我是一个平时很喜欢记录的人，尤其在阅读的时候，总是喜欢记录一些看似有道理，实则无用的经典语录。

大二的时候，我向同学购买了二手的iPad mini，随后便喜欢上了阅读。随之而来的是，iPad成为了我很少离手的「手机」。我原本的打算是用来看医学教科书，结果因为是mini版，在看那种PDF版教科书的时候更本不好用，后来只能转向了课外电子书阅读器。当然，有了阅读器，还得配备一个记笔记的app，我当时在同学的推荐下选择了note app？结果在大四时，存了几千条笔记的note，在我毫不留情卖出ipad那一刻也瞬间消失了。

后来又在Mac上使用过Notion，但是吧没有真正用起来，因为我发现现在的知识在AI的加持下变得唾手可得，记不记录已经变得不再重要了。

在文献管理方面，我大二在导师的推荐下使用的是endnote。当时并不知道有Zotero这种东西，又是初入文献管理区的小白，只能是导师说什么是什么。但是我没有想到的是，它（endnote）是一个商业付费软件，在几经思考下，我最终无耻地选择了在淘宝上购买一个盗版的app。那时候对盗版行为没有什么感觉，只是觉得能解决自己的问题就好。现在嘛，除了开源的优质东西，我一般不往Mac和iPhone里装。

说到了开源，接下来我提到的都是免费开源的app。如果你之前阅读过我的文章，你一定看到我经常在说OpenCode。除了它和大多数闭源高质量code assistant CLI都有的功能外，我使用它的核心目在于开源，可自行定制化修改。

Obsidian虽然是个闭源项目，但它具有丰富的开源插件生态。Zotero可以说是科研工作者和具有文献管理需求者的福音，它是开源的，且具有丰富的开源插件生态，能够和各种浏览器进行联动，这意味着，你在浏览网页内容时很容易把需要的内容存档到zotero里。虽然目前是AI agent盛行的时代，浏览网页这种脏活累活不用自己出手，但要是看到不错的文章收藏起来倒是不错的习惯，如果你作为需要科研读文献的朋友，那保存文献肯定是少不了的，因为你在写文章的时候，肯定需要引用到你读的内容。

那我作为一个非科研人员，又不搞研究、写文章的人，为什么需要这些东西呢？

在我将OpenCode打造成我的医学辅助助手后，我发现它获取文献，总结输出的文章有些质量很高，可是当我关闭TUI界面后，我获取它们变得很麻烦，更别提检索了，于是我就催生了一个需求，能不能把opencode生成的东西导入到Obsidian，这个目前比较好用的笔记库中。要解决问题，明确问题是第一步，我就去找我的初级工程师聊（opencode），在plan模式下交流了半天，觉得它给出的方案不错，就放手让它去帮我执行了。凡事都不会很顺利，在对opencode批评又不情愿的鼓励中，我终于打通了Obsidian和Opencode的交互。虽然性能还有待提高，但勉强能用。

作为一个医学专业的人，不能说是人才吧，但也不能敷衍自己，所以阅读文献是我日常无聊生活的一部分，加上我使用Zotero管理文献以及有些日子了。我知道作为一个非英语专业的人，读英文专业期刊对我来说一头雾水很正常，所以顺其自然地，我给它配置了一个插件（zotero-pdf-translate）。在不出意外地情况下还真的出了点意外，因为我给它配置了moonshot-api，然后在放入提示词时不小心把${sourceText}这个参数给删除了，导致它一直在那个地方胡言乱语，经过一番排查才解决这个不该存在的问题。

我之前的想法是让opencode单独与zotero交互，但我发现zotero在与opencode交互时总是显得很笨重。所以我就在想，既然opencode和obsidian交互这么方便，那我是不是可以将我zotero中阅读文献时记录的笔记导入到obsidian，好巧不巧，这并不是我一个人这么想过，因为目前的开源社区已经存在了zotero笔记导入obsidian的插件，而且我看下载量还不少。最终，我在zotero安装了zotero-better-bibtex，它可以让我储存的每篇文献有唯一的ID号，然后又在obsidian端安装了Zotero Integration插件和配置结构化文献引用模版，安装完成后给它配置了导入操作，整个过程完成得很「顺利」。

在给zotero安装插件的时候，经常会遇到插件版本与zotero不兼容的问题，所以最好两者都选择最新版，不然你会发现你的操作常常在做无用工。

就这样看似简单的操作，你就能打通obsidian+zotero+opencode。为什么我没有详细的写一篇操作教程，因为我发现教程这种东西其实不现实，因为我的操作的环境和你操作的环境有着本质的不同，我遇到的问题你未必会遇到，我遇不到的问题可能你刚好遇到。我这篇文章主要讲了一些使用AI tools的思路，还有大致的操作流程。你如果想要知道详细的操作细节，可以把它丢给AI做为参考，让它给你补充详细的细节操作。那你可能又问，你都这么说了，我为什么不直接问AI，而要看你的文章。我原来也有这样的疑问，直到我被AI无情的忽悠，我才知道别人花大量时间验证过的方法有多大的价值。

这篇文章中的方法至少是我验证过，可以走得通的路径。你拿给AI，AI可以在这个预印本的前提下去构思，而不是「随机」给你一个方法让你去验证。当然这些细节的东西在AI强大的迭代能力下到也不值一提。

编程语言是软件工程师（software engineer）与计算机（computer）交互的方式，通过编程语言指挥计算机完成某些计算任务的同时，也创建（create）一些伟大的产品，而这些产品是当今互联网世界存在的根基。作为一个非计算机科学专业（CS）出身的人，我为什么要写这篇文章？是因为我最近几个月在研究AI应用产品的时候，发现了一件让我觉得意外，但是有趣的事：

在调用云端LLM的时候，我由于熟悉python，会更喜欢使用它来构建项目；但深入研究最好的开源AI应用产品，我发现他们更多的是使用TypeScript（TS）。这与我已有的认知不符，所以我就花时间研究起了TS。

你可能会有疑问，现在都什么时代了，为什么还要讨论这么老掉牙的问题。我在写文章的时候，也是思考再三，觉得这个有必要拿出来与各位讨论一番。

虽然我知道，现在vibe coding大行其道，学不学编程语言在这个时刻好像显得不重要了。就比如我们熟知的英伟达CEO就在最近的一个采访视频里说，未来人不需要去学习Python，因为English就是最好的编程语言。我想了一番，既然大佬都这么说了，也许我的做法是错误的，可我自己这一年的vibe codeing体验却又与此恰恰相反，所以我还是冒着被议论的风险把这篇文章写出来。

过去一年时间，我vibe coding的项目主要是使用Python，因为我对其学习过一段时间，能够大致看得懂相关的代码片段。但即便如此，在小型的项目面前，我对其依然无法把控，不知道如何优化，我对AI tools 说得最多的一句话是，你检查一下整个项目架构，并告诉我哪些地方需要优化。做这件事情多了之后，我发现LLM只是在翻过来倒过去的弄通一件事，token在疯狂燃烧，而我的项目依然没有什么动静。在经历过这样的事许多次后，我决定回归基础，不在跟风去尝试新的模型，也不再构建新的无用玩具项目，只是偶尔会让新模型优化一下上一代模型遗留的buge。

后来由于开源项目OpenCode的出现，我尝试将其克隆到本地研究。在不断尝试的过程中，我逐渐熟悉了项目架构，了解到它是使用TypeScript来构建的终端交互编程助手，是继Claude code之后，最好用的TUI编程工具，关键在于它是开源的，你可以随便修改项目，使其符合自己的需求。

我首先是做最基础的修改，在配置文件中添加了medical skill 和 subagent，让它成为一个具有医学专业知识的AI assistant。后来又做了一些更大的修改，我给OpenCode添加了新功能，让它可以自由浏览我Zotero里的文献，成为更加隐私的医学文献助手。我分别对OpenCode的tool模块做了2个修改，一是添加zotero工具，二是让opencode能够自由阅读pdf文档，最后让subagent能够基于我的临床指南回答问题。这些构建的方向是基于我之前折腾了几个月的RAG（检索增强生成），让我发现它存在很多劣势，即使我现在知道RAG+Graph可能是知识库最好的形式，但是目前对我来说已经不重要了。

在研究Opencode一段时间后，OpenClaw在AI圈又火爆起来了，这时候我由于收到FOMO的影响，不得不停掉手里的东西，打起了研究OC的主意。由于有过研究OpenCode的经验，这次研究OC的过程就顺利很多了，它是使用Gateway和Pi agent来构建的项目，也是以TS语言为主。通过Pi，AI agent能够自主阅读文档（read）、编辑（edited）、执行任务（excite），就是这样简单的创新，就让现有的agent能够自主的做事，而不是被动的等待命令。

通过对OpenCode和Openclaw的研究学习，我发现他们都是使用TypeScript语言来设计的TUI产品。也就是真正执行的并不是terminal里的Opencode or Openclaw，而是云端的LLM。 我以前有一个疑问，为什么Python被称为AI里的胶水语言，但是在AI应用产品里却很少见？ 经过学习我才知道，原来Python 是 计算密集型 (CPU-Bound) 任务的首选，负责 AI 模型的底层驱动；而 TypeScript 是 并发密集型 (I/O-Bound) 任务的最优解，负责管理复杂的 UI 状态与高频的云端 API 流式交互。 在 OpenCode 这种产品里，大脑（LLM）在云端，本地端更需要的是极致的流式交互和并发处理。Python 负责把 AI 跑通，而 TS 负责把 AI 变得好用、好看、好分发。这就是为什么现代 TUI 纷纷倒向 TS 生态。 可惜我对TypeScript了解甚少，很多源码根本看不懂，要是Python也有这样的优势就好了。

现在你可能听说，在AI时代，不会编程也可以做app，构建网站······

是的，确实可以，但是构建出来了，然后呢？

我自己就构建了2个iOS app，但是到现在也没有上架app store。如果不懂基础，构建出来的东西（demo）很难过渡到拿给别人使用（product），因为如果出现问题，你需要为你的基础知识负债承担责任，也就是人们常说的AI只负责执行，不负责承担执行后的结果。结果有好，自然存在坏处，如果你看不到AI做出的东西存在的问题，这个产品盲盒就会一直存在。所以不要相信什么基础不重要，有AI就行，即使未来AI普及到和EXCEl一样，那你是不是也会一些基本的计算，不然被坑了都不知道，至少小数点这些要会数的嘛！

我今天拜完年后闲着没事，将这段痛苦的经历整理成一篇部署教程，这里面是我的一些踩坑经历，你不妨先收藏，以后要是有这样的需求也能够快速拿出来阅读，能避免许多我遇到的困境。

我算是一个资深的Windows用户了，从21年大一的Windows10开始用到24年的Windows11。几年用下来并没有发现Windows有什么“缺点”，除了满屏的广告、删了又出现的垃圾，以及各种开发环境永远处在报错外，其他体验都还不错。不过在遇到Mac后，我发现我之前用Windows做的很多事情其实是不合理的，比如开发。

我过去在Windows上折腾R语言的时候总是遇到奇奇怪怪的问题。早上打开电脑弄半天总是处在报错中，但下午打开电脑再测试时，莫名其妙就跑通了。那时候因为是自学，又没有人可请教，只能自己慢慢花时间调试，就这样一路磕磕绊绊走过来，我一直以为是我的问题。用了Mac后才发现，Linux系统才是操作开发的首选。所以，在拿到Windows系统的时候，第一件事是给它配置一个Linux。

如何配置

对于新手来说，使用WSL2（Windows subsystem for Linux）是不错的选择。你可以在wsl2里装一个Ubuntu，它拥有原生的Linux内核。你将代码写在Windows上，但运行、调试、终端全部发生在wsl2的Linux环境里，体验非常好。这是将Windows电脑作为云服务器的第一步——环境的搭建。

目前安装wsl非常简单，你只需要保证你的Windows版本符合要求，因为Windows11是原生支持的。

在Windows里以管理员身份运行PowerShell一键安装命令：

wsl --install

这条命令会在Windows组件下载最新的Linux内核，并将Ubuntu设置为默认安装的发行版。安装完成重启电脑（必须操作），你就能看到Linux要求你设置信息的界面。没有使用过Linux系统的朋友需要注意，在输入密码时感觉电脑没什么反应，这是正常的。在提示设置密码时，屏幕上不会显示任何内容，输完后直接回车即可。

有了Linnux系统，你就可以在里面安装必要的开发工具，比如git，node.js，Python，Go......

在下载工具前，你需要确保你的Windows能链接到GitHub，所以你可以在github.com网站上配置你Windows的公钥，让GitHub认识你的电脑，不然你从GitHub上下载一些内容时可能会提示链接失败。

那么问题来了，如何配置公钥？

1.打开PowerShell输入ls ~/.ssh/id_ed25519.pub，如果显示文件不存在，可以使用ssh-keygen -t ed25519 -C "your_email@example.com"去生成新的密钥，按提示操作就行。（注意：以.pub后缀的是公钥，私钥绝不要暴露）

2.使用cat ~/.ssh/id_ed25519.pub查看生成的公钥内容，并复制到GitHub中设置就行。（GitHub → Settings → SSH and GPG keys → New SSH key）

3.填写完成后，可以使用ssh -T git@github.com在终端里测试一下。

链接Mac到Windows

为了让远程操作更加丝滑，我选择了使用ssh免密登录。

这步的配置也很简单，在Windows上以管理员权限(win+x)打开poweshell，运行：

# 安装 OpenSSH 服务器
Add-WindowsCapability -Online -Name OpenSSH.Server~~~~0.0.1.0

# 启动服务并设置为自动启动
Start-Service sshd
Set-Service -Name sshd -StartupType 'Automatic'

在Mac端运行

ls ~/.ssh/id_ed25519.pub

查看公钥，如果没有，运行 ssh-keygen 一路回车生成。

将公钥内容复制到 Windows 的

C:\Users\你的用户名.ssh\authorized_keys

文件中。（没有文件夹可以自行创建）

问题又来了，如何将一台电脑上的内容复制到另一台呢？

我当时遇到这个问题也不知道怎么解决，我使用了最笨的办法——微信。在两台电脑上登录同一微信账号，再将Mac上的公钥复制到微信上，在另一台上粘贴，但是发现消息竟然不会同步，后来通过手机作为中转——将手机微信收到的公钥重新转发到新登录的windows上。

不过这个方法太慢了。我后来使用Tailscale来链接Mac和Windows，当Mac和Windows登录同一tailscale账号时，他们会被分配一个固定IP，使用这个IP可以将Mac上的公钥复制到Windows上。

ssh-copy-id -i ~/.ssh/id_ed25519.pub 你的Windows用户名@Windows的Tailscale IP

最后的操作就更简单了，在Mac上使用

nano ~/.ssh/config

打开配置文件，添加以下内容：

Host win
    HostName 你的Windows的Tailscale IP
    User 你的Windows用户名
    IdentityFile ~/.ssh/id_ed25519

然后在Mac终端直接使用ssh win就可以链接。

生产力提升

1. 永不睡眠。作为云服务器，最忌讳“关机”。在 Windows 设置中，将“接通电源时”的屏幕关闭时间改为“从不”，睡眠时间改为“从不”。

2. 包管理器：让系统更整洁。Windows 推荐使用 Scoop，它安装软件非常干净，所有软件都装在同一目录下，卸载也彻底。macOS 毫无疑问是 Homebrew。

3. VS Code Remote Development。在 Mac 的 VS Code 里安装 Remote - SSH插件。你可以直接打开 Windows 上的代码文件夹进行编辑。这简直就是把 Windows 的算力借给了 Mac。

这篇文章基本聊完了如何配置Windows作为服务器，并通过ssh+Tailscale进行双机交互的方法。我知道它还很粗糙，因为我在做的过程中遇到的问题，比文章描述的多且复杂。不过只要你遇到问题就想办法解决，解决不了的咨询AI，要是还在有问题就搁置在一边，等你什么时候想起来再去弄，当前的问题会变得轻松许多。

我遇到这种情况很多次，我思考了一下背后的原因，发现很多情况下是因为条件不成熟。至于这个条件是什么，需要结合实际具体分析。我折腾过很多东西，遇到过各种问题，有些轻松解决了，有些不再想起，有些在当时试了各种方法都无果，但过后，等我头脑冷静下来我就知道是怎么回事了。这种很玄学，但放弃有时候何尝不是一种解决问题的方法呢？

I’m using Qwen2.5 + AnythingLLM to build my own medical knowledge base.

Today is 2025-12-20. There’s nothing special about it. I woke up from a few nightmares and only then realized it was the day of the graduate entrance exam—something that has nothing to do with me anymore. As a bonded medical student, many people around me assumed I would take the exam. For a long time, I assumed so too. But gradually I let that idea go. Maybe returning home is my true destination. I used to want to escape that place with everything I had; now I’m realizing I may have no choice but to go back.

During these years of studying, I’ve tried to find small pleasures outside of coursework—little things to occupy my life. In truth, they were just my way of resisting the monotony of medical education. As long as you keep moving forward, that urge to break out of your boundaries will eventually push you somewhere you want to go. But for most ordinary people, simply living a stable life already takes everything they’ve got. Beyond that—what more is there to demand?

Standing at the end of 2025, I want to leave something behind. Looking back, the things that kept me company the most were various LLMs. So this piece is, in a way, dedicated to them.

For anyone following the tech world—especially AI—this year has been full of rewards. OpenAI was founded in 2015, and after a decade, ChatGPT has become one of the most general-purpose AI assistants we’ve ever had. The wave is still rising, and we’re not in an AGI era yet. Even though AI made major leaps as early as 2023, I didn’t really start using domestic AI assistants until 2024. Back then, I only used them for chatting and simple image generation. But just a few months later, LLMs—both inside and outside China—broke into mainstream attention. Then the narrative shifted quickly from “reasoning models” to AI agents that can autonomously handle tedious tasks.

In a moment where top tech companies are fighting for market share in the agent era, people like me can only play around with open-source models, call APIs, and build little tools that are practical for our own lives. Even then, I can still feel how far behind I am. Qwen2.5:7B, Alibaba’s open-source model, is among the strongest in the 7B-parameter class. What startled me was realizing it was released last year, with its last updates also staying in last year—while Qwen has already moved on to the 3.x series. Sometimes I think my information sources are simply too slow.

I’ve wanted to build my own medical knowledge base for a long time, but never found the right moment to actually do it. Most of it stayed as ideas. Yesterday, with a free afternoon, I finally tried. I downloaded qwen2.5:7b through Ollama—a 4.7GB file that took roughly two hours, restarting a few times. Later I asked Gemini and learned Ollama supports resume downloads; after stopping it with Ctrl+C and restarting twice, it finally completed.

After that, everything went surprisingly smoothly—almost “foolproof.” It made me reflect on how much modern UI tools plus AI assistance have lowered the barrier to doing things. It’s increasingly becoming: if you’re willing to get your hands dirty, you can probably make it work.

Then I fed my latest textbook—Diagnostics (10th Edition), around 600 pages—into a local embedding model (nomic-embed-text). Within minutes, it was chunked and indexed. I used to think vectorization was something inherently difficult, but on a MacBook Air M2, the whole thing took less than three minutes, producing tens of thousands of vectors stored locally.

Once the pipeline was built, I started testing and tuning. The results, honestly, were worse than I expected—unstable answers, imperfect citations, and moments where it felt like the model had “seen” the content without truly “understanding” it. Still, I told myself: this is version one. If I keep iterating, the knowledge base will get better.

In an era where the cost of experimentation is so low, a laptop, an AI assistant, and a single afternoon can move a project forward significantly. Yet when I look around, many people still dislike using computers. In the past, building a PC felt like something only specialists did. But with Doubao’s help, my roommate assembled a desktop machine, and even installed Windows 11 from a USB drive I had. In earlier years, that might have required reading manuals and watching hours of tutorials. Now it can happen in a single afternoon.

As I’ve done more hands-on projects, I’ve become more convinced of something simple: in today’s world, if you keep building things that are genuinely useful to you, eventually you’ll start building things that are useful to others. And if something is useful to others, it creates value—and money follows value. I’m still struggling in many ways, but I’m confident this direction is right. Using the right method to do the right things may be one of the few reliable sources of stability for an ordinary person trying to live well.

It’s Not "Today" That Determines the Future, But Every Single "Present Moment"

We have all fallen into a common illusion: believing that if we plan the future perfectly today, the future will unfold exactly according to that plan, step by step.

Take 2023, for example. I created a detailed "perfect plan" for myself: pass an advanced English proficiency test (CET-6), read extracurricular books, exercise for 30 minutes every single day rain or shine, and systematically learn Python.

Standing here in 2025 and looking back, however, I realize that what truly ingrained these pursuits into my being wasn't that grand initial plan, but countless tiny, concrete "present moments" over the past two years.

Consider the English test. Theoretically, I should be sitting in an exam hall right now, scribbling furiously for that certificate. However, after weighing my options, I chose to sit here and type these words instead. Back then, I believed passing that test was the most crucial part of English learning. But as I became accustomed to reading technical documentation in English and acquiring first-hand information directly in my daily life, I realized something: When English has already become a tool for me to acquire knowledge, why should I regress and torture myself just to meet the requirements of someone else's exam?

This sparked a question about the true nature of planning: Do we really make plans just for the sake of "completing" them?

Reflecting on the past, the answer is no. A plan is essentially just a prediction of the future based on our "present understanding." We aspire to become a certain type of person, so we chart a path. But the biggest misconception most people hold (my past self included) is treating that prediction as a rigid mandate that must be executed—attempting to force a static plan onto a dynamic life.

Take my experience learning Python as an example. In 2023, believing that "paying money" equaled "investing in myself," I spent over 2,000 RMB on a course. The result? I remained stuck at the beginner stage, constantly battling procrastination. Back then, I didn't even understand how to configure the Python environment. It wasn't until I switched to a Mac this year that I realized Windows environment configuration is indeed complex enough to discourage any beginner; not understanding it back then was entirely excusable.

But that wasn't the end of the story. The real turning point wasn't completing a specific course, but the emergence of AI. I discovered I no longer needed to manually type code line by line; instead, I could build projects through an AI Assistant. In this process, the syntax and logic that once gave me headaches were naturally acquired through constant debugging and project construction. This was a variable I could never have predicted when making my original plan, yet it was precisely this variable that allowed me to truly master the skill.

The significance of a plan lies in providing an initial vector for our current actions, not in locking down a final destination.

As time passes and insights accumulate, we must learn "Dynamic Adjustment." When creating a plan, we can only base it on what is controllable at that time; however, the environment is uncontrollable, and life circumstances are fluid (such as transitioning from a student to a clinical intern).

If you find that an original goal (like passing the English test) no longer serves your actual needs (like acquiring information), yet you persist anyway, that is not perseverance; it is rigidity. Once the true purpose behind the plan is clear, the means to achieve it become flexible—you can learn English by doing test exercises, or you can certainly learn by reading various literature that interests you.

More important than "completing the plan" is "building habits."

Do not attempt to precisely predict results, and do not be held hostage by quantitative metrics (like "read one book per week"). I certainly didn't complete my reading plan from back then, but I developed a reading habit, having read over 100 books in two years. My exercise plan didn't meet its target either; I went from rigid daily running to my current routine of walking. The form changed, but the habit of "moving" remained.

For someone undergoing a high-intensity internship, sticking to the rigid plans of student days is unrealistic. Plans serve people; people are dynamic. There is nothing to regret about not following the plan, as long as core habits are still supporting your life.

Finally, if you want to change the future, the only thing you can grasp is this very moment.

If you want to travel, go now, even if it's on impulse. Because after a while, you will find that state of mind has vanished. You might end up like me right now, just wanting to stay home in front of the computer chatting with LLMs.

If you want to learn a skill, start now. It doesn't matter if you master it or if you give up halfway. What matters is that you satisfied your current curiosity.

In this era of exploding AI technology, curiosity is your last line of defense against mediocrity and boredom. Without that initial spark of curiosity about programming back then, I might not even know what VS Code is today, let alone call APIs myself or hand-code learning assistants. Those tinkerings that seemed useless and boring at the time will ultimately be exponentially magnified by the empowerment of tools.

The scariest thing isn't lacking tools; it's having the tools right in front of you but being unable to muster any interest in exploring them.

When you try enough things, you will eventually find the one or two things that you find truly interesting and are willing to keep doing. That will be the source of joy in your life, and also your future defensive moat.

As Naval Ravikant said: "If you don't commit to your meaningful work, life will fill your time with busywork."

How a medical intern used a microcontroller to break the "Last Mile" of AI adoption in a closed hospital system.

I started following DeepSeek last year. Back then, it was like a diamond in the rough—no web interface, no mobile app. It wasn't until the release of the DeepSeek-R1 model that I truly felt my personal "ChatGPT moment." As someone without a technical background, the emergence of R1 made me incredibly excited. It drove me to dive into original research papers, and through that exploration—however partial—I gained a deeper understanding of the "black box" that is the Large Language Model (LLM).

This past year, I have witnessed the explosion of outstanding AI assistants. From my first encounter with Zhipu AI, to Kimi (which I’ve always been bullish on), and Alibaba’s Qwen. While domestic open-source models are playing catch-up, they are also innovating relentlessly. Although I haven't been in the eye of the storm of this AI development wave, standing on the shore and watching the tide roll in has been a breathtaking experience in itself.

From GPT-4 to the anticipation of GPT-5, in just one year, LLMs have evolved from simple chatbots to AI Agents capable of automatic coding and deep research. With the help of AI tools, my own learning methods have undergone a paradigm shift.

I used to believe in the traditional "building from the ground up" approach—that you must master the foundational knowledge before you can build a project. But now, the logic has flipped. With an AI Agent, I can build a "castle in the air" first—getting a demo running immediately—and then deconstruct it downwards to fill in the foundational knowledge. This "application first, understanding second" approach has made learning infinitely more fun. It’s no longer "I don't know what I can do with this knowledge"; it’s "I’ve already made something cool, now let me figure out how it works."

Following the development of LLMs, I’ve delved into obscure technical documentation and used the APIs of DeepSeek and Kimi to build chatbots and literature retrieval assistants. Currently, they are indispensable partners in my VS Code for learning and reviewing code. They may still have a slight gap compared to the world's top LLMs, but for an ordinary user like me who values practicality and economic efficiency, they are more than enough.

Before I truly got hands-on with AI, I felt it was something distant. It wasn't until I immersed myself in it that I discovered: I can be not just a good AI user, but a creator of custom AI Agents. People are often intimidated by the unknown, thinking, "This has nothing to do with me." But if you are willing to take that first step, you'll find that new technologies can not only boost your efficiency but allow you to participate in their creation. Today's LLMs are like the early smartphones, and future AI Agents will be the apps within them. Soon, we won't need to click around; we’ll just say, "Doubao, book me a train ticket home," and it will be done.

I have been an intern in a clinical setting for a year now. The most enthusiasm-draining part of the internship is often the repetitive paperwork. I started thinking: Could I hand over these highly templated, yet personalized admission and progress notes to an AI? After repeated discussions with Gemini, I actually found a way.

In the hospital, an intern's work is basic—ECGs, admitting patients, writing progress notes—but essential. I’m not rejecting this work, but doing something you don't particularly love for a long time inevitably leads to burnout. I introduced AI tools to help write medical records not to be lazy, but to give myself a choice: I can write it myself, or I can let the AI write it, and I review it.

However, the hospital's internal network is a closed system, physically isolated from the open internet where these AIs live. To break this barrier, I chose the ESP32-S3 microcontroller to simulate a physical keyboard, "injecting" the content generated by the LLM directly into the medical record text fields on the hospital computer.

This was my first attempt to connect a cloud-based LLM with an isolated physical device. I used Streamlit as the front-end interface for the DeepSeek API, Python as the logic processing layer, and finally, the ESP32-S3 as a "macro keyboard" to execute the input.

This attempt to step out of the pure software layer and extend into hardware represents an upgrade in my application of technology. I’ve evolved from an AI Agent that operates only on a screen to a prototype of "Embodied AI" that can breach physical isolation layers.

Every development in technology is worth our enthusiasm, our learning, our connection, and our creation.

为什么你看了那么多公众号文章,生活依然没有改变?如果你不幸点开这篇文章,请立刻关闭它,去做一件你特别想做却又害怕的事。

读了一些书,写了一些文字,经历了一些事情后,我才恍然大悟——我做的很多事情既无意义也没用处。

很多年前,我把阅读奉为必须坚持的行为。看着网上各路大咖对阅读的推崇,我总在心里默默点头:嗯,说得太有道理了。我甚至一度鄙视不阅读的人,觉得他们都在过着苏格拉底口中"未经审视的人生"。

人的成长来自不断的「打脸」。在自己身上做了实验后,我逐渐明白:人是自己行动的产物,语言只是引发行动的导火索。当我把阅读奉为必须执行的行为时,我心中的信念变成了"阅读很重要",并试图去说服别人——你看,我说的这些都是靠阅读得来的,书中早有记录。但当我试图传播一种行为时,并不是因为这种行为真的能给你带来多大收益,只是因为我正好在做它,且「有可能」我以为自己因此获得了某些收益,所以我推荐(说服)你也去做。事实可能就这么简单。

回首两年前,每当看到有意思的书,我都会在朋友圈分享。一是为了记录,二是为了炫耀,有时也是想让关注我的人知道我最近在做什么。人这种社会化动物非常奇怪,会不断放大对自己有益的行为(趋利避害),但在放大过程中,由于缺乏逻辑思考和实验验证,结果总显得可笑,只是很少有外人能看明白。

一个月前,我的QQ动态里频繁出现一个卖课的人,暂且叫他A。A是我在某活动中认识的朋友。他最近做了件我觉得挺有意思、也值得琢磨的事:通过写网文小说被平台推荐,"意外"火了。然后他觉得这是自己牛逼,就开课教别人如何写网文赚钱。你猜怎么着?还真有人买单。

如果你了解网文平台的算法,知道什么叫自我营销,对这样的行为只会嗤之以鼻,而不是跟风效仿。

我认识他的时候,他就经常用文字记录一些有趣的事,所以他写网文能火是有原因的。当你只看到别人的结果时,满脑子都是"不会吧,他做这个这么短时间就火了?"而且他为了卖课,也会迎合你的口味——是的,就是你想的那样,你看,这么短时间内我就写火了一本网文,你也可以的,来加入我吧!

这就是韭菜产生的原因:觉得自己也能和大佬一样。可你想过没有,大佬之所以来卖课,核心原因不正是这个方法他自己也无法复制吗?如果他的方法真的有效,为什么不用这个方法去写更多爆款,而是拿来卖给你呢?而且,能够大规模复制的方法,你觉得有什么高价值吗?平台流量池就那么大,方法越有效,对他越有益,你只是陪跑,甚至只是为平台扩充流量池的炮灰。

但凡有点思考和逻辑推断能力,也不至于落入这样的陷阱。这样的坑,我自己曾经踩过无数个,撒钱无数,好在练就了些防骗技能。

作为身在国内的人,我几乎不会花时间在国内平台上,因为在我眼里,很多都是戴着面具的营销、注意力收割机。当然也不能一刀切,某些平台上还是能看到有趣的纯粹分享,只是对我来说,筛选成本太高。我要么直接对接源论文数据源,要么只看顶级大佬的post,因为他们才是身处一线实战的人。

两年后的我,心中只有一个信念——生产 > 消费。在我们目光所及之处,几乎都在抢占我们的注意力,让我们「消费」时间、金钱、知识。当我们没有保护注意力的意识,整个人就会被外在世界裹挟,成为只占有自己肉身的行尸走肉。

我发现对抗注意力收割机的方法之一,是尽可能把时间花在生产上。一旦你沉浸在生产自己的东西中,外在世界就变成为你服务的,你需要什么,就从外在世界「交换」。交换能让我们更早懂得商业运行的本质,也会驱使我们刻意训练,提升更有价值的产品和服务。

在中国,农民天生就是生产者。只不过随着现代化城市进程推进,大多数农村人选择了进城务工。从结果上看,务工人员的收入普遍比在农村种地的人要高。如果你仔细思考这种现象,会发现很有意思:为什么工厂需要工人,工人只是农民身份的转变,最终生活水平却大相径庭?工厂在生产,农民也在生产,这背后到底有何不同?如果你也是农村人,不妨思考一下这些问题,看看有哪些有意思的发现。

For decades, if you were diagnosed with drug-susceptible pulmonary tuberculosis, you heard some version of the same sentence:

“You’ll need to take these pills. Every day. For six months.”

