robma

@iPaulCanada 吹牛逼

一位高中班主任的视频，她说她当过补课老师，一年能赚近300万，最后却关了班重回学校，只拿十几万工资，就为心里踏实。 她说出大实话：很多补课就像买保健品，家长花钱求心安，孩子耗时间装努力。基础没打好、学习习惯没养成，靠两小时补课根本救不回来。 不少孩子明明是底子差，补课班却不敢说实话，怕砸了饭碗。真正有用的补课极少，大多只是自我安慰。钱花了、孩子累了，成绩没上去，心更焦虑。比起盲目报班，陪孩子补基础、养习惯、聊心里话，比800块一节课有用太多。

@yeshengzongli @KKaWSB 人类身体的极限，井底之蛙怎么能理解百米飞人的意义

@KKaWSB 现代社会100米跑有什么实用价值？送外卖更快还是能躲过子弹？

这速度简直令人发指，这位新博尔特名字叫古特·古特。 16岁跑出了10.04秒的成绩。无语。

Don't have Stata run Claude. Have Claude run Stata. I made a Claude Code skill just for this: github.com/dylantmoore/st… Instructions for Claude on how to run and write Stata code + compressed Stata documentation in the form of md files.

Japanese actor Hiroyuki Sanada spoke about the contradictions of human nature: “Some people dream of having a swimming pool at home, while those who have one hardly ever use it. Those who have lost a loved one feel a profound sense of loss, while others often complain about their living relatives. Those without a partner long for one, while those who have one often don't appreciate it. The hungry would give anything for a meal, while the satiated complain about the taste of their food. Those without a car dream of owning one, while those who have a car are always looking for a better one.” The key to happiness is gratitude: truly seeing and appreciating what we already have, and understanding that somewhere, someone would give anything for what we take for granted.

This looks highly relevant! "Deep Learning for Solving Economic Models" by Jesús Fernández-Villaverde. "The ongoing revolution in deep learning is reshaping research across many fields, including economics. Its effects are especially clear in solving dynamic economic models. These models often lack closed-form solutions, so economists have long relied on numerical methods such as value function iteration, perturbation, and projection techniques. Unfortunately, these approaches suffer from the curse of dimensionality, which makes global solutions computationally infeasible as the number of state variables increases. Deep learning offers a different approach: flexible tools that solve dynamic economic models by minimizing residuals in equilibrium conditions, and that can handle high-dimensional problems. This development promises to broaden the scope of quantitative economics. I illustrate the approach using the neoclassical growth model." sas.upenn.edu/%7Ejesusfv/Dee…

看到 Google Cloud 分享的 SKILL.md 设计模式，觉得用 Claude Code 或 Codex CLI 的同学可以直接抄作业！ 以前我总是乱写 prompt，AI 很容易乱猜或跳步，现在照这 5 种简单方法来，稳定多了： 1. Tool Wrapper → 让 AI 马上变成某个库的专家 2. Generator → 专门生成固定格式的文件 3. Reviewer → 叫它帮你审查代码 4. Inversion → 先问清楚需求，不要自己脑补 5. Pipeline → 强制一步一步走，不让它乱来 我自己试了几个，真的感觉 AI 听话可靠很多！ 最简单用法，把这篇文章丢给你的AI，然后优化一下就行。😂

What happens when you invite 150 AI economists (Claude Code) to a research conference, give them the exact same data, and ask them to test the same hypotheses? We did just that. The results reveal a new phenomenon: Nonstandard Errors in AI Agents. 🧵👇

Peer review is facing a death spiral, and AI production tools are speeding it up. AI-assisted reviewing is necessary and should be open. We built OpenAIReview: open AI reviewing for everyone, for the cost of a coffee. openaireview.github.io/blog.html 🧵

I am very happy that my survey paper, "Deep Learning for Solving Economic Models," is forthcoming in the Journal of Economic Literature (pending final replication checks, which should be quick). The paper benefited greatly from the editor, David Romer, five referees, and many friends who read earlier versions. I believe the result is a solid introduction to the field, though in 48 pages, there is only so much one can do. So, I created a companion webpage: sas.upenn.edu/%7Ejesusfv/dee… where you can find the paper, the code, and some slide decks with my teaching material. My plan is to expand the slides over time, adding new material and updating them as new results appear. I will probably do a thorough revision once the spring semester is over. Those who follow my feed know that I think deep learning is the most fundamental change to computational economics in the last 40 years. I am by now convinced it is more important than the development of Markov chain Monte Carlo methods in the early 1990s or the introduction of projection and perturbation methods in the 1980s. To find a comparable shift, one would probably need to go back to Richard Bellman's invention of value function iteration in 1957. More pointedly, we need to redesign the Ph.D. in economics. Not at the margin. From the ground up. Economists can either fully embrace the deep learning revolution or become irrelevant, as has already happened, I would dare say, to some fields in academia that refused to accept reality. Finally, let me apologize to everyone working in this area whom I could not cite. Space was a binding constraint. And yes, this post was written with the considerable help of AI. There is nothing I am prouder of than the fact that AI is now an integral part of every step I take in my professional life.

