Bugjay

Math & code enthusiast obsessed with problem-solving. Runner🏃‍.

[Bugjay @bugjay4]

26 年目标：至少开源两个收入源，提升个人收入反脆弱性。最后，All in writing！

[Bugjay @bugjay4]

@Graceruansu 《本杰明富兰克林自传》
https://t.co/xMXZyAtxS6

[Bugjay @bugjay4]

https://t.co/UIohwORzaQ

[Bugjay @bugjay4]

@kenki_kids You've found your way to a happy life.

[Bugjay @bugjay4]

@Timbazp 现在就是不太想消费，买了也没什么感觉，索性不买，还省了很多烦心事。

简单最重要

[Bugjay @bugjay4]

@Wujizhuzhu 保养的真好啊

[Bugjay @bugjay4]

@AlllexHu 都是复制品，有啥意义

[primordial intelligence @primal_brainer]

don’t let a job stop you from reading like ur unemployed

[Bugjay @bugjay4]

@AndyAIGPT 不给力啊，多给一个月也可以的。

[Bugjay @bugjay4]

@Li665508Li 基本上用不到，大家一起求学而已。

[Bugjay @bugjay4]

有没有比沉浸式翻译更好的替代方案？

用了快一年，已经成刚需了。但最近总觉得，大模型本身似乎也能完成大部分翻译和总结工作，而沉浸式翻译的订阅费用又不算便宜。

想找一个体验接近、成本更低的解决方案。大家有什么推荐吗？

[Bugjay @bugjay4]

@TaiPingJieZong 放弃了，昨晚看了看，看不懂🥲

[Bugjay @bugjay4]

早上看到大佬的推荐，但是没有深入去研究。下午忙完一看看涨停了，赶紧晚上研究研究这只股票。

[Serenity @aleabitoreddit]

专门写给我的中文读者：

绿的谐波（LeaderDrive，688017，577.3亿人民币）是我在布局人形机器人赛道时最青睐的中国上市标的。

他们的业务涵盖：

谐波减速器（据称占有超过60%的国内市场份额，以及1800多家全球客户）

人形机器人旋转关节减速器

直线执行器

电机/关节，以及许多其他核心零部件。

并且他们正在进军行星滚柱丝杠（planetary roller screws）领域。

目前占据着绝对的主导地位：超过60%的国内市场份额以及1800多家全球客户。

主要客户包括优傲机器人（Universal Robots）、优必选（UBTech）、智元机器人（Agibot），而特斯拉（Tesla）、Figure 以及绝大多数其他人形机器人研发商都很可能是他们的潜在客户。

如果你看一下这些零部件的 BOM（物料清单），粗略估计它们占到了每台研发出的人形机器人的 4-15%，随着他们进军更多细分领域，这个比例可能还会更高。

我认为没有人能对这类公司进行精确的财务建模，其前景更多取决于未来几年内会有多少人形机器人实现量产。

这其实是对一家能够在每台生产出的人形机器人中占据可观份额的公司，进行的一种方向性做多（directional long）。

在可扩展的规模化量产方面，中国显然是该领域的领导者，许多西方玩家无法将成本降到绿的谐波那样的水平。

随着我们向实体人工智能（physical AI）的规模化迈进，我极其看好机器人这个赛道。

[Bugjay @bugjay4]

@imwsl90 咸鱼 30 块一年🌚

[Bugjay @bugjay4]

@demonyins 没用，想用的时候要会员就搁置了。现在又被收回去，那肯定也不用啊，淘汰的技术。

[Bugjay @bugjay4]

@Kongkongda5882 富不过三代，也是这个说法

[Bugjay @bugjay4]

@TaiPingJieZong 相聚是缘分，珍惜这段在同一个平台交流的时光

[Bugjay @bugjay4]

@imwsl90 真相了，创造永远是最难的，任凭工具再利，也难替那灵光一现。

[Bugjay @bugjay4]

@snowboat84 @drfeifei AI 越想理解世界、模拟世界，就越绕不开物理

[Bugjay @bugjay4]

The next stage of AI may not be about making machines better at talking.
It may be about making machines better at understanding the world.
Fei-Fei Li’s essay on World Models is important not because it introduces one specific model, but because it gives us a clearer framework for thinking about where AI is going next.
She breaks World Models into three capabilities:
1Renderer
A renderer generates what we see: images, videos, visual scenes.
Most of today’s image and video generation models fall into this category.
But there is a key limitation:
Looking real is not the same as understanding reality.
A video can look beautiful while still getting space, physics, object relations, and causality wrong.
It can be a moving illusion.
1Simulator
This is the most important part to me.
A simulator does not just generate pixels.
It generates a world state that can be computed, interacted with, and tested.
A good simulator needs geometry, physics, dynamics, materials, and causality.
This is what makes it useful for:
•robot training;
•autonomous driving;
•architecture and design;
•industrial digital twins;
•complex decision-making;
•testing actions before taking them in the real world.
A renderer makes the world look real.
A simulator makes the world behave real.
1Planner
A planner decides what to do next.
Given a goal and an environment, it chooses actions.
If the renderer is about seeing, and the simulator is about understanding how the world works, the planner is about acting inside that world.
Together, these three capabilities point toward a bigger shift:
from language intelligence
to spatial intelligence.
Over the last few years, AI has learned to write, summarize, translate, chat, and code.
But the next frontier is not just generating better text or prettier videos.
It is building systems that can understand, simulate, and act in the physical world.
This matters for robotics, autonomous vehicles, AR/VR, games, cities, manufacturing, and many other domains.
But I think it also matters at a personal level.
Most personal AI workflows today are still about output:
•summarize this;
•write this;
•make a to-do list;
•draft this email;
•create this slide.
Useful, yes.
But limited.
The more interesting future is a personal world model.
An AI system that understands:
•your long-term goals;
•your active projects;
•what is stuck;
•what information matters;
•how tasks depend on each other;
•how today’s actions affect future outcomes;
•what tradeoffs you are really making.
In other words, AI should not only help us express thoughts faster.

[Fei-Fei Li @drfeifei]

https://t.co/Kt50ttQRMJ

[Bugjay @bugjay4]

@Catfishingblue @Russell3402 现在的学生注意力都是负的，根本听不进去