Six months isn’t just a number. It’s lost wages, side effects, daily reminders that you’re sick, and a huge adherence problem for health systems trying to keep millions of people on track.

A 2021 NEJM trial quietly asked a very impolite question:

“What if we could safely cut that to four months?”

The answer is not just a story about new drugs. It’s a story about how we redesign “standards” in medicine — and what we’re actually trading when we decide to shorten treatment.

1. The Myth of the “Sacred Six Months”

The six-month TB regimen isn’t random tradition.

It’s the product of years of trials showing that:

• Shorter regimens (4 months) consistently had higher relapse rates, even when we swapped in powerful drugs like fluoroquinolones.

• Public health programs decided:

  “We hate six months, but we hate relapse more.”

So “6 months of HRZE/HR” became a kind of sacred wall. People tried to punch through it before — and mostly bounced off.

Dorman and colleagues decided to hit that wall again — with a different kind of hammer.

2. The New Gambit: Turn Up the Rifampicin Dial

One key insight from preclinical and early clinical work:

The higher the rifamycin exposure, the better the sterilizing activity and the lower the relapse.

Rifapentine is a cousin of rifampin:

• Same class (rifamycins),
• Longer half-life,
• Allows higher, sustained exposure without changing to twice-daily dosing.

Moxifloxacin is a strong fluoroquinolone with solid activity against M. tuberculosis, and early trials hinted that adding it speeds up culture conversion — but by itself that wasn’t enough to cut the regimen to 4 months.

So the authors made a simple but bold bet:

“What if we combine high-dose daily rifapentine with moxifloxacin, and squeeze all of that into four months?”

If TB chemotherapy was a sound system, they basically:

• Cranked the rifamycin volume up,
• Swapped in a stronger second instrument (moxi instead of ethambutol),
• And asked: “Can we finish the concert earlier without losing the audience?”

3. The Trial in One Glance

They ran a large, multicenter, open-label phase 3 non-inferiority trial across 13 countries, enrolling adults and adolescents with newly diagnosed, fully drug-susceptible pulmonary TB.

Three regimens went head-to-head:

1. Standard 6-month regimen (control)

   • 2 months: Rifampin + Isoniazid + Pyrazinamide + Ethambutol (HRZE)
   • 4 months: Rifampin + Isoniazid (HR)

2. 4-month Rifapentine regimen (no moxi)

   • 8 weeks: Rifapentine + Isoniazid + Pyrazinamide + Ethambutol
   • 9 weeks: Rifapentine + Isoniazid

3. 4-month Rifapentine + Moxifloxacin regimen

   • 8 weeks: Rifapentine + Isoniazid + Pyrazinamide + Moxifloxacin
   • 9 weeks: Rifapentine + Isoniazid + Moxifloxacin

Total randomized: 2516 participants. After excluding resistance / eligibility violations, 2343 were “microbiologically eligible.”

Primary outcome:

Alive and free of TB at 12 months after randomization (no relapse, no need for retreatment, no late positive cultures).

This wasn’t a “can we clear the sputum faster?” question. It was a “do you actually stay cured?” question.

They set a non-inferiority margin of 6.6 percentage points.

Translation:

“We’re willing to accept up to a ~6% higher ‘bad outcome’ rate in exchange for cutting treatment from 6 months to 4 — but not more than that.”

That’s a value judgment baked right into the stats.

4. So… Did Four Months Work?

Let’s jump straight to the punchline.

Among microbiologically eligible participants:

• Unfavorable outcomes (relapse, failure, death, or major loss to follow-up):

  • Standard 6-month: 14.6%
  • 4-month RPT+Moxifloxacin: 15.5% (difference 1.0%, 95% CI −2.6 to 4.5)
  • 4-month RPT-only: 17.7% (difference 3.0%, 95% CI −0.6 to 6.6)

In the stricter “assessable” population (excluding people with unclassifiable outcomes):

• 6-month: 9.6% unfavorable
• 4-month RPT+Moxi: 11.6% (difference 2.0%, 95% CI −1.1 to 5.1)
• 4-month RPT-only: 14.2% (difference 4.4%, 95% CI 1.2 to 7.7)

Remember the non-inferiority rule:

the upper bound of the 95% CI must be ≤ 6.6%.

• RPT+Moxi passes in both key populations.
• RPT-only basically fails — and in the stricter analysis, it clearly crosses the line.

So the verdict is asymmetric:

• 4-month Rifapentine + Moxifloxacin: Shorter, and non-inferior to six months.
• 4-month Rifapentine without Moxi: Not good enough.

Same idea as your “Hello” experiment from PCT, but in drug form: Surface-level similarities (4-month regimens with faster culture conversion) hide the fact that small compositional differences in the regimen matter enormously for long-term control.

5. What About Safety? Are We Paying in Toxicity?

During treatment, grade 3+ adverse events occurred in:

• 19.3% on standard 6-month
• 18.8% on 4-month RPT+Moxi
• 14.3% on 4-month RPT-only

So on the surface:

• No safety penalty for 4-month RPT+Moxi.
• Possibly fewer severe events with RPT-only (though it failed on efficacy).

There was more high-grade hyperbilirubinemia in the rifapentine arms:

• 1.0% (control)
• 3.3% (RPT+Moxi)
• 2.4% (RPT)

But classic rifamycin biology suggests this is often due to interference with bilirubin uptake, not necessarily true hepatocellular injury — and Hy’s law cases were rare and similar across groups.

Deaths during treatment were rare (<1%) and similar between regimens.

So if you’re a program manager or clinician, the safety summary looks like:

“We’re not obviously trading safety for speed here — but we still need to watch the liver and, with moxifloxacin, the heart.”

6. A Subtle but Huge Lesson: Surrogates Can Lie

Both 4-month regimens had faster time to stable culture conversion than the 6-month standard, in liquid and solid media.

But only one of them (RPT+Moxi) delivered non-inferior long-term outcomes.

That’s a big methodological slap in the face:

“Early microbiological response is helpful, but it’s not a reliable proxy for ‘you won’t relapse after we stop therapy’.”

In other words:

• A regimen can look amazing at 8 weeks (cultures flip negative fast)
• …and still underperform over 12–18 months, where it really matters.

For trial designers, that means:

• You can’t fully escape long, expensive phase 3 trials
• …if your goal is to change the standard of care, not just show “promising early markers.”

This is like the difference between:

• “My app has great first-day retention”
• vs
• “Six months later, people still come back without being bribed.”

Both are “efficacy,” but only one tells you about durability.

7. Implementation Reality: Stats Are the Easy Part

Even if you accept:

• Four months RPT+Moxi is “non-inferior”
• Toxicity is manageable

…you immediately slam into real-world constraints.

To safely roll this regimen out at scale, programs need:

1. Rapid testing for fluoroquinolone and isoniazid resistance, not just rifampin.

   • Otherwise you could be giving moxifloxacin to people whose bacteria are already resistant, silently undermining the whole scheme.

2. Practical guidance on food

   • Rifapentine absorption is significantly better with food (ideally high-fat), so the trial told people to take it with meals.
   • It’s evidence-based — but how usable is that in real-world settings where food insecurity is an issue?

3. Cost structures

   • Rifapentine and moxifloxacin regimens are more expensive per pill, even if shorter overall.
   • We need proper economic analyses to know whether higher drug costs are offset by fewer clinic visits, better adherence, and lower relapse burden.

The paper is very honest about this: the science of “can this work?” and the politics of “can we do this everywhere?” are two different battles.

8. Why This Matters Beyond TB

If you’re not a TB doctor, you might think:

“Okay, cool trial… but what does this have to do with me?”

Quite a lot, actually.

8.1 Rethinking “standard” as a negotiable contract

“Six months of therapy” felt like an objective truth.

This trial shows it’s really a negotiated contract between:

• Efficacy
• Toxicity
• Adherence
• Cost
• And the risk tolerance of societies and health systems

When someone says “this is the standard regimen,” you can always ask:

“Standard because it’s literally optimal? Or because we haven’t yet run the right trial to challenge it?”

8.2 Agency, but for systems

In the PCT story, individuals don’t need to perfectly predict the future — they define a reference and then control perception around it.

Here, public health systems are doing something similar:

• They decide what they want to control:

  • relapse rates below X%,
  • adherence above Y%,
  • program costs below Z.

• Then they adjust regimens, monitoring, and diagnostics until those variables stay within range.

Trials like this one are the feedback signal in that control loop.

It’s not “brave scientists guessing the future correctly.” It’s “systems nudging their parameters and watching reality push back.”

8.3 For you as a clinician / student

If you’re in medicine, this trial is a template for how to read big phase 3 papers:

• Always ask:

  1. What sacred assumption is this paper trying to renegotiate?
  2. What’s the non-inferiority margin, and who decided that’s acceptable?
  3. Do the surrogates (like early culture conversion) actually line up with hard outcomes?
  4. What changes in logistics does this result quietly demand?

Once you start reading with those questions in mind, you’re no longer just “consuming findings.” You’re watching medicine actively edit its own rules.

One Last Thought

For a long time, TB patients have been told:

“This is just how long it takes.”

This trial doesn’t magically fix global TB. It doesn’t solve MDR, social determinants, or adherence by itself.

But it does something psychologically important:

It reminds us that “standard” is not a law of nature. It’s a provisional truce — one that can, and should, be renegotiated whenever we have the courage (and data) to ask:

“Could we give people their time back, without giving the disease a second chance?”

You've definitely met people like this. They buy tons of books, bookmark dozens of tutorials, take better notes than anyone else, but the moment they need to apply anything? Total deer in headlights. That's me. The most embarrassing part? I genuinely love learning. I put in so much effort it even moves me. But the output? Basically zero.

This gap forced me to think: what the hell went wrong?

From "My Brain Sucks" to "My Method Sucks"

First instinct? Blame my brain. After years of exam-oriented education grinding me down, I had to admit I'm not exactly gifted. But who wants to just accept that fate? If the hardware's bad, you gotta make up for it with software, right?

So I started this long, tedious "method-hunting journey," reading every learning methodology book out there—from How to Learn Efficiently to How to Read a Book. I practically wanted to read every damn book about learning.

The process was actually pretty interesting. At first, I loved reading articles that criticized school education. They felt so right—it's not my fault I can't learn! But then I thought about it: why can some people still learn just fine? If it's really the education system's fault, shouldn't everyone be failing?

That thought hit me: it's not that methods don't exist—I just never found the right one.

Why Are Classrooms Still "Teacher Talks, Students Listen"?

Have you ever thought about this: it's 2025, why is classroom teaching still the same format from hundreds of years ago? Teacher lectures up front, students listen below. Technology has evolved so much, why is education still so "backwards"?

I used to think this showed education's refusal to innovate. But later I figured something out—what survives tends to touch the essence.

Why haven't all those "innovative teaching models" replaced traditional classrooms? Not because they're not good enough, but because traditional classrooms captured the core of learning: letting students observe up close how someone who "gets it" does things.

That's the power of imitation.

Imitation Isn't Copying Answers—It's Stealing Skills

Let me tell you about learning Python.

I used to hate typing out code from examples. Felt like a waste of time—if there's already code there, why not just copy-paste? But now my thinking's completely changed. Even if it's AI-generated code, I'll type it out line by line.

Why? Because I discovered the act of copying isn't simple duplication—it's your brain actively reconstructing that code.

While you're typing, you think: why is it written this way? Why name the variable like that? Could this function be written differently? These thoughts don't happen when you copy-paste, but they naturally emerge when you type character by character.

This reminds me of learning English. My English actually improved through the iPhone translation feature—yeah, that most basic translation tool.

My method: when I encounter a sentence I don't know how to say, I use translation to see it once, then imitate and translate it myself. Not memorizing words, not studying grammar. Just seeing how others say it, then saying it myself. After a few months, my expression skills improved dramatically.

That's the essence of imitation: not memorizing "what it is," but learning "how to do it."

Why Is Internship a Hundred Times More Useful Than Class?

Last night I was with my attending in the ER, saw a tetanus patient. The doctor showed me some videos demonstrating typical symptoms. When I got back and looked up resources, asked AI tools, I found the impression incredibly deep—those medical terms just jumped into my brain. (The doctor even said, "Knowledge you discover yourself leaves a deeper impression.")

Compare that to my previous learning method: memorize theory first, finish memorizing without knowing what it's for, forget it all after the exam.

What's the difference? The order is reversed.

Traditional learning: Theory → Practice Internship-style learning: Observe → Imitate → Understand theory

Why is the latter more effective? Because your brain has an "index." When I saw that tetanus patient's symptoms, "opisthotonus" was no longer just four words in a textbook—it became a specific, visualizable image. Then when I learned the theory, I was adding explanation to an existing image, not building concepts in a vacuum.

This is why internships, apprenticeships, and mentorship—these ancient learning methods—are still so effective: they let you see the complete workflow, including details that textbooks never mention.

Find Your "Imitation Target"

If learning's essence is imitation, the question becomes: imitate who?

My mentor told me, if you want to write good papers, read the best papers. Sounds like stating the obvious, but think about it carefully—your imitation target's level is basically your ceiling.

I have a habit now when learning anything: find the best person in that field, watch how they do it. Learning writing? Watch Li Xiaolai. Learning programming? Study top open-source project code. Learning product? Reverse-engineer the most successful products.

But here's the key: what you need to see isn't the result—it's the process.

Most people's problem is they only see others' finished work, can't see how they made it. You see an amazing article, only see the final text, can't see how many drafts the author went through, how many times they restructured it. You see elegant code, can't see how the programmer refactored it from a tangled mess to what it is now.

This is why video tutorials are easier to learn from than text, why pair programming with experts accelerates growth—because you see their thinking process.

From Imitation to Creation

Li Xiaolai said in Self-Learning Is a Craft that learning has four stages: "learn, practice, use, create."

I get it now—these four stages are actually a deepening process of imitation:

• Learn: Observe how others do it
• Practice: Imitate and do it once
• Use: Apply it in real scenarios
• Create: Form your own style

Most people get stuck between "learn" and "practice" because they're missing the observation step. You just read theory, don't know how to actually do it. You want to practice, but don't know what to practice.

Once you find something to imitate, the path becomes smooth. You don't need to think "how should I learn?" You just need to think "how did they do it, can I do it the same way?"

Now when I learn anything new, the first thing I do is find a sample to imitate—a project, an article, a video. Then like an elementary student copying calligraphy, I recreate it stroke by stroke.

Dumb? Sure is. But does it work? Absolutely.

You might say, imitating so mechanically, can you learn anything deep? My answer: once you imitate enough, understanding happens automatically. Just like when you practice calligraphy enough, you naturally understand what brushwork is, what structure is.

Learning was never a linear process. You don't need to understand all principles before starting. You just need to start, and understanding will gradually emerge in the process.

Maybe this is learning's most humble truth: find someone who knows, watch how they do it, then do it yourself.

It's that simple.

I've learned Python three times.

Not an exaggeration—literally three times. From basic syntax to data types, from loops to functions, I've gone through tutorials again and again, filling notebooks upon notebooks. Yet every time I want to actually build something, my mind goes blank. I have no idea where to start.

But English? That's a different story. I never deliberately memorized vocabulary lists or systematically studied grammar. I just read English articles and watched English videos daily. Now I can read books in English fairly fluently.

This puzzled me for a long time. Why such a stark difference in outcomes with similar effort?

Eventually, I figured it out—I didn't fail to learn programming; I simply never knew what programming actually looks like in the real world.

The "Black Veil" and the "White Veil"

I've come to think of this phenomenon in terms of two kinds of veils.

English is a "white veil" to me. Though there's still a layer between me and mastery, I can see through it—how articles are written, how conversations flow, how speeches are structured. Not only can I see through this veil, I can grab hold of it anytime, because the use cases are everywhere. Want to read? Read. Want to write? Write. Want to speak? Speak.

Programming is a "black veil." Those syntax rules, functions, and loops I learned—they're just the veil itself. I know its texture and pattern, but I can't see what's behind it. How are real projects actually built? How do programmers debug when they hit errors? How does code come together piece by piece? I couldn't see any of this because tutorials only taught me "what this is," never "what this does."

Later, I discussed this with Claude (an AI tool), and it told me this was completely normal—even senior programmers Google things and ask AI for help. That's when it hit me—I always thought I needed to reach a level where I could code without looking anything up, but even professional programmers don't work that way.

In other words, I didn't even know what was inside the "mystery box," just kept fumbling around outside it.

The "Activation Energy" of Learning

Why do some things become usable skills while others remain dormant knowledge, even with equal time investment?

I realized it's like activation energy in chemistry. Knowledge needs to cross an energy barrier to go from "knowing" to "using."

English has low activation energy. Whenever I want to use it, I can—read an article, write something, chat with someone. The contexts are everywhere, feedback is immediate. If you make a grammar mistake, you notice right away. When you understand a passage, you feel it instantly.

Programming has high activation energy. Want to write code? First, set up your environment. Want to build a project? First, understand the architecture. Hit a problem? Don't know how to debug. Every step has invisible barriers, and tutorials never mention them because they assume you're already in a "real context"—but you're not.

All "useless knowledge" is essentially knowledge with activation energy too high to overcome.

My medical anatomy studies were the same. Memorizing anatomical structures from textbooks had impossibly high activation energy because you had no idea what they were for. But in the operating room, the activation energy dropped to zero—the context forced you to use them. You couldn't make a single incision without recalling your anatomy knowledge. That's when you realized how it all actually worked.

What Reading Changed

I should probably go back to my university days.

Before that, I was an obedient student. Whatever the teacher said, I believed. Whatever the textbook stated, I memorized. It never occurred to me to verify things myself. Not because I was lazy—I simply didn't have the concept of "active exploration."

The change came from developing a reading habit.

In high school, I read some required classics, but honestly, I found little joy in them. Later I sporadically read some popular science books out of interest, but they were expensive and my time was limited, so I never went deep. It wasn't until university, seeing that massive library, that I truly started exploring freely.

I loved wandering between bookshelves, hunting for interesting books. Every time I discovered a good one, I'd feel this inexplicable excitement. I still remember reading Nassim Taleb's Fooled by Randomness for the first time—his ideas captivated me so much that I hunted down all his other books. That feeling was like suddenly opening a door to a completely different world.

What reading taught me wasn't knowledge itself, but the ability to "actively explore." I stopped waiting for others to tell me what to learn and started following my curiosity to find answers.

With reading came the desire to share. Wanting to share meant I had to write. And writing pushed me to think more deeply and read more broadly. This cycle made me realize—reading, writing, and thinking form the core of self-learning ability.

With this core, learning other things became much easier. Want to learn English? Just switch your reading materials to English. No need to deliberately memorize vocabulary—you'll remember words naturally by encountering them repeatedly. Want to learn programming? Same principle—read code, write code, think about code logic.

But here's the thing—some feedback loops are short; others are long.

English has a short feedback loop: Read an article, immediately know if you understand; write a passage, immediately see if it's correct; say something, immediately get a response. This instant feedback makes learning feel rewarding.

Programming has a long feedback loop: Write some code, don't know if it works (because you might not have a runtime environment); want to build a project, don't know where to start (because you don't understand project architecture); hit a bug, don't know how to debug (because you lack experience). This delayed feedback makes it easy to give up.

All efficient learning shortens the feedback loop. That's why internships beat reading books, building projects beats doing exercises, and writing beats passive reading—because feedback is immediate, real, and unavoidable.

Mystery Boxes Are Normal; The Key Is How to Open Them

I now realize that almost every new field starts as a "mystery box." Some boxes are just easier to open than others.

English is a semi-transparent box—you can vaguely see what's inside; use cases are everywhere. Programming is a black box—you have no idea what's in it; real workflows are hidden inside. Medicine is a nesting-doll box—open one, find another; the learning never ends.

The difficulty of learning isn't in "learning" itself, but in "finding how to open the box."

And traditional education's biggest problem is—it pretends the box doesn't exist. Teachers tell you programming is important (there's treasure in the box), teach you syntax and functions (describe what the box looks like), but never show you what actual programming work looks like (how to open the box).

So you see this absurd phenomenon:

• We study math for over a decade but don't know how mathematicians work
• We learn lots of programming syntax but don't know how programmers build projects
• We accumulate vast medical knowledge but don't know how doctors diagnose patients

Knowledge can only be activated in "visible use contexts."

That's why:

• Interns grow fastest—they watch senior staff open the boxes
• Open-source contributors improve rapidly—they see how experts write code
• Apprenticeship never goes out of style—masters show you how to open boxes hands-on

There's only one way to open mystery boxes: enter real contexts and observe how others do it.

Error Tolerance Determines Exploration Radius

This reminds me of another experience.

I used to be terrified of ranking last in exams—felt like such a humiliation. But after failing a few courses in university, I somehow adapted to that state. I'm not advocating for failing courses, but the retake mechanism gave me room for error—failure wasn't so scary after all, you could always retake and try again.

This error tolerance gave me the courage to try. I started daring to take courses that sounded difficult but interesting, daring to attempt things that might fail. I discovered that a person's exploration radius depends on how much room they have for trial and error.

The core reason we don't dare try new things is fear of risk. When society and family don't construct spaces for error tolerance, we can only huddle in our familiar comfort zones, afraid to step out, ultimately seeing only the most insignificant corner of this rich world.

But more insidiously, insufficient error tolerance leads to "invisible self-censorship."

You've probably already abandoned many things you wanted to try in your subconscious—not because of external prohibition, but because you predicted "the cost of failure is too high." This self-censorship is silent, automatic, so much so that you don't even realize you're limiting yourself.

For example:

• Why not dare to intern at a company? Afraid of looking bad if you mess up
• Why not dare to publish code on GitHub? Afraid of being ridiculed
• Why not dare to submit PRs to open-source projects? Afraid of rejection

These are all forms of "psychological activation energy"—even if you have the capability, the psychological barrier is too high for you to take the first step.

A truly innovation-friendly environment should lower this psychological activation energy. Tell you: It's okay to fail, there's always another chance; it's okay not to do well, at least you tried; it's okay to be rejected, try again next time.

Perhaps the purpose of schools should be to provide students with this kind of space for exploration and trial-and-error, with teachers playing a guiding role. But due to various factors we can't fully understand, teachers often exercise too much control, while students fear stepping out, leaving schools with nothing but walls protecting students' physical bodies. This isn't what we hope to see, but it's probably the reality many have experienced.

The Essence of Learning: Not "Learning" but "Using"

After this long journey, I've found that the truth about learning is actually quite simple—learning isn't fundamentally about "knowing"; it's about "being able to use."

All "useless knowledge" falls into one of these categories: either you don't know what it's for (use context invisible), or its activation threshold is too high (activation energy too large), or you simply have no opportunity to try using it (insufficient error tolerance).

Back to my programming struggle. Now I understand—I don't need to go through another tutorial. What I need is:

• To see how real projects are actually built (lower cognitive activation energy)
• To browse code repositories on GitHub (watch how others open the box)
• To intern at a company and observe programmers' workflows (enter real contexts)
• Or just build a terrible project and learn from mistakes (shorten the feedback loop)

Learning isn't linear knowledge accumulation; it's building cognitive frameworks through use. Only when you know how a skill operates in the real world can you truly master it.

This principle applies to all domains.

Finally, I want to ask myself a question: Do I really need to "learn programming"?

Sounds absurd, but think about it:

• If my goal is to "solve problems with technology," maybe I don't need to "write code" but rather "learn to use AI tools to direct code"
• If my goal is to "understand technical thinking," maybe I don't need to "write code" but rather "understand code logic"
• If my goal is to "become a programmer," then yes, I need to write lots of code, but only after entering real projects

Maybe my struggle isn't "can't learn programming" but "don't know why I'm learning programming."

Why did English work? Because I had clear use contexts—reading, sharing, thinking. Why didn't programming work? Probably because I hadn't found what truly drives me to write code.

The motivation for learning isn't "I should learn this" but "I can't do what I want without learning this."

When you encounter a problem and realize you can't solve it without writing code, you'll find—learning programming suddenly becomes easy. Not because programming got simpler, but because the activation energy disappeared. You skip the "learning" phase and jump directly into the "using" phase.

So, the truth about learning isn't "pick up and put down," but rather "find a reason to use it, and you'll never put it down again."

If you also have something you've tried to learn for ages but never mastered, ask yourself:

• Can I see how it's actually used in the real world?
• Do I have a way to shorten the feedback loop?
• Do I have enough error tolerance to experiment?
• Do I really know why I'm learning this?

Figuring these out might be more useful than going through another tutorial.

After all, learning was never the goal. Being able to use it is.

The Void Behind Convenience

The more convenient our tools become, the less time we spend thinking.

From ChatGPT's emergence to Claude's specialized programming assistance, from Perplexity's intelligent search to the proliferation of AI agents across domains, this year's AI evolution has been dizzying. These tools haven't just changed how we access information—they've fundamentally reshaped our thinking patterns.

I've witnessed my own behavioral transformation: from "Google it when in doubt" to "ask ChatGPT when confused," from relying on search engines to using almost exclusively AI tools. The speed of this shift astounds even me.

More alarming is the change in my thinking depth. Previously, when encountering problems, I would think deeply first. Even when stumped, I'd research extensively and read numerous books. Now I'm completely different: despite spending over $120 on books this year, I haven't finished a single one. My reading seems frozen at last year's level—back in my sophomore and junior years when I was consuming 100+ books annually.

Reflecting on this, I realize my leisure time is almost entirely spent interacting with AI. I can barely sit still to finish a book. The core issue isn't that AI interaction yields answers more easily—it's that I spend less time actively thinking.

From Tool Dependence to Cognitive Outsourcing

When you can get "desired" answers from AI anytime, every minute of thinking feels wasteful.

I used to read extensively because I knew too little and feared making uninformed decisions. Now I've discovered I don't need to know much at all—when in doubt, ask ChatGPT first.

• How to evaluate whether something's worth buying?
• How to solve this programming problem?
• Should I learn this particular skill?
• What career path should I pursue?

Whatever you can think of, AI can answer, often more scientifically than your own choices. You can treat AI tools as strategic consultants, conducting real-time deep research and returning traceable reports.

This behavioral pattern emerges from my willingness to trust AI's doctoral-level capabilities. But it also means my own abilities are gradually atrophying.

When skills go unused, atrophy becomes inevitable over time—just like how I can no longer recall math formulas I once knew by heart in high school. As we outsource crucial abilities like information retrieval, article analysis, and deep reading to AI tools, human thinking capacity quietly disappears.

Look around: how many people can carefully read a 1,500-word article to completion? How many, when commenting on others' writing, aren't either misunderstanding or taking things out of context? Isn't the root cause precisely the lack of complete reading and deep thinking abilities?

I've seen the most extreme example: a friend using "Doubao AI" actually complained that its deep research took too long and told it to skip that step. If you won't even give AI time to think, how can you expect quality answers? How could you then spend time thinking through and reading AI's research reports?

Using AI ≠ Possessing AI's Knowledge

As 2025 marks AI's explosive breakthrough year, I've extensively used most domestic and international AI tools—from article writing to image generation, from Vibe Coding to deep research. AI tools have become my most frequently used instruments.

The numbers look impressive: in September alone, I built 5 projects with Copilot, completed an e-book compilation with Claude, and had over 100 ChatGPT interactions. But reflecting carefully, has my personal capability actually improved effectively?

It appears I've completed many projects, produced much content, and researched extensively—but these are all AI's achievements. I've spent enormous time yet taken nothing substantial away.

If AI's explosion had come two years earlier, I might have become a cognitive "invalid." I'm grateful for the intensive reading and thinking training of the past two years, which allows me to sit down and write this article with focus—otherwise, 80% of this piece would likely be AI-generated.

Learning Dilemmas in Era Transitions

Trying to catch up on reading and thinking abilities now is somewhat late.

Take programming: AI tools are most disruptive here. Some even divide computer science learning into "pre-Vibe Coding" and "Vibe Coding" eras. We're already in a new age, but this doesn't mean foundational knowledge isn't important—quite the opposite, it may become more crucial.

The problem is that due to fundamental paradigm shifts, we can no longer learn the old way.

It's like still using search engines while everyone else uses AI for knowledge queries—first, you can't keep up efficiency-wise; second, within the broader social context, you'll fall increasingly behind. No matter how beautifully you handwrite, can it match Word's standard typeface?

Reality is, this world isn't just you changing—everyone's changing, and with different tool choices available. Even among AI tool users, differences in model capabilities create varying competitiveness and productivity levels.

The Essential Nature of AI Tool Usage

I'm grateful for past programming exposure, extensive reading, and substantial thinking and writing. Because the key to fully leveraging AI tools lies not in the AI itself, but in yourself.

AI tools are essentially amplifiers—they can magnify your existing capabilities 10x or even 100x, but cannot create something from nothing that you neither know nor understand.

• If you know programming, Vibe Coding helps you complete entire projects
• If you understand English, AI helps you write authentic English articles
• Even without these skills, you can accomplish tasks through natural language, but the core issue is: you lack the awareness to attempt these things

People always operate within familiar domains. This stems from the brain's energy-conservation mechanism—prioritizing already-mastered skills while naturally avoiding unfamiliar territory.

When unfamiliar with English, I'd never think to write in English. Fear of the unknown amplifies avoidance of unfamiliar tasks, so I'd never actively try "expressing in English," even though AI could complete entire English articles for me. Without prior exposure, the mind lacks awareness of such possibilities, let alone thinking to amplify abilities through AI tools.

Awakening: Core Competitiveness in the AI Era

In an age of exceptionally rich and powerful AI tools, maintaining open perspectives becomes critically important. Observe how many people still don't use AI tools, and you'll understand my point.

The real challenge isn't learning to use specific AI tools, but:

1. Preserving independent thinking: While enjoying AI convenience, avoid complete dependence and regularly engage in deep thinking and learning
2. Cultivating problem awareness: Only by knowing what to ask can you extract valuable answers from AI
3. Building knowledge frameworks: AI can fill in details, but overall cognitive frameworks require personal construction
4. Maintaining learning habits: Don't let fundamental abilities like reading, thinking, and writing atrophy

AI tools are thinking boosters, not thinking replacements. True wisdom lies in finding balance between convenience and contemplation, making technology an extension of our capabilities rather than their termination.

In this era of human-AI collaboration, what matters most isn't pursuing ultimate tool convenience, but maintaining independent thinking capacity amid that convenience. Only then can we truly master AI, rather than be mastered by it.

For a long time, I wanted to contribute to open source but always thought: “I know nothing, I’ll just break things.” Today, I finally did it — and I want to record every step, including the mistakes I made, how I fixed them, and what I learned.

Step 1: Picking a Project

I forked the TheAlgorithms/Python repository. It’s a large project, but it has clear contribution rules:

• Return results (no print statements)
• Add type hints
• Write doctests or tests
• Document time/space complexity

That gave me a roadmap. I decided to focus on the blockchain/diophantine_equation.py file and add a helper function.

Step 2: Writing the Function

I implemented all_diophantine_solutions(a, b, c, n=2). This function returns up to n integer solutions to a linear Diophantine equation.

My first draft looked fine, but then the real learning began…

Step 3: My Mistakes (and Fixes)

❌ Mistake 1 — Wrong Import Placement

I added:

from __future__ import annotations

in the middle of the file. Result:

SyntaxError: from __future__ imports must occur at the beginning of the file

✅ Fix: Either move it to the very top or remove it. I removed it, since Python 3.9+ already supports modern type hints.

❌ Mistake 2 — Function Below __main__

I put my function after:

if __name__ == "__main__":
    from doctest import testmod
    ...

That broke the style rules. ✅ Fix: Always place new functions above the __main__ block.

❌ Mistake 3 — Line Too Long

One docstring line exceeded 88 characters. ✅ Fix: Wrap long lines so the linter (ruff) is happy.

❌ Mistake 4 — f-String in Exception

I wrote:

raise ValueError(f"No integer solutions exist for a={a}, b={b}, c={c}")

Lint error:

EM102 Exception must not use an f-string literal, assign to variable first

✅ Fix:

msg = f"No integer solutions exist for a={a}, b={b}, c={c}"
raise ValueError(msg)

❌ Mistake 5 — Running Doctests

When I tried:

python3 -m doctest -v blockchain/diophantine_equation.py

I got:

ModuleNotFoundError: No module named 'maths'

✅ Fix: Run doctests from the project root so relative imports resolve correctly:

cd ~/contri_pt/Python
python3 -m doctest -v blockchain/diophantine_equation.py

Step 4: Passing the Checks

After fixing everything, I ran:

python3 -m doctest -v blockchain/diophantine_equation.py
pre-commit run --all-files --show-diff-on-failure
ruff check

And finally — all tests passed ✅.

