actuators in the forearm
• Each finger has 4 degrees of freedom
• The wrist has 2 degrees of freedom
• Tendon-driven system: 3 thin, flexible control cables (tendons) per finger run from forearm actuators, through the wrist and into the fingers.
• Advanced wrist routing: Cables switch from a lateral (horizontal) stack on the forearm side to a vertical stack on the hand side, plus a transition zone to minimize stretch, torque, friction, and crosstalk during yaw/pitch movements.
• Integrated control cable channels in the finger segments and precise routing (behind some joints, forward others) for selective bending.

The full filing is in the thread below if you want to read more about it:

[gotcha @chris3he]

@forgivenever @CkitYN 有羊毛吗