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Cursor的 Composer1 模型限免了
 大家可以用用看 是我最近主力模型 优势就是快  几乎不用等就完成了 准确率还行 当然有些场景有点智障 换替补GPT5就搞定了

Cursor的 Composer1 模型限免了 大家可以用用看 是我最近主力模型 优势就是快 几乎不用等就完成了 准确率还行 当然有些场景有点智障 换替补GPT5就搞定了

独立开发者 自由职业 作品 - 简单简历 https://t.co/xMu5JFIGnr 五分钟打造程序员的金牌简历 课程 - 慕课网精英讲师 https://t.co/NTyFFrvHwL 经历 - 不上班的1000天 https://t.co/bonuLQCCsY 视频 - https://t.co/aQYLgujIyC

avatar for Viking
Viking
Fri Nov 07 14:29:44
How much doe?

How much doe?

Founder | Author | Speaker Building @beltstripe. Healtech/EdTech/Agric I'm Not The Man Of Your Dreams. Your Imagination Wasn't This Great.

avatar for Sani Yusuf
Sani Yusuf
Fri Nov 07 14:28:57
RT @kimmonismus: The worlds smartest AI agent system is
1) opensouce and- weight
2) from china

Let that sink in.

RT @kimmonismus: The worlds smartest AI agent system is 1) opensouce and- weight 2) from china Let that sink in.

Co-founder & CEO @HuggingFace 🤗, the open and collaborative platform for AI builders

avatar for clem 🤗
clem 🤗
Fri Nov 07 14:19:19
The paper:

https://t.co/zYNA51w6fv

2/2

The paper: https://t.co/zYNA51w6fv 2/2

Research Scientist @meta (FAIR), Prof. @Unige_en, co-founder @nc_shape. I like reality.

avatar for François Fleuret
François Fleuret
Fri Nov 07 14:18:03
There is a paper from 2017 that introduced a trick that I love but never seen used.

Consider two linear layers f and g that you initialize with the same parameters, and then you use

h(x)=f(relu(x))+g(-relu(-x))

Then at initialization, h is linear!

1/2

There is a paper from 2017 that introduced a trick that I love but never seen used. Consider two linear layers f and g that you initialize with the same parameters, and then you use h(x)=f(relu(x))+g(-relu(-x)) Then at initialization, h is linear! 1/2

Research Scientist @meta (FAIR), Prof. @Unige_en, co-founder @nc_shape. I like reality.

avatar for François Fleuret
François Fleuret
Fri Nov 07 14:18:02
There is a paper from 2017 that introduced a trick that I love but never seen used.

Consider two linear layers f and g that you initialize with the same parameters, and then you use

h(x)=f(relu(x))+g(-relu(-x))

Then at initialization, h is linear!

1/2

There is a paper from 2017 that introduced a trick that I love but never seen used. Consider two linear layers f and g that you initialize with the same parameters, and then you use h(x)=f(relu(x))+g(-relu(-x)) Then at initialization, h is linear! 1/2

The paper: https://t.co/zYNA51w6fv 2/2

avatar for François Fleuret
François Fleuret
Fri Nov 07 14:18:02
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