李丁泽宇

🎓 Adobe研究员 @dingzeyuli

[李丁泽宇 @lidingchatroom]

嘉宾张明瑞离开了 Meta，加入了一家名为 Reflection ai 的初创公司。这家被称为“美国版 DeepSeek”的公司究竟在做什么？AI 时代的面试和研发逻辑又发生了哪些巨变？本期节目我们复盘明瑞求职心路历程，并探讨大厂与小公司的差异
Youtube: https://t.co/vqnoUcrOjK
音频Podcast: https://t.co/t7L85Ojxai

[李丁泽宇 @lidingchatroom]

我们组招人啦！

[Ding Li @dingzeyuli]

Hey friends 👋 my team is hiring!

We're looking for a Senior Research Engineer to build the future of GenAI video authoring with us at Adobe Research. Frontier research + fast prototyping.

Got cool demos? Send 'em my way!

📩 [email protected]
🔗 https://t.co/c9l0g7vh7E

[Eric Xu (e/Mettā) @xleaps]

https://t.co/V1JH2pPLBa

[Boris Cherny @bcherny]

I wanted to share a bunch of my favorite hidden and under-utilized features in Claude Code. I'll focus on the ones I use the most.

Here goes.

[李丁泽宇 @lidingchatroom]

@starbot_ever 但也慢到令人发指

[李丁泽宇 @lidingchatroom]

The more I vibe code, the more I want to work. It’s addictive and empowering.

[Andrej Karpathy @karpathy]

A few random notes from claude coding quite a bit last few weeks.

Coding workflow. Given the latest lift in LLM coding capability, like many others I rapidly went from about 80% manual+autocomplete coding and 20% agents in November to 80% agent coding and 20% edits+touchups in December. i.e. I really am mostly programming in English now, a bit sheepishly telling the LLM what code to write... in words. It hurts the ego a bit but the power to operate over software in large "code actions" is just too net useful, especially once you adapt to it, configure it, learn to use it, and wrap your head around what it can and cannot do. This is easily the biggest change to my basic coding workflow in ~2 decades of programming and it happened over the course of a few weeks. I'd expect something similar to be happening to well into double digit percent of engineers out there, while the awareness of it in the general population feels well into low single digit percent.

IDEs/agent swarms/fallability. Both the "no need for IDE anymore" hype and the "agent swarm" hype is imo too much for right now. The models definitely still make mistakes and if you have any code you actually care about I would watch them like a hawk, in a nice large IDE on the side. The mistakes have changed a lot - they are not simple syntax errors anymore, they are subtle conceptual errors that a slightly sloppy, hasty junior dev might do. The most common category is that the models make wrong assumptions on your behalf and just run along with them without checking. They also don't manage their confusion, they don't seek clarifications, they don't surface inconsistencies, they don't present tradeoffs, they don't push back when they should, and they are still a little too sycophantic. Things get better in plan mode, but there is some need for a lightweight inline plan mode. They also really like to overcomplicate code and APIs, they bloat abstractions, they don't clean up dead code after themselves, etc. They will implement an inefficient, bloated, brittle construction over 1000 lines of code and it's up to you to be like "umm couldn't you just do this instead?" and they will be like "of course!" and immediately cut it down to 100 lines. They still sometimes change/remove comments and code they don't like or don't sufficiently understand as side effects, even if it is orthogonal to the task at hand. All of this happens despite a few simple attempts to fix it via instructions in CLAUDE . md. Despite all these issues, it is still a net huge improvement and it's very difficult to imagine going back to manual coding. TLDR everyone has their developing flow, my current is a small few CC sessions on the left in ghostty windows/tabs and an IDE on the right for viewing the code + manual edits.

Tenacity. It's so interesting to watch an agent relentlessly work at something. They never get tired, they never get demoralized, they just keep going and trying things where a person would have given up long ago to fight another day. It's a "feel the AGI" moment to watch it struggle with something for a long time just to come out victorious 30 minutes later. You realize that stamina is a core bottleneck to work and that with LLMs in hand it has been dramatically increased.

Speedups. It's not clear how to measure the "speedup" of LLM assistance. Certainly I feel net way faster at what I was going to do, but the main effect is that I do a lot more than I was going to do because 1) I can code up all kinds of things that just wouldn't have been worth coding before and 2) I can approach code that I couldn't work on before because of knowledge/skill issue. So certainly it's speedup, but it's possibly a lot more an expansion.

