Eric Zhao

iOS App, Android App & Django Backend Developer, Former Windows Client Developer, C/C++/Golang/Java/Objective C/Python.

[Eric Zhao @zhaopenghui]

@jerry_230925 @0xVeryBigOrange 这两年日光盘都有好几个了

[Michael McNair @michaeljmcnair]

Arguing that Elon Musk’s success is due to “narrative control”, luck, or riding others coattails is such an implausible claim that it functions as a useful litmus test for a persons analytical judgment.

This isnt about whether you like Elon Musk. I don’t know him, and I am largely agnostic about him as a person. But I do know his record as a CEO, and studying management and business strategy has been a major part of my job for the past 20yrs. From that perspective I can tell you that Musk isn’t just a good CEO. He is one of the most effective CEOs of our generation.

When I hear people write off Elon’s achievements bc someone else started these companies, it is a clear tell that they don’t understand business. Ideas are a dime a dozen. They are not what makes a great CEO. Execution is. And part of execution is recognizing a good idea when you see one and understanding how to build something around it that actually works.

Tesla was months from bankruptcy when Musk took control. It’s now the company that forced every major automaker on earth to retool their entire product strategy. SpaceX was a startup that serious people in the aerospace industry dismissed as a fantasy. It now conducts more orbital launches than the rest of the world combined and has driven launch costs down by an order of magnitude. Starlink is on track to become one of the most consequential communications infrastructure projects in history. These aren’t narrative achievements. Theyre tangible businesses that work, at scale, in industries where failure is the default condition.

And there’s a consistent pattern where Elon has repeatedly looked crazy, and then been right. The people who called reusable rockets a dream watched a booster fly back and land itself. The people who said a mainstream consumer EV company was impossible watched Tesla restructure the global auto industry. This is a person who has repeatedly seen something others cant see yet, absorbs the ridicule, and then builds toward it anyway.

The PayPal criticism this author pushes is another perfect ex. Do you know how he became CEO? Elon identified the importance of network effects in the late 90s and realized he could take advantage of cheap capital during the internet bubble to pay users to join his network. He was labeled a lunatic. Losing money upfront to lock customers into your network is well understood now but it wasn’t back then. Confinity was forced to merge bc they couldn’t compete with it…and that’s based on Peter Thiel’s own account in Zero to One.
Elon was considered reckless at the time. But he was right.

And now we have people criticizing Musk’s Mars goal. But as Ben Thompson explained, Mars is the strategic North Star that forces you to radically confront the cost structure required to achieve it. Which leads you down the only path that actually scales, without settling for easier short-term solutions. If you’re serious about putting a city on Mars, full reusability is non-negotiable. And that engineering logic turns out to be what dramatically lowers launch costs. Which unlocks Starlink at scale. And Starlink creates the revenue flywheel that funds everything else. An Arianespace executive called reusability a dream in 2013 and said it was impossible. But the dream isnt the destination. It’s the constraint that forces you down the only engineering path that actually works. And it’s why SpaceX is a trillion company today.

You can write off one company as luck. You can write off two as fortunate timing. But at some point the sheer weight of success across different industries and challenges stops looking like coincidence and starts looking like a big flashing signal.

When someone executes repeatedly in industries where lack of execution destroys almost everyone else, the correct analytical move is to update your model. If you can’t see that Elon is a great CEO, then you’re just revealing the limits of your own analytical process.

[Eric Zhao @zhaopenghui]

Blow my mind

[Michael Andregg @michaelandregg]

We've uploaded a fruit fly. We took the @FlyWireNews connectome of the fruit fly brain, applied a simple neuron model (@Philip_Shiu Nature 2024) and used it to control a MuJoCo physics-simulated body, closing the loop from neural activation to action.

