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Parallel Experiments avatar

Parallel Experiments

Stay informed. Stay authentic.
Welcome to the public part of my brain. Here I share curations and thoughts.
Created with ❤️ by @linghao.
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Channel creation dateSep 22, 2018
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Mar 25, 2025
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16.05.202523:59
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30.01.202523:59
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Popular posts Parallel Experiments

03.05.202507:20
https://julian.digital/2025/03/27/the-case-against-conversational-interfaces/
这篇可以一起看,标题比较钓鱼(作者自己也承认了),但其实是对怎样的 UX 能最大发挥 AI 效用很好的思考。
AI should function as an always-on command meta-layer that spans across all tools. Users should be able to trigger actions from anywhere with simple voice prompts without having to interrupt whatever they are currently doing with mouse and keyboard.

Productivity and collaboration shouldn’t be two separate workflows.


P.S. 这个博主的文章都很赞,比如 https://julian.digital/2023/07/06/multi-layered-calendars/https://julian.digital/2020/09/04/a-meta-layer-for-notes/
27.04.202502:33
https://store.steampowered.com/app/1569580/Blue_Prince/
强烈推荐,2025 开年至今个人玩到最惊艳的游戏。结合解密和roguelike,puzzle有多层depth,好玩耐玩💯
#game
03.05.202507:12
https://koomen.dev/essays/horseless-carriages/
我是觉得拿工业革命时期的例子来类比 AI 时代的种种有点 cliche 了,不过这篇中心论点和例子都挺到位,还有交互。
In most AI apps, System Prompts should be written and maintained by users, not software developers or even domain experts hired by developers.
18.04.202504:53
https://newsletter.pragmaticengineer.com/p/the-philosophy-of-software-design

A Philosophy of Software Design 作者 John Ousterhout 做客 The Pragmatic Engineer. #podcast #software_design
19.04.202505:31
https://arxiv.org/abs/2305.18290 #llm #ai

今天深入学习了 DPO,再次感叹扎实的数学功底对 AI/ML Research 的重要性……

原始的 RLHF 是用 pairwise human preference data(A 和 B 哪个更好)去训练一个 reward model,然后用 RL 来训练主 policy model,objective 是 minimize negative log likelihood + regularization(比如 PPO 就是通过新旧 policy 之间的 KL Divergence 来做 regularization)。这样的缺点在于 RL 是出了名的难搞,而且还需要一个 critic model 来预测 reward,使得整个系统的复杂性很高。

DPO 的思路是,观察到 RLHF 的 objective 本质上是 minimize loss over (latent) reward function,通过一番 reparameterization 等数学推导,重新设计了一个 minimize loss over policy 的 objective,绕过了中间这个 reward model,让 gradient update 直接增加 policy model 生成 winner response 的概率并降低 loser response 的概率,大幅简化了流程。

拓展阅读:
- KTO: 更进一步,不需要 pairwise comparison,只用对 individual example 的 upvote/downvote 也可以学习到 preference。
- IPO: 解决 DPO 容易 overfit 的问题。
01.05.202505:04
Interesting opinion piece. I'm most impressed by the sheer number of links in this post 😅
https://www.latent.space/p/clippy-v-anton
24.04.202506:48
https://www.youtube.com/watch?v=lcjdwSY2AzM

这期介绍 principle of least action 的视角很独到,还科普了几位相对不怎么被提及的科学家的贡献 👍
01.05.202517:38
支持一下友邻!很有意思的一个人!👇
15.05.202514:26
New landing page design and live at https://deeptime.now 🎉 and deeptime is now in beta, all features are free! Sign up today! #DeeptimeNow
Reposted from:
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散步中
01.05.202517:38
朋友来找我做博客嘉宾,我说我没录过,我也不是谦虚你另请高明吧,但他说是想聊聊搬到SF的体验,我说我可以:

https://www.xiaoyuzhoufm.com/episode/680eee0d7a449ae8581a3820
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