Leverage. LLMs are exceptionally good at looping until they meet specific goals and this is where most of the "feel the AGI" magic is to be found. Don't tell it what to do, give it success criteria and watch it go. Get it to write tests first and then pass them. Put it in the loop with a browser MCP. Write the naive algorithm that is very likely correct first, then ask it to optimize it while preserving correctness. Change your approach from imperative to declarative to get the agents looping longer and gain leverage.

Fun. I didn't anticipate that with agents programming feels *more* fun because a lot of the fill in the blanks drudgery is removed and what remains is the creative part. I also feel less blocked/stuck (which is not fun) and I experience a lot more courage because there's almost always a way to work hand in hand with it to make some positive progress. I have seen the opposite sentiment from other people too; LLM coding will split up engineers based on those who primarily liked coding and those who primarily liked building.

Atrophy. I've already noticed that I am slowly starting to atrophy my ability to write code manually. Generation (writing code) and discrimination (reading code) are different capabilities in the brain. Largely due to all the little mostly syntactic details involved in programming, you can review code just fine even if you struggle to write it.

Slopacolypse. I am bracing for 2026 as the year of the slopacolypse across all of github, substack, arxiv, X/instagram, and generally all digital media. We're also going to see a lot more AI hype productivity theater (is that even possible?), on the side of actual, real improvements.

Questions. A few of the questions on my mind:
- What happens to the "10X engineer" - the ratio of productivity between the mean and the max engineer? It's quite possible that this grows *a lot*.
- Armed with LLMs, do generalists increasingly outperform specialists? LLMs are a lot better at fill in the blanks (the micro) than grand strategy (the macro).
- What does LLM coding feel like in the future? Is it like playing StarCraft? Playing Factorio? Playing music?
- How much of society is bottlenecked by digital knowledge work?

TLDR Where does this leave us? LLM agent capabilities (Claude & Codex especially) have crossed some kind of threshold of coherence around December 2025 and caused a phase shift in software engineering and closely related. The intelligence part suddenly feels quite a bit ahead of all the rest of it - integrations (tools, knowledge), the necessity for new organizational workflows, processes, diffusion more generally. 2026 is going to be a high energy year as the industry metabolizes the new capability.

[Thariq @trq212]

people keep asking me what it's like to work on Claude Code

[李丁泽宇 @lidingchatroom]

文字版：https://t.co/76lH4dacwP

[李丁泽宇 @lidingchatroom]

嘉宾张明瑞离开了 Meta，加入了一家名为 Reflection ai 的初创公司。这家被称为“美国版 DeepSeek”的公司究竟在做什么？AI 时代的面试和研发逻辑又发生了哪些巨变？本期节目我们复盘明瑞求职心路历程，并探讨大厂与小公司的差异
Youtube: https://t.co/vqnoUcrOjK
音频Podcast: https://t.co/t7L85Ojxai

[李丁泽宇 @lidingchatroom]

最近想要加个generator inlet能插大电池。电工加个inlet的quote都成千上万了，而且感觉态度都很差。技术水平就不知道了。

自己搜了搜，homeowner是可以自己做的，问了问邻居，有些自己diy的，也能过city的inspection。

然后看了看华州电工证，发现门槛高的很 呵呵呵

[Rainier @mtrainier2020]

这边电工太废了。年初装的几个插座，3相220v， 2.4kw。
在最忙的时候，今天坏了两个。
我怀疑电工用了比较差的switch。
打电话让他来修，约到了周二…..
哎。

[Rainier @mtrainier2020]

这篇文章很长，分析了美国为了制衡中国在很多方面越来越像中国。

但是文章中有很多地方没有深入去探讨。

比如，政策的刚度，执行力，以及政策的决策流程。
比如最近的例子，美国是首先提出对中国造的船征收吨位费这个政策的。中国是跟进的。
但是从政策的制定，落地，执行，反馈整个流程来讲。美国的这边的政策是比较混乱的。而中方的反制措施是非常坚定和迅速的。
根本的原因就是，中国虽然是一个集权统治政权，对内的执行是专业的，态度是坚决的，除非遇到巨大的反弹，否则是不会改变的。
而美国这边的政策，尤其是这一届，比较随机，比较随心所欲。 比如对H1b的10万美元的费用。政府部门和移民局之间没有商量清楚。以及对全球各国的关税，都是随心所欲的，慌慌张张的。

这是外表的。

内在的根本原因是，中美之间的决策机制与责任机制完全不一样。

而这种决策机制与责任机制的变更对美国而言是动摇宪法的。 除非修宪是不可能做到的。
而在这个层面来讲，中美是很难趋同的。

再从经济政策方面来讲，美国的经济是一堆财阀，寡头控制的。 这话很难听，但是事实就是如此。 跟俄罗斯的寡头不一样，俄罗斯的寡头是石油寡头。美国的寡头是，科技，金融，能源。