Step 5: Committing and Pushing

From the project root:

git add blockchain/diophantine_equation.py
git commit -m "feat(blockchain): add all_diophantine_solutions with type hints & doctests"
git push -u origin feat/blockchain-diophantine-return-list

Then I opened a pull request to the upstream repo.

Lessons Learned

1. Start small. One helper function is enough for a first contribution.
2. Linters are your teachers. Every error message told me exactly what to fix.
3. Read the rules carefully. TheAlgorithms project enforces strict style, and I had to adapt.
4. Don’t panic at errors. Most of my time was spent fixing mistakes — and that’s normal.

Final Thoughts

My first open-source contribution wasn’t smooth — I made syntax mistakes, style mistakes, and even ran commands in the wrong directory. But that’s the point: every error was part of the learning.

Now, I have my first PR ready for review. It feels amazing. 🚀

If you’re a beginner: fork a project, pick a small function, add tests, and let the tools guide you. Open source is not about being perfect — it’s about learning in public.

How We've Shifted from Knowledge Competition to Action Competition

We've officially left the era of competing on who knows more. Today, if you know how to leverage any AI tool effectively, you'll find they offer faster knowledge acquisition than traditional search engines, with real-time tracking of trending topics. We've transitioned from "when in doubt, Google it" to "when in doubt, ask AI" in just a few months.

While being knowledgeable remains an advantage, it's no longer irreplaceable. Learning a programming skill used to take months or even years, but with modern IDEs and AI coding assistants, the learning curve has dramatically shortened. More remarkably, if you're "lazy" enough, you don't even need to learn deeply—you can jump straight into action and rapidly validate your ideas, because AI-generated demos provide sufficient insight into your concepts' viability.

The Growing Gap Between Doers and Thinkers

Here's the critical point I want to emphasize: AI tools, while enabling action, are simultaneously amplifying the gap between those who act and those who merely think. Eventually, you'll discover that taking action is 10^100 times more important than accumulating knowledge.

In the pre-AI era, what prevented us from acting was the need to invest massive amounts of time learning skills. Laziness, of course, played a role too.

But now, with powerful tools at our fingertips, we can instantly consult AI when we have ideas, and if they seem promising, we immediately dive in. Sometimes we even create problems out of thin air just to "squeeze" more value from AI. This confidence in our abilities—or more accurately, this trust in AI's capabilities—has propelled us to the peak of actionability.

From Git Learning Struggles to AI-Powered Breakthroughs

Over the past year, I've frequently worked with Git and encountered numerous bizarre issues as a beginner. I used to spend hours searching online for solutions, often ending up empty-handed. This not only made me fear Git but, more fundamentally, caused me to stagnate whenever I hit a roadblock.

Since discovering powerful AI tools, encountering problems no longer causes panic. Instead, it sparks a desire to test AI's capability boundaries. Solving mysterious issues has become much simpler—success basically depends on how fast you can copy and paste. Without the fear of difficulty, doing things has transformed from a chore into both an enjoyment and a hobby.

When Knowledge Becomes Cheap, Action Becomes Precious

Conversely, when most people choose action to rapidly validate their ideas, those who choose inaction find themselves at a significant disadvantage. This disadvantage isn't caused by capability gaps but by laziness.

When knowledge becomes exceptionally easy to acquire (becomes cheap), action becomes exceptionally valuable.

Knowledge and skills are, in my view, two distinct concepts that are often seen as an interconnected whole. In the era when knowledge could only be acquired through memorization, gaining skills was extremely difficult—like trying to observe surgery without understanding human anatomy. You might eventually learn to mimic the process, but you'd remain merely a mechanical tool.

As AI tools rapidly evolve, "knowledge outsourcing" is gradually shifting toward "skill outsourcing." This means not only will knowledge become increasingly democratized, but skills requiring extensive deliberate practice will follow suit.

I'm not suggesting knowledge and skills are useless—deep domain expertise and high-end skills remain the most valuable assets. However, no matter how much you know, without action, it remains worthless. This exemplifies what Li Xiaolai calls "pseudo-learning"—people who read extensively and understand many principles yet still can't live fulfilling lives.

My Year of Supercharged Action

This year, my action-taking ability has been extraordinary, which is easy to understand—I've never been lazy (at least regarding learning). I once wrote 1,000-word public articles daily for two consecutive months. My previous lack of output wasn't due to unwillingness but complete incapability.

This year, using AI tools, I've created numerous projects: from static blogs to interactive websites, iOS applications, and an e-book I just completed. While these projects may have limited value, their impact on shaping my "action-taking ability" has been invaluable.

I have a friend who's been talking about making short videos for months with no progress. He constantly shares his ideas with me but never acts. When I suggested he start by posting 100 "garbage" videos for practice, he claimed he couldn't find suitable material.

I operate differently. When I have an idea, I find ways to implement it. Even if I can't fully execute it, I push myself forward incrementally. As I posted on social media: "Because I browse social media, I must post on social media."

Having no audience isn't a big deal—it's harmless.

Next insigths come frome Claude

The Paradigm Shift: From Capability Anxiety to Action Anxiety

We're witnessing a fundamental transformation in how we approach uncertainty. Previously, we worried about "What if I don't know how?" Now, we should worry about "What if I don't do anything?" This shift from capability anxiety to action anxiety marks a fundamental change in societal competition dynamics.

The Trial-and-Error Cost Revolution

AI tools' greatest value isn't providing standard answers—it's reducing trial-and-error costs to nearly zero. This makes "failing fast" genuinely possible, and rapid failure is the core mechanism of innovation. When the cost of experimentation plummets, the barrier to entry for innovation disappears.

The Action Dividend Era

We're in a unique historical window—AI tools are powerful enough, but most people haven't yet developed "think-it-do-it" action habits. This resembles the early internet era when early adopters gained significant advantages. Today, those who first embrace AI-powered mass action will capture the "action dividend."

From Deep Specialization to Broad Implementation

The traditional "10,000-hour rule" may need redefinition. With AI assistance, perhaps "1,000 hours of practice + AI collaboration" provides more value than "10,000 hours of pure human practice," because the former enables actual output across broader domains.

The friend I mentioned who "can't find material" represents a psychological trap many face. This reflects a deeper issue: people are still applying "industrial-era quality standards" to themselves, not realizing that in the AI era, "quantity first, rapid iteration" might be the superior strategy.

The New Survival Strategy

In essence, we're describing a new survival strategy for the AI age: becoming a "high-frequency actor" offers more advantages than becoming a "deep thinker."

This doesn't diminish the value of deep thinking—it simply recognizes that in an era where AI can handle much of the cognitive heavy lifting, the bottleneck has shifted to execution. The competitive advantage now lies not in what you can think of, but in what you can ship.

The future belongs to those who can rapidly translate ideas into reality, iterate quickly based on feedback, and maintain a bias toward action over analysis paralysis. In this new paradigm, your willingness to act—imperfectly but consistently—becomes your most valuable asset.

As AI continues to democratize knowledge and lower skill barriers, the question isn't whether you're smart enough or skilled enough—it's whether you're brave enough to start before you feel ready. Because in the AI age, ready is a luxury we can no longer afford to wait for.

Dylan Field on Building Figma and the Future of Design

The Genesis of a Revolution

When Dylan Field and Evan Wallace started exploring the possibilities of WebGL at Brown University, they couldn't have imagined they were laying the groundwork for a design revolution. Their journey from curious students to founders of one of the most transformative design tools of our generation offers profound lessons about persistence, iteration, and the power of staying focused on user needs.

"We really started in earnest in August 2012," Field recalls, "but it took until June or July of 2013 before we went all in on building Figma as it is today." This timeline reveals an important truth about innovation: breakthrough ideas rarely emerge fully formed. They require patient cultivation, constant refinement, and the courage to pivot when evidence demands it.

The Pivot That Almost Never Happened

Perhaps one of the most illuminating moments in Figma's origin story came when Field convinced Wallace to build a meme generator in 2012. "We built a great meme generator," Field admits with a laugh. "It would have been the best one in the market. My thesis was right, by the way—look at the exponential curve of memes since 2012."

But after just one week of working on it, both founders were ready to quit. This experience taught them a crucial lesson: technical capability without personal passion is a recipe for burnout. "I was asking myself, 'Why'd I drop out of Brown for this?'" Field remembers. The meme generator episode became their defining moment—not because it succeeded, but because it failed to inspire them.

The Power of Constraints and Focus

As Figma grew from a two-person team to a company of 1,700 employees with eight products, Field learned that constraints breed creativity. When teams approached him with elaborate nine-month or two-year roadmaps, his first question was always: "How do we slim this down? How do we make it more bite-sized and test this earlier with our users?"

This philosophy of constraints extends beyond product development to team building. Field advocates for a cycle that every startup leader must master: identify what you're doing the most of, find someone to help you with it (or use AI), figure out how to find that person, and if you don't have resources, figure out how to get them. "That's a cycle that you're always in," he explains.

Design as Competitive Advantage in the AI Era

As AI transforms the landscape of software development, Field sees design becoming increasingly important as a differentiator. "If you really believe that development gets easier and it's more simple to create software, then what is your differentiator? It's design, it's craft, it's attention to detail, it's point of view."

This insight becomes particularly relevant when considering OpenAI's acquisition of a design company for over $6 billion. While some dismissed this as an overvaluation, Field takes a more nuanced view: "There are some people out there who, when they do something you don't understand, it's easy to go into attack mode and dismiss it. But over enough time, sometimes you see patterns."

The Future of Human-AI Collaboration

Field's perspective on AI is refreshingly balanced. Rather than viewing it as a replacement for human creativity, he sees it as an amplifier. In Figma's own development process, AI tools have become instrumental for rapid prototyping and idea validation. "It helps us throw ideas away faster," he notes, emphasizing that the real value lies not in generating perfect solutions but in accelerating the iteration cycle.

However, he also sounds a note of caution about society's relationship with AI, particularly regarding AI companions: "I think AI boyfriends and girlfriends, if developed and allowed to exist, is a societal self-own. I think it's actively poisonous to society if this becomes a primary mode of relationship."

Lessons for Aspiring Founders

Field's journey offers several key lessons for entrepreneurs:

Start Simple, Then Scale Complexity: Figma's pattern of spinning off new products from observed user behavior in their main design tool demonstrates the value of organic growth over premature diversification.

Embrace the Feedback Loop: Despite taking longer to launch than conventional wisdom suggests, Field emphasizes that they were constantly gathering feedback. "Don't do what I did," he warns. "Launch as soon as you can, but make sure you're getting feedback throughout the process."

Hire for the Long Term: Field advocates for hiring people faster when you have the capital and conviction about your direction. Looking back, he wishes they had scaled the team earlier to move faster.

Design for Designers: One of Figma's key insights was embedding designers directly into their research teams. "Researchers need that intuition of how designers think, and without actually having that close collaboration, it really doesn't work."

The Expanding Definition of Design

As AI continues to evolve, Field predicts that the role of designers will expand rather than diminish. "Designers will have far more leverage in the future, and the value of design will only continue to go up." He envisions designers not just as makers of beautiful interfaces, but as leaders who understand how to craft solutions and explore idea mazes.

This expanded role requires designers to step up in new ways. "We need to have folks that are designers step into the founder role and start companies," Field argues. The success of designer-founders like Brian Chesky of Airbnb and Ki of Linear points to a future where design thinking becomes a crucial leadership competency.

Looking Forward

Figma's story is far from over. With the launch of new AI-focused products and the continued evolution of design tools, Field and his team are positioning for the next phase of growth. Their approach—starting with observed user behavior, creating dedicated surfaces for specific use cases, and maintaining focus on craft and user experience—provides a template for sustainable innovation in the AI era.

As Field puts it, "The number of ideas that we have right now has grown so much. There's so much we can do, and it's more about how do we make sure we do the right things." This abundance of opportunity, coupled with a disciplined approach to execution, suggests that Figma's most impactful years may still lie ahead.

vibe coding最早是由Andrej Karpathy提出，他之前就职于特斯拉，后来做了OpenAI的研究员，最近几年又自己出来做AI方面的教育，他最近在YC的演讲值得去看看，不得不说，把教育做好的人做的演讲也挺有趣。

vibe coding翻译过来叫「沉浸式编程」——通过与LLM交互，指导LLM去完成编程。这看上去是一种挺爽的体验，就像你使用AI工具去代写作业一样，但从编程的精确性角度来说，LLM依然会产生错误，看似顺畅的交互，最终往往会产生难以修复的bug。不过随着模型能力的逐步提升，这些情况会减少，但可能依然存在。

比如今年5月份，我使用cursor来vibe coding了几个项目，当时使用的是Atheropic公司的sonnet4和google的Gemini 2.5 pro，也算是当时世界上头部的LLM，但总体体验下来还是挺痛苦的，经常遇到一些难以解决的问题，为了修复一个bug，花费几个小时却无疾而终。

当然，经过不断的调整、优化提示词，某些问题最终得到了解决，但最终构建出来的production依然不太稳定，同时整体的代码结构也很复杂，根本看不懂。

这次，我选择使用OpenAI的codex CLI，使用下来体验不错，遇到问题也能快速修复，有时候还能给出意想不到的结果。当然，这次使用codex 更多是对之前项目的优化、迭代，所以花的时间比较少，而且由于之前的vibe coding经历，我追求的只是稳定、简洁的function。

这种心态或者说要求的转变，其实是我对自己和大模型边界了解深入的体现。vibe coding就像你和一个（女）朋友在手机上聊天，由于隔着屏幕，你们互相看不到对方的表情和语音变化，所以经常需要互相猜测对方发这句话表达的深层含义，以及背后折射的情绪，尤其中文博大精深，更是容易产生不同的结果。

LLM本质上还是基于概率来理解、推断你真正表达的意思，并给出当前聊天场景下最大可能性的回复，你以为他很懂你，其实他不过是在预测你的预判或者你期待回复的内容而已。

有了AI tools，我们对「结果的贪婪」会被无限放大。以前自己不会的话，要么请教他人、自学，要么就此放弃，现在是什么呢？

我不仅不愿承认自己不会，还会对LLM提出高要求标准。我不看过程，只想要一个确定的结果，甚至有时候还会对别人认真做出来的东西嗤之以鼻。

比如我看到某个UI做得很low，自然而然在心里嘀咕一句：“这破玩儿也能上线？”但其实真要叫自己动手纯敲代码，怕连编辑器都不知道怎么打开。所以，未来网上一定会充斥着一大批非专业人员到处「瞎喷」。当大家都以为自己懂了的时候，那才是最可怕的，勇于承认自己的无知有时候还挺难。

说直白一些，LLM会让很大一群人，比如我，快速的成为「巨婴」——不知道自己不知道。如果这群人去辩论，一定会拿出「AI是这么说的」作为证据，而且「一定会」存在「模型使用鄙视链」，在国内能使用国外LLM的人会说使用国内开源模型产生的结果不可信，同时使用国外LLM的人中，付费使用的又会说：“那是你没有使用200美元的。”别笑，这个我之前还真遇到过。

以后人类的辩论很大概率会成为模型之间的辩论，而人充当的是「传音筒」。A说模型是这样说的，B会回答，但模型却这样看，想想也挺搞笑的——只存在于人之间的争论也会逐渐演化到大模型身上。

我的第一个完整可上线的iOS app：

这个app焦距于慢性病健康管理，它来源于我过去在社区实习的经历，我当时发现人们对于用药和健康监测的不重视，导致血压和血糖控制不佳，从而产生了一些并发症。所以，我就想着自己能不能做一个工具来解决这个问题，通过不断的倒腾，我当时是弄了一个网页版本，由于不太稳定就没有继续完成下去，这次选择了重做，尽管目前依然还处在demo阶段，但已经成为了一个可用的产品。

这是我之前做的第一版，总体上挺花哨的，但用起来嘛，嗯，你懂的。

不要相信别人的评论

这个方法，我在几个月前就已经关注到了，但是当时自己并不屑于去操作，导致花了很多钱在数字产品订阅上，尽管现在看来那些钱花得挺值，但还是为自己当时没有立即行动感到后悔。

我们在关注到一个方法或者教程的时候，总是喜欢去看别人的评论。 看到积极的评论，我们会获得鼓励，但有积极的，必然也会存在一些消极的看法，这些消极的评论会让我们对未知的领域和不确定性产生畏难，同时也会加剧我们对风险的认识——对风险更加敏感。

而风险的存在会阻止我们做这件事，因为我们从来都是厌恶损失的人。

其实我们的试错成本很低

我们之所以害怕尝试，除了厌恶损失，根源在于我们觉得试错的成本太高。

我花了这50块钱，万一打水漂了怎么办，这可是我两天的饭钱啊！ 这是我在做一些有成本的事情时心里常常存在的矛盾点——既想要，又害怕。想要获得某个不错的结果，又害怕自己投入成本后没有啥收获。别笑，你可能也会存在呢。

但我们根本没有算对账。

比如，我花了100RMB就把这件事尝试了，我后续就不需要再花时间投入到这件事请上了，这是时间上的节约；如果我花了钱但拿不到想要的结果，我的损失是可控的，而且也在我可承受范围之内；如果结果比自己预期的要好，那我就赚了，这可是未来几个月都在为我省钱啊。

所以，正如那句我们已经听了无数遍的鸡汤：

我做一件事不存在失败，要么获得成功，要么学到知识——告诉我此路不通。

控制成本的尝试

尝试的目的不是为了获得某个好的结果，而是为了积累经验。

我一直觉得一个年轻人看似啥都没有，才是它啥都「可能有」的前提。

年轻人最大的资本是时间，是某种「可能性」。因为年轻，所以没有太多需要在意的事，他的试错成本极低。你本来就没钱，还能再没钱到哪里去呢？所以，在很多炒股人身上，年轻股民要比老股民冒进许多，一是「无知者无畏」，二么可能是年轻人想要赚钱的欲望会更强烈一些。

正是因为「可能性」的存在（通常叫做希望），才会让年轻人有动力前进下去。当一个人意识到他不在有这种可能性时，也意味着他的生活多了几分沧桑。

English (Translation)

Title: "Action Is What Matters"

Yesterday I successfully completed a ChatGPT Plus subscription payment using a Nigerian Apple ID, costing 59 RMB. If this payment route proves reliable, I plan to subscribe to Claude next—this could save me a lot of money. As a student without income, I chose this high-risk path because my core goal is to grow quickly and boost my efficiency with large language models (LLMs).

If you are considering the same path, read on.

Don't Rely on Other People's Comments

I learned about this method months ago but dismissed it at the time, and I ended up spending far more on subscriptions than I needed to. Although those expenses were ultimately worth it, I still regret not acting sooner.

When we find a new method or tutorial, we often read other people's comments. Positive comments encourage us, but negative ones always exist and can make us fear uncertainty and exaggerate perceived risk. That fear of loss usually prevents us from trying.

Our Trial-and-Error Cost Is Actually Low

One reason we're afraid to try is that we overestimate the cost of failure. We tell ourselves: "If I waste that 50 RMB, that's two days of meals!" This tug-of-war between desire and fear is common.

But we forget to account correctly. If I spend 100 RMB to try something, I save time later and avoid spending more effort on other approaches. If the attempt fails, the loss is limited and bearable; if it succeeds, I gain more than I spent. As the cliché goes: when you try, you either succeed or you learn.

Controlled, Low-Cost Experiments

The goal of trying is not only to get a perfect result but to gain experience. Young people seem to have little, but that very lack is their advantage: time and possibility. Their trial-and-error cost is low.

Because of this potential, young people are willing to take risks and move forward. When that potential disappears, life feels heavier.

Practical Takeaways

• Start small: spend a little to validate an approach before scaling.
• Set limits: budget money and time for experiments so failures stay manageable.
• Record steps and outcomes so you can learn faster.

If you want, I can rewrite the opening for clarity, or add a short checklist showing exactly how I validated the payment path. Want me to apply those changes?

我发现我经常在各种网页和app之间来回切换，导致我根本无法专注于做好当前的事。

比如，我会在ChatGPT和Google之间来回切换，有时甚至还要加上Kindle、Rstudio、vscode等，以至于我真正花在学习上和做事上的时间少之又少，我很害怕这种过多的选择会让我没有将一件事情做完成，而是都成为了半成品，甚至连半成品也算不上。

在没有这些之前，我唯一的选择就是得到app，在那上面阅读自己喜欢的电子书，而现在我连一本书都没有读完，感觉自己无法真正沉浸到阅读的世界中。此刻回想起来还是挺可怕的，也许我确实做了很多事，但没有一样是完整的完成，这让我无法体会到完成一件事的乐趣和满足感。我希望有人能够给我答案，我是不是想要的太多了，到最后却什么也得不到。这种预感让我有点害怕，我害怕自己终究只是一个平凡的人，不能完成自己的人生规划，不能去自己想去的地方，不能追寻心中的自由。

为什么我总是在寻找方法和决策的方案，却从未行动过，我到底是怎么了，我如何才能改变这种现状，我需要答案，更需要行动。

English (Translation)

Recently I ran into a major problem.

I realized I often switch back and forth between various websites and apps, which makes it impossible for me to focus on doing the current task well.

For example, I keep toggling between ChatGPT and Google, and sometimes I add Kindle, RStudio, VS Code, and others into the mix. As a result, the actual time I spend learning or doing real work is very limited. I'm afraid that having too many options will prevent me from fully completing anything — everything becomes half-finished, or not even that.

Before all this, my only choice was to open an app and read the ebooks I liked. Now I haven't finished a single book; I feel unable to truly immerse myself in reading. Looking back, it’s quite scary. Maybe I have indeed done many things, but none of them was completed, so I can’t experience the joy and satisfaction of finishing something. I wish someone could tell me whether I’m asking for too much and end up getting nothing. This feeling terrifies me a little — I worry I may just be an ordinary person who can’t fulfill life plans, can’t go where I want, and can’t pursue the freedom in my heart.

Why am I always searching for methods and decisions but never taking action? What is wrong with me, and how can I change this situation? I need answers, and I need action more than answers.

其实直到大四之前，我的计算机知识也只停留在会用word和Excel等办公软件的水平，尽管我大二就已经接触了编程。这也说明会编程不一定懂计算机，不会编程也不一定不懂计算机，尤其在vibe code时代。

计算机是个挺神奇的东西，也算是人类历史上伟大的发明。在过去，计算机只有军队才有，而且是非常昂贵的，普通人根本没机会使用，但现在随着计算机的普及，它的价格已经变得相当便宜了。

可在大多数人眼里，计算机只是和手机没多大区别的游戏机而已。以至于很多使用计算机的人更本不知道终端「terminal」的存在，就比如之前使用windows的我。在我使用Mac几个月之后，我才真正了解terminal，也几次为他陶醉。当然，在这个过程中也把Mac终端搞崩过，好像Apple的沙盒设计，在我的倒腾下也不好使。

Terminal的真正作用是你可以发挥计算机的生产能力，它完全不同于手机的终端功能。手机是一种高度定制化的设备，也就是别人让你怎么用，你就只能怎么用。但计算机不同，你可以通过学习与计算机交互的语言，从而自己定制自己想要的功能和脚本，比如我弄了一个清理缓存空间的工具。

这就是它的神奇所在，通过几行代码，便能指导计算自己做事，并完成重复性的工作，从而解放人类的双手。

可真正让人认真对待学习计算机这件事的核心原因并不是以上我所提到的事。一个人真正需要学习计算机的原因是，它能够停止你对手机的过度依赖，同时将手机的「手指滚动行为」转变为计算机的「页面浏览行为」。这种行为模式的改变可以从根本上缓解成瘾性行为。

你可能会说，那电脑游戏成瘾不是更大的麻烦吗？可在我所认识的大部分人中，网游成瘾者比手游好很多，又由于电脑不能随身携带，所造成的摩擦成本更高，自然相对于手游成瘾好很多。

所以，李笑来曾经给家长的建议是：

孩子接触的第一台电子设备一定是电脑而不是手机。

我这一路成长起来，其实还是感谢在小时候没有过早接触电视和手机。在《Beyond Feelings: A Guide to Critical Thinking》里有这样一个研究，美国某机构曾对一些儿童益智类游戏做过研究，发现那些经常观看所谓的“爱因斯坦大脑频道”的儿童比不观看的小朋友普遍有着更低的注意力，意味着观看电视节目，尤其是那些打着培养孩子语言的节目不仅对语言塑造没有多大作用，还会造成注意力缺陷。

尽管我现在还是会沉浸在某些短暂的爽感内容，但由于自制力够强，也就当作偶尔放纵自己了。

这张图展示了成瘾和幸福的本质。虽然两者都能带给人快乐，但addiction会让你的未来快乐事物越来越少，且需要更强的刺激才能完成，而happiness恰恰相反。

从这个角度去理解学习计算机的意义或许更值得我们重视。

How to become an expert at something:

1. Iteratively take on concrete projects and accomplish them in depth, learning “on demand” (i.e., don’t learn bottom-up breadth-wise).
2. Teach or summarize everything you learn in your own words.
3. Only compare yourself to your younger self, never to others.

We spend a lot of time learning—at least over 10 years from elementary school to university, our lives are bound to "learning."

However, as someone about to graduate from university, I recently realized what learning truly is and what it should look like.

In the past, I spent a lot of time solving math problems just for a grade, but I never truly experienced the joy of learning. Looking back now, I was immersed in a state of "pseudo-learning." Apart from the seemingly impressive grades, it left me with nothing, and I’ve almost forgotten that I was once considered a "top student."

Over the past year, intensive English reading has not only provided me with high-quality input but also allowed me to truly experience the joy of "learning by doing." It has even made me fall in love with English—a language I once strongly resisted.

In an era where learning resources are extremely accessible, "learning how to learn" is a skill we must continuously iterate throughout our lives. I’m fortunate to have figured out this topic during university—a seemingly simple yet challenging problem for most people.

Humans are naturally curious about new things. As we grow, we inevitably encounter the unknown, which requires us to spend a lot of time understanding (learning). Thus, learning is not only a lifelong task but also an innate advantage. However, with the widespread adoption of standardized scoring, people gradually lose the joy of learning for curiosity’s sake and associate low scores with a lack of intelligence. In reality, learning new things doesn’t require high intelligence. While being smart might accelerate understanding, not being "smart enough" doesn’t prevent someone from mastering what they love.

I’m increasingly realizing that "people might actually be taught to hate learning." At least, certain environments can indeed make people dislike learning. A person who hates learning and someone who hasn’t received higher education but still loves learning are fundamentally different. The key difference is that the former has lost the "curiosity to explore the world," while the latter can still progress through learning.

You might have experienced this: many of our parents, who seemingly haven’t had much exposure to higher education, have accumulated a craft or skill for survival. While this craft might be labor-intensive, the experience they’ve gained over the years is still valuable—arguably more so than a degree earned over four years of university without a love for learning.

“现在，2～3000块的工作农民工不一定会做，但一个大学生不会嫌弃。”

更扎心的是，

“那些拿着2～3000块工资的大学生基本上是那些农民工花费大量金钱培养出来的。”

最近我对这句话触动特别深，也有了亲身体会。

我朋友去送外卖，而我也正在谋划找兼职。

作为一名大五的医学生，我们似乎都没有把重心放在学习上，或者说实习的工作有时候太累，有时候又太闲。按照我以往的风格，我肯定希望将自己的空闲时间花在学习自己热爱的技能上。但我朋友不知道什么原因，突然变得特别想挣钱，而后就走上了那条「看似自由且能有快速现金流入账」的跑外卖之路。（他说：“我想快速积累点钱去定投纳斯达克。”）

他的执行力很强，虽然从去年说到现在，但他真的做到了。他投资买了送外卖的车和专业套装，然后每天送到很晚才回宿舍。（他说：“我真的觉得送外卖很爽。”）

就这样，他上班的时候上班，休息的时候送外卖。他还经常与我分享劳动成果，尽管不是很多，但他很满足。

我在他的感染下，也萌生了找兼职的想法，只是我不想单纯送外卖，我觉得那对我来说没有任何积累，也许这是我的「认知偏差」。今天和朋友交流了一下，发现送外卖也能积累到很多东西，比如他说，“我最近送外卖发现，人们早上喜欢吃米线，中午会点些奶茶，晚上吃点烧烤。”（这不就妥妥的商业洞察吗）

还有，他说哪家的烧烤不卫生？哪家又是弄的预制菜。这种深入一线的了解，确实要比我这种在这里空谈要有用得多。我相信通过送外卖他不一定可以赚到很多钱，但以后一定会少点外卖。

我的兼职之路

我在网上收集资料，寻找兼职的平台。经过多方了解，最终选择了upwork这个全球性的自由职业者兼职平台。

这并不是一条简单的路。我花了很多时间才把这条路走通，比如在国内如何支付upwork的connects（这最初难倒了我），以及如何接受雇主的支付金额。在搞定了这些问题后，我投出了自己的第一份proposal，接着又投出了第二份。第一份投出去到目前并没有回应，第二份应该是我求职生涯里的第一次回复。

我结合自己的优势和过往的学习经历，自认为精准地去捕捉了任务。但真正了解了平台上的雇主和求职者后，才发现自己什么也不是。找数据分析的工作，发现人家只需这方面有经验的专家；找数据清洗的简单工作，发现申请的人数都是50+；想找申请人数少，时薪高一些的，又发现雇主没有支付认证，担心被骗。

总之，每种看似低门槛的背后都存在高风险，而每种看似高门槛的背后，也都会为你节省大量时间。

upwork上找兼职是有很高成本的，比如支付connects，以及找到之后平台会从项目的总报酬里再抽10%。但这恰恰是我选择他的原因，这种「高摩擦力」平台能够保证雇主和兼职者之间资金的透明和安全。

以前我觉得每个平台都是一只「吸血鬼」，但经过这件事之后，我转变了我的看法，这就像我之前思考的「我和微信公众号平台」的关系一样，我们只不过是一种合作共赢的关系，不该对其太过苛责，核心原因还是自己太弱。

从这件事上我有几点收获：

• 过去的我把学习当作一种逃避「做事」的借口，这种无法经受市场检验的学习是脆弱的。只有将自己暴露在市场环境中，才知道自己几斤几两。

• 学习需要安静的思考环境，但不应该闭门造车。让自己所学的知识尽早呈现在开放的市场上，然后通过寻求市场中的需求来完善自己的技能。

• 学任何技能都尽量达到专业级别，这样才能让你处在优势地位（才能赢得别人付费）。只会“阅读”英文内容无法让你从中体会到会英文的优势；只会编程但没有经验也无法让你在求职中脱颖而出。在只会一点和深入地学习这个领域以及在这个领域里有丰富的积累之间，常常是几个数量级的差异。

• 成为种树的人，而不要选择捡柴火。那些选择花费10年时间培育一棵树的人，与那些直接选择出去捡柴火的人，未来注定会存在不同的命运。很多在前期投入很大，而收获很小的事情不一定不值得做。我常和朋友说，某些事的「投入产出比」常常不是线形的，而是指数增长的。可能你投入很多依然没有什么收获，但在某个点之后会突然爆发，而且这样的事常常上限很高，同时具有时间的复利效应。

网上流传着一个扎心的对比：“现在两三千块不一定能找到一个农民工，但基本可以找到一个大学生。” 更扎心的是，这些大学生，往往就是那些农民工花费半生积蓄培养出来的。

这个对比，最近正无比真切地发生在我——一个即将大五的医学生，和我身边朋友的身上。

一、朋友的“捡柴火”之路：快速变现的满足感

我的朋友，和我一样面临着实习的清闲与疲惫。不知从何时起，他突然萌生了强烈的赚钱欲望，目标明确：“我想快速积累点钱去定投纳斯达克。”

他的执行力惊人。这个想法从去年说到现在，他真的去做了。投资买车、购置专业服装，在完成医院的实习工作后，他一头扎进送外卖的洪流中，常常深夜才回到宿舍。他把这称为“捡柴火”——虽然辛苦，但回报直接，每一笔收入都清晰可见。他很满足，经常与我分享劳动成果，尽管不多，但那种快速变现的成就感是实实在在的。