A few things I want to say about what this means and where we're going at @eonsys. 🧵

[BBC News 中文 @bbcchinese]

【最新消息】伊朗官方电视台确认该国最高领袖阿亚图拉·阿里·哈梅内伊已经身亡。

[棋局 @qiju2030]

王阳明的龙场悟道，其实就是教人怎么自救。

大多数人讲王阳明，总爱说他龙场悟道，好像某天夜里灵光一闪，宇宙真理“啪”地一下砸到脑门上，从此心学诞生，圣人就位。

这个说法顶多当个传奇故事听，但对理解这段经历几乎没什么帮助。你真把他当圣人看，这个故事跟你有啥关系？

可如果你把时间往前拨一点，把王阳明先从神坛上请下来，还原成一个活生生的人，这事儿就完全不一样了。

王阳明去龙场之前，是遭逢人生巨变的失败者。三十多岁，科举出身，真心信儒家那套修身齐家治国平天下。然后他在朝堂上硬刚权贵，结果被抓，下狱，廷杖。相当于清华北大毕业，刚被提升为处长，一腔理想还没施展出来，然后就被双开了。

接下来就是龙场。注意，龙场不只是偏远艰苦。在当时，那地方几乎等同于社会性死亡。瘴气、疾病、信息断绝，属于在文明世界之外。相当于今天，被发配去一个连信号都没有的地方。

去那里的官员，默认结局只有两个：病死，或者精神先垮掉。因为真正打击不在环境苦不苦，而在于原来那条人生奋斗路径失效了。

我们大多数人的痛苦，本质是还在赌一件事：再撑一下会不会有转机，再努力一点会不会被看见。可在龙场那里，几乎做什么都得不到反馈。还不是负反馈，是压根没反馈，连骂你的人都没有。

你读书给谁看，你修德谁来评判，你坚持到底是为了什么。没有观众，没有回报，没有上升通道，没人关心，这就让努力甚至是活着本身都失去了意义。

后人说龙场悟道，说得很玄，其实过程一点都不浪漫。对于王阳明来说，根本不是想通什么哲学问题，而是被逼着解决一个生存级问题：如果世界已经完全不按规则给你反馈，那你还怎么活？

王阳明给出的答案，后来被包装成一句很漂亮的话——心即理。但翻译成人话，其实很简单：
自我判断权，我不外包了。

以前他的默认模式，是外面有一套正确秩序，圣人榜样在那里，经典也在那里，我只要不断靠近，世界迟早会承认我——这是一个彻底外部判断并给出结算的系统。

可在龙场，这套系统彻底失灵。王阳明要想活下去，或者不疯掉，只能换算法。而新的算法就是：
行为本身，就是意义来源。

我这么做，不是因为将来有用，不是因为会成功，不是因为会被看见，而是因为这是我认可的。我不这样做，我精神会先死。

很多人讲知行合一，容易讲成道德要求。但如果一个人一直停在“我知道该怎么做，但我什么都改变不了”，人很快就会崩溃。

知行合一真正干的，是把评价闭环锁回自己身上：
我判断，我行动，我自己确认，不再等外部系统给反馈。

这一点非常重要。因为一旦你不再依赖外部结算，你就变得极难被摧毁。环境再烂，它最多限制你能得到什么，但无法再定义你是谁，这就是另一种意义上的“无欲则刚”。

这其实就是创伤后成长最真实的样子。不是熬过去变坚强了，而是原来的那套认知系统已经无法支撑你继续活下去，必须被迫重写一套。

等王阳明后来回到社会，他整个人已经完全不一样了。他不证明自己，不被评价牵着走，成败对他影响很小，但行动力极强。

所以龙场悟道，根本不是什么顿悟宇宙真理，而是一个人在彻底被世界抛弃之后，学会自己给行为结算意义。

这个故事放在今天更有意义。因为很多现代人的痛苦，和龙场有一个隐秘的相似点：
努力和反馈之间的关系，正在失效。

但很多人还在等一个可能永远不会再来的外部确认。

王阳明在龙场做的那件事，说到底只有一句话：
当世界不再给你正反馈和奖励时，你要学会自己给行为结算意义。

否则，你就会一直站在原地，等一个不回应你的世界，把你拖垮。

[Eric Zhao @zhaopenghui]

@tualatrix 如果宝宝闹的厉害，试试半水解或全水解的奶粉

[Eric Zhao @zhaopenghui]

@waylybaye 孩子是动力

[Django @djangoproject]

Some of the biggest companies in the world use Django, but the project's budget is comparable to a single bay-area engineer's salary.