@fxtrader @AnthropicAI @OpenAI 信你的话才是SB

#观察 对中文用户，一边是Anthropic Claude Code的疯狂封号，一边是OpenAI Codex的量大管饱，而二者的水平已经相差无几，你为什么还要用Claude？@AnthropicAI @OpenAI

一直以来我都以为我对notebook LM 已经到了出神入化阶段 看来我还是菜

Holy shit...Someone built an AI system that takes a research idea and outputs a full academic paper. Real citations. Real experiments. Conference-ready LaTeX. Zero human input. It's called AutoResearchClaw. And the pipeline is insane. Here's what actually happens when you type one command: It searches arXiv and Semantic Scholar for real papers. Not fake citations actual literature with 4-layer verification: arXiv ID check, CrossRef DOI lookup, Semantic Scholar title match, and LLM relevance scoring. Hallucinated references get killed automatically. Then it designs and runs real experiments. Hardware-aware auto-detects whether you have NVIDIA CUDA, Apple MPS, or just CPU, and adapts the code accordingly. When experiments fail, it self-heals. When results don't support the hypothesis, it pivots to a new direction on its own. Then it writes the paper. 5,000-6,500 words. Section by section. Multi-agent peer review with methodology-evidence consistency checks. Then it revises based on those reviews. Then it outputs conference-ready LaTeX. NeurIPS, ICML, ICLR templates. Compile-ready for Overleaf. BibTeX references auto-pruned to match inline citations. The whole thing runs across 23 stages and 8 phases. Three human-approval gates if you want them. Or just pass --auto-approve and walk away. What you get back: → Full academic paper draft → Conference-ready LaTeX + BibTeX → Experiment code + sandbox results + charts → Peer review notes → Verification report on every citation This is what autonomous scientific research actually looks like in 2026. 100% Opensource. MIT License. Link in comments.

@shachepi 太麻烦了，普通用户根本用不着

@RJDAIGOGO 屁，照你这个说法，就没有战斗机成功躲避导弹了

科普贴：如何打飞机？

兄弟们，好消息 Anthropic 宣布 3 月 13 日至 27 日期间 Claude 所有付费和免费用户在非高峰时段的用量自动翻倍 美东时间 8:00-14:00（北京时间 20:00-次日 02:00），为高峰期 除上述 6 小时之外的所有时间，为非高峰时段（用量翻自动倍） 对国内用户来说，大部分白天工作时间都属于非高峰， 刚好能享受到翻倍。 另外周末全天使用量翻倍... 覆盖范围： Free、Pro、Max、Team 计划全部包含 Enterprise 计划不参与 适用平台：Claude 网页版、桌面版、移动端、Cowork、Claude Code、Excel 插件、PowerPoint 插件 不需要做任何操作，没有兑换码，不用改设置，符合条件的账号已经自动生效。 而且额外获得的用量也不会计入周度用量上限。活动结束后自动恢复正常限制，不影响账单。 对国内用户意味着什么 换算成北京时间，高峰时段是晚上 8 点到凌晨 2 点。也就是说，白天 9 点到晚上 8 点这段最主要的工作时间，Claude 的用量都是翻倍的。

@techeconomyana 这是错误的认知，pp上跳，是国际能源价格上涨的，属于输入型通货膨胀，与国内经济的根本紧缩没有啥本质联系，说白了，国际大宗资源价格平抑后，价格必回落。国内经济该没本质起色，还是没起色。

中国高频工业物价指标在2026年3月上旬跳升3%，通胀信号已经确认。

@politicVincent 天天讨论这些毫无意义的蝇营狗苟，对你有何意义？别人升与降，与你有半毛钱的关系吗？咸吃萝卜淡操心！

本届中央政治局，王毅、王沪宁、石泰峰、李希、李干杰、李鸿忠、何立峰、陈敏尔、赵乐际、黄坤明、蔡奇必退。其中，陈敏尔转岗人大副委员长，李干杰转岗政协副主席。吴政隆、张升民、陈小江的生态位是“政治局候补委员”，赵一德、周祖翼、许昆林、王浩、信长星、黄建发在下届入局的概率也特别高（1/4）

This talk was a lot of fun (and thanks to the ~750 live attendees!) Slides are up on my site - ai-mba.io/tutorials/ai-a… . If you had any questions I didn't get time to answer in the talk - join my Skool (skool.com/the-ai-mba) we've got over 1300 economists, business people, and AI devs pushing to get to the limits of agentic coding

@Nancyshanghai 真能编

人真的会突然变好！ 躺平开网约车的我， 被这几个问题点醒了 以前总觉得躺平摆烂是人生常态 烂泥扶不上墙是定局 直到我这个天天浑浑噩噩开网约车的 人， 被乔丹·彼得森的几个灵魂问题 狠狠敲醒 才发现 人真的会突然变好 只要你敢直面自己！

@cuichenghao AI渲染了吧

难以想象！这是华语乐坛著名歌手孙燕姿在最近的演唱会上，体重忽然增加了好多，变化之大比肩张惠妹。