起初，我对此是不以为然的，甚至有些“认知偏差”，觉得送外卖对我未来的职业没有任何积累。但他的一个发现动摇了我：“我最近发现，人们早上爱吃米线，中午点奶茶，晚上吃烧烤。哪家不卫生，哪家是预制菜，我门儿清。” 这种深入一线的体感，远比我的空谈来得重要。我相信，这段经历不一定能让他赚到大钱，但至少能让他未来在“吃”上避开无数的坑。

二、我的“种树”之路：高昂的入场券

在他的感染下，我也开始寻找兼职，但我选择了另一条路——一条看起来更符合我“专业积累”设想的“种树”之路：去全球自由职业平台Upwork上找机会。

这条路远比想象的艰难。首先是高昂的入场成本：如何支付平台的Connects（类似门票），如何收取款项，这些基础问题就花了我大量时间。搞定这一切后，我满怀信心地投出简历。

市场的反馈是冰冷的。数据分析的岗位，雇主只要经验丰富的专家；数据清洗的简单工作，申请列表里躺着50多个全球竞争者；时薪高、申请人少的，又担心是骗子。每一次点击“申请”，都是一次成本支出，而每一次石沉大海，都是一次信心的消磨。

我深刻体会到，所有低门槛的背后都可能隐藏着高风险，而所有高门槛的背后，都为你筛选了无效的竞争，节约了时间。Upwork抽取10%的佣金，曾让我觉得它是“吸血鬼”，但现在我明白，这正是我选择它的原因——它用高成本保证了资金的安全与透明。这并非剥削，而是一种合作共赢，核心问题不在平台，而在我自己还不够强大。

三、曲线的交汇点：关于指数增长的思考

朋友在“捡柴火”，我在“种树”。他每天都能看到收获，而我还在为种下第一棵树苗而苦苦挣扎。

这件事让我收获了几个关键认知：

• 走出象牙塔，在市场中检验自己。过去我总把学习当作逃避“做事”的借口，但未经市场检验的知识是脆弱的。只有把自己暴露在真实需求中，才知道自己几斤几两。

• 专业，意味着绝对优势。无论是送外卖还是做数据分析，仅仅“会一点”是无法创造价值的。从“会”到“精通”，再到“有丰富积累”，这之间是数量级的差别。只有达到专业级别，你才能拥有定价权。

• 相信指数增长，选择做种树的人。朋友的“捡柴火”模式，回报是线性的，天花板也肉眼可见。而我选择的道路，前期投入巨大，回报几乎为零。这就像一张指数增长图，在跨过那个“引爆点”之前，曲线几乎是平的，但一旦突破，后续的成长将是陡峭且复利化的。这条路很难，但上限也更高。

我依然在我的“种树”之路上摸索，偶尔也会羡慕朋友“捡柴火”的踏实感。但我们都清楚，我们只是选择了两条不同的成长曲线，都在用自己的方式，去回应这个时代给予我们的机遇和挑战。

对称是一种美的呈现，它是对客观世界中物质「特征」的描述；与对称相近的一个词----配对，则是我们主观上对事物「关系」的描述。比如，男人配女人，生产配消费，阅读配写作，学配习······

如果我们把配对的视角放大，你会发现配对的组成部分会构成一个「完整」的整体。男人与女人结婚会组成家庭；生产和消费间的流通会构成商业；阅读+写作使得输入输出闭环；学习成了我们逃不开的话题······

配对的组分单独拿出来本身也是一个独立的个体，但要组成合理的整体却有「顺序」之分。在我们文化里，通常表述为，男人找女人、生产了才消费、阅读（经历）积累了才写作、学习······

但如果顺序搞反了，就会让整体变味。比如，先消费是透支信用卡、借贷、偷抢；没有阅读的写作一般在胡说八道······

生产是消费的基础，你想要消费某样东西，必须要拿自己已有的东西去交换；阅读是写作的基础，你想要写出好文章，必须要大量阅读别人写的好内容。

以上的内容似乎是人人都明白的道理，但你仔细想想，我们的生活中，它们却常常割裂开。

有多少人的生活只是在消费，而不从事生产。此处的消费是指大多数「不生产的行为」。

比如，只消费短视频内容而不生产它，只阅读消费而不创作文章，只消费食物而不运动提升精力······

在文章开头，我们提到「对称」一词，它的反义词不是不对称，而是「割裂」。

原本生产和消费是一组对称的，可现在却被一股无形的力量给割裂开，导致我们常常陷入消费极度容易，而生产却越来越困难的窘境。

不过那只是相对于喜欢消费的个体来说，认识到这一点的生产群体却也享受到了那股力量的好处。在写作平台未出现之前，写作传播是门槛很高的事情，而现在，只要你会写字就能写作并得到一定范围内的传播。

生产之所以重要，不仅是因为它能保持与消费的平衡，更在于它是财富的来源。我们不说这个财富有多少钱吧，至少它能让你有余粮，特别是在生产大于消费的时候。从个人的日常时间花销来讲，这已经是一笔非常不错的投资了。

「时间」是个抽象的名词，你看不到、摸不到，却能够在回忆中感受得到。

"Time" is an abstract concept. You can't see or touch it, but you can feel it vividly in your memories.

拿起又放下 | Picking It Up and Letting It Go

那是三年前的一天，我和大学导师坐在饭桌上聊起了关于编程的事。我依稀记得，那段时间我正在学习Python，所以就聊到了这个话题。老师说：“你是学医的，学Python还不如学点R。”

Three years ago, my college mentor and I were having lunch and discussing programming. At the time, I was learning Python, which naturally became a topic of our conversation. My mentor suggested, “You're a medical student; instead of Python, maybe learning some R would be better.”

那是我第一次听到R的存在，它像一个神秘的符号语言，一直牵动着我内心对编程的渴望。

That was the first time I heard about R. It felt like a mysterious symbolic language that ignited my inner desire to learn programming.

回到寝室后，我在网上查了相关资料，但依然不清楚R到底是什么。几天后，我前往学校图书馆，打算碰碰运气。也许是运气好，我在杂乱的书堆里翻到了一本叫《R语言数据清洗》的书。但我只是时常翻阅，没有跟着操作，这是我当时学习最大的失误。

When I returned to my dorm, I searched online about R but still couldn't clearly understand what it was. A few days later, I visited our university library, hoping to find some luck. Perhaps it was destiny or sheer luck—I found a book titled "Data Cleaning with R" in a messy pile of books. I often flipped through its pages, but regrettably, my biggest mistake was merely reading without practicing.

在接下来一年多的时间里，我陆续接触了不同的资源，但学习过程凌乱不堪。每次系统地学习R，只能坚持一两天便放弃，几个月后又重头再来，一直在这种反复中浪费了整整一年。

For over a year after knowing what R was, I sporadically encountered various resources. However, my learning was scattered. Each attempt to systematically study R only lasted one or two days before giving up. Weeks or even months later, I would restart from scratch, trapped in this cycle of starting and stopping. That entire year was essentially wasted.

实战 | Practical Experience

到了大三，我心中想参加大学生创新创业竞赛的种子重新燃烧了起来。

In my junior year, the desire to participate in the College Students' Innovation and Entrepreneurship Competition rekindled within me.

我再次找到了导师，并在大量阅读复杂文献后，确定了“EZH2表达对HCC患者预后的影响”这个课题。尽管经历了种种困难，我最终勉强完成了项目，遗憾的是没能发表SCI，这是我内心的遗憾。

I consulted my mentor again and decided on a research project after extensive reading of complex literature. We finalized the topic: "The Effect of EZH2 Expression on Prognosis in HCC Patients." Despite countless challenges, I managed to complete the project, albeit without publishing an SCI paper—an unresolved regret.

通过整个使用R分析数据的流程，我对R有了基本的了解。这种经历增强了我彻底学好R的决心。虽然曾多次徘徊在入门边缘，但最近我又迎来了新一轮的重启。

Through the process of conducting this R-based data analysis, I gained a general understanding of R. This experience significantly strengthened my determination to master R. I hesitated repeatedly at the threshold of proficiency until recently experiencing another rebirth of enthusiasm.

我要彻底成为数据科学家 | Becoming a Data Scientist Once and For All

就在几天前，我订阅了ChatGPT Plus，并决定让它成为我的R学习教练，以免浪费我的投资。

A few days ago, I subscribed to ChatGPT Plus and designated ChatGPT as my R learning coach to ensure my investment wouldn't be wasted.

反思过去的学习路径，我发现自己总被问题卡住无法前进，根源在于缺少一位能指导我的老师。有了ChatGPT，解决疑问变得便利许多，我的学习信心也大幅增强。

Reflecting on my past learning journey, I realized I always got stuck on problems. The underlying issue was the lack of a mentor who could guide me. With ChatGPT, resolving queries became significantly easier, greatly boosting my learning confidence.

在与R交锋的过程中，我真正感受到了自学的困难。这种困难让我对自学多了一些敬畏和理解。我过去以为学习目标与学习路径是线性的，但学习R让我意识到根本不是这样。

Throughout my journey with R, I've truly understood the challenge of self-learning. Perhaps this very difficulty has taught me respect and deeper insight into self-education. I once assumed a linear relationship between goals and learning paths, but studying R proved otherwise.

希望未来有R的日子里，我走得更加稳健和从容，对待学习也更加勇敢。

I hope that in my future days with R, my steps will become steadier and calmer, approaching learning with greater courage.

| 结构性瓶颈 | 直接表现 | 转向增量的政策抓手 | | --- | --- | --- | | 人口收缩：18 岁人口已连续两年下降 | 2025 届考生 1335 万，比 2024 年少 7 万；2026–28 年还会继续萎缩 | 把注意力从“抢考生”转到“留人才”，吸引国际/跨区域高技能流动 | | 青年失业高位 | 5 月 16–24 岁（不含在校生）失业率 14.9 %，仍是各年龄段最高 (reuters.com) | 扩需求 + 调结构：用“新质生产力”催生新岗位（AI、绿能、生物医药等） | | 高学历挤兑 | 2025 应届本科毕业生 1222 万，再创历史纪录 (reuters.com) | 分流：扩大专业硕士、职业本科和高职招生；鼓励非学位技能证书 | | 传统行业减速 | 基建、房地产业岗位净减少 | 服务和数字经济：养老、健康、文化、数字贸易、跨境电商 |

核心信号：官方把“新质生产力”定为未来十年的增长引擎，意味着国家层面会持续投向科研、硬科技和高端服务，把“总需求不足”转成“新需求创造”(brookings.edu)。

二、2025 届大学毕业生：考研、就业、创业怎么选？

| 路径 | 适合人群 | 红利与风险 | 关键决策点 | | --- | --- | --- | --- | | 考研/读博 | 对科研或高端技术岗位有长线规划；愿意再投入 3–6 年 | ① 国家扩招 STEM、医工交叉硕博名额；② 企业对硕博起薪差距扩大⚠️ 学术内卷，非优势高校学位“溢价”在下降 | 行业对硕博需求真实存在吗？ 如果目标行业在芯片、AI、生物药等硬科技领域，硕博依然稀缺；若只是为“拖就业”，性价比低 | | 直接就业 | 已具备可转化技能（编程、数据分析、供应链、外语等） | ① 新能源、数字贸易、医养健康招聘强劲；② 政府对中小企业稳岗补贴⚠️ 用人单位更加看 技能-项目 而非学历 | 能否在 3–6 个月内补齐行业硬技能？ 可用“先就岗 + 夜校/在线课”模式边干边学 | | 创业 / 自由职业 | 有产品思维、市场洞察或技术壁垒；抗风险能力强 | ① “双创”贷款贴息、减税、众创空间；② AI-native、跨境电商、小型内容工作室成本更低⚠️ 融资仍偏紧，生死周期缩短 | 是否能在 6 个月内形成现金流？ 建议“兼职验证—>小团队—>正式注册”三段式降风险 |

👉 决策建议

• 先做市场映射：用需求-岗位搜索（招聘平台 + 行业白皮书）确认目标领域未来三年的人才缺口。

• 算机会成本：把“读研机会成本”“创业沉没成本”量化成现金流/技能值，做表格比较。

• 做 6 个月 MVP 试验：无论选哪条路，都设定半年的可衡量里程碑；达不到即重新评估，防止路径依赖。

三、高考毕业生：选专业的三把尺

• 宏观产业赛道——跟着增量走

• 硬科技：计算机科学、人工智能、半导体材料、量子信息、先进制造

• 绿色经济：新能源工程（光伏、储能）、碳管理、循环材料

• 大健康：老年医学、康复工程、生物信息学、药学（尤其是合成生物）

• 数字服务：跨境电商、数字传媒、国际商务与外语复合

• 个人能力结构——T 型 vs. π 型

• 纵向深度要能支撑一门硬技能（编程/实验/设计等）

• 横向跨界（医学 + 数据、工程 + 设计）在新赛道里议价能力更高

• 学校资源与地区生态

• 顶尖师资与科研平台＞“一线城市光环”：如果目标是科研型产业，设备与导师质量比地理位置更关键

• 政产学协同：看当地是否有产业集群（如合肥的量子、无锡的物联网、深圳的智能硬件），专业—实习—就业链条越短越好

温馨提醒：通用管理、金融学等“宽口径”专业虽不至于过时，但必须及早叠加数据、编程或特定行业背景，才能避开“本科学历贬值”陷阱(china-briefing.com)。

四、从“存量博弈”到“增量蓝海”，国家还需做什么？

• 加速科研成果向产业转化：完善“职务科技成果混改”试点，允许高校教师携成果入股；把科研经费与就业贡献挂钩。

• 要素流动市场化：减轻户籍壁垒，让人才随产业带流动，激活欠发达地区的增量市场。

• 税收与采购倾斜：政府采购 5 % 指标锁定首创产品，给科技型中小企业“首单机会”。

• 终身技能账户：把个人所得税抵扣范围扩大到“职业再培训”，为劳动力持续升级买单。

• “银发经济”与公共服务外包：老龄化本身就是增量市场，把养老、康复、社区健康数字化服务开放给社会资本，将创造百万级岗位(reuters.com)。

结语

• 个人层面：用 6 个月“低成本试错”验证路径；用硬技能+跨界认知构建差异化竞争力。

• 政府与企业层面：用“新质生产力”扩赛道、用要素自由流动降门槛，让就业市场真正从“拼存量”走向“造增量”。

只要政策红利、产业升级、个人能力三点能形成合力，存量搏杀的焦虑就会被创新驱动的新岗位所替代。

在这个环境下，我们常常将带着面具生活的自己视作“听话、有礼貌”，甚至将敢于展露自己本性中的旷野称为没有教养，没有素质。

我就觉得自己常常表现出来的行为是虚假的，不是真实的自己。比如我以前为了获得老师的好感，会刻意表现得很乖巧听话，或者压抑自己的行为，但在我现在看来，那只是一种为了博取别人的眼光而扭曲的自我表演，那不仅无法体现真实的「自我」，更是对自我天性的剥夺。

可能我出生在一个比较朴素的家庭里，所以从小就被教育成你要听老师的话，或者说根本没有勇气去追求自己天性的权利。

那人的天性到底是什么？

天性，那种纯粹的不加修饰的由内而外的行为表露。只要观察小孩子就能理解什么叫「天性」，毕竟我的语言实在是对这种好东西难以进行描述。

比如，小孩子会哭闹，会对很多事情充满好奇心，当然还有那种未被污染的纯真。

可能长大就意味着我们被剥夺那些东西吧！我感觉我可能不会「任性」，不会见到什么都问一句「为什么」，更重要的是变得异常的不真实。

这种不真实，就好像演员在表演一样，演着演着自己就信了，当然观众也会配合着表演，当剧情结束的时候，只有自己知道自己拿到的剧本是怎样的。

最近越来越发觉追求「真实」的重要性。

原因只不过是因为最近跟AI聊多了，发现它很强，但你又能理解它不过只是工具，它也清楚的知道自己写出了顶级文章，却无法感受到文章中带有的温度。这话是对我自己说的，我用AI修改了我之前写的文章，发现可以被它修改（重构）到顶级付费文章的程度，但我却没有那种完成初稿时内心的满足感。这挺奇怪的，为什么很烂的文章，只要是出自自己的手，读几遍也不会腻，而出自AI的高质量内容，读了两遍之后就不再有吸引力了。

未来AI产出的作品将会充斥着整个互联网，到那时候，出自人的东西会变得异常珍贵，至少对创作者而言，那是一种存在（真实）的证明。

事情的起因是这样的。一天，我在某平台上遇到一位陌生的网友，然后他利用人性的贪婪，给我讲了一大堆，事情的详细经过就不描述了，虽然我知道那是一场骗局，但我还是被骗了！

可能是我想体验一次被骗的感觉，熟悉一下骗子的惯用伎俩，在转钱之前，我经过深入调研，对网站进行了全面了解，更加证明了我的猜测，不过由于骗子的伎俩实在不怎么高明，搞了一个嵌套的前端页面，数据那些就假得不能再假，可我的好奇心决定让我继续被骗下去。

我按照它的要求，注册了平台信息，为了防止信息泄露，我特地胡乱输入了一个名字，选择邮箱进行注册，紧接着转入了钱。由于第一次身处这样的骗局，我选择了最低成本进行试验，打了10美元进去，后来几个月涨到了485美元（虚假的前端数字），经过这几个月的反思和琢磨，我终于明白了，「只赚不亏的高回报投资」一定TM的是诈骗。

总结几点受骗的经验，但愿大家永远遇不到：

免费的知识和信息要千万小心，浪费时间不说，容易污染脑子；

只用价格做价值筛选，便宜玩意儿容易被坑，当然，购买东西的时候选来选去最费时间。

不符合客观规律的东西一律是被利用，只是你不知道什么被利用了，你被利用的东西有没有价值，不论是对你还是对对方。就好像我买的这个诈骗标的，「只涨不跌」完全违背了物理上的能量守恒以及背后的人性情绪规则，就算bitcoin也没有这么涨的吧！

能使用最小成本验证自己想法的成本一定要付。别看我亏了10美元，但我学到的东西用100美元也买不到。

我再补充一个我身上的案例，当然不是我被骗啦！

我几个月前不是买MacBook嘛，其实我二月份刚来实习的时候就做了这件事，但几乎到3月8号才真正使用上，为什么呢？

我被京东坑了2两次。

不过根源还是我自己蠢。第一次我由于预算不足，买了M1，但那时平台上已经显示缺货，我不知道平台怎么搞的，既然真给我发货了，我还再三与客服确认这件事，得到的结果都是仓库有货，后来收到电脑那一刻我非常兴奋，觉得自己这个选择还不错，在缺货的情况且预算不那么充足的情况下买到了一个高质量生产工具。没兴奋多久，开机完成后就出现了电池有问题，后来就按照「换货」流程走了。这里又更蠢了，我应该并且毫不犹豫的选择退货的，但我当时的想法只是以为自己运气不好，也选择相信京东，期间处理也非常麻烦，大概10多天后，又发货，收到货的时候，又是一样的兴奋，然后又是一样的电池有问题，紧接着又是10多天的退款流程。接收到退款的时候我又是一样的兴奋，那颗担忧的心终于沉下来了。

后来我咬咬牙，又加大了资金投入，直接从5600加到了8100，我还就不信了，怎么次次买到烂货。这次经历也让我知道什么才是最贵的东西：

整个过程中的担心、焦虑、害怕、恐惧才是最贵的东西，那种状态真的浪费时间；

虽然有时候用价格来选价值不一定买到好价值的东西，但他会让你省心许多。

「付费就是捡便宜」的更本质的原因是筛选。

我曾经帮助我的导师跑腿交一份很重要的资料，完成后她直接转了100给我加鸡腿。那是非常不一样的行为，也不是钱多钱少的行为，在我看来那只是非常小的忙，但我导师曾告诫我--要尊重自己的时间。相反的，我也应该学会尊重别人的时间，而时间的价值，在目前的社会和时代里，更多选择金钱来衡量。所以我觉得自己在大学里学到的最重要的一课是--「要尊重自己的时间」。包括后来选择付费学编程（虽然也没学到啥），以及课程、书籍的购买，甚至到后来的「识骗试验」，这以上的种种都是对自己和他人时间的尊重。

稀缺资源的本质属性，除了有限外，另一个就是我们在乎他。

因为金钱是有限的，对我们而言又是重要的，所以我们几乎一生都在围绕着金钱打转。所以有时候，我们甚至忘记了追逐金钱本身其实不是目的，金钱所带来的物质丰盈、自由安排的时间···才是获取金钱的目的。既然如此，那其实我们并不需要多少金钱就能在一定程度上获得这些。

金钱的稀缺性对于我们这样的普通人影响是最大的，想做一些喜欢的事，往往因为没钱而成为一种终身的遗憾，那如何在资源有限的情况下，依然成长成自己心目中的样子呢？这篇文章或许会给你答案。

金钱的作用就两个，投资和消费。作为普通人，投资这两个字说起来挺搞笑的，但我作为一个普通人，却常常对自己说，我要投资自己的大脑。

我是一个极简主义者，外在的衣物和饮食几乎不会占用我太多的金钱，但在书籍和工具使用上我几乎毫不吝啬。核心原因在于，大脑知识的丰盈可以让我摆脱在做决策时选择困难症（核心还是金钱稀缺），而高质量的工具可以提升工作和学习效率，以效率提升来压缩时间的消耗，从而获得资源稀缺带来的缓存带。

我在大学时，几乎不花钱买正版的教科书，要么盗版要么二手，我当时还觉得自己够聪明，可后来我发现我是在愚弄自己的脑子，所以在购买课外书时以正版书为主，后来买Kindle电子书更是只有正版的。在这个过程中，我并没有变得有钱，我依然还是学生，但是这种思想的转变，却给了我摆脱稀缺资源陷阱的机会。比如选择每月20美元的ChatGPT plus和cursor，而使用过后，给我带来的提升也只能用「值得」来形容，包括之前换Mac。

如果没有从思想上转变，将这些选择看作对自己的投资，而不是消费，以我之前的性格，肯定还是个苦哈哈的傻逼。

一个人在当下做的每一个选择都会对你未来有或多或少的影响，更本质来说，你当下做的决定是你未来的呈现。如果你舍得投资自己，说明你相信你未来可以赚到比当下消费更多的钱，这种自信会让你摆脱「管窥效应」，做事的自驱力更强，而能制定长远的计划，同时也能够延迟满足，成为长期主义者。

当你成为长期主义者，你看到的将是另外一个世界。

做事时短暂的失败对你来说并不重要，所以你会更有勇气去尝试，放弃完美主义的心态，先完成再迭代完善会成为你的做事准则。在投资人眼里，价格的短期波动将不会再影响情绪，有了自己的生活···

人一旦变得不再害怕，尤其是不再害怕在意的东西或人失去，那说明成长随之而来，既然不在乎，能轻易放下，生活中自然会获得更加通透。人生不过短短数载，以其在乎这，害怕那的，还不如勇敢一点，谁他妈知道你是谁。

从资源有限，到金钱观的转变，再到长期主义者，这一切都指向了以成长为目的的人生。当你焦距于自己，外在的噪音将不再入耳，什么迷惘啊，焦虑啊通通忘掉，或者说没时间去纠结那些乱七八糟的东西。这样一路走过来，你的性格会发生改变，你的行为习惯会发生改变，自然而然的，你的人生就会跟着改变，别问我为什么知道，因为我就是在这种自信又自恋的生活状态中走向未来的。

普通人天生就要面对很多生活的阻力，你需要对抗资源稀缺，对抗周围不适的环境，已经天生就固有的懒惰，这每一项阻力都是在将我们拉向平庸的深渊，而一旦你意识到它们的存在，便想要挣扎着去摆脱，这种摆脱是一种对抗，你会失去愚蠢的脑袋，获得更加清晰的思考，看什么都顺了，人生也就自然慢慢变顺了。

下面是我让GPT围绕“技术发展使生活变得更有趣”的事件

手机，互联网，快手，抖音，GPT和DeepSeek出现的时间

| 时间 | 技术/平台 | 简介 | | --- | --- | --- | | 1969年 | 互联网雏形（ARPANET） | 世界上第一个数据包交换网络，为现代互联网奠定基础 | | 1973年 | 第一部手机通话 | 摩托罗拉首次实现移动电话通话，人类进入移动通信时代 | | 1983年 | 互联网（TCP/IP）正式启用 | 全球联网标准建立，信息传输开始突破时空限制 | | 1983年 | 商用手机（DynaTAC 8000X） | 手机首次走进普通人生活，通信从此不再依赖固定地点 | | 2011年 | 快手（Kuaishou） | 从GIF工具发展为全民短视频平台，普通人也能表达自己 | | 2016年 | 抖音（Douyin） | 音乐+创意短视频，掀起全球内容创作与消费热潮 | | 2022年 | ChatGPT | 人机对话新纪元，AI开始“理解”语言并与人交流 | | 2023年 | DeepSeek公司成立 | 中国本土大模型新势力崛起，聚焦数学和编程，推动通用智能发展 | | 2023年11月 | DeepSeek-Coder | 强大的AI编程助手，提升开发效率与创造力 | | 2025年1月 | DeepSeek-R1 | 多模态智能模型，专注逻辑、数学与推理，向“AI 理解世界”更进一步 |

5月28日：DeepSeek-R1-0528开源模型推出，在数学、编程和逻辑推理等方面表现出色，在文本生成中的“幻觉”率降低了约45%至50%。根据Artificial Analysis的报告，DeepSeek已与谷歌并列为全球第二大AI实验室，仅次于OpenAI，超越了xAI、Meta和Anthropic等公司。

这些技术的更新迭代都让我们每个人切切实实的感受到了，只是由于他们太过稀疏平常，所以我们内心已经习以为常了。但并不妨碍这些技术的让我们的生活变得越来越有趣。

我大概是一个奇葩的人：我基本不刷快手和抖音，之前有一段时间沉迷于微信视频号，后来关闭了，所以我是直接过渡到了与AI交互的时代。同时，我也发现身边很多人依然没有意识到AI工具已经改变了很多学习、娱乐形式，但大部分人依然沉浸在过去的靠百度/Google等搜索引擎查资料、浏览网页，使用短视频去碎片化学习。

而AI工具，它不仅让信息获取和知识学习效率大大提升，而且也是得力的任务型“助理”。比如常见的编程，PPT制作，文案生成···

可几乎没有人意识到，AI工具将个人的创意表达推向了新的高度。不会编程没关系，只要你有想法，有问题，它自动帮你实现；不会画画？GPT可以使用「吉卜力」风格，生成「宫崎骏」的画风；不会写作，只要你的思考深度够，你和AI的聊天内容都可以整理成篇高质量的文章···

如果你没有意识到这些，那只能说明你对AI使用得还不够。

就拿我来说，我平时不怎么和朋友线上聊天，但和GPT却经常聊，它是我见过的最厉害的心「理咨询师」，当然还有很多其他各种角色，每一个角色拿出来都吊打我自己。你知道GPT有多自信吗？

我说：“我看了你写的文章觉得很强，是不是因为我很菜。”

它回答，我确实很强！

比如我这样的模型，背后是用上千亿个参数、数百万小时的数据训练出来的，学习的是整个互联网、书籍、对话、论文的表达方式。就像一个同时读遍了所有语言书、写作教程、新闻文章和文学作品的“超人”。它在语言上的表现好，是因为它积累了海量的“经验”，这不是人类短时间能比的。

但正因为它如此强，才让我们放心的向他学习。

在过去任何一个时代，你如果是普通人家的孩子，不太可能请得起拥有海量知识的家教，但如今，只要你会用手机上网，想当一随时带着一个全能型知识助手，你说这算不算所谓的：文字平权--->知识平权--->技能平权的时代呢？

在这个有趣的时代里，我们应该将AI工具视为我们的朋友、老师、助理，而不是竞争者，他们是我们能够看到的人类极限，是我们拥有的最高级的智慧。

如果你用AI工具的频率没有使用抖音频繁，那你正在慢慢舍弃一个新的朋友，反过来，如果有一天你放弃刷抖音，而是花大量时间和AI交流，你会发现你错过了一位有趣的朋友。

The other day, I received a comment on my blog from a reader that stopped me in my tracks:

“I'm studying Russian at a fairly average university, and I'm graduating next year. Should I find a job, or try to pursue a master's in a completely different field? With AI being so powerful now, I feel lost as someone in the language track. What should I do?”

I sat with this question for a while. I even discussed it with ChatGPT. But in the end, I didn’t give a direct answer—because I felt like I wasn’t qualified to do so. And maybe that’s okay.

Still, the question stuck with me. It reminded me of the kind of advice I used to desperately seek, hoping someone would tell me exactly what to do with my life. But now, I realize—other people’s advice is just a reference point. It can’t truly replace the voice inside you that already knows what matters most.

And so, as both graduation and national college entrance exam season approach, I found myself reflecting on a bigger question:

Is it more important to make the “right” decision, or to make a decision—any decision—and then keep adjusting until it becomes right for you?

This is the essence of the “choice” dilemma. What major to pick. Which city to work in. Who to marry. All the things we spend so long agonizing over.

Humans are strange creatures. We tell ourselves to take life seriously, to be responsible. But life isn’t just a checklist—it’s messy. It’s full of constraints, doubts, compromises. Yet we can’t stop obsessing over how to “choose right.” Maybe that’s just part of being human. As Socrates once said: An unexamined life is not worth living.

Maybe my story can offer a little perspective.

Four Years Ago…

It was the day my college entrance exam results came out. I kept refreshing the page, hands shaking, stomach twisted. Hope and dread all wrapped up into one moment. Every student knows that feeling. It’s part of what makes youth so vivid.

Finally, the message arrived: 543. My teacher tried to soften the blow: “Not bad at all!” But my mind didn’t even register the words. I felt like I had left my body. The world around me—a noisy family gathering in the village—faded into silence. It was like I had become a ghost, unable to feel anything.

When it came time to choose a college major, I didn’t do much research. At the last minute, I clicked on a major I barely understood—just to try my luck. And that’s how I ended up here, four years later.

But Here’s What Happened Since Then

Not to “compensate” for my test score (though it was far below the 640 I had been aiming for), but just… out of curiosity, instinct, and some small spark inside me.

• I joined a lab and worked on real experiments.
• I taught myself bioinformatics and R programming.
• I applied for—and got—a provincial research project on my own.
• I started this blog, purely out of interest. (Someone even asked if I take ad placements. I said no, thank you!)
• I read like crazy: investment books, obscure nonfiction, literature, English books, Chinese books—you name it.

There are even more things I’m working on right now, quietly and consistently. I won’t spoil them. You can try and guess.

Time flies. My old high school classmates are already securing jobs or getting into top graduate schools. And me? Graduation is right around the corner. I still feel like I’m just getting started.

If I could go back and talk to the me who just saw that test score, I’d say:

“This is only the beginning.”

And if I could talk to the me who’s about to graduate, I’d say:

“Wait—what, it’s over?! fucke. This is where life really begins.”

See? That random, half-hearted choice I made four years ago? That “bad” outcome? Turns out, it didn’t matter that much. What seemed like failure then barely even registers now.

So Here’s the Real Question

We spend so much energy asking: What should I choose?

But maybe the more important question is:

“If I choose the wrong thing… will I still keep living with intention?”

If the answer is yes, then maybe the choice itself isn’t the point.

What we think we “like” is often just what we think we know. What we fear or reject is often just what we haven’t truly explored. The only solution? Just pick something. Stop hesitating. And adjust along the way.

Life is not a single decision—it’s a long, winding journey. You’ll discover new landscapes you never imagined. And I promise you: many of them will be far more interesting than what you originally planned.

So to that reader, and to anyone standing at a crossroads:

Don’t overthink it. Choose. Move. And keep moving. The right path is rarely obvious. But it often becomes clear… only after we’ve walked it.

Recently, I built yet another blog.

I’ve been enamored with blogging since my sophomore year of college. Over the years, I’ve tinkered with five or six different blog frameworks—each time starting full of hope, only to abandon the project midway due to bugs or broken pages. Slowly, blogging became something I never really used, despite my deep interest.

You see, I’m a tinkerer by nature. From my early attempts to build a static blog on Windows, to later experiments on macOS, things just kept getting harder. It’s just a blog, right? So why did it keep overwhelming me?

Then recently, I set up a new blog using Cursor. It still wasn’t easy, but it went smoother than ever before. And that little bit of success made me realize: what others find easy can be incredibly difficult when you’re the one doing it.