虽然美国在试图建立美国经济的国家队，甚至学国资委，但是两者的力度完全不在一个层面上的。

把垄断资本主义驯化成国家资本主义，不流血根本是搞不成的。
在经济层面上来说，两者也是很难converge的。

如果这两个无法converge，其他方面，都是形似而已。

还是那个问题，
看美国什么时候建设美国特色的国家电网。
看美国什么时候教育改革，自己完善自己的人才培养体系。

如果这两个搞不定，无从converge起。

[Ding Li @dingzeyuli]

If you’re at UIST 2025, come say hello. my email: [email protected]

We’re hiring interns and full-time researchers.

My team builds generative-video prototypes for next-gen authoring tools. If that excites you, let’s connect at UIST or online afterward. #UIST2025

[李丁泽宇 @lidingchatroom]

@yuwen_lu_ 你是用figma写博客吗 👀

[李丁泽宇 @lidingchatroom]

花5年读博，错过就业风口，到底值不值？ 如果时间倒流，你还会选择读博吗？嘉宾们复盘了这笔人生的“投资”：它带来了经济上的机会成本，但也赋予了更强的职业韧性和未来的可能性。这笔账，你怎么算？ #博士 #PhD #读博 #人生选择

完整版：https://t.co/HL4tYvwL17
AI文字版：https://t.co/Vww0WBhrbL

[李丁泽宇 @lidingchatroom]

当AI能取代我们做Demo，HCI研究员的价值还剩什么？一个直击灵魂的问题：当一个不会写代码的设计师都能用AI快速做出精美的原型，我们HCI（人机交互）研究员的独特竞争力在哪里？这是一个值得所有从业者思考的挑战 #人机交互

完整版：https://t.co/HL4tYvwL17
AI文字版：https://t.co/Vww0WBhrbL

[李丁泽宇 @lidingchatroom]

简历石沉大海？你可能少了最关键的一步。 对于研究方向明确的博士生来说，找到完美匹配的岗位后，最重要的一步是什么？是确保你的简历能被Hiring Manager看到！嘉宾分享了如何通过Networking和内推，让你的申请脱颖而出 #求职技巧

完整版：https://t.co/HL4tYvwL17
AI文字版：https://t.co/Vww0WBhrbL

[李丁泽宇 @lidingchatroom]

Startup vs 大厂，到底哪个Offer更好拿？这是一个经典问题，但答案可能和你想象的不一样。两位嘉宾背景相似，但在“哪里更好拿Offer”这个问题上却有完全相反的体验。听听他们的分析，评论区说说你的看法！
- #Startup #大厂 #求职 #Offer #面试 #科技公司 #选择

[李丁泽宇 @lidingchatroom]

面试官不会告诉你的“潜规则”：别拿梦中情司练手！一个血泪教训：千万不要一开始就把自己最想去的公司全投了！嘉宾分享了她“过于自信”导致错失机会的经历，以及更稳妥的面试策略。找工作的你一定要看！#面试技巧 #求职攻略

完整版：https://t.co/HL4tYvwL17
AI文字版：https://t.co/Vww0WBhrbL

[李丁泽宇 @lidingchatroom]

从“非顶尖大厂不去”到海投200份简历，你经历过吗？找工作时的心态变化太真实了！从一开始的雄心勃勃，到后来只求一个offer。嘉宾分享了她海投200多份简历的心路历程，相信很多人都能感同身受。#求职 #找工作 #简历 #面试 #心态

完整版：https://t.co/HL4tYvwL17
AI文字版：https://t.co/Vww0WBhrbL

[李丁泽宇 @lidingchatroom]

2025求职真相：除了LLM，其他都是“冷门”专业？ 感觉今年的就业市场有点魔幻？我们聊了聊一个残酷的现实：似乎所有资源都涌向了LLM，连强化学习、机器人等传统热门方向都感受到了寒意。你的方向还好吗？

完整版：https://t.co/fUTjXBkcAo
AI文字版：https://t.co/Vww0WBhrbL

[李丁泽宇 @lidingchatroom]

当了一年Founder，我为什么最后还是选择去找工作？毕业前，我曾满怀激情地投身创业，寻找投资、打磨产品… 但现实是，一年后我还是走上了找工作的路。这是一段宝贵的经历，也让我对职业道路有了新的思考。