If your company uses Django, please ask them to donate! It's a great way to say thanks, and really helps keep the framework going.

[Kimi.ai @Kimi_Moonshot]

🚀 Hello, Kimi K2 Thinking!
The Open-Source Thinking Agent Model is here.

🔹 SOTA on HLE (44.9%) and BrowseComp (60.2%)
🔹 Executes up to 200 – 300 sequential tool calls without human interference
🔹 Excels in reasoning, agentic search, and coding
🔹 256K context window

Built as a thinking agent, K2 Thinking marks our latest efforts in test-time scaling — scaling both thinking tokens and tool-calling turns.

K2 Thinking is now live on https://t.co/YutVbwktG0 in chat mode, with full agentic mode coming soon. It is also accessible via API.

🔌 API is live: https://t.co/EOZkbOwCN4
🔗 Tech blog: https://t.co/n7xxaszqzF
🔗 Weights & code: https://t.co/4ukcXB0iP6

[Eric Zhao @zhaopenghui]

@BoraBoraBaye 现在还能请到缅甸的吗？

[Eric Zhao @zhaopenghui]

@DavidHuSG1 @zzxwill 原来住低楼层会有，换到十楼以上以后就很少了，每次fogging 的时候偶尔有蚊子飞上来。

[Eric Zhao @zhaopenghui]

@DrZhou_fengshui 苏轼：着力即差

[@levelsio @levelsio]

Unfollow everyone trying to sell you their courses

Follow everyone who actuallly builds stuff

[Eric Zhao @zhaopenghui]

👍

[Andrej Karpathy @karpathy]

New 3h31m video on YouTube:
"Deep Dive into LLMs like ChatGPT"

This is a general audience deep dive into the Large Language Model (LLM) AI technology that powers ChatGPT and related products. It is covers the full training stack of how the models are developed, along with mental models of how to think about their "psychology", and how to get the best use them in practical applications.

We cover all the major stages:
1. pretraining: data, tokenization, Transformer neural network I/O and internals, inference, GPT-2 training example, Llama 3.1 base inference examples
2. supervised finetuning: conversations data, "LLM Psychology": hallucinations, tool use, knowledge/working memory, knowledge of self, models need tokens to think, spelling, jagged intelligence
3. reinforcement learning: practice makes perfect, DeepSeek-R1, AlphaGo, RLHF.

I designed this video for the "general audience" track of my videos, which I believe are accessible to most people, even without technical background. It should give you an intuitive understanding of the full training pipeline of LLMs like ChatGPT, with many examples along the way, and maybe some ways of thinking around current capabilities, where we are, and what's coming.

(Also, I have one "Intro to LLMs" video already from ~year ago, but that is just a re-recording of a random talk, so I wanted to loop around and do a lot more comprehensive version of this topic. They can still be combined, as the talk goes a lot deeper into other topics, e.g. LLM OS and LLM Security)

Hope it's fun & useful!
https://t.co/75mXcUBI8L

[Eric Zhao @zhaopenghui]

@shatufeii @Rumoreconomy 观测需要光，光本身的能量会干扰观测的粒子。

[Eric Zhao @zhaopenghui]

@Be_Waterman 编程方面应该是比ChatGPT好一些

[Eric Zhao @zhaopenghui]

@neubaye @maffany2011 @thinkingcui 差不多，储备金+GIC+淡马锡

[Eric Zhao @zhaopenghui]

@neubaye @maffany2011 @thinkingcui https://t.co/ZZANHFnicn

[Eric Zhao @zhaopenghui]

@thinkingcui @maffany2011 根源还是儒家的内卷，各大补习中心大部分都是华人。