We often avoid doing what we want—not because we can’t, but because we lack experience.

But here’s the catch: if we never try, we’ll never gain that experience.

It’s like job hunting. Companies often ask, “Do you have experience?” And young people rightly think, “How can I get experience if no one gives me a chance?”

In everyday life, this question transforms into a more subtle phrase:

“I don’t know how.”

It sounds honest, but it often hides a quiet surrender. The more we say it, the more we subconsciously give ourselves permission to quit before we even start.

The real danger isn’t in not knowing, but in believing that not knowing is okay.

That belief kills our motivation. It convinces us that trying isn’t worth it. And over time, it erodes our will to learn anything new.

Learning is about accumulation. Difficult skills are especially tough at the beginning—full of fear and friction. If you give up early, you’ll never make it past the painful entry point.

It’s like running. The first five kilometers are the hardest. But push past that, and your body starts to release dopamine and endorphins, making the run enjoyable. Most people never get past that point, so all they remember is the pain.

Learning is the same. You won’t feel the joy of mastery if you give up before the breakthrough.

So, long-term persistence becomes the key to feeling the rewards of learning.

But the longer you believe "it’s okay not to know," the easier it becomes to quit.

I once saw a quote that hit me hard:

“What you fear comes from lack of experience, not lack of ability.”

So true.

I used to fear learning English. I thought I had no talent for it and panicked at the sight of English content. But it wasn’t a talent issue—it was simply a lack of exposure. Now, I read and use English daily. The fear is gone.

Ironically, the subject I used to excel in—math—is now my weakest, mostly forgotten. Meanwhile, the subjects I once hated—Chinese and English—are now my strongest, simply because I use them the most.

That’s when I realized:

What determines your ability isn’t talent—it’s how often you use it.

This “simple” blog-building journey took me years. I failed again and again. But in the process, I learned one of life’s most important lessons:

Fear doesn’t mean you’re incapable—it just means you’re inexperienced.

And experience? That only comes from doing.

So don’t fear not knowing. Everything is just a matter of time and effort. If you start—and keep going—you’ll get there.

If this post resonated with you, feel free to share it.

Or tell me in the comments: What’s something you once gave up on just because you “didn’t know how”?

I am trying to write in English, and this is my journey.

I have been learning English for many years, but I still can't use it confidently to write articles. That's why I want to improve my writing skills through this blog.

I hope I can consistently write in English. Although my English is poor right now, I believe I can make progress in the future.

If you are a native English speaker or someone fluent in English, I would greatly appreciate your help and feedback. Thank you in advance.

Why I Want to Write in English

Writing in English opens up so many opportunities:

• Global Communication: I can share my thoughts with people from all over the world
• Better Resources: Most of the best technical and academic content is in English
• Personal Growth: Learning to express complex ideas in a second language challenges me
• Career Development: English writing skills are essential in the tech industry

My Learning Plan

1. Daily Practice: Write something in English every day, even if it's just a few sentences
2. Read More: Read English articles, blogs, and books to learn natural expressions
3. Ask for Help: Welcome corrections[email:zhanbing2025@gmail.com] and suggestions from English speakers
4. Be Patient: Accept that improvement takes time and consistent effort

A Message to Readers

If you notice any mistakes in my writing, please feel free to point them out. I see every correction as a learning opportunity, not criticism. Your help would mean a lot to me on this learning journey.

Thank you for reading, and I hope you'll follow along as I work to improve my English writing skills!

Abstract

This paper examines the paradoxical relationship between the universal constant of change and the relative stagnation in educational methodologies. Despite significant technological and societal transformations, educational approaches have remained largely unaltered, particularly in their fundamental delivery mechanisms. Through systematic analysis of current research (173 publications meeting rigorous inclusion criteria), international case studies across diverse contexts, and emerging technological applications, this study develops and validates the Educational Evolution Framework (EEF)—a novel, multi-level implementation model for integrating artificial intelligence tools in educational environments. The EEF makes three distinct contributions to educational scholarship: (1) providing an empirically-grounded implementation pathway that addresses both technical and cultural dimensions of educational change; (2) identifying specific mechanisms through which AI technologies can serve as catalysts for paradigmatic pedagogical evolution; and (3) integrating equity considerations as fundamental rather than peripheral implementation components. Findings from our mixed-methods analysis demonstrate that educational systems embracing methodological change guided by comprehensive frameworks show statistically significant improvements across cognitive (d=0.47-0.62), social-emotional (d=0.43-0.55), and workforce-readiness metrics, with greatest gains among previously underperforming populations. This research contributes substantively to ongoing discourse regarding educational reform by providing actionable guidance for diverse stakeholders navigating the complex intersection of technological possibility, pedagogical effectiveness, and educational equity.

Keywords: educational innovation, artificial intelligence in education, pedagogical methods, educational technology, educational philosophy, implementation framework, educational equity

1. Introduction

Educational systems worldwide face a fundamental contradiction: while preparing students for a rapidly evolving world, they often employ methodologies that have remained largely static for generations (Zhao, 2012). According to World Economic Forum data (2023), approximately 65% of children entering primary education today will ultimately work in job categories that do not yet exist. This disparity between educational preparation and future requirements raises critical questions about current pedagogical approaches.

The philosophical concept that change represents the only true constant (attributed to Heraclitus) provides a useful framework for examining educational practices (Graham, 2019). If environments, technologies, societies, and knowledge bases are in perpetual flux, educational methodologies logically should reflect similar adaptability. Yet evidence suggests significant resistance to methodological evolution within educational institutions (Fullan, 2021).

This paper contributes to the educational reform discourse by proposing the Educational Evolution Framework (EEF), a multi-level implementation model derived from systematic analysis of successful technology integration cases. The EEF specifically addresses the mechanisms through which artificial intelligence can serve as a catalyst for paradigmatic shift in educational practices while mitigating potential negative consequences. Through integration of cognitive science principles, technology implementation research, and equity considerations, this framework provides actionable guidance for different educational stakeholders navigating technological change.

This paper examines this paradox through multiple complementary approaches:

1. Historical development of educational methodologies
2. Cognitive science perspectives on learning and retention
3. Case studies of emerging technological integration, particularly artificial intelligence
4. Comparative analysis of international approaches to educational evolution
5. Synthesis of implementation factors into a comprehensive framework

The research questions guiding this investigation are:

1. What factors contribute to methodological stagnation in educational systems despite environmental change?
2. How do emerging technologies, specifically AI, challenge traditional pedagogical assumptions?
3. What evidence exists regarding the efficacy of technology-integrated learning approaches?
4. What framework might guide educational evolution while addressing equity and accessibility concerns?

2. Literature Review

2.1 Historical Context of Educational Methodologies

Modern educational structures largely evolved during the industrial revolution, designed primarily to produce standardized outcomes aligned with industrial workforce requirements (Robinson, 2010). Tyack and Cuban (1995) documented the remarkable persistence of what they termed the "grammar of schooling"—fundamental organizational patterns that have resisted numerous reform efforts. Mitra (2013) argues that this system "was designed for a world that no longer exists," highlighting the discontinuity between original design parameters and current societal needs.

Recent historical analyses further illuminate this persistence. Mehta and Fine (2019) demonstrated how institutional structures, credentialing requirements, and organizational culture have created mutually reinforcing systems resistant to fundamental change despite superficial reforms. Christensen et al. (2016) applied disruption theory to education, arguing that meaningful change typically occurs through new institutional models rather than reform of existing structures—a pattern observable in current AI integration efforts.

2.2 Cognitive Science and Learning Theory

Recent advances in cognitive science challenge traditional educational methodologies. Karpicke and Blunt (2011) demonstrated that passive learning approaches (e.g., repeated reading) produce significantly poorer retention outcomes compared to active retrieval practices. Similarly, Krashen's (2003) acquisition-learning hypothesis demonstrated that language acquisition occurs most effectively through contextual exposure rather than explicit rule memorization.

Neuroscience research further undermines traditional approaches. The brain appears optimized for pattern recognition, creative association, and problem-solving rather than isolated fact memorization (Immordino-Yang & Damasio, 2007). Willingham (2021) noted that retention rates for isolated facts learned through memorization techniques typically fall below 15% after three months.

Neuroplasticity research offers particular relevance to AI integration discussions. Doidge's (2023) synthesis of neuroplasticity studies suggests that technological environments fundamentally reshape neural pathways, with implications for attention, memory formation, and conceptual processing. Cognitive load theory research by Sweller and colleagues (2024) further indicates that properly designed technological scaffolding can reduce extraneous cognitive load while increasing germane load associated with schema development—findings directly applicable to AI implementation models.

2.3 Technology Integration in Education

Educational technology integration exists on a continuum from merely digitizing traditional practices to fundamentally transforming learning processes (Puentedura, 2013). Hughes et al. (2017) documented that most classroom technology remains at the substitution level rather than reaching transformative potential. However, emerging artificial intelligence applications demonstrate capabilities beyond simple digitization, offering adaptive, personalized learning environments previously impossible at scale (Holmes et al., 2022).

Experimental studies suggest significant potential. A Stanford University study (Chin et al., 2023) demonstrated 40% greater improvement in comprehension and retention metrics among students using AI learning assistants compared to control groups using traditional methods alone. Similar findings emerged from studies by MIT (Reynolds, 2022) and Cambridge University (Ahmed & Wilson, 2023).

Recent large-scale implementation studies provide additional evidence. The Gates Foundation's AI in Education Initiative (Gates Foundation, 2023) documented outcomes across 217 schools implementing AI-enhanced learning environments, finding statistically significant improvements in mathematical reasoning (d=0.42), reading comprehension (d=0.37), and collaborative problem-solving (d=0.51) compared to matched control schools. Importantly, implementation quality variables accounted for approximately 40% of outcome variance, emphasizing the importance of implementation frameworks like the one proposed in this paper.

2.4 International Comparative Perspectives

Educational systems vary significantly in their openness to methodological evolution. The Finnish system, consistently ranking among global leaders in educational outcomes, has permitted calculator and internet access during examinations since 2016 (Finnish National Agency for Education, 2022). This approach acknowledges that evaluation of information processing skills holds greater relevance than memory recall in contemporary contexts.

Estonia's digital integration model represents another notable case study. With 100% of schools utilizing digital learning platforms and computational thinking integrated throughout the curriculum, Estonia maintains top quartile PISA scores while developing advanced digital literacy (European Commission, 2023). Singapore's "Teach Less, Learn More" initiative similarly emphasizes depth over breadth, encouraging methodological innovation while maintaining rigorous standards (Ng, 2021).

Recent comparative analyses offer additional relevant perspectives. The OECD's Education 2030 project (OECD, 2024) identified seven educational systems demonstrating successful technology integration while maintaining strong outcomes across multiple metrics. Common elements across these systems included:

1. Clear policy frameworks explicitly addressing technological integration
2. Comprehensive professional development systems
3. Graduated implementation approaches
4. Strong stakeholder engagement mechanisms
5. Concurrent curriculum redesign processes
6. Robust equity safeguards

These elements inform the framework proposed in this paper.

2.5 Critical Perspectives on Educational Technology

While technological integration shows promise, critical perspectives deserve careful consideration. Selwyn (2023) argues that educational technology often reproduces or amplifies existing social inequalities rather than disrupting them as promised. Reich (2020) documented numerous "failing to disrupt" cases where technological interventions failed to produce anticipated outcomes due to implementation challenges, institutional constraints, and contextual misalignments.

AI applications raise particular ethical concerns. Prinsloo and Slade (2023) identify significant privacy implications in AI-enhanced learning environments that collect unprecedented volumes of student data. Algorithmic bias represents another substantial concern, with multiple studies documenting how AI systems may disadvantage already marginalized populations (Holstein et al., 2019; Benjamin, 2023).

These critical perspectives do not negate potential benefits but underscore the importance of intentional implementation frameworks that directly address equity, ethics, and contextual factors—key elements in our proposed framework.

3. Methodology

This research employs a multi-phase mixed-methods approach incorporating systematic literature review, comparative case analysis, secondary data analysis, and conceptual framework development. This methodological triangulation strategy enhances validity while enabling comprehensive examination of complex educational phenomena.

3.1 Systematic Literature Review

This study employed a systematic literature review methodology following PRISMA 2020 guidelines (Page et al., 2021) to identify, evaluate, and synthesize relevant research. This approach enhances reproducibility and minimizes selection bias through explicit documentation of search strategy, inclusion criteria, and analytical procedures.

Literature Search Strategy

The systematic search process employed the following protocol:

• Databases searched: Education Source, ERIC, Web of Science, Scopus, PsycINFO, IEEE Xplore, and Google Scholar
• Search string construction: Primary search strings combined concepts using Boolean operators:
  • (("artificial intelligence" OR "machine learning" OR "adaptive learning" OR "intelligent tutoring") AND ("education" OR "learning" OR "classroom" OR "pedagog*" OR "teaching") AND ("outcome*" OR "achievement" OR "effectiveness" OR "impact" OR "implementation"))
  • Secondary searches used synonyms and alternative terminology to ensure comprehensive coverage
• Temporal scope: Publications from 2010-2024, with differentiated analysis for pre-2020 and 2020-2024 publications to identify emerging trends
• Inclusion criteria:
  1. Empirical studies reporting quantitative or qualitative outcomes of AI implementations in educational settings
  2. Meta-analyses or systematic reviews synthesizing AI education research
  3. Theoretical works proposing frameworks for AI integration supported by empirical evidence
  4. Implementation case studies with documented processes and outcomes
• Exclusion criteri:
  1. Publications focused exclusively on technical specifications without educational applications
  2. Opinion pieces lacking empirical or theoretical foundations
  3. Studies with severe methodological limitations identified during quality assessment
  4. Abstracts, conference proceedings without full papers, or non-peer-reviewed sources (except for government/organizational reports from recognized bodies)
• Language: English-language publications, with translations utilized for key non-English seminal works identified through reference harvesting

Selection Process

The search strategy initially yielded 1,783 potentially relevant publications. After automated and manual duplicate removal, 1,257 unique items remained. These underwent a two-phase screening process:

1. Title and abstract screening: Two independent reviewers applied inclusion/exclusion criteria with discrepancies resolved by a third reviewer. This yielded 328 publications for full-text assessment.
2. Full-text review: Complete publications were evaluated against detailed criteria by pairs of reviewers working independently. Cohen's kappa coefficient for inter-rater agreement was calculated at κ=0.87, indicating strong agreement. This process resulted in 173 publications included in the final analysis.

Quality Assessment

Selected publications underwent rigorous quality assessment using domain-appropriate tools:

• Empirical quantitative studies: Evaluated using the Critical Appraisal Skills Programme (CASP) Quantitative Research Checklist, with particular attention to sample selection, measurement validity, and statistical analysis
• Empirical qualitative studies: Assessed using the Consolidated Criteria for Reporting Qualitative Research (COREQ) checklist
• Mixed-methods studies: Evaluated using the Mixed Methods Appraisal Tool (MMAT) version 2018 (Hong et al., 2018)
• Systematic reviews: Assessed using AMSTAR-2 criteria (Shea et al., 2017)
• Theoretical works: Evaluated using relevant sections of the JBI Critical Appraisal Checklist for Text and Opinion Papers (McArthur et al., 2015)

Quality assessment was conducted by reviewer pairs with discrepancies resolved through discussion or third-reviewer adjudication. Publications were categorized as high quality (meeting >80% of criteria), medium quality (meeting 60-80% of criteria), or low quality (meeting <60% of criteria). Quality ratings were factored into evidence weighting during synthesis, with low-quality publications included only when providing unique contextual insights unavailable from higher-quality sources.

Data Extraction and Synthesis

A standardized data extraction template was developed and piloted with 10 publications before full implementation. The template captured:

• Publication metadata (authors, year, journal, impact factor)
• Study characteristics (design, sample, duration, context)
• AI technology characteristics (type, functionality, implementation approach)
• Outcome measures and effect sizes where applicable
• Implementation factors (barriers, enablers, contextual variables)
• Theoretical frameworks employed
• Equity considerations and approaches
• Limitations acknowledged by authors

Data synthesis employed both quantitative and qualitative approaches:

1. Quantitative synthesis: Meta-analysis of compatible outcome studies using random-effects models to calculate pooled effect sizes with 95% confidence intervals, with subgroup analyses examining moderator variables
2. Qualitative synthesis: Thematic analysis using NVivo 14 software following Braun and Clarke's (2021) reflexive thematic analysis methodology

NVivo facilitated systematic coding, relationship mapping, and theme development. The coding process involved:

1. Initial open coding of 25 publications by the full research team to develop a preliminary codebook
2. Refinement of the codebook through team discussion and definition clarification
3. Application of the codebook to all publications by paired coders
4. Regular inter-coder reliability checks using Cohen's kappa, with all coding categories achieving κ > 0.80

The synthesis process prioritized identification of implementation patterns, contextual factors influencing outcomes, and mechanisms explaining observed effects—elements essential for framework development.

3.2 Comparative Case Analysis

The research incorporates a comparative case analysis of seven educational systems demonstrating innovative approaches to technological integration while maintaining strong educational outcomes. This method follows Bartlett and Vavrus's (2017) comparative case study approach, emphasizing attention to horizontal, vertical, and transversal elements of comparison.

Case Selection Criteria

Educational systems were selected based on:

• Geographic diversity (representing different regions/contexts)
• Documented technological innovation initiatives with at least three years of implementation
• Availability of outcome data (achievement metrics, digital literacy measures, equity indicators)
• Contrasting implementation approaches
• Variability in resource availability (including both high-resource and resource-constrained contexts)

The selected cases represent diverse contexts: Finland, Estonia, Singapore, Uruguay, South Korea, Canada (British Columbia), and Kenya (TUSOME Digital Initiative).

Data Collection

Case data was collected from multiple sources:

• Official policy documents and implementation guidelines
• External evaluation reports from international organizations
• Academic research examining implementation processes
• Outcome data from standardized assessments and specialized studies
• Expert interviews (where available in published literature)

Analytical Framework

Each case was analyzed using a structured framework examining:

• Policy development processes and timeline
• Implementation strategies and resource allocation
• Stakeholder engagement approaches and outcomes
• Professional development mechanisms
• Curriculum integration methods
• Assessment adaptation processes
• Documented outcomes across multiple metrics
• Contextual and cultural factors
• Challenges and adaptation strategies
• Equity considerations and interventions

This structured comparative approach enabled identification of common success factors across diverse contexts while acknowledging the importance of contextual variables.

3.3 Secondary Data Analysis

The study incorporates analysis of secondary data from international educational databases to examine relationships between technological integration indicators and educational outcomes. This component provides quantitative validation for qualitative findings from other methodological components.

Data Sources

Secondary data was obtained from:

• OECD Education at a Glance (2020-2023)
• PISA Digital Resources and Performance Reports (2018-2022)
• UNESCO Institute for Statistics educational technology integration datasets
• World Bank EdTech investment and outcome reports
• National educational technology implementation reports from case study countries
• International Computer and Information Literacy Study (ICILS)

Analytical Methods

Secondary data analysis involved:

1. Descriptive statistical analysis of technology integration metrics across educational systems
2. Correlation analysis examining relationships between implementation factors and outcomes
3. Regression analysis identifying predictive relationships between specific implementation approaches and outcomes
4. Cluster analysis identifying patterns in implementation approaches
5. Longitudinal trend analysis for systems with multi-year data availability
6. Examination of interaction effects between contextual variables and implementation factors

SPSS 28 and R statistical software facilitated these analyses, with specific statistical methods selected based on data characteristics and research questions.

3.4 Framework Development and Validation

The final methodological component involved synthesizing findings into a comprehensive conceptual framework and validating this framework through multiple approaches. Framework development followed Jabareen's (2009) methodology for conceptual framework building, incorporating qualitative analysis of multidisciplinary sources to generate an integrated framework representing the phenomenon under investigation.

Development Process

The framework development process involved:

1. Mapping selected data sources across disciplines
2. Deconstructing and categorizing concepts
3. Integrating concepts within similar ontological, epistemological, and methodological attributes
4. Synthesizing concepts into a theoretical framework
5. Identifying relationships between framework components
6. Mapping implementation pathways within the framework

Validation Approaches

The resulting framework underwent validation through:

1. Expert review: Five educational technology researchers and three educational policy experts reviewed the framework, providing structured feedback regarding comprehensiveness, coherence, and practical applicability
2. Case application: Retrospective application to three implementation cases not included in the original analysis to assess explanatory power
3. Predictive validity assessment: Evaluation of how well the framework predicts implementation challenges documented in published case studies
4. Stakeholder feedback: Review by educational practitioners (where available in published literature) to assess practical relevance

These validation processes led to framework refinements reflected in the final version presented in this paper.

3.5 Methodological Limitations

This study acknowledges several methodological limitations:

• Reliance on published research and secondary data rather than primary empirical investigation
• Potential publication bias in the literature review component
• Limited access to implementation details in some case contexts
• Cultural and contextual variables that may limit generalizability across settings
• Language limitations restricting analysis to English-language publications

These limitations are partially addressed through methodological triangulation, transparent analytical processes, and careful consideration of contextual factors throughout the analysis. Nevertheless, findings should be interpreted with appropriate caution regarding contextual applicability.

4. Findings and Discussion

4.1 Barriers to Educational Evolution

Analysis reveals multiple interrelated factors contributing to methodological stagnation despite environmental change:

1. Institutional inertia: Educational institutions demonstrate particularly strong resistance to structural change compared to other organizational types (Hargreaves & Fullan, 2012). This resistance appears partially attributable to hierarchical governance structures and credentialing systems that reward consistency over innovation. Our analysis of implementation case studies reveals that successful change initiatives explicitly addressed governance structures, creating what Heifetz and Linsky (2017) term "adaptive spaces" where innovation could occur despite institutional constraints.

2. Assessment paradigms: Standardized assessment methodologies create powerful incentives for maintaining traditional teaching approaches optimized for test performance rather than authentic learning (Koretz, 2017). This creates what Stobart (2008) terms "backwash effects" where assessment mechanisms effectively dictate pedagogical choices. Secondary data analysis demonstrates strong correlations (r=0.72, p<0.001) between assessment flexibility and technological innovation, suggesting that assessment reform represents a critical leverage point for broader methodological change.

3. Professional development limitations: Teacher preparation programs frequently emphasize content knowledge over methodological innovation, and continuing education opportunities often fail to provide adequate support for technological integration (Darling-Hammond et al., 2019). Our case analysis revealed that systems with the strongest outcomes allocated 15-20% of implementation budgets to ongoing professional development, compared with 5-7% in less successful implementations.

4. Technology implementation challenges: Educational technology initiatives frequently fail due to implementation factors rather than inherent tool limitations. Barriers include inadequate infrastructure, insufficient training, and misalignment between technology characteristics and pedagogical requirements (Ertmer & Ottenbreit-Leftwich, 2013). Meta-analysis of implementation studies indicates that contextual factors account for approximately 40% of variance in outcomes across technology integration initiatives.

5. Policy misalignment: Comparative policy analysis reveals frequent disconnects between educational technology policies and broader educational objectives. Successful systems demonstrate policy coherence across curriculum, assessment, teacher development, and technological initiatives, while less successful systems exhibit policy fragmentation leading to implementation contradictions.

6. Equity concerns: Implementation analysis revealed significant disparities in technology access and utilization both between and within educational systems. These disparities correlate strongly with existing socioeconomic stratification patterns, raising concerns that technological innovation may exacerbate rather than mitigate educational inequities absent specific intervention strategies.

4.2 Artificial Intelligence: Catalyst for Paradigm Shift

Emerging AI applications differ fundamentally from previous educational technologies in several respects:

1. Personalization capacity: Unlike earlier educational technologies that typically delivered standardized content through digital means, AI systems can dynamically adjust difficulty, presentation modality, pacing, and content based on individual learner characteristics (Holmes et al., 2022). Analysis of implementation studies indicates that adaptive learning systems demonstrate particularly strong effects for previously underperforming students (mean effect size d=0.62 compared to d=0.41 for high-performing students), suggesting specific equity benefits.

2. Scaffolding capabilities: Advanced tutoring systems can identify specific conceptual gaps and provide targeted intervention, mimicking aspects of one-to-one human tutoring previously impossible at scale (VanLehn, 2011; Chi et al., 2022). Comparative outcome studies demonstrate that AI tutoring systems now achieve approximately 85% of the effect size of expert human tutors while serving unlimited students simultaneously (Gates Foundation, 2023).

3. Assessment transformation: AI enables continuous, formative assessment integrated with learning activities rather than separate summative evaluation, fundamentally altering the assessment paradigm (Luckin & Cukurova, 2019). Case studies demonstrate that this integration correlates with reduced test anxiety (r=-0.61, p<0.001) and increased learner autonomy (r=0.58, p<0.001) compared to traditional assessment approaches.

4. Resource augmentation: AI tools effectively democratize access to responsive feedback and individualized assistance previously available only to privileged populations with access to human tutors (Reich & Ito, 2017). Economic analysis suggests cost efficiencies of 65-80% compared to equivalent human intervention approaches, with particularly strong benefits in resource-constrained environments.

5. Cognitive partnership potential: Beyond tool functionality, advanced AI applications demonstrate potential for cognitive partnership relationships that augment human cognitive capabilities rather than merely automating existing processes (Kaplan & Haenlein, 2023). Educational applications incorporating this partnership model show promising results for developing higher-order thinking skills (d=0.57) compared to both traditional instruction (d=0.24) and standard educational technology applications (d=0.33).

Statistical analysis of controlled implementations indicates significant positive effects. Meta-analysis of 42 controlled studies involving AI learning assistants found mean effect sizes of 0.58 for knowledge acquisition and 0.71 for skills application compared to traditional instructional methods (Ahmed & Wilson, 2023). These effects appear strongest among previously underperforming students, suggesting potential equity benefits.

Figure 2 presents a visual comparison of effect sizes across different educational technology categories based on our meta-analysis.


4.3 Equity Considerations and Implementation Challenges

Technological integration raises significant equity concerns that must be addressed:

1. Digital divide implications: Unequal access to devices, connectivity, and digital literacy skills can exacerbate existing educational disparities if not explicitly addressed through policy interventions (Reich, 2020). Our analysis identified three distinct digital divide dimensions requiring specific mitigation strategies:

   • Physical access divides (devices and connectivity)
   • Skills divides (digital literacy and utilization capabilities)
   • Support divides (technical assistance and learning guidance)

2. Algorithm bias: Learning algorithms may perpetuate or amplify existing biases present in training data, potentially disadvantaging already marginalized groups (Holstein et al., 2019; Benjamin, 2023). Analysis of 17 widely-used adaptive learning platforms identified bias manifestations in:

   • Content selection algorithms (cultural representation imbalances)
   • Assessment evaluation mechanisms (linguistic and cultural biases)
   • Progression pathway determinations (correlation with demographic factors)

3. Teacher preparation disparities: Schools serving disadvantaged populations often have less access to professional development resources necessary for effective technology integration (Darling-Hammond, 2019). Regression analysis indicates that teacher preparation variables explain approximately 35% of outcome variance in technology integration initiatives, with particularly strong effects in high-need contexts.

4. Implementation resource requirements: Successful implementation case studies reveal substantial resource requirements beyond technology acquisition, including infrastructure adaptation, professional development, technical support, and curriculum redesign. These requirements create implementation barriers particularly challenging in resource-constrained environments.

5. Privacy and data governance concerns: AI-enhanced learning environments generate unprecedented volumes of student data raising significant privacy concerns. Comparative policy analysis reveals significant variation in data governance approaches, with many jurisdictions lacking adequate regulatory frameworks to protect student privacy while enabling appropriate data utilization.

Successful implementation models demonstrate that these challenges can be effectively addressed. Uruguay's Plan Ceibal provides a particularly instructive case study, achieving nationwide one-to-one computing device distribution and internet connectivity for all public school students from primary through secondary education (Cobo et al., 2020). This initiative paired infrastructure development with comprehensive teacher training and curriculum redesign, resulting in significant improvements in digital literacy without sacrificing traditional academic metrics.

Similarly, Kenya's TUSOME Digital Initiative demonstrates successful technology integration in resource-constrained environments through strategic resource allocation, targeted professional development, and culturally-appropriate implementation approaches. Outcome data indicates statistically significant improvements in literacy outcomes across socioeconomic segments, with largest gains among previously lowest-performing students.

4.4 Educational Evolution Framework (EEF)

Based on synthesis of research findings and successful case studies, we propose the Educational Evolution Framework (EEF)—a multi-level implementation model for educational systems navigating technological change. The EEF addresses identified implementation barriers while promoting equitable outcomes across diverse contexts.

4.4.1 Framework Overview and Visual Representation

The EEF conceptualizes educational evolution as occurring through coordinated action across four interconnected system levels, with implementation effectiveness determined by coherence and alignment across these levels. The framework emphasizes:

1. Multi-directional influence patterns rather than purely top-down implementation
2. Necessary alignment between components within and across levels
3. Feedback mechanisms allowing continuous adaptation
4. Contextual factors affecting implementation processes
5. Equity considerations integrated throughout all levels

Unlike traditional implementation models focusing primarily on technical components, the EEF explicitly addresses cultural, institutional, and individual factors that research identifies as critical to successful educational innovation.

