以前的叙事是：CS PhD = 改命，大厂 = 上岸。 现在有人从美国 CS PhD 退学，去做现场电工/新能源设备，虽然累、晒，但至少每天解决的问题是具体的。 而是很多白领路径正在失去确定性，体面也越来越像一种高成本幻觉。

OpenAI Robotics is hiring, looking for exceptional full-stack hardware, ops, systems, and ML engineers to help us program and manufacture robots that are useful for society. AI should be able to help people in the physical world. In the short term, we are focused on robots to support skilled workers to build our future infrastructure; in the long term, we imagine everyone having a personal robot doing anything they need. Our world simulation research program, led by Aditya Ramesh (@model_mechanic), has evolved over the past year into OpenAI Robotics. Progress is rapid, and based on a foundation of co-design between robotics hardware and ML research. If you love working hands-on across the robotics stack and want to build the future, please consider joining us. Send an email with your background and evidence of exceptional accomplishment to: robotics-recruiting@openai.com

OpenAI Robotics is hiring, looking for exceptional full-stack hardware, ops, systems, and ML engineers to help us program and manufacture robots that are useful for society. AI should be able to help people in the physical world. In the short term, we are focused on robots to support skilled workers to build our future infrastructure; in the long term, we imagine everyone having a personal robot doing anything they need. Our world simulation research program, led by Aditya Ramesh (@model_mechanic), has evolved over the past year into OpenAI Robotics. Progress is rapid, and based on a foundation of co-design between robotics hardware and ML research. If you love working hands-on across the robotics stack and want to build the future, please consider joining us. Send an email with your background and evidence of exceptional accomplishment to: robotics-recruiting@openai.com

当生活几乎被 AI 填满后，我开始意识到：工作流可以自动化，表达可以被润色，代码可以被重写，但孤独、爱、亲密关系这些东西，依然绕不开人本身。 越追求效率，越发现自己真正缺的不是效率，而是连接。

A Norwegian neuroscientist spent 20 years proving that the act of writing by hand changes the human brain in ways typing physically cannot, and almost nobody outside her field has read the paper. Her name is Audrey van der Meer. She runs a brain research lab in Trondheim, and the paper that closed the argument was published in 2024 in a journal called Frontiers in Psychology. The finding is brutal enough that it should have changed every classroom on Earth. The experiment was simple. She recruited 36 university students and put each one in a cap with 256 sensors pressed against their scalp to record brain activity. Words flashed on a screen one at a time. Sometimes the students wrote the word by hand on a touchscreen using a digital pen, and sometimes they typed the same word on a keyboard. Every neural response was recorded for the full five seconds the word stayed on screen. Then her team looked at the part of the data most researchers had ignored for years, which is how different parts of the brain were communicating with each other during the task. When the students wrote by hand, the brain lit up everywhere at once. The regions responsible for memory, sensory integration, and the encoding of new information were all firing together in a coordinated pattern that spread across the entire cortex. The whole network was awake and connected. When the same students typed the same word, that pattern collapsed almost completely. Most of the brain went quiet, and the connections between regions that had been alive seconds earlier were nowhere to be found on the EEG. Same word, same brain, same person, and two completely different neurological events. The reason turned out to be something nobody had really paid attention to before her work. Writing by hand is not one motion but a sequence of thousands of tiny micro-movements coordinated with your eyes in real time, where each letter is a different shape that requires the brain to solve a slightly different spatial problem. Your fingers, wrist, vision, and the parts of your brain that track position in space are all working together to produce one letter, then the next, then the next. Typing throws all of that away. Every key on a keyboard requires the exact same finger motion regardless of which letter you are pressing, which means the brain has almost nothing to integrate and almost no problem to solve. Van der Meer said it plainly in her interviews. Pressing the same key with the same finger over and over does not stimulate the brain in any meaningful way, and she pointed out something that should scare every parent who handed their kid an iPad. Children who learn to read and write on tablets often cannot tell letters like b and d apart, because they have never physically felt with their bodies what it takes to actually produce those letters on a page. A decade before her, two researchers at Princeton ran the same fight using a completely different method and ended up at the same answer. Pam Mueller and Daniel Oppenheimer tested 327 students across three experiments, where half took notes on laptops with the internet disabled and half took notes by hand, before testing everyone on what they actually understood from the lectures they had watched. The handwriting group won by a wide margin on every question that required real understanding rather than surface recall. The reason was hiding in the transcripts of what the two groups had actually written down. The laptop students typed almost word for word, capturing more total content but processing almost none of it as they went, while the handwriting students physically could not write fast enough to transcribe a lecture in real time, which forced them to listen carefully, decide what actually mattered, and put it in their own words on the page. That single act of choosing what to keep was the learning itself, and the keyboard had quietly skipped the choosing and skipped the learning along with it. Two studies. Two countries. Same answer. Handwriting makes the brain work. Typing lets it coast. Every note you have ever typed instead of written went into your brain through a thinner pipe. Every meeting, every book highlight, every idea you captured on your phone instead of on paper was processed at half depth. You did not forget those things because your memory is bad. You forgot them because typing never woke the part of the brain that would have made them stick. The fix is the thing your grandmother already knew. Pick up a pen. Write the thing down. The slower road is the faster one.

xAI 算法开源后，解读内容铺天盖地。 我敢说一句颠覆多数人认知的实话： 市面上 95% 的分析，是 AI 批量生产的同质化废话， 连源码文件名都没翻过一次。 「多互动」「多发帖」「账号要垂直」 这种谁都会说的话，说了等于没说。 真正藏在 xAI 算法深处的机制， 大多数人连名字都没听过， 却在教别人怎么运营 X。 你见过哪篇解读，是真的牛逼有价值，欢迎推荐！ 如果没有我就来个收费的文章😁 绝对牛逼！ 开玩笑，我也是每天在一点点的啃~~ 但是真的惊喜不断！

everything you need to know about how the team built the new @raycast from the ground up honestly worth a read 👉 raycast.com/blog/a-technical… there's nothing to hide

比较有趣的入门教程 该项目希望用一条更“实践优先”的路径来学习现代强化学习：从经典控制出发，逐步连接到 LLM 后训练基础、DPO/GRPO/RLVR、Agentic RL 和 VLM RL 等现代应用。 GitHub: github.com/walkinglabs/hands… WebPage: walkinglabs.github.io/hands-…

现在有人觉得 CS 可能是天坑，但 AI 不是天坑，我觉得这种说法部分正确。 AI 也要分清 LLM 和非 LLM，做 LLM 也要分工业界 care 的东西和工业界不 care 的东西。1% vs 99% 的分化在算法岗位上依然存在。 AI 是个很大的 topic，很多细分方向一样找不到工作。