Seniorious

CS & Logic Undergrad @ Peking U

[Mistivia @mistivia]

《赛博朋克2077》里面有个设定是义体使用过度会变成赛博精神病。LLM和agent用多了也是这样。很多蓝V给人的感觉就是那种赛博精神病状态。

[Kasra @kasrak]

This is me

I ended up leaving OpenAI, not because there's anything wrong with OAI (I actually think they're underrated as a biz), but because to me, building commercial software doesn't feel fun anymore

Because agents write ~all the code:
- There’s less human to human collaboration, so it’s a lonelier experience
- You’re constantly context switching and not going as deep into the code, which means there’s ~zero flow state
- Most of the job becomes code review and trying to reduce complexity, which is very mentally draining

Again just my experience, and none of this specific to OAI. There are many people who seem to be enjoying building software in this era!

But I've always wanted to try making films, and now seems as good a time as any

[thetechpencil @TheTechPencil]

@sama

[Joel David Hamkins @JDHamkins]

The ordinal ω·2+1 is

[Kinai @Kinai_24]

Category theorists be like "it is immediate to see that the diagram commutes"

The diagram:

[HANK @hankinbeijing]

一顶贝雷帽，初见与永别

[Arthur B. @ArthurB]

We're still so early

[Ziqian Zhong @fjzzq2002]

New blog: Why does Kimi identify as Claude and Sonnet 4.6 as DeepSeek? We find that this can come from a subliminal-learning-like effect: If you speak like Claude, you become Claude.

[Seniorious @Seniorious5]

严肃学习

[jianlin.su @Jianlin_S]

Deconstructing Scaling Laws: The Triad of Optimization, Architecture, and Data
https://t.co/jbqiFXsIJN

[Tencent Hy @TencentHunyuan]

For a finite set of integers (A), how much faster can (|A+A|) grow than (|A-A|)?

A 1969 theorem gave an upper bound of 2 for the exponent. For more than 50 years, the best constructions barely exceeded 1.1.

With help from our research agent Hyra and the Hy3 model, we found an explicit construction showing that the optimal exponent is exactly 2.

A 50-year-old problem, solved.

Paper: https://t.co/OU7lfH0hXR
Hyra blog: https://t.co/NeQEekUSZ3
Formal proof: https://t.co/LMbGdKqo2D

[Kimi.ai @Kimi_Moonshot]

Releasing the model weights and technical report of Kimi K3.

Kimi K3 is our most capable model: a 2.8T MoE model with native visual understanding and a 1M-token context window.

New model architecture: 2.5x the intelligence per unit of compute, not just more params.

Alongside Kimi K3, we're opening up more of the stack behind it — high-performance attention kernels, MoE communication library, and infrastructure for running agent environments at scale.

Model weights: https://t.co/7m7eEg6Y0B
Tech report: https://t.co/yeu6cjpMCT
Tech blog: https://t.co/YTfiMSNM1f

[Kai @real_kai42]

经常听到有人说，
“我们人太少了，所以来不及 xxx，赶紧上吧”
“太 rush 了，别 xxx 了，直接上”
“我们是小公司，没必要那么讲究，直接上”

但我的理解恰恰相反，分享一个小故事
大一的时候，高级程序设计的老师推荐我们买显示器，然后说“如果你比较有钱，那就买二手的；如果你没钱，你就买全新的”，这与大家常规理解有点相反

老师说：“显示器坏掉的话，维修成本非常高。
如果你有钱，你买二手的，坏了就再买一个，你可以用自己的钱去承担这个风险；
如果你没钱，那你是承担不起这个维修风险的，那就买全新的，多花钱，用官方保修来替你承担这个风险”

我觉得这是那节课我学到最有意思的东西。
所以我在读书的时候，没啥钱，所有 apple 设备都会买 ac+，因为我承担不起我主力设备坏掉的风险。🤣而且学生党时间多，稍微有点问题都能去骚扰天才吧去修。（而且遇到过，屏幕连续三次出了问题，直接给我从 2016mbp，换新成 2019mbp。可惜当时 mbp 性能太差了，三年没啥提升）
现在自己有工资了，所有设备就不买 ac+ 了，自己能承担坏掉的风险，并且也没多少时间去大老远跑去维修

扯远了，回到最初的问题。
如果时间宽裕，或者团队大/资源多，是完全可以不验证上一些事情的，因为输得起。就像很多公司会内部赛马，或者上很多实验性项目。

但如果时间急迫，团队小/资源少，反而应该挤出一点时间去做验证，至少跑通 happy path。因为输不起，对很多小公司来说，一次重金投入的实验失败，基本等于倒闭

类似的 meta idea 在生活中用到的地方很多
- 我 出国/租车 基本都会买保险。同样是因为我不算有钱，我需要用保险替我承担风险
- 有钱人的豪车反而不会买保险。因为豪车太多且豪车保险贵，有钱人完全可以用自己的资金去承担风险。

[Jensen Huang @JensenHuang]

For my first post, I’m sharing a letter @NVIDIA signed on why open models matter.

AI will transform every industry, power every company, and be built by every country.

Open models strengthen safety and cybersecurity, accelerate innovation and diffusion, and enable sovereignty.

The world needs both frontier closed models and frontier open models.

https://t.co/AUKzoQ5Ikb

[Hema shushu @hemashushu]

自从ai兴起之后，似乎什么程序语言、操作系统、cpu指令集、基础软件通通都变得不重要的，也没什么人再提起了，但我觉得这些领域远远没达到完善的程度，基础软件更是千苍百孔。

[Seniorious @Seniorious5]

@lgswdn_ChrisZ 为什么呢