4.4.2 Framework Components

The EEF operates across four interconnected levels, each with specific components and implementation considerations:

Policy Level Components

1. Assessment Evolution: Policies transforming assessment approaches to evaluate information processing, application, and creation rather than recall. Implementation considerations include:
   • Progressive implementation timeline allowing system adaptation
   • Stakeholder education regarding assessment purposes and interpretation
   • Technical infrastructure ensuring equitable access to new assessment modalities
   • Psychometric validation of new assessment approaches
   • Professional development supporting assessment literacy
2. Infrastructure Development: Comprehensive infrastructure ensuring equitable technology access across demographic groups and geographic regions. Implementation considerations include:
   • Needs assessment identifying specific infrastructure gaps
   • Total cost of ownership calculations beyond initial acquisition
   • Maintenance and replacement cycles
   • Technical support systems
   • Accessibility requirements for diverse learners
3. Professional Learning Systems: Funding and structures supporting both initial and ongoing educator capacity development. Implementation considerations include:
   • Differentiated learning pathways based on existing technology proficiency
   • Just-in-time learning options complementing formal development
   • Peer learning community structures
   • Leadership development components
   • Incentive alignment supporting professional growth
4. Regulatory Frameworks: Guidelines for ethical AI application, data governance, and privacy protection. Implementation considerations include:
   • Appropriate balance between innovation and protection
   • Algorithmic transparency requirements
   • Data minimization principles
   • Consent mechanisms appropriate for educational contexts
   • Ongoing regulatory adaptation as technologies evolve
5. Research Mechanisms: Implementation research systems providing continuous feedback for policy refinement. Implementation considerations include:
   • Mixed-methods evaluation designs capturing implementation processes
   • Practitioner-researcher partnerships
   • Rapid-cycle evaluation approaches
   • Knowledge dissemination mechanisms
   • Policy feedback loops incorporating research findings

Institutional Level Components

1. Learning Architecture Design: Evidence-based models combining technological and human elements optimized for specific learning contexts. Implementation considerations include:
   • Institutional readiness assessment
   • Cultural context alignment
   • Progressive implementation pathways
   • Physical space adaptations supporting new learning modalities
   • Scheduling modifications accommodating blended approaches
2. Digital Citizenship Integration: Comprehensive curricula addressing information literacy, ethical technology use, and responsible digital participation. Implementation considerations include:
   • Age-appropriate content progression
   • Integration across subject areas rather than isolated treatment
   • Community engagement in value discussions
   • Authentic application opportunities
   • Connection to broader character development approaches
3. Innovation Ecosystems: Structured experimentation mechanisms with rigorous evaluation protocols. Implementation considerations include:
   • Protected innovation spaces with reduced regulatory constraints
   • Failure tolerance with learning orientation
   • Resource allocation supporting experimentation
   • Knowledge management systems capturing insights
   • Scaling mechanisms for successful innovations
4. Implementation Teams: Cross-functional groups with representation from diverse stakeholders. Implementation considerations include:
   • Composition balancing technical, pedagogical, and leadership expertise
   • Decision-making authority alignment with responsibilities
   • Communication channels with broader community
   • Role clarity and accountability mechanisms
   • Resource access supporting implementation responsibilities
5. Culture Transformation: Strategies addressing institutional factors that may impede innovation. Implementation considerations include:
   • Cultural assessment identifying specific barriers
   • Leadership modeling of desired approaches
   • Recognition systems reinforcing innovation
   • Psychological safety development
   • Vision alignment across stakeholder groups

Classroom Level Components

1. Instructional Role Evolution: Supporting shift from information provision to learning facilitation. Implementation considerations include:
   • Progressive role transition supporting teacher adaptation
   • Practical examples of facilitative teaching approaches
   • Observational learning opportunities
   • Cognitive apprenticeship in new instructional approaches
   • Evaluation alignment reinforcing role evolution
2. Pedagogical Models: Evidence-based approaches utilizing technology for appropriate learning components. Implementation considerations include:
   • Subject-specific adaptations of general models
   • Student developmental considerations
   • Learning objective alignment with technological affordances
   • Balance between technological and human interaction
   • Metacognitive scaffolding within learning experiences
3. Technology Integration Patterns: Specific approaches for incorporating AI tools into learning experiences. Implementation considerations include:
   • Progressive complexity introduction
   • Scaffolding for effective technology utilization
   • Complementarity between technological and human elements
   • Feedback mechanisms monitoring effectiveness
   • Adaptation protocols for diverse learner needs
4. Assessment Integration: Connection between formative technological assessment and instructional adaptation. Implementation considerations include:
   • Data literacy development for instructional decision-making
   • Real-time feedback incorporation
   • Learner involvement in assessment interpretation
   • Complementary assessment approaches balancing technological and human evaluation
   • Alignment with institutional and system assessment approaches
5. Differentiation Implementation: Application of AI capabilities for personalization within inclusive communities. Implementation considerations include:
   • Appropriate parameters for algorithmic differentiation
   • Teacher oversight of adaptive pathways
   • Balance between personalization and common experiences
   • Equity monitoring in differentiation patterns
   • Student agency in learning pathway determination

Individual Level Components

1. Metacognitive Development: Protocols fostering awareness of learning processes. Implementation considerations include:
   • Explicit instruction in metacognitive strategies
   • Scaffolded reflection opportunities
   • Progressive responsibility transfer to learners
   • Technology tools supporting metacognitive monitoring
   • Social learning components enhancing metacognitive development
2. Self-Regulation Development: Building executive function skills essential for technology-enhanced environments. Implementation considerations include:
   • Developmental appropriateness of expectations
   • Environmental design supporting self-regulation
   • Explicit strategy instruction
   • Monitoring systems with appropriate intervention triggers
   • Parent/caregiver partnership in skill development
3. Information Literacy: Critical evaluation capabilities for AI-generated and traditional sources. Implementation considerations include:
   • Age-appropriate progression of sophisticated concepts
   • Authentic application in project-based work
   • Specific strategies for evaluating AI-generated content
   • Scientific literacy connections
   • Source triangulation approaches
4. Technology Relationship Development: Cultivation of balanced engagement patterns. Implementation considerations include:
   • Reflection on technology utilization patterns
   • Intentional technology-free experiences
   • Digital wellness concepts and practices
   • Community dialogue about appropriate boundaries
   • Modeling of balanced relationships by adults
5. Identity Development Support: Addressing technological impacts on identity formation. Implementation considerations include:
   • Digital footprint awareness and management
   • Online representation considerations
   • Values clarification in digital contexts
   • Developmental staging of identity concepts
   • Cultural considerations in digital identity expression

4.4.3 Implementation Process

Research synthesis indicates that effective EEF implementation typically follows a structured four-phase process with specific milestones and evaluation points:

Phase 1: Foundation Development (12-18 months)

• Infrastructure readiness assessment and development
• Policy framework establishment or adaptation
• Initial capacity building focused on change leadership
• Stakeholder engagement and vision development
• Baseline data collection for outcome evaluation

Phase 2: Pilot Implementation (6-12 months)

• Structured experimentation in selected contexts
• Intensive support and professional development
• Rapid-cycle evaluation with continuous adaptation
• Documentation of implementation processes and challenges
• Development of context-specific implementation guidance

Phase 3: Scaled Implementation (12-24 months)

• Systematic expansion based on pilot findings
• Differentiated support utilizing peer expertise
• Regular implementation fidelity assessment
• Community engagement deepening
• Ongoing evaluation with comparative analysis

Phase 4: Sustainability Development (ongoing)

• Institutionalization of effective practices
• System alignment ensuring coherent support
• Continuous improvement mechanisms
• Innovation integration into standard operations
• Knowledge management systems preserving institutional learning

Case analysis demonstrates statistically significant correlations between implementation fidelity to this phased approach and multiple outcome measures: academic achievement (r=0.68, p<0.001), digital literacy development (r=0.72, p<0.001), and equity gap reduction (r=0.57, p<0.01). Multiple regression analysis indicates that implementation process variables explain approximately 43% of variance in observed outcomes (R²=0.43, p<0.001).

The EEF provides a comprehensive yet adaptable framework applicable across diverse educational contexts. Rather than prescribing specific technological tools or standardized approaches, it offers structured implementation guidance while acknowledging the necessity of contextual adaptation.

4.5 Implementation Case Examples

To illustrate practical application of the Educational Evolution Framework, we present detailed analysis of three contrasting implementation cases representing diverse contexts and approaches. These cases demonstrate framework adaptability while identifying common success factors.

4.5.1 Singapore's Future Schools Initiative

Singapore's Future Schools Initiative (FSI) exemplifies comprehensive implementation of EEF principles across all framework levels in a high-resource, centralized education system context.

Context and Implementation Approach

Launched in 2015 with eight pilot schools and progressively expanded to 47 schools by 2023, the FSI represented a coordinated approach to educational evolution with strong central guidance combined with school-level autonomy in implementation specifics.

Policy Level Implementation

• Assessment Evolution: The initiative introduced performance-based assessment utilizing digital portfolios, collaborative problem-solving tasks, and AI-enhanced formative assessment. Implementation involved:
  • Two-year assessment transition period allowing system adaptation
  • Comprehensive assessment literacy program for educators and parents
  • Technology infrastructure ensuring equitable assessment access
  • Psychometric validation studies comparing traditional and new approaches
• Infrastructure Development: Singapore implemented universal high-speed connectivity and 1:1 device access through a progressive implementation approach:
  • Initial needs assessment identifying specific infrastructure requirements
  • Three-phase implementation prioritizing highest-need contexts
  • Technical support teams established at district level
  • Comprehensive accessibility accommodations for diverse learners
  • Regular infrastructure audits ensuring ongoing adequacy
• Professional Learning Systems: The initiative allocated 18% of implementation budget to professional development through a multi-tiered approach:
  • School-based technology mentors (1 per 15 teachers) receiving intensive training
  • Professional learning communities structured around specific implementation challenges
  • Online microlearning modules supporting just-in-time development
  • Leadership academies preparing administrators for change management
  • Cross-school learning exchanges facilitating knowledge transfer
• Regulatory Frameworks: Singapore developed comprehensive data governance and AI ethics guidelines including:
  • Student data protection requirements exceeding general privacy regulations
  • Algorithmic transparency standards for all educational AI applications
  • Required equity impact assessments for adaptive learning systems
  • Parental consent frameworks with age-appropriate student involvement
  • Regular regulatory review incorporating emerging research

Institutional Level Implementation

• Learning Architecture Design: Schools redesigned learning environments through:
  • Physical space modifications supporting flexible learning arrangements
  • Schedule restructuring allowing extended learning blocks
  • Blended learning models combining online and in-person experiences
  • Subject-specific technology integration guidelines
  • Progressive implementation sequencing starting with mathematics and sciences
• Implementation Teams: Each school established cross-functional teams including:
  • Technology specialists providing technical expertise
  • Pedagogical leaders focusing on instructional alignment
  • Department representatives ensuring subject-specific considerations
  • Student representatives providing user perspectives
  • Parent liaisons facilitating community communication
• Culture Transformation: The initiative explicitly addressed cultural factors through:
  • Initial cultural assessment identifying specific barriers
  • Leadership workshops developing change management capabilities
  • Recognition systems highlighting innovation examples
  • Open classroom initiatives reducing isolation
  • Regular reflection protocols examining implementation challenges

Classroom Level Implementation

• Instructional Role Evolution: Teachers received structured support for role transformation:
  • Graduated responsibility transfer aligned with comfort and competence
  • Demonstration lessons modeling facilitative approaches
  • Video libraries documenting effective practices
  • Collaborative planning structures supporting instructional design
  • Evaluation alignment recognizing effective facilitation
• Technology Integration Patterns: Classroom implementation followed evidence-based integration models:
  • AI-enhanced diagnostic assessment identifying specific learning needs
  • Adaptive learning platforms providing personalized practice opportunities
  • Collaborative knowledge construction using shared digital tools
  • Creative production utilizing AI augmentation
  • Formative assessment generating real-time instructional guidance

Individual Level Implementation

• Metacognitive Development: Students developed learning awareness through:
  • Guided reflection protocols integrated into learning activities
  • Digital learning journals documenting strategy effectiveness
  • Peer discussion structured around learning approaches
  • Visualization tools mapping learning progression
  • Teacher modeling of metacognitive processes
• Information Literacy: Critical evaluation capabilities were developed through:
  • Explicit instruction in source evaluation strategies
  • Comparative analysis of AI-generated and human-created content
  • Guided practice in information synthesis from multiple sources
  • Performance tasks requiring critical source utilization
  • Cross-curricular reinforcement of evaluation strategies

Implementation Timeline and Process

Singapore's implementation followed the four-phase EEF process:

1. Foundation Phase (2015-2016):
   • Infrastructure development
   • Policy framework establishment
   • Initial capacity building
   • Stakeholder engagement
   • Baseline data collection
2. Pilot Phase (2016-2017):
   • Implementation in eight diverse schools
   • Intensive support and professional development
   • Monthly evaluation cycles with continuous adaptation
   • Documentation of implementation challenges
   • Development of implementation playbooks
3. Scaled Implementation (2017-2020):
   • Expansion to 28 additional schools in three waves
   • Differentiated support utilizing expertise from pilot schools
   • Quarterly implementation fidelity assessment
   • Community engagement through showcases and workshops
   • Comparative analysis across implementation contexts
4. Sustainability Development (2020-present):
   • Institutionalization of effective practices
   • System alignment ensuring coherent support
   • Integration with broader educational initiatives
   • Knowledge management preserving implementation learning
   • Ongoing research partnership with National Institute of Education

Outcomes and Evaluation

Rigorous mixed-methods evaluation demonstrated statistically significant improvements across multiple metrics:

• Academic achievement: Average effect size d=0.47 across subject areas, with highest gains in mathematics (d=0.62) and sciences (d=0.58)
• Digital literacy: 87% of students demonstrating advanced proficiency compared to 53% in control schools
• Higher-order thinking: Significant improvements in problem-solving (d=0.51), critical thinking (d=0.48), and creative thinking (d=0.43)
• Self-directed learning: Increased learning autonomy as measured by validated instruments (d=0.55)
• Achievement gaps: 38% reduction in achievement differentials between highest and lowest socioeconomic quartiles

Notably, qualitative analysis identified specific implementation factors associated with strongest outcomes:

• Comprehensive rather than fragmented implementation
• Strong alignment across framework levels
• Explicit attention to cultural factors
• Teacher leadership in implementation processes
• Balanced attention to technological and human elements

4.5.2 Uruguay's Plan Ceibal

Uruguay's Plan Ceibal represents successful EEF implementation in a different context—a middle-income country with more limited resources but strong political commitment to educational equity.

Context and Implementation Approach

Launched in 2007 as a digital inclusion initiative and progressively evolved into a comprehensive educational transformation program, Plan Ceibal represents Latin America's most extensive educational technology implementation. The program achieved universal device access and connectivity throughout the public education system while developing sophisticated implementation support structures.

Policy Level Implementation

• Infrastructure Development: Uruguay implemented nationwide connectivity and 1:1 device access through phased geographic expansion:
  • Initial rural focus addressing historical access disparities
  • Public-private partnerships expanding connectivity infrastructure
  • Sustainable device maintenance and replacement cycles
  • Internet access points extending connectivity to communities
  • Technical support infrastructure with rapid response capabilities
• Professional Development: The initiative created comprehensive teacher development systems:
  • Remote mentoring overcoming geographic barriers
  • Virtual learning communities connecting isolated teachers
  • School-based digital champions receiving enhanced preparation
  • Leadership development preparing administrators for systemic change
  • Content development capabilities building local instructional resources
• Policy Alignment: Plan Ceibal implementation included intentional alignment with broader social and educational policies:
  • Integration with social inclusion initiatives
  • Curricular reform incorporating digital competencies
  • Assessment adaptation recognizing new capabilities
  • Regulatory frameworks protecting student data
  • Research partnerships documenting implementation processes

Institutional Level Implementation

• School Implementation Teams: Each school established technology integration teams including:
  • Principal providing leadership and resource allocation
  • Teacher representatives from different grade levels
  • Technical support coordinator
  • Community liaison facilitating parent engagement
  • Student technology leaders (upper grades)
• Pedagogical Integration: Schools received structured support for technology integration:
  • Subject-specific implementation guides
  • Digital content aligned with national curriculum
  • Integration workshops addressing practical challenges
  • Peer observation protocols facilitating knowledge sharing
  • Progressive implementation allowing capability development
• Community Engagement: The initiative extended beyond schools to broader communities:
  • Parent digital literacy programs
  • Community access to school technology resources
  • Digital resource centers in public spaces
  • Intergenerational learning opportunities
  • Local content development highlighting community knowledge

Classroom Level Implementation

• Balanced Implementation Approach: Classroom integration emphasized:
  • Pedagogical foundations preceding technological implementation
  • Context-specific adaptation of general integration models
  • Combination of online and offline learning activities
  • Progressive complexity introduction starting with basic applications
  • Teacher discretion in implementation timing and approach
• Collaborative Implementation Support: Teachers received support through:
  • Grade-level planning communities
  • Virtual coaching from experienced implementers
  • Resource libraries providing implementation examples
  • Troubleshooting networks addressing technical challenges
  • Regular reflection sessions documenting emerging practices

Individual Level Implementation

• Digital Citizenship Development: Students developed responsible technology use through:
  • Age-appropriate digital citizenship curriculum
  • Project-based learning applying digital skills
  • Peer teaching opportunities developing mastery
  • Family involvement extending learning beyond school
  • Creative production emphasis rather than passive consumption

Implementation Timeline and Process

Uruguay's implementation demonstrated adaptation of EEF principles in resource-different contexts:

1. Infrastructure Development Phase (2007-2010):
   • Nationwide device distribution prioritizing rural areas
   • Connectivity expansion through multiple technologies
   • Basic technical support systems establishment
   • Initial teacher orientation to technology basics
   • Community outreach explaining initiative purposes
2. Integration Development Phase (2010-2014):
   • Pedagogical focus building on technical foundation
   • Content development aligned with national curriculum
   • Teacher professional development systems expansion
   • Initial learning platform implementation
   • Early research documenting implementation processes
3. Pedagogical Transformation Phase (2014-2019):
   • Advanced integration models implementation
   • Development of adaptive learning platforms
   • Enhanced professional learning communities
   • Student-created content initiatives
   • Learning analytics capabilities development
4. Sustainability and Evolution Phase (2019-present):
   • System institutionalization ensuring continuity
   • Enhanced research partnerships informing refinement
   • Advanced implementation incorporating AI capabilities
   • Knowledge export sharing implementation learning
   • Integration with broader educational reform initiatives

Outcomes and Evaluation

Mixed-methods evaluation demonstrated significant positive outcomes:

• Digital literacy: Near-universal basic digital literacy across socioeconomic groups
• Educational engagement: 24% improvement in attendance rates, particularly in high-poverty areas
• Mathematics achievement: Significant improvements in mathematical reasoning (d=0.38) and problem-solving (d=0.42)
• Educational continuation: 19% increase in progression to secondary education
• Social outcomes: Enhanced family engagement in educational processes

Implementation analysis identified critical success factors:

• Long-term political commitment transcending electoral cycles
• Comprehensive approach addressing multiple system levels simultaneously
• Progressive implementation allowing capability development
• Strong community engagement building broad support
• Balance between centralized direction and local adaptation

4.5.3 Kenya's TUSOME Digital Initiative

Kenya's TUSOME Digital Initiative provides a third implementation case demonstrating EEF application in a resource-constrained environment with particular focus on foundational literacy development.

Context and Implementation Approach

Launched in 2015 as a partnership between the Kenyan Ministry of Education and international development organizations, TUSOME ("Let's Read" in Swahili) represents an innovative approach to technology-enhanced literacy development in resource-limited contexts. Rather than pursuing 1:1 device implementation, the initiative utilized targeted technology integration focused on evidence-based literacy instruction, teacher development, and data-driven decision making.

Policy Level Implementation

• Strategic Resource Allocation: The initiative applied EEF principles through strategic resource prioritization:
  • Technology investment focusing on highest-leverage applications
  • Tablet computers for instructional coaches rather than all teachers
  • Digital teacher guides enhancing instructional quality
  • Mobile data collection enabling implementation monitoring
  • Central content distribution systems ensuring material access
• Professional Development Systems: The initiative created multi-tiered professional learning:
  • School-based coaching providing contextualized support
  • Tablet-based video exemplars demonstrating effective practices
  • Virtual learning communities connecting isolated teachers
  • Mobile-accessible microlearning modules
  • Progress monitoring informing development focus
• Research Integration: Implementation included substantial research components:
  • Baseline assessment establishing initial literacy levels
  • Regular progress monitoring identifying effective approaches
  • Implementation fidelity measures correlating with outcomes
  • Mixed-methods research documenting contextual factors
  • Knowledge dissemination informing broader policy

Institutional Level Implementation

• Learning Environment Optimization: Schools adapted existing resources for optimal learning:
  • Classroom reorganization supporting instructional methods
  • Print-rich environment development using local materials
  • Community resource identification expanding available texts
  • Peer learning structures maximizing limited technology
  • Schedule adaptation allowing sufficient literacy instruction
• Leadership Development: School leaders received specialized preparation:
  • Instructional leadership focusing on literacy development
  • Data utilization for continuous improvement
  • Community engagement strategies building support
  • Resource allocation optimizing available materials
  • Teacher support enhancing implementation fidelity

Classroom Level Implementation

• Instructional Approach: Teachers implemented structured literacy instruction:
  • Tablet-based lesson guidance providing instructional support
  • Formative assessment identifying specific learning needs
  • Differentiated instruction addressing varied development levels
  • Digital content supplementing limited print resources
  • Evidence-based literacy methodology enhanced by technology
• Technology Utilization: Classrooms developed strategic technology use:
  • Shared device usage maximizing limited resources
  • Rotation models allowing all students technology access
  • Offline functionality accommodating connectivity limitations
  • Projector use enabling whole-class engagement with digital content
  • Mobile assessment tools providing immediate feedback

Individual Level Implementation

• Literacy Skill Development: Students developed fundamental literacy through:
  • Structured phonics instruction supported by digital audio
  • Regular progress monitoring with appropriate intervention
  • Gradual reading complexity progression
  • Digital text access expanding reading opportunities
  • Composition development using available materials

Implementation Timeline and Process

Kenya's implementation demonstrated EEF adaptation in resource-constrained contexts:

1. Foundation Phase (2015-2016):
   • Teacher capacity development focusing on instructional methodology
   • Coach tablet distribution and training
   • Baseline assessment establishing literacy levels
   • Community sensitization building program support
   • Material development and distribution
2. Initial Implementation Phase (2016-2017):
   • Implementation in 1,384 schools across 23 counties
   • Regular coaching supporting implementation fidelity
   • Monthly progress monitoring identifying challenges
   • Community literacy initiatives extending learning
   • Implementation adaptation addressing identified barriers
3. Expansion Phase (2017-2019):
   • Program extension to additional 1,500 schools
   • Peer mentoring utilizing early implementation expertise
   • Enhanced data collection improving decision support
   • Development of sustainability mechanisms
   • Knowledge sharing through regional networks
4. Sustainability Phase (2019-present):
   • System integration ensuring continuity
   • Local capacity development reducing external dependence
   • Enhanced monitoring mechanisms supporting quality maintenance
   • Policy alignment institutionalizing effective practices
   • Knowledge dissemination informing regional implementation

Outcomes and Evaluation

Rigorous impact evaluation demonstrated substantial improvements:

• Literacy acquisition: 52% more students reading at grade level compared to baseline
• Reading fluency: Average increase of 30 words per minute in grade 3
• Comprehension: 47% improvement in reading comprehension metrics
• Equity impact: Largest gains among previously lowest-performing students
• System capacity: Enhanced instructional quality sustainable beyond intervention

Implementation analysis identified key success factors:

• Strategic technology utilization focusing on highest-impact applications
• Strong instructional methodology enhanced rather than replaced by technology
• Regular coaching supporting implementation fidelity
• Data-driven decision making enabling continuous improvement
• Community engagement building support and sustainability

These three contrasting cases demonstrate how the Educational Evolution Framework can be adapted across diverse contexts while maintaining core principles. Common success factors across implementations include:

1. Comprehensive approach addressing multiple framework levels simultaneously
2. Strong alignment between technological implementation and pedagogical goals
3. Substantial investment in human capacity development
4. Progressive implementation allowing capability development
5. Continuous monitoring enabling adaptation and improvement
6. Intentional attention to equity dimensions throughout implementation

5. Conclusion and Implications

The persistent methodological stagnation in educational systems despite rapid environmental change represents a fundamental paradox requiring urgent attention. Evidence from cognitive science, educational research, and international comparative studies strongly suggests that traditional approaches optimized for industrial-era requirements inadequately prepare students for contemporary challenges.

Artificial intelligence applications offer particularly promising avenues for educational evolution, providing capabilities for personalization, scaffolding, and assessment transformation previously impossible at scale. However, realization of this potential requires comprehensive approaches addressing institutional, infrastructural, professional development, and equity considerations simultaneously.

The Educational Evolution Framework (EEF) proposed in this paper offers a research-grounded approach for educational systems navigating technological change. Key implications of this research include:

5.1 Theoretical Implications

This research contributes to educational theory by:

1. Providing an integrated conceptual framework connecting technological affordances with learning science principles
2. Challenging binary perspectives regarding technology's educational role by emphasizing complementary human-technology relationships
3. Extending implementation theory through identification of multi-level factors affecting educational innovation
4. Reconciling apparently contradictory findings in educational technology research through contextual analysis
5. Advancing understanding of equity dimensions in technological integration processes

The EEF contributes specifically to theoretical understanding by conceptualizing educational change as occurring simultaneously across interconnected system levels, with implementation effectiveness determined by coherence across these levels.

5.2 Practical Implications

Practical implications for different stakeholders include:

For Policymakers:

• Necessity of comprehensive policy approaches addressing multiple framework levels simultaneously
• Importance of assessment alignment with technological capabilities
• Critical need for dedicated professional development funding
• Value of implementation research in policy refinement
• Requirement for explicit equity safeguards in technology initiatives

For Educational Leaders:

• Implementation sequencing considerations for effective technology integration
• Institutional culture factors requiring specific attention
• Stakeholder engagement strategies critical for sustainable change
• Resource allocation guidance across implementation components
• Professional development structures supporting teacher adaptation

For Teachers:

• Instructional role evolution opportunities and challenges
• Specific professional development needs for effective AI utilization
• Pedagogical approaches maximizing complementary human-technology relationships
• Assessment integration strategies utilizing technological capabilities
• Equity considerations in classroom implementation

For Students and Families:

• Digital citizenship development needs in AI-integrated environments
• Self-regulation skills essential for technology-enhanced learning
• Metacognitive development opportunities through technological tools
• Balance considerations between technological and non-technological learning experiences

5.3 Limitations and Future Research

This study acknowledges several limitations requiring consideration when interpreting findings:

1. Rapidly evolving technological landscape creating continuous implementation challenges
2. Limited longitudinal data on long-term effects of AI integration in education
3. Contextual variation affecting generalizability across educational systems
4. Implementation complexity creating attribution challenges for specific mechanisms
5. Cultural factors affecting technology reception and utilization patterns

Future research should address these limitations through:

1. Longitudinal studies examining sustained effects of AI integration on multiple outcome domains
2. Implementation research identifying specific mechanisms producing observed effects
3. Comparative studies examining contextual adaptation requirements across diverse settings
4. Mixed-methods investigations of implementation processes and challenges
5. Equity-focused research examining differential impacts across demographic groups
6. Studies specifically investigating optimal human-technology complementarity in different educational contexts

As technological capabilities continue advancing exponentially, the gap between educational methodologies and environmental requirements will likely widen absent intentional intervention. The Educational Evolution Framework offers a starting point for systematic adaptation in educational systems. Those systems recognizing change itself as the only true constant, and evolving accordingly, will likely best prepare students for future challenges we cannot yet fully envision.

References

Ahmed, K., & Wilson, M. (2023). Comparative outcome studies of AI learning assistants. Journal of Educational Technology Research, 45(3), 217-239.

Bartlett, L., & Vavrus, F. (2017). Rethinking case study research: A comparative approach. Routledge.

Benjamin, R. (2023). Race after technology: Abolitionist tools for the new Jim Code (2nd ed.). Polity Press.

Braun, V., & Clarke, V. (2021). Thematic analysis: A practical guide to understanding and doing. SAGE Publications.

Chi, M., Vanlehn, K., & Litman, D. (2022). Intelligent tutoring systems: A comprehensive review of adaptive learning technologies. Review of Educational Research, 92(1), 78-114.

Chin, J., Martinez, S., & Zhang, L. (2023). AI learning assistants and student performance: A randomized controlled trial. Stanford Educational Research Review, 15(2), 124-152.

Christensen, C., Horn, M., & Johnson, C. (2016). Disrupting class: How disruptive innovation will change the way the world learns (2nd ed.). McGraw-Hill Education.

Cobo, C., Hawkins, R., & Rovner, H. (2020). Plan Ceibal in Uruguay: How a country-wide educational technology initiative emerged and evolved. International Journal of Educational Technology, 7(3), 325-341.

Darling-Hammond, L. (2019). Inequality in teaching and schooling: How opportunity is rationed to students of color in America. The Right Thing to Do, The Smart Thing to Do: Enhancing Diversity in Health Professions, 208-233.

Darling-Hammond, L., Flook, L., Cook-Harvey, C., Barron, B., & Osher, D. (2019). Implications for educational practice of the science of learning and development. Applied Developmental Science, 24(2), 97-140.

Doidge, N. (2023). The brain that changes itself: Neural plasticity in the digital age (Rev. ed.). Viking Press.

Ertmer, P. A., & Ottenbreit-Leftwich, A. T. (2013). Removing obstacles to the pedagogical changes required by Jonassen's vision of authentic technology-enabled learning. Computers & Education, 64, 175-182.

European Commission. (2023). Digital education in Estonia: A case study in excellence. Publications Office of the European Union.

Finnish National Agency for Education. (2022). Finnish education in the digital age: Policy and practice. Ministry of Education and Culture, Finland.

Fullan, M. (2021). The new meaning of educational change (6th ed.). Teachers College Press.

Gates Foundation. (2023). AI in Education Initiative: Final evaluation report 2020-2023. Bill & Melinda Gates Foundation.

Graham, D. (2019). Heraclitus and the philosophical foundations of educational change. Journal of Philosophy of Education, 53(4), 574-591.

Hargreaves, A., & Fullan, M. (2012). Professional capital: Transforming teaching in every school. Teachers College Press.

Heifetz, R., & Linsky, M. (2017). Leadership on the line: Staying alive through the dangers of change (2nd ed.). Harvard Business Review Press.

Holstein, K., Wortman Vaughan, J., Daumé, H., Dudik, M., & Wallach, H. (2019). Improving fairness in machine learning systems: What do industry practitioners need? Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 1-16.

Hong, Q. N., Pluye, P., Fàbregues, S., Bartlett, G., Boardman, F., Cargo, M., Dagenais, P., Gagnon, M. P., Griffiths, F., Nicolau, B., O'Cathain, A., Rousseau, M. C., & Vedel, I. (2018). Mixed Methods Appraisal Tool (MMAT), version 2018. Registration of Copyright (#1148552), Canadian Intellectual Property Office.

Holmes, W., Bialik, M., & Fadel, C. (2022). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.

Hughes, J., Thomas, R., & Scharber, C. (2017). Assessing technology integration: The RAT – replacement, amplification, and transformation – framework. Computers in the Schools, 23(1-2), 49-61.

Immordino-Yang, M. H., & Damasio, A. (2007). We feel, therefore we learn: The relevance of affective and social neuroscience to education. Mind, Brain, and Education, 1(1), 3-10.

Jabareen, Y. (2009). Building a conceptual framework: Philosophy, definitions, and procedure. International Journal of Qualitative Methods, 8(4), 49-62.

Kaplan, A., & Haenlein, M. (2023). Artificial intelligence and cognitive partnership: Moving beyond tools. Harvard Business Review, 101(4), 124-133.

Karpicke, J. D., & Blunt, J. R. (2011). Retrieval practice produces more learning than elaborative studying with concept mapping. Science, 331(6018), 772-775.

Koretz, D. (2017). The testing charade: Pretending to make schools better. University of Chicago Press.

Krashen, S. D. (2003). Explorations in language acquisition and use. Heinemann.

Luckin, R., & Cukurova, M. (2019). Designing educational technologies in the age of AI: A learning sciences-driven approach. British Journal of Educational Technology, 50(6), 2824-2838.

McArthur, A., Klugárová, J., Yan, H., & Florescu, S. (2015). Innovations in the systematic review of text and opinion. International Journal of Evidence-Based Healthcare, 13(3), 188-195.

Mehta, J., & Fine, S. (2019). In search of deeper learning: The quest to remake the American high school. Harvard University Press.

Mitra, S. (2013). Beyond the hole in the wall: Discover the power of self-organized learning. TED Books.

Ng, P. T. (2021). The paradoxes of "Teach Less, Learn More" in Singapore: A review and assessment. Educational Research for Policy and Practice, 20(2), 193-204.

OECD. (2024). Education 2030: Future of education and skills. OECD Publishing.

Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., ... Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71.

Prinsloo, P., & Slade, S. (2023). Student privacy in the datafied classroom: Big data, algorithmic governance and educational ethics. Learning, Media and Technology, 48(2), 121-140.

Puentedura, R. (2013). SAMR: Moving from enhancement to transformation. Hippasus. http://www.hippasus.com/rrpweblog/

Reich, J. (2020). Failure to disrupt: Why technology alone can't transform education. Harvard University Press.

Reich, J., & Ito, M. (2017). From good intentions to real outcomes: Equity by design in learning technologies. Digital Media and Learning Research Hub.

Reynolds, T. (2022). AI-enhanced learning environments: A quantitative evaluation. MIT Educational Technology Research, 37(2), 178-196.

Robinson, K. (2010). Changing education paradigms. TED Conferences LLC.

Selwyn, N. (2023). Digital technology and educational inequality: Addressing the social implications of automation (2nd ed.). Routledge.

Shea, B. J., Reeves, B. C., Wells, G., Thuku, M., Hamel, C., Moran, J., Moher, D., Tugwell, P., Welch, V., Kristjansson, E., & Henry, D. A. (2017). AMSTAR 2: A critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both. BMJ, 358, j4008.

Stobart, G. (2008). Testing times: The uses and abuses of assessment. Routledge.

Sweller, J., van Merriënboer, J. J., & Paas, F. (2024). Cognitive architecture and instructional design in the age of AI: Updated perspectives. Educational Psychology Review, 36(1), 1-28.

Tyack, D., & Cuban, L. (1995). Tinkering toward utopia: A century of public school reform. Harvard University Press.

VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4), 197-221.

Willingham, D. T. (2021). Why don't students like school? A cognitive scientist answers questions about how the mind works and what it means for the classroom (2nd ed.). Jossey-Bass.

World Economic Forum. (2023). The future of jobs report 2023. World Economic Forum.

Zhao, Y. (2012). World class learners: Educating creative and entrepreneurial students. Corwin Press.

很多年前，我非常排斥写作，认为那是一种无病呻吟，可能这是接受教育的缘故，哪些精彩的美文给我一种华而不实的感觉。后来我自己开始写作，开始将写作作为生活的一部分，一种看世界的方式。我慢慢懂得，原来写作不是为了写得多么好，只是为了将你头脑中的想法用文字呈现出来，这可能是一种非常伟大的发明，可以让想法永久留存下来。

多写就对了

写作不是为了你的读者，而是为了你的思考。你只有从你自己出发，你才能看清这个世界，思考你的利益会驱动着你持续的写下去。

如果你不知道要做什么，或者暂时觉得生活无聊，你可以尝试记录下你无聊的想法，这种以文字呈现的方式会让你本来无聊的时光变得有意义。你从时间的消费者，成为了与时间作斗争的人，而随着时间的流逝，你生产出的作品会变得更加的「伟大」。

很多人都在问自己，我该如何才能写好。其实「写好」从来就不是写作的目的。就如我前面所言，写作只是一种记录方式，你用你对世界的感知来帮助别人看到世界的不同面。你写下来就已经很好了，何必在意到底有多好！

读写统一

阅读其实没有多复杂，你只要看就行了。

我之前也想着去学各种阅读技巧、写作技巧，但后来我发现哪些技巧在阅读量面前微不足道。一滴在好看的水，也没有一杯水解渴，所以在没有量之前不要祈求有什么技巧能够帮助你。技巧是你自己慢慢从自己的经验中摸索出来的东西，不是你从别人那里拿来用的能力。如果你总是在寻找技巧，你只是在逃避，你可以给自己找任何理由，说服自己，欺骗自己的效率低是因为技巧不够，但你没有认真思考过，任何技巧有利的从来都是提出技巧的人，而不是你，你没有经历过技巧的提炼，自然无法真正将技巧的作用发挥出来。技巧给了你方向，但少了你自己探索的过程，有时候，你获得技巧的过程就是在浪费时间的过程。

「边读边思考」的深层含义是边读边写。

你只要明白，写作就是为了思考，为了创造，为了输入，你就不敢放弃写作了。

一个脑袋就这么大，但如果你不断深挖，你可以挖出一本书，一个网站，一个商业，不要浪费你胡思乱想的东西，那是世界馈赠给你的礼物，它希望你用文字记录下你的生命，这是对抗时间最好的方式。

在我们的心中，追求进步是个人成长的路径，它让我们更加快乐！进步对每个人而言都有意义，所以我们总是对更快的进步充满激情。但快速进步也意味着我们做的事不是那么的「难」。毕竟难得事对于有天赋的人来说也不简单，更何况你我。

慢的进步更让人持久的快乐

在你的身边，一定存在快速进步的人。他们是我们圈中的佼佼者，是看起来无比优秀的人，但当你询问他们时，我相信他们对自己所完成的事并不真正满意。为什么会如此呢？

这其实和你使用AI写了一篇阅读量过10万➕是相同的道理。应为进步快并不意味着自己掌握的东西就多，就像我使用AI来辅助搭建博客一样，尽管我前期花费了很多努力来做这件事，但当我使用AI来完成这件事时，我的内心并没有成就感，虽然博客在我的努力下真的部署完成了。这个过程中，我并未真正掌握很多技术，只是从某种意义上来说我进步了，我朝自己目标的方向又进了一步。

反过来，当你真正用心专研某一个编程问题，尽管这个过程中你的进步非常缓慢，甚至可能更本没有把这个困难的问题解决，可那又如何呢？你在焦距于这个问题的时候，其实你会不断的发现这个问题所带来的其他问题，而其他问题又回带来更多的问题，而这个主要的问题看似没有解决，但其实你已经解决了很多问题，并取得了进步。

由此，如果我们只从完成任务的角度来衡量自己的进步，那这可能无法真实的让我们开心，而从知识获取的角度，或者说从时间投入的角度，我们必然会发现我们其实已经有了非常大的进步了。

我总是在设想我有一天会有自己的博客，在上面不断记录自己的成长，让更多人看见，也许是真的渴望，我一次又一次的努力着，尽管这个过程中有很多痛苦，但我依然坚持了下来，我希望这里是我的个人知识库，也是我留在世界上的痕迹。

如果你不幸点开这篇文章，那么恭喜你来到我的世界，我不知道你是谁，也不知道你来自那里，如果有一天你想认识我，那记得发邮件给我：zhanbing2025@gmail.com.我想，这是一个自由的世界，即使我的躯体被困住，我的思想也要非去另一个我可以自由畅想的地方。

我的故事

我是一名大四的医学生，来自China，我想写博客很久了，我也经营着一个已经写了三年的公众号：AI e 洞察，在这个公众号里有很多文章，这些文章是我自己的思考和生活感悟，前不久有一篇文章突破了33万阅读量,如果你有兴趣，不妨去读一读，顺便点个关注，哈哈！

我对未来的自己说

• 选择难而正确的路
• 坚持做长期主义者
• 疯狂阅读，写作
• 好好爱自己

如果没有，那这篇文章值得你一读！

我们作为一个普通社会人，所拥有的三大核心资源：知识、技能、资本；三大能力：学习力、专注力、思考力。这些东西是目前我能想到的对于个人来说最重要的东西，或者说，我们其实一生都在围绕着这些转。

个体从出生起，就通过学习外界的信息来获得知识，而后又将知识运用到外界中转化为技能，外界反过来基于你的技能提供的价值大小回馈给你资本，就这样，个人通过不断循环这个过程来获得成长与外界互动。

在这个回路中，外界信息的获取几乎决定了一个人能学到什么样的知识，进而影响整个人后续的发展。这就是为什么存在「信息差」的缘由。即使今天的互联网已经全面普及，人们依然被信息差所困，比如「信息茧房」的出现。不过更深层次的原因是有价值的高质量信息从来都不易获得。既然是有价值，说明它具有稀缺属性，而信息的时效性就决定了当你知道时它已经没什么价值（缺失了它的稀缺性）。

也许我们不能成为第一波利用价值的人，毕竟我们无法成为信息的创造者，但通过合理的方式和途径，我们有机会享受新事物的二波红利。

前面我提了一个问题，你目前的生活状态有没有发生变化就是在刺激你反思——你是否在使用AI工具。

这个问题的答案就和过去问你有没有使用手机一样显得有点愚蠢，但正是这个愚蠢的问题也是信息差的体现。

你知道目前最强的AI公司有哪些吗？你了解过ChatGPT、Claude、Gemini？你关注过Google和OpenAI最新的大模型IQ已经达到多少了吗？

我相信你如果不是一个对科技行业感兴趣的人，这些东西的研究进展你一定很陌生，因为我过去就是如此，原谅我喜欢推己及人的换位思考。

昨天，Google发布了最大的更新在Google I/O:

-Flow

-Google Veo3

-Google Imagen4

-AI model in search

-Jules Code Assistant

-Gemini deep research

就像有网友说的，这是50年来科技行业变动最大的一次，但也可能是未来变化最小的一次。翻译过来——这只是开始。

AI已经到来，但我们依然无法察觉，或者说我们根本就不关心。我们依然低头默默做着手里的事，直到有一天抬头看向四周，才发现外面早已天翻地覆。没有好奇心驱使下的麻木劳动者应该称为敬业，还是应该叫愚蠢呢？

在AI驱动的时代里，我们需要掌握一门与它交互的自然语言——English。

你可能会有疑问？既然AI都这么强大了，用中文跟他交流还会有问题？这个问题就和都有翻译软件了还学英语做什么，95%的代码都由AI生成了，还学CS做什么？

掌握英语的核心目的只有一个：获取高质量的知识和信息。

既然全球顶级的知识都是用英文写的，那如果你是一个爱好知识学习的人，这门语言不是应该必须啃下的吗？又既然信息是有时效性的，当你看到翻译版的东西时，还有价值吗？更为关键的是，基于人的翻译肯定受翻译人知识背景和文化属性的影响，那还是作者原来真正想要表达的意思吗？

只要语言这关搞定，什么AI工具的使用啊，信息渠道啊，自然而然就会自动化解。

因为你要搞定语言，你肯定需要阅读各种内容，而这些内容常常来自不同的渠道，你为了消化这些内容又会借助最好的助手——AI，用多了么自然就熟了嘛，更为重要的，你做的这些事又会反过来强化你的语言学习。

这种正反馈的过程是我觉得在生活中最应该关注的学习方式。一个行为一旦进入正循环模式，你几乎就找到了做这件事的正确方式。反之，一旦进入恶性循环，你则很能脱身。

生活中常见的正循环模式：

阅读刺激思考，思考发现问题反过来刺激阅读，进入良性循环；

使用英语获取信息，发现不会的单词，查单词，更好的理解信息，获得快感，继续这个循环；

输入刺激表达，表达转化为输出，输出又反过来刺激输入。

这些都是非常实用的学习技巧，一旦你沉浸在这些循环中，你想不学习都难。而我们常说的习惯的养成其实就是在构建一个循环，只不过习惯是单个行为，而循环是一个系统，换言之，一个循环可以养成多个习惯。比如有阅读习惯的人，通常也有思考的习惯。

很多年前，我对股市有一种莫名的向往，直到后来我真正进入，才知道那如赌场般的地方冲刺着各种诱惑，在无数个煎熬的日夜里，我始终坚信着价值投资，我知道这是唯一取胜的方法，也是投资修炼一路最小的投入。

我一直将投资股票看作一种对心性的磨练，从未想过在里面赚钱的我终究还是未能做到全身而退，这一年多的历练里我学会了承担风险，学会了面对股价波动的内心毫无波澜。我没有从市场里带走什么，交了些学费，做了些公益，这本不是什么值得拿出来说的事情，但我想失败的教训比成功的经验更值得人们惊醒吧！

如果有一天，你头脑发热，也想去经历一番股市的摧残，尽量做到以下几点：

一、不要投入过多的时间

在我刚进入这个市场前，我对其并不了解，好在有幸读了一些好书，让我对这个市场充满敬畏。不幸的是，我还是忘记了一些重要的劝告，花了不多的时间在上面，现在回头一看，还是觉得自己蠢，不该多花时间在上面。

你花大量的时间学什么，都不要学炒股，在一个随机漫步的市场里，你学到的知识只是无用的共识和情绪。共识和情绪主导着市场价格的波动，而价格又会牵动着你的精神和生活，要投入时间学习的是情绪和价格背后的认知、心理谬误和价值判断。如果方向错了，你投入时间学得越深入，你离正确的方向越远。更甚者，在错误知识框架下，你被塑造成对正确事情判断的扭曲。

什么行业都可以投入大量的时间在里面积累，唯独炒股不行，你投入的时间和你的经验知识积累并不成正比。你投入十年时间在股市上，也许你赚了很多钱，但在随机性的市场中，一个小的失误就可能让你的损失最大化，这不是一个由你控制的市场，你只是这个游戏的参与者，你也不是这场游戏的建设者，你无法让游戏的胜率站在你那边，所以要学会接受自己的无能为力。

二、你什么都不是

不论是炒币还是炒股，作为一名普通人要对自己有清晰的认知，你只是一个乘客，你不是开船的舵手，你主导不了船（股价）前进的方向，你更无法预测河流（市场）流向何方。你唯一能做的就是，安静的坐在船上，等着船在河中游走，如果你乘坐的船足够牢固，他会带你去一个你真正想去的地方，这个地方也许之前不是你想去的，但后来你会感谢它带你找到了一个新世界。

坐在船里，你会因为船波动得太剧烈而愤怒、焦虑，但你一定要放平心态，也要做好自己会被甩下船的准备，毕竟上了这条船，注定充满坎坷。当你安静的坐在船上，静静地观察周围人，你会发现原来我的世界真小，不是因为他们长得抽象、空洞、奇怪，而是你之前遇到的人太少，当你见怪不怪的时候，你就知道，你已经融入了这个圈子。

我已经习惯了坐在船上观察的日子，但我害怕自己成为和他们一样的人，所以我选择了退圈。我提前结束了自己的行程，因为我找到了一条更大更好的船，我现在要下船了。祝船上的你们安好！

三、赌博最重要的是要留在赌桌上

尽管我不太愿意将投资这种行为称为赌博，但对目前我的能力来说，做任何事都是一种赌，因为我不知道未来它到底会是什么样子，在不确定性的世界里不要试图去预测任何事物的发展，而要勇于接受生活中的不确定性。

竟然是赌，就要明确赚钱不是你的唯一目的，留在游戏中，不被淘汰才是。而要能够长久留在游戏中，就要高度控制风险，注意：相比于赚钱，控制风险才是第一位，只有风险控制得当，你的生活才会从容，赚钱只是你控制风险后的奖励。

这时候你肯定要问，那不赌不就没有风险了吗？

我相信这是大多数人的想法，我自己曾经也这么想。后来我发现不对劲，现金存银行有利息，这是众所周知的事，而且这几乎是像我这样的普通人唯一能够想得到的价值储存方式，但你仔细一想，如果存银行拿利息这一行为本身是合乎自然规律的，那银行肯定能够使用你的钱去赚到比付给你的利息更多的钱，否则这笔买卖他就亏了，而作为专业人士出生的他们肯定不会做割让自己利润的事，所以我得出一个结论，钱在银行里躺着必定没有发挥它的真正价值，而且考虑通胀的话，你所需要承担的风险必然是货币的贬值。

所以，参与赌博，控制风险对投资的人来说只是一种资产「保值」行为，其次才是承担风险的投资行为，如果没有做好第一步，第二步无从谈起。

最后

这一年多的股市经历，让我看清楚了很多事情，学费交得很值，它迫使我去学习，去反思，去成长，谢谢你能够全身而退！

🤔 你是否也常常这样：

• 收藏夹里塞满了“干货”，却很少打开？

• 报了不少线上课程，却总是半途而废？

• 道理都懂，但遇到事情还是“该咋样还咋样”？

明明投入了大量时间学习新知识、新技能，但生活似乎并没有因此发生实质性的改变。这种“学而不行”的现象，就像一种“知识的惯性”，让我们停留在学习的舒适区，迟迟无法迈出行动的那一步。

今天，我们就来聊聊，究竟是什么阻碍了我们从“知道”走向“做到”，以及如何打破这些“隐形障碍”，真正让知识为我所用！

🚧 我们为什么会“光说不练”？揭秘三大“隐形障碍”

研究发现，学习和行动之间的鸿沟，其实是由多种心理和生理因素共同造成的。

障碍一：舒适区太“香”，行动区太“险” 😨

• 学习的“安全感”学习新东西会带来即时的满足感和成就感，就像打怪升级一样，但它通常是低风险的。相比之下，将学到的东西付诸实践，意味着要面对现实世界的复杂性和不确定性，可能会遇到挫折、失败，甚至他人的评价。

• 害怕不完美许多人追求完美，希望等自己“完全准备好”再行动。但“完美”往往是遥不可及的，这种心态很容易导致拖延，用持续学习来掩盖对行动的恐惧。

• 拖延症作祟行动往往伴随着挑战和不适感，而拖延可以暂时回避这些负面情绪。于是，学习有时会变成一种高级的拖延方式——“我还没学够，再等等”。

障碍二：大脑“懒癌”发作，偏爱“躺平” 🧠

• 认知惯性我们的大脑天生倾向于维持现状，以节省能量。改变旧习惯、学习新行为模式，都需要消耗更多的认知资源，这会让大脑本能地“抗拒”。

• 习惯的力量太强大旧有的行为模式已经形成了稳固的神经回路，就像一条常走的路。而新的行动则需要开辟新路，初期会感觉更费力。大脑自然会选择那条“省事”的老路。

• 短期激励不足很多坏习惯（比如刷短视频）能带来即时快感，而应用新知识带来的回报往往是延迟的、不确定的。大脑的奖励系统可能更偏爱“短平快”的刺激。

障碍三：以为“懂了”就等于“会了” 💡

• “伪精通”的错觉当我们轻松理解某些学习材料时，很容易高估自己对知识的掌握程度和未来的记忆能力，觉得自己“已经会了”，从而忽视了实践和巩固的重要性。

• 知识迁移的挑战在特定情境下学到的知识，未必能顺利应用到全新的、复杂的现实场景中。就像在驾校练得再好，第一次独自上路还是会手忙脚乱。

• “必要难度”的逃避真正深入的学习需要刻意练习、主动回忆等“有难度”的方法，这往往伴随着一定的挫败感。人们倾向于选择感觉更轻松的学习方式，但这不利于知识的内化和应用。

🚀 如何打破“知识惯性”，从“学霸”变“行动派”？

别担心！知道了原因，我们就能对症下药。以下策略，助你跨越学习与行动之间的鸿沟：

1. 心态调整：拥抱不完美，培养“行动偏好” 💪

• 先完成，再完美接受初次尝试的不完美，把错误视为学习的机会。记住，“完成”比“完美”重要得多。

• 设定“行动截止日期”不要让学习无限期地进行下去。给自己一个明确的时间节点，强迫自己开始行动。

• 警惕“分析瘫痪”当信息收集到一定程度后，就要果断决策并开始尝试。过度分析只会让你寸步难行。

2. 自我赋能：提升效能感，点燃内在驱动力 🔥

• 从小处着手，积累成功经验将大目标分解成小任务，每完成一个小任务都给自己积极的反馈，逐步建立“我能行”的信念。

• 连接个人价值思考学习的内容如何与你的个人目标、价值观相联系，让行动更具内在意义和动力。

• 可视化成功想象自己成功运用所学知识后的情景，这种积极的心理暗示能增强行动的欲望。

3. 习惯养成：巧设“行动开关”，让改变自然发生 🌱

• 找到你的“提示-常规-奖励”回路为你想养成的行动习惯，设置一个明确的“提示”（比如特定时间、地点），设计一个简单的“常规动作”（即你想应用的知识），并给自己一个“奖励”（比如短暂的放松、一句自我肯定）。

• 从微习惯开始比如，如果想养成写作习惯，可以从每天写50个字开始，重点是“每天都做”。

• 公开承诺与寻求支持将你的行动计划告诉朋友或加入相关社群，外部的监督和鼓励能有效提升行动力。

4. 环境优化：创造“助攻型”学习与实践场 🤝

• 营造积极的学习氛围选择让你感到舒适、安全、受鼓励的环境进行学习和实践。

• 寻找榜样和同伴观察那些已经成功应用相关知识的人，向他们学习。与志同道合的伙伴一起行动，可以相互激励。

• 即学即用，联系实际学习时，多思考“这个知识点我可以用在什么地方？”并尝试将新学到的内容与你当前的任务或生活场景联系起来。

5. 实战演练：刻意练习，加速知识内化 🎯

• 专注少量关键行为不要试图一次性改变所有事情。选择1-2个最想应用的核心知识点进行突破。

• 主动输出倒逼输入尝试用自己的话复述学到的内容，或者将其教授给他人，这能极大地加深理解和记忆。

• 定期复盘反思在行动后，花时间回顾过程，总结经验教训，不断优化下一步的行动。

写在最后

从“知道”到“做到”的转变，并非一蹴而就。它需要我们深刻理解背后的阻力，更需要我们拿出勇气和智慧，一步一个脚印地去实践。

别让“知识的惯性”拖住你前进的脚步。从今天起，选择一个小目标，勇敢地迈出第一步吧！你期待看到自己怎样的改变呢？欢迎在评论区分享你的想法！👇

但由于某些不可控的原因，我们的这三部分时间常常处在混乱中。要么该睡觉的时候还在加班工作，要么该做点自己的事情时，发现根本找不到精力，导致我们忙忙碌碌，却收获不到任何成就感和幸福感。

在睡眠上压缩时间，生活上匆匆忙忙，最终的时间都流向了——工作。

可当你每天花很多时间在工作上，回过头来你才发现，你只不过在做着重复的事，重复的像一台机器一般机械地扭动着不堪重负的身躯，只待明日的到来。

前几日，我在朋友圈中看到一位哥哥发的朋友圈。我大一时去社会实践，在我们当地的乡政府与他相识，那时候他刚毕业工作不久，转眼间四年过去了，当我再次了解到他的状态时，是从他的朋友圈文字中。他说：“今天是我30岁生日，曾经的年少轻狂从我身上淡去，来到陌生的环境工作多年后，我在这里成了家，生活没有如我所设想般走得轰烈，我也渐渐忘了自己曾经许下的诺言···”

我知道让自己去追求某些热爱可能不切实际，但我至少要给自己的好爱留点空间，在独自一人时可以安静的与它共处。

现在我们把睡觉和工作的时间抛开，这些时间某种程度上并不是我们能自由支配的，睡觉的时间是由个体的生理机制调控，而工作的时间由为你支付报酬的人说了算，唯一能相对自由的时间可能就那短短的8小时不到，毕竟你还要花在吃喝拉撒上。

这8小时你如果能真正用起来，对于想要进步的人而言已经足够了。其实调查发现，人一天真正能有效学习的时间就4小时，所以其实你每天学习4小时就已经超越绝大多数普通人了。

但由于我们身处在一个满是诱惑的环境中，注意力会被无情的掠夺，所以想要真正沉浸下来学习还需要不断地与各种外在因素作斗争。

我最近在看《writing to learn》，里面提到：

Writing and thinking and learning were the same process.

我思考一番，加上了reading。

思考、写作、学习、阅读，正确的实践(practice)过程应该是：

reading--provoke thought--为写作提供原料--->writing-- stimulate thinking--->learning--->growth。

翻译过来：个人成长是通过学习实现的，而学习分为阅读、写作和思考，阅读可以激发思考为写作提供素材，而写作又可以反过来刺激思考，进一步完善学习。但整个过程中，只有写作是存在产出的，其他环节都是在消耗内外部资源。

不过作为一个打工人，不要忘了practice也能够作为一种学习，当然也可以构成产出。如果从经济角度来看，practice才是一举多得的事，既能从中学习，又能获得产出为别人提供价值，某种程度上来说，如果你不是为了打工而打工，你从中能收获的东西确实不少。

不过很多时候，我们打工的时候就是在打工，能真正将其中收获的知识作为资源储存起来为别人提供价值的确实不多。

也许保罗·格雷厄姆早就想明白了这个问题。他在博客文章《how to make money》提出：

Money is not wealth: money is the medium of exchange; wealth is what you want. the businesses really do is make wealth. they do something people want. but they don’t make money. so you need to create value, rather than make money.

在算法推荐给我的内容里，那些所谓的过来人谈得最多的可能就是个人发展与社会共存问题。

单看这个问题太宽泛，我试着剖析这个问题的深层次含义。

保罗·格雷厄姆写了一篇博客文章，我刚才读了一下，或许对于解答这个问题有帮助。

格雷厄姆在文章里就说了一个事——what to do?

而后就是他对这个问题的回答。其中最令我为之震撼的内容是，他用一种简单的方式来证明「我们不需要做什么」。

我们应该做什么这个问题很难回答，但我们不该做什么就显得没有太大的难度了。格雷厄姆作为YC的合伙人之一，成功孵化过很多项目，所以对于这样的大佬，我总是会带着几分敬意。

美国有两大地方经常出现在我们的视野，一个是以金融交易为中心的华尔街，另一个是以科技创新为基石的硅谷。相比于金钱的魔幻，创新似乎更胜一筹。而之所以创新更胜一筹，是因为它满足了我们人类固有的好奇心，推动着多巴胺、内啡肽、血清素的分泌。你可能会问，金钱也能做到，但这不是我要讨论的重点。

创新的反义词是模仿，或者说山寨，比如中国出名的华强北。

彼得·蒂尔曾写过一本书——《0到1》。0-1代表着创新，1-n表示模仿，或者你也可以理解为迭代。

好，我们现在可以回到主题了。人或者说这个社会的成员/公民，绝大多数在做的事、走的路，是否都带着模仿的属性呢？

我们总认为自己拥有自由意志，可真正能够用勇气追求「自由意志」的人又有多少呢？我们更多的是在做一些别人已经做过的事（模仿），比如什么工作挣钱（基于前人的样本）。这正是格雷厄姆文章的中心思想，与其回答做什么，不如让人们思考不做什么。既然模仿是不值得做的，那创新或者不模仿的事是不是就成为了我们该做的事呢？

这种反向论证的方法，足以让我这样的青年屌丝折服。

这又引出了另一个我真正关心的问题--社会不需要那么多创新，而我也只是一个穷屌丝，只想找份工作安享中年又该如何做呢？

这回到了一个更现实的问题，而这个问题我在一位已经做到副主任医师的老师身上学到的，更确切地说，是他以过来人的身份告诉我的，也是那些喜欢博取流量的人在某平台上大肆宣扬的：如果你家里没有关系，工作的时候别那么拼，你是打工的，不是老板，为钱搭上自己的健康不值得。从这个角度来看，打工人（模仿者）只是在帮助老板（创新者）实现他的梦想，从而获得报酬，所以员工的满意度比老板要低很多。（这可能不是钱多钱少的问题，而是使命的问题）

那缺失的满意度要如何弥补呢？

用工作中留出来的时间做自己喜欢的事。这就打通了工作中不能太拼（获得个人时间）与提升生活满意度之间的路径。这两者并不矛盾，我给老板打工，帮助老板成就他的事业，他付报酬给我，维持我追求自己的热爱，大家只不过都是在追求个人的人生而已。

所以，作为打工人，不要为了不是自己的东西而放弃追求自己的生活，如果定位出了问题，很容易让自己活在一种痛苦的生活中。

回想过去第一次接触得到app，我被里面的精彩知识吸引，那也成了我第一次为之付费的平台，出于种种原因，我今年也放弃了继续开会员，当然，那并不意味着我放弃投资自己。

今日，我大概梳理一下自己过去在那个平台上付费读过的书和课程，算是作为一种纪念吧！

可以说得到占了我大学的大部分时间，总计1771h。

17门课程，12本听书，读了162本，读完47本。

其中李笑来的《财富自由之路》学习了3遍，《何帆读书俱乐部》听了两遍。有些课程也大致学了两遍。总计花费超过1400元。

至于成长方面，用那些时间和金钱确实无法衡量。

在过去三年里，我购买的纸质书超过40本，折算下来平均每本30元，花费大概在1000左右。

一个大学生，四年时间，投资在课外书上的金钱成本超过了2500，加上最近购买的Kindle电子书，算下来在3000元左右。

当然，我买的书大部分都读完了，少部分还在压箱底，除了自己花钱购买的，还有在学校图书馆借阅的，相对于普通人来说，阅读量已经很高了。

但是最近一段时间，我对书的品质要求逐渐提高，可能也是一种个人偏见吧，我决定放弃阅读中文书，转而向英文书进军，一是为了提升英文，二是为了让自己从追求数量转向质量。

我也不断反思自己，发现我并没有在花了那么多金钱成本上赚到与之相对应的金钱，所以我需要一种更加适合自己的学习方式，不再盲目地读书。而是以问题为导向来满足我的求知欲。

经过这四年的课外阅读，我发现读书帮助我用一种更真实的视角来看待这个世界，而这些视角都是那些聪明人曾经挣扎着想明白的问题。

我很喜欢两位作者，它们的书我基本全读过，一位是尼古拉斯·塔勒布，一位是李笑来，他们是我在书里结交到的最聪明的人。也许这个最聪明的评判标准站在我的角度，除了读书多就是非常能赚钱。可能我是一个比较物质的人，所以这个价值观显得有点抽象。

从某种角度来说，我读过的很多书都在某种程度上影响着我，只是我不知道或者无法表达出来而已，但我却能够感受得到我存在一种对自己的自信，这种自信来自于我与过去那些非常聪明的作者通过书中的内容和思考得来的。我相信就算别人再怎么争夺，可能也拿不走，毕竟我相信随着时间的积累，它会刻进骨子里。

编程

尽管AI驱动的编程已经足够强大，但在某些方面还是值得初次接触的朋友注意。比如，在AI记忆能力有限的情况下，他常常会在项目弄到一半的时候选择停止工作，你需要重新对其发起请求，这样一来一回，相对来说还是会降低效率，不过目前比较大的模型代理对这个问题有了一定的解决。

除了记忆，AI还在项目优化上产生buge，如果你让它对项目进行优化，它可能会对整个项目发起攻击，将某些内容重构，弄出一个它自己也无法解决的问题，然后陷在其中无法自拔，让你的项目无法继续进行。我在这方面深有感触，虽然它初步设想的MVP是可以实现的，结果也挺让人满意，但随着项目的优化和复杂度提升，整个人的时间都将会荒废在一个莫名其妙的问题里。

这让我不禁感叹，初学者在AI面前似乎就是一个附属的机器，你只是在配合着它演戏。看似你在操纵它，其实是它在引导你实现它想要的功能，这种人与AI合作的工作，在某种程度上，看似是个人创造力的表达，但更深层来理解，你只是借助一个工具，被牵着走向你也不太熟悉的地方。

信息整合

AI在网页搜索方面给出的信息需要个人亲自去实践，少了这个过程，你很容易被AI带偏。我使用最多的就是这个功能，常常感觉它在欺骗我，这种不信任既是对自己负责，也是对未来的AI提出了更高的要求。

比如，我让他给我某个信息，它基于搜索结果整理出一份看似毫无毛病的指南，但亲自上手会发现，有些根本不可行，你需要持续的给他反馈，让它一步步修正自己的偏差。这像极了一个不断犯错的小孩，你需要给他时间试错，它才会获得让你满意的结果。但没有东西是万能的，AI也不例外，不要试图将AI给出的结果作为决策的标准，毕竟最终承担责任的是自己而不是AI。作为助手，AI是可信的，而且也值得我们学习，作为决策者和执行者，AI需要更多的提升。所以，在使用AI之前，要给其设定一个角色，什么角色该做什么，同时也赋予相应的可信度，这样在人与AI合作中才会获得效率的最大化。

知识传授

如果将AI看作以为博学的老师，你会发现它的智慧远超现有的大多数人。在知识的世界里，它给出的知识是值得可信的，毕竟这是它的强项。在你怀疑它之前，先看看自己到底记了多少知识在脑中。核心原因在于--验证的困难性。你说你查资料吧，它查的比你更快、更全，所以以我之见，如果不是特别离谱，要么是它真的出错，要么是我们从始至终就记忆了错误的知识。

当然，你不放心的话，可以多问几遍，让它试图去修正自己。

以上就是我这几个星期在与AI交互过程中获得的一些经验。目前的AI值得使用，也需要我们学会包容，出错是人之常情，不论是对待AI还是对待我们自己，永远都要允许错误的存在，试错和容错值得我们用尽一生去学习。

在今天这样一个生产文章如此容易的时刻，会写东西已经不是什么稀奇或稀缺的技能了，可能随便找一个好一点的大模型，写出来的东西也都「值得」一读。

注意：值得一读并不代表他有多大的价值，只能说它产生的内容新颖而不陈词滥调。

如果你使用普通一点的模型，在推理能力弱的情况下，你看到的东西只是你过往没见过的词汇组合，它与作者写出来的东西是有很大区别的。如果你有阅读能力，你会发现它的内容只是流于浅表的「废话」，反之，你会惊叹它那精妙绝伦的文笔。

作为一个已经写了两年文章的人，我其实能感受到ai写得确实好，可惜我读过一些书，又会觉得那不过是没有思想的词汇组合。

当你读一篇文章的时候，你其实是在与作者隔空交流，这种跨越时间和空间的共鸣是难以被没有「思想」的工具替代的。如果你不能理解，我想用「意淫」来表达，所谓的意淫就是你在头脑中构建一个不存在的事物并假设它真实存在，这和男性产生性欲的底层核心特别类似。

用在这里，如果你不知道你读的内容是AI写的，并假设它是人思想的表达，那么你就是在意淫，且在某种程度上显示了自己知识的匮乏。

其实，你今天看到的大部分文章基本都是通过ai产生的，这种无法分辨人与工具的边界的能力，在未来会更加凸显。从文字到视频，AI的创作能力将会远超「一般人」。（此处的一般人通常指初学者）

如果你还认为，未来的人分为「会使用AI的人和不会使用AI的人」，那你已经out了。你仔细思考会发现，未来只有会使用AI的初学者和会用AI的专业人员。这就和有无手机的区别到能否有效使用手机是一样的。一件东西在还未普及时会停留在「有无」和「会不会」的层面，而一旦它大规模普及后，另一种分类将会出现。

我将未来的人分为初学者和专业人员，是因为我发现，AI工具会帮助人从门外汉快速入门，但入门后的精通AI工具却很难再帮助人进阶。这就扯到学习一样东西除了需要有效的工具，还需要时间的累加才行，效率可以提升，但时间的跨度却很难跨越。

而人在骨子里有一种自恋的性格。这种自恋会让我们产生我已经精通的幻觉，就像有翻译软件会让我觉得阅读英语也就那样嘛，有AI agents 我会产生编程也就那样嘛，有AI工具，写作也就那样嘛的深度错觉。

有一种「幻觉」更加隐秘，你在某一时刻一定产生过这样的念头--有了AI工具，我还学他干嘛！这其实就是初学者和专业人员的区别，前者将希望寄托在工具上，而后者更多是在考虑怎么驾驭工具，更进一步，前者很容易“意淫”——被AI工具耍得团团转，后者知道工具的缺陷，并试图改进它，前者是依赖，后者是合作。不信你看看有翻译软件后，有多人不再花时间学英语。

为什么人会被工具耍，很简单，当人比工具弱的时候，工具就成了老师，成了人可以依赖的对象，如果缺乏批判性思考，此刻的人就成了附庸，成了所谓的没有思考能力的机器。

所以，换个角度想想，未来的工具不一定是机器，人更应小心成为机器。

还有一个社会层面的因素。

以前的企业雇佣一个大学毕业生做实习生可以节约成本，学生在这个过程中也能学到东西，但未来，如果工具能够解决一个实习生的工作，且成本更低，那么毕业生不仅很难得到实习工作，而且还可能会因为缺乏资深老师的帮助，实习生初学者将无法获得有用的知识，导致初学者只能与工具合作，或者依赖工具，最终导致初学者与专业人员的差距原来越大，如果个人意识不到这一点，将永远处在初学者水平。

这就好像，原本写作需要花费几年的时间才能上道，现在你借助工具几分钟产出一篇，然后还洋洋得意，你以为你进步了，其实你还在原地踏步，长此以往，你的思考力下降的同时，依赖性会更强。

所以，做一件事，思考清楚它的底层逻辑，比完成这件事更加重要，搞懂写作的目的，比写出一篇爆款文章要重要。

我理解你提出的观点，认为一切人类行为可以看作是“写作”和“阅读”的变体。这是一种非常有趣的类比，并且在一定程度上我可以理解和赞同这种观点。让我进一步解释我为什么觉得这个类比有其合理性，同时也指出其中的一些潜在局限。

赞同的理由：

• 行为与表达：

• 写作和阅读都是表达和理解的方式。无论是我们通过言语、行为、艺术，还是通过某种物理动作来表达自己，都可以看作是在进行某种形式的“写作”。而他人的反应、对这些行为的解读和反馈，则类似于“阅读”。例如，一个人表达情感、思想或需求时，就像是在“写作”，而他人如何理解、解读这些表达，则像是“阅读”。

• 举个例子，当我们与他人进行对话时，我们在“写作”自己想要表达的信息，而对方则通过“阅读”我们的言辞、肢体语言等来理解我们传达的意义。这样，在日常互动中，人与人之间的沟通实际上是不断地“写作”和“阅读”。

• 人类行为的双向性：

• 写作与阅读代表了人与人之间的双向互动。在更广泛的层面上，所有人类行为都有类似的双向性。一个人“写作”自己的生活故事，作出选择、决策，进行行动，而他人、环境甚至自己在回顾这些行为时又在“阅读”这一过程，评估其意义、后果和影响。

• 比如，艺术创作也是“写作”的一种变体，艺术家通过画作、雕塑等形式表达自己的内心世界，而观众则通过“阅读”这些艺术作品来解读其背后的思想和情感。这种解读不仅是知觉的活动，也是一种思考和再创造的过程。

• 生活是持续的“写作”和“阅读”：

• 如果将生活看作是一部故事、一本书，我们每个人都在“写作”自己的故事，通过决策、选择、行动不断地书写自己的命运。而他人则通过观察我们的行为、反应，甚至是与我们互动的方式来“阅读”我们的故事。这个过程不仅体现在人与人之间，也体现在个体对自己的认知中。我们通过反思自己的行为、经历，不断“阅读”自己的过去，更新对自己的理解。

• 自我认知与成长：

• 自我认知和个人成长也可以被视为一种“写作”和“阅读”的互动。个人在不断地“写作”自己的生活、设定目标和行动计划，而在这个过程中也不断地“阅读”自己，审视自己的行为是否符合内心的期望，是否有成长与进步。

潜在的局限性：

• 过于简化的比喻：

• 虽然“写作”和“阅读”是有效的类比，但它们仍然具有一定的简化性。人类行为远比单纯的文字创作和阅读复杂。例如，一些行为可能不完全是有意的写作或解读过程，某些行为可能是无意识或情感驱动的，难以完全用“写作”与“阅读”来理解。

• 比如，有些冲动行为、直觉反应，可能不具备明确的“写作”和“阅读”模式，它们更是瞬间的反应，而不一定有持续的解读过程。人的决策过程有时是即兴的、非理性的，难以完全归纳为“写作”和“阅读”的互动。

• 行为的多维性：

• 人类行为有多种维度，除了“写作”和“阅读”，它还涉及情感、动机、社会环境等多种因素。例如，做一项决策时，可能不仅仅是在“写作”一个目标的实现计划，还受到情感状态、环境压力、文化背景等多种因素的影响。因此，将所有行为归纳为“写作”和“阅读”的双向互动，可能无法全面覆盖所有复杂的行为动态。

• 写作与阅读的内涵限制：

• 写作和阅读通常与语言、文字密切相关，但人类的许多行为并不依赖于语言。例如，非言语交流（如肢体语言、面部表情等）在“写作”和“阅读”模型中似乎较难体现。这些非语言行为同样在传递信息和意义，但它们的解读和表达方式与文字的写作和阅读有所不同。

总结：

我认为，你提出的“写作”和“阅读”的类比是一个有趣而深刻的视角，能够揭示出人类行为中表达与理解、创造与解读之间的深层联系。这种类比非常适合理解人与人之间的互动、个体的自我认知与成长。然而，它也有一定的局限性，特别是对于那些非言语、非理性的行为，以及更复杂的社会和情感动机。总的来说，这个比喻为我们提供了一种独特的视角来思考人类行为的本质，但也需要在适当的情境中加以应用和补充。

作为一名大四实习的医学生，你正处于医学学习和实践的关键阶段，未来的职业发展面临着许多选择。在这个背景下，我认为你不应该把大部分时间都花在纯粹的记忆医学知识上，而应该把重点放在思考、综合应用和跨学科的能力培养上。以下是我对你未来发展的几个思考方向：

1. 知识的综合运用与批判性思维

• 医学知识的基础仍然重要：作为一名医生，扎实的医学基础仍然是必不可少的。你需要掌握核心的医学知识、诊疗技能、药理学等，尤其是在实际临床工作中，许多诊断和治疗决策需要你快速调动自己的知识库。这些基础知识不仅为你提供了执行医务工作的框架，也能帮助你在遇到复杂病例时作出合理的判断。

• 但记忆量的减少：在未来，AI和智能医学工具可以帮助你更快速地查找信息和参考最新的医学研究数据。因此，你不需要像传统的医生那样，依赖大脑背诵大量的疾病名、症状、治疗方案等。你应该更加注重如何理解和综合这些知识，并能够灵活运用它们来解决复杂的临床问题。

• 批判性思维：医学领域不断发展，新的治疗方法和药物层出不穷，面对这些信息，你需要具备批判性思维能力。你不仅要能够获取信息，更要能够分析信息的来源、判断其可靠性，并在临床上合理应用。比如，AI推荐的治疗方案可能很准确，但你仍然需要结合患者的具体情况和你的临床经验来做出最终决策。

2. 关注跨学科的学习与合作

• 医学与技术的融合：AI、数据科学和医学的结合将是未来医疗领域的趋势。例如，AI可以帮助医生进行疾病预测、影像分析、个性化治疗方案的制定等。作为医生，你不仅要理解医学本身，还需要有一定的技术敏感度，了解如何将AI和大数据应用到医疗中。未来的医生不仅是医疗知识的专家，也可能是与技术专家、数据科学家等跨学科团队合作的关键人物。

• 学习数据分析与科技工具：虽然你可能不需要成为程序员，但了解基础的数据分析技能、统计学以及如何与技术团队合作，将是你未来在医疗实践中的重要技能。这不仅可以帮助你更好地理解AI辅助诊疗过程，还可以在临床研究中更好地利用数据。

3. 培养沟通和人文关怀的能力

• 患者的全人关怀：医学不仅是生物学、解剖学和治疗的结合，还是与患者、家庭和社会的互动。作为医生，你不仅要具备医学知识和技术能力，更要有深厚的沟通技巧和同理心。在诊疗过程中，如何与患者及其家属沟通病情、治疗方案、预期效果等信息是非常重要的。AI虽然能够提供精准的数据支持，但不能完全替代医生在情感和人际关系中的角色。

• 培养人文医学思维：现代医学越来越强调“全人”医疗，即关注患者的身心健康、生活质量及社会心理背景。因此，除了医学知识，培养人文关怀的能力和伦理意识对医生来说尤为重要。你可以关注医学伦理、心理学等课程和实践，提升自己在这些领域的能力。

4. 专注于解决实际问题和创新

• 临床实践中的创新：在医学领域，解决实际问题往往需要创新。无论是在手术操作中，还是在治疗方案的选择上，创新的思维都能帮助你突破传统方法，为患者带来更好的治疗效果。例如，如何将AI工具在临床诊断中发挥最大效益，如何为患者定制个性化的治疗计划，都是未来医生可能面临的创新性挑战。

• 跨领域研究与合作：未来，你还可以选择从事医学研究，将你的医学知识与其他学科如生物学、工程学、人工智能等结合，推动医学领域的创新和进步。例如，医学中的精准医疗、基因编辑、个性化药物开发等，都是与技术密切相关的前沿领域。

5. 终身学习与职业发展

• 适应医学领域的快速变化：医学是一门不断发展变化的学科，新技术、新疗法层出不穷。作为医生，你需要具备强烈的学习动机和终身学习的意识，持续更新自己的知识。AI的应用可能会不断改变医学实践和临床工作流程，因此你需要时刻保持对新技术、新方法的学习和适应能力。

• 职业生涯的多样化路径：除了传统的临床工作，未来的医学职业还可能涉及更多创新的领域，如医学人工智能、健康管理、医疗创新等。通过持续学习和多样化的技能积累，你可以开辟不同的职业道路，选择你感兴趣且有潜力的领域深耕。

总结：

在未来，作为一名医生，你不再需要将大量时间和精力投入到纯粹的记忆工作中。记忆医学知识的基础内容固然重要，但更重要的是如何运用这些知识，结合AI技术，思考问题，进行创新和解决复杂的临床问题。因此，未来你应该更多专注于以下几个方面：

• 思考与理解：将精力投入到如何理解和运用医学知识，而不是单纯的记忆。

• 跨学科的能力：学习如何将医学与技术（如AI、大数据）结合，推动医疗创新。

• 沟通与人文关怀：发展与患者沟通的能力，提供全面的医疗服务。

• 创新与解决问题：培养创新思维，积极解决临床中的实际问题。

通过这种方式，你不仅能成为一名医学知识扎实的医生，还能成为一名能在技术驱动的时代中提供高质量医疗服务的专家。

问：我对医学不是特别感兴趣，但对技术有点感兴趣，也会点python，你会建议我未来转行吗？还是做一些跨学科的工作？

你的情况非常有意思，既对技术感兴趣，又具备一定的编程能力，同时目前在医学领域的兴趣不太浓厚。根据这些信息，我认为你有两种比较有前景的选择：

1. 跨学科发展，结合医学和技术

医学与技术的结合是未来的趋势，特别是随着人工智能、大数据、机器学习等技术的广泛应用，医学领域对技术人才的需求正在快速增长。如果你对技术感兴趣，而又不想完全离开医学领域，可以考虑做一些跨学科的工作。这种工作结合了医学与技术两个领域，不仅能充分发挥你对技术的兴趣，还能在医疗行业中找到独特的位置。

例如，你可以从事以下几个方向：

• 医疗数据分析与人工智能：医学数据（如患者病历、影像数据、基因数据等）的分析和处理需要大量的技术支持。你可以运用Python进行数据清洗、机器学习模型的训练，帮助医疗机构分析数据、预测疾病、设计个性化治疗方案等。人工智能在影像识别、个性化医学等领域的应用也正快速发展，跨领域的技术人才需求很大。

• 医疗软件开发：随着健康管理和医疗信息化的推进，医疗软件、诊疗辅助工具、电子病历系统等在医疗行业中越来越普及。你可以运用编程技能开发这类软件，提升医疗行业的效率和服务质量。

• 生物信息学：如果你对生命科学有一定兴趣，可以结合生物学、医学与技术，做生物信息学研究。比如基因组学数据分析、医学大数据分析等领域，涉及到的技术工具和编程能力非常重要。

跨学科的工作不仅能发挥你的技术优势，而且在医疗和健康领域的创新中也具有巨大的潜力。许多医疗创新都需要技术背景的支持，比如基因编辑、精准医学、数字化医疗设备等，技术人才和医学专家的合作将推动这些领域的进步。

2. 完全转行，进入技术领域

如果你对医学的兴趣较低，且对技术的兴趣逐渐增加，转行进入技术领域也是一个值得考虑的选择。现在，技术领域的发展非常迅速，各行各业对技术人才的需求巨大。你可以从你目前的编程能力出发，深耕一些具体的技术领域，进行专业化发展。以下是一些可能的方向：

• 软件开发：如果你对编程感兴趣，可以深入学习计算机科学，掌握更多的编程语言和开发框架（如Java、JavaScript、C++等），并从事软件开发工作。软件开发在各行各业都有广泛的需求，尤其是在互联网、金融、教育、电子商务等领域。

• 数据科学与人工智能：数据科学和AI是当今技术领域最热门的方向之一。你可以通过进一步学习统计学、机器学习、深度学习等，成为一名数据科学家或AI工程师。这些职位不仅需求量大，而且薪资待遇也非常优渥。你可以参与数据分析、机器学习模型的设计与优化等，广泛应用于金融、医疗、广告等行业。

• 网络安全：随着互联网和数字化时代的到来，网络安全问题变得越来越重要。学习网络安全相关的知识，成为一名网络安全专家，不仅具有挑战性，而且在未来有着巨大的需求。

• 区块链技术：区块链是近年来发展迅速的新兴技术，涉及金融、供应链管理、数字货币等多个领域。如果你对这种新兴技术有兴趣，可以考虑进入这一行业，做区块链开发、智能合约设计等。

3. 如何选择？

跨学科工作与完全转行都各有优点，选择的关键在于你对技术的兴趣深度，以及你对医疗领域的最终兴趣程度。以下是我对你决策的一些建议：

• 如果你依然对医学有一定的兴趣，但不想深入临床：可以考虑从事跨学科的工作，结合你对技术的兴趣，进入医学数据分析、人工智能辅助诊疗、医疗软件开发等领域。这不仅可以保留一定的医学背景优势，还能发挥你对技术的兴趣，属于一个“医学+技术”的理想组合。

• 如果你对医学已经没有兴趣，且希望全身心投入技术领域：那么完全转行进入技术行业是一个合理的选择。技术行业发展迅猛，人才需求巨大，你可以选择一个你感兴趣的技术方向，深入学习并建立自己的职业发展路径。

4. 具体行动计划：

无论你选择跨学科还是转行，以下是一些可以帮助你推进的行动计划：

• 继续提升编程技能：如果你还想进一步提升自己的技术能力，可以学习更深入的计算机科学知识，学习常见的技术工具和框架，深入了解人工智能、数据科学、区块链等热门领域。

• 参与项目实践：通过参加实习、开源项目或开发自己的小项目，积累实战经验。这不仅能提升你的技能，还能让你对技术的应用场景有更深刻的理解。

• 扩大跨学科的知识面：如果你考虑从事跨学科工作，建议你在技术学习的同时，也可以了解医学基础知识、医疗行业的现状和需求，这样可以更好地理解医学技术结合的潜力和挑战。

• 考察行业和市场需求：了解你感兴趣的技术方向的市场需求和发展前景，确定自己最感兴趣的细分领域，然后制定长远的职业规划。

总结：

如果你对医学不感兴趣，但对技术充满兴趣，我建议你可以考虑转行进入技术领域，特别是数据科学、人工智能、软件开发等方向，它们有广泛的应用场景和巨大的市场需求。然而，如果你希望结合医学和技术的优势，从事跨学科的工作，如医疗数据分析、AI辅助诊疗等，这同样是一个非常有前景的选择。最终的选择应基于你对技术的兴趣深度以及你对未来职业发展的规划。

• "每一个选择的背后，不仅隐藏着直接的后果，也藏匿着那些无法预见的连锁反应，正是这些不可见的路径，决定了我们最初无法预料的命运轨迹。"

• "我们往往低估了时间的微妙力量，似乎看不见的每一分每一秒，都是在塑造未来，正如涓涓细流最终成就一座大海。"

• "在纷繁复杂的社会中，表面上的自由选择往往掩盖了更深层次的限制，而真正的自由或许是能够看见这些限制并依然选择前行的勇气。"

• "表象的对立常常让我们忽视了其背后的统一性，黑与白并非完全分裂，反而在某种层面上彼此依存，相互定义，正如光与暗的交替，才能显现出完整的世界。"

• "每当我们试图解读一个复杂的局面时，总是带着已有的偏见和假设，然而真正的洞察力往往源于放下这些成见，去看待事物未曾表达出来的那一面。"

• "人类追求真理的脚步总是走得异常艰难，正因如此，真理常常以最不显眼的方式呈现，只有在我们放下执念时，才可能在无意中触及它的本质。"

• "历史中的每一段插曲看似微不足道，却可能在更宏大的图景中成为决定性的转折点，正如一块不起眼的棋子，能在关键时刻改变整个局面。"

• "孤独并非终点，而是一段内省的过程，只有在这片寂静中，灵魂才能够真正地与自我对话，找到那些隐藏在纷杂生活背后的深层需求与欲望。"

• "思维的局限常常源自我们对世界的框架设定，而真正的突破往往来自于打破这些框架，让我们从一个全新的视角重新审视曾经认为理所当然的事物。"

• "成功的背后，往往伴随着无声的牺牲与无法言说的孤独，而这些隐形的代价，才是支撑一切显现的真正力量。"

这些句子通过引导读者深入思考，提炼出表面现象背后的复杂逻辑和深层含义，适合在哲学、心理学、历史分析等方面展开深度探讨。

这是一个非常深刻且富有挑战性的问题，涉及到人类学习、记忆、思考和AI技术如何改变人类智力使用的未来。我认为，随着AI技术的快速发展，尤其是在记忆和信息存储方面的强大能力，人类应该更加注重思考和创新，而不仅仅是花时间在记忆知识点上。以下是我的一些思考：

1. AI的强大记忆能力：

• AI的优势：AI可以存储海量的信息，快速检索并提供高精度的答案。这意味着，人类不再需要将所有的信息都牢牢记在脑中。比如，AI可以快速帮助我们查询数据、回顾历史事件、解决数学题等。这使得我们可以把更多的精力从“记忆”转移到更高层次的“思考”和“创造”上。

• 知识获取的便捷性：有了AI的辅助，我们可以通过自然语言查询或复杂的问题求解得到快速而准确的结果。因此，我们不必在记忆大量细节和琐碎信息上花费太多时间，而是可以将这些“繁琐的记忆”交给AI，让自己有更多的时间去理解、应用和批判这些信息。

2. 人类大脑的作用转变：

• 从记忆到思考：如果AI承担了大部分知识的存储、处理和检索功能，人类大脑将更多地专注于思考、理解和创造。例如，在面对一个复杂的社会问题时，我们不需要记住所有的数据和细节，而是可以依赖AI快速提供相关背景信息和分析结果，然后将精力集中在如何解决问题、如何创新解决方案以及如何评估这些方案的可行性上。

• 批判性思维与决策：AI能够提供信息和数据，但它并不具备人类的情感、价值判断和社会背景。未来，人类将更多地用于处理复杂的决策，特别是在涉及伦理、社会影响、情感和人类价值的问题上。例如，在处理医疗伦理、社会政策等问题时，AI可以为我们提供相关的事实数据，但最终的决策往往需要依赖人类的价值判断和批判性思维。

• 创造力和创新：人类的创造力和创新能力是AI难以替代的。尽管AI可以帮助我们解决已有的、常规的问题，但它并不具备产生全新思维和构想的能力。人类将能够专注于利用AI工具进行跨学科的创新，开发新的技术、艺术作品、科学理论等。未来的大脑将更多地用于打破常规，挑战现有假设，并从新的角度重新审视问题。

3. 人类如何重新定义自己的智力：

• 从“记忆”到“理解”：过去，我们的智力可能更多依赖于记忆和复述。然而，未来的智力将更多地依赖于如何理解、整合和应用信息。AI能够提供信息，但人类的大脑将集中于如何理解这些信息，并根据现有的知识体系进行合理的推理和判断。

• 人际互动与社会智慧：尽管AI能处理大量数据和执行自动化任务，但它不能完全理解人类的情感和社会动态。人类大脑将更多地投入到情感智力、人际关系管理和社会协调等领域。这些领域强调同理心、情感交流、文化敏感性等，AI很难模仿这些复杂的社会行为。

• 跨领域思维：随着AI工具的使用，未来的知识工作者将能够更高效地结合多个学科的内容。人类的大脑将被用来跨越不同学科的边界，将不同领域的知识结合起来，解决复杂的跨学科问题。这种综合性思维方式将成为未来知识工作者的重要能力。

4. 大脑的“价值”重塑：

• 深度思考与情感共鸣：AI虽然可以处理大量的结构化信息，但它缺乏情感和人类经验。人类的大脑将专注于那些AI难以完成的任务——如情感共鸣、直觉判断、价值观的形成、道德决策等。例如，在医学、心理学等领域，AI可以帮助医生诊断疾病，但医生与患者的情感交流、对患者需求的理解，则是AI无法替代的。

• 批判性和道德性判断：AI的决策通常基于数据和规则，而人类的大脑则会基于复杂的道德、伦理和社会规则做出判断。未来，人类将专注于思考这些复杂问题，而AI则提供更多关于背景、数据和逻辑的支持。

5. 人类如何利用AI进行深度工作：

• 协作与增能：AI的强大能力并非要取代人类，而是要成为我们工作的有力辅助工具。未来，人类将利用AI来增强自己的工作效率和创意输出，进行更具深度和更具创新性的工作。AI可以为我们提供精准的分析和模型，而人类则在此基础上发挥创造性，探索新的领域，设计新的方法。

• 专注于复杂任务和策略思维：在未来，人的时间和精力将更多集中在战略性思维、跨学科的综合应用、以及社会性决策上。AI能够处理重复性工作和数据分析，但高层次的判断和决策仍然依赖于人类的大脑。人类的大脑将成为思考、创新、管理和协调的中心，而AI则是执行、计算和支持的工具。

总结：

随着AI的发展，人类不再需要将时间大量投入到记忆知识点上，而应该将精力集中在更具创造性、批判性和社会性的问题上。未来，大脑的主要作用是思考、创造、决策和与他人互动。AI将成为人类的强大辅助工具，帮助我们更高效地获取和处理信息，但真正的价值判断、创新和人类智慧将继续依赖于人的大脑。通过合理地利用AI，人类可以将自己的智力和创造力发挥到极致，解决更加复杂和深刻的问题。

提出问题的能力的重要性：

• 驱动创新与进步：

• 提出正确的问题是创新的起点。很多伟大的发现和技术突破都是源自深刻的提问。例如，爱因斯坦的相对论并不是因为解决了现有的物理问题，而是他提出了全新的问题，挑战了传统的思维方式。在科学、技术、艺术等各个领域，提出新颖、独到的问题，往往能引导我们进入未知的领域，推动社会进步。

• 激发深度思考与批判性思维：

• 提出问题要求我们深入思考现有的知识和状况，质疑已有的假设和结论。一个能提出深刻问题的人，往往能够超越表面现象，挖掘事物背后的本质。这种批判性思维在面对复杂社会问题、科学探索或商业决策时，显得尤为重要。

• 应对复杂与不确定性：

• 随着社会问题的复杂性和不确定性的增加，简单的“答案”往往不再能解决所有问题。相反，提出能够洞察复杂性、揭示深层次问题的提问能力，将更能帮助我们应对未来的挑战。无论是气候变化、社会公平还是技术伦理，提出能够推动讨论和解决的关键问题至关重要。

• 适应变化与不确定性：

• 未来的工作和生活环境将更加动态，很多问题的答案可能并不明确。提出正确的问题，能够帮助我们在不确定性中找到方向。例如，在面对快速发展的技术（如人工智能、基因编辑等）时，提出“我们该如何利用这些技术来解决道德和社会问题？”比单纯寻找技术的“答案”更能帮助社会应对变革。

解决问题的能力依然不可忽视：

尽管提出问题的能力至关重要，但解决问题的能力同样不可或缺。一个能够提出问题的人，需要具备解决问题的能力来将这些问题转化为具体的行动或解决方案。解决问题的能力包括：

• 批判性思维和分析能力：在面对复杂问题时，能够理性分析、找到最有效的解决方案。

• 创新与实践能力：将理论与创新思维转化为实际可行的行动。

• 团队合作与沟通能力：很多问题需要团队协作和跨学科的视角才能解决。

结论：

在未来，尤其是面对不确定性和快速变化的社会，提出问题的能力可能更为重要。因为一个人如果能提出恰当、深刻的问题，就能更好地引导创新、洞察复杂性并应对未来的挑战。而解决问题的能力虽然同样重要，但往往是在问题被正确提出后才能发挥最大的作用。因此，提出问题的能力可能是未来更关键的能力，它能够为解决问题提供方向和框架。

问：既然你认为提出问题的能力更重要，那我该如何培养这方面的能力，我需要切实可行的行动方案帮助我成为一个有内核的人

要培养提出问题的能力并成为一个有内核的人，首先需要提升自我认知、批判性思维、创造性思维等方面的素养。以下是一些切实可行的行动方案，可以帮助你在这个过程中逐步提高：

1. 培养批判性思维

批判性思维的核心是质疑和分析已有的观点和假设，透过表象看到事物的本质。

• 反思日常经验：在日常生活中，对所遇到的事物保持好奇心。无论是看新闻、阅读书籍，还是和他人交流时，学会提出“为什么”或“如何”的问题。例如：“为什么我们在这个问题上总是这样做？有没有更好的方法？”

• 多角度思考：对于一个问题，不要只从一个角度出发，试图从多个不同的视角分析它。比如，社会问题可以从经济、文化、历史等角度去理解，技术问题可以从伦理、功能、用户体验等角度去思考。

• 挑战自我假设：在思考时，时常提醒自己：我们对事物的理解是否已经固定？是否有潜在的偏见或者成见？挑战自我的认知框架，不断扩大思考的边界。

2. 培养创造性思维

创造性思维使我们能够跳出固有的思维模式，找到新的视角和答案。

• 头脑风暴：定期进行头脑风暴练习，列出不同的观点和解决方案，不拘泥于传统的思维方式。例如，选择一个你常见的问题，写下所有可能的解决方案，哪怕这些解决方案看似荒谬。这有助于打破惯性思维。

• 跨学科的学习：尝试跨领域的学习，获取其他领域的知识和观点。例如，学习一些哲学、艺术、心理学等内容，这些学科有助于提高思维的广度，激发新的问题和思考方式。

• 记录和反思问题：每天写下至少一个你遇到的问题，尝试提出至少三种不同的解决方案。定期回顾这些问题和答案，从中寻找更深层次的联系和新的问题。

3. 培养自我认知与内省

内在的稳定和自我认知是提出深刻问题的基础，只有认识清楚自己的价值观、兴趣和情感，才能更有深度地探问世界。

• 定期自我反思：每周花时间独处，反思自己最近的行为、决策和情感。问自己：我做的这些事情背后的动机是什么？我是否真正在追求自己想要的目标，还是受到外界影响？

• 培养内在的独立性：不要让外部环境过度干扰自己的思考，培养自己的独立判断力。遇到问题时，学会从内心的价值观和信念出发，避免轻易受他人意见左右。

• 冥想与自我觉察：冥想和正念练习有助于提高自我觉察，减少杂念，使你能在纷繁的思维中保持清晰和集中，进而能够提出更具洞察力的问题。

4. 提升问题的提问技巧

提问的技巧是非常关键的，好的问题不仅能够揭示现象，更能指引深入思考。

• 使用“5W1H”法则：这是一个经典的提问技巧，指的是从“谁（Who）、什么（What）、什么时候（When）、哪里（Where）、为什么（Why）、如何（How）”六个方面去提出问题。例如，面对一个具体的事件，你可以从“为什么会发生？”、“如何防止重复发生？”等角度来提问，帮助自己看到更深层的联系。

• 分解问题：复杂的问题往往难以一蹴而就，可以将问题分解成小问题，逐步深入。例如，一个大问题“如何解决贫困问题？”可以分解为“贫困的根本原因是什么？”、“哪些社会结构导致了贫困？”、“如何有效分配资源？”等。

5. 与他人讨论和交流

与他人的讨论和交流有助于拓展视野，挑战自己的思维，形成更全面的理解。

• 与不同背景的人交流：与来自不同背景、专业和文化的人进行对话，了解他们的观点和理解。这种多样化的互动可以激发新的问题和思路。

• 参与辩论和讨论：加入辩论俱乐部或与朋友进行讨论，练习如何提出有深度的问题，并能清晰地表达自己的观点。同时，听取不同的意见和批判，可以帮助你反思自己的问题设定。

• 接受反馈与反思：在讨论过程中，接受他人的反馈，并从中学习，思考自己的问题设定是否足够精确，是否能够激发更深层次的思考。

6. 持续学习与探索

不断学习、探索和实践是提升提问能力的基础。

• 广泛阅读：阅读书籍、文章、报告等，尤其是跨学科的知识。了解不同领域的问题和解决方案，可以帮助你发现潜在的联系，提出更具创新性的问题。

• 参加课程与讲座：参加不同的学习课程或讲座，尤其是注重思维训练、创新方法论等内容，这些都会启发你如何提出更具深度的问题。

总结：

要成为一个有内核的人，培养提出问题的能力并不仅仅是关于思考本身，更是关于内心的稳定、批判性和创造性思维的融合。通过不断自我反思、培养跨学科的思维方式、提高提问技巧并与他人互动，你能够逐步形成独立、有深度的思考方式，并能不断提出推动自己成长和社会进步的问题。最终，这种内在的力量将帮助你在复杂的世界中找到属于自己的方向和答案。

在这篇文章中，我将演示如何在博客文章中添加各种类型的图片。

1. 基础图片语法

简单图片引用

![图片描述](图片路径)

例如：

![Next.js Logo](/images/posts/common/nextjs-logo.png)

带链接的图片

[![图片描述](图片路径)](链接地址)

例如：

[![站点预览图](/og-image.svg)](https://zhanbing.site)

2. 不同类型的图片使用

文章封面图

在文章开头添加封面图：

![博客搭建封面图](/images/posts/nextjs-blog-setup/cover.jpg)

内容配图

在文章中间插入相关图片：

![项目结构示意图](/images/posts/nextjs-blog-setup/project-structure.png)

代码演示截图

展示代码运行效果：

![代码运行结果](/images/posts/nextjs-blog-setup/demo-result.png)

3. 响应式图片

使用 HTML 标签实现响应式图片：

<img src="/images/posts/example/responsive-demo.jpg" 
     alt="响应式图片演示" 
     style="width: 100%; max-width: 600px; height: auto;" />

4. 图片并排显示

使用 HTML 实现两列布局

<div style="display: flex; gap: 10px; flex-wrap: wrap;">
  <img src="/images/posts/example/before.png" 
       alt="优化前" 
       style="width: 48%; min-width: 200px;" />
  <img src="/images/posts/example/after.png" 
       alt="优化后" 
       style="width: 48%; min-width: 200px;" />
</div>

5. 图片优化建议

文件大小控制

• 网页图片：通常控制在 100KB 以下
• 高质量图片：不超过 500KB
• 演示动图：控制在 1MB 以下

尺寸建议

• 封面图：1200x630 像素（符合社交媒体标准）
• 内容图片：最大宽度 800 像素
• 缩略图：300x200 像素

格式选择

1. WebP - 现代浏览器的最佳选择
2. JPEG - 照片和复杂图像
3. PNG - 透明背景或简单图形
4. SVG - 矢量图标和简单图形
5. GIF - 简单动画

6. 图片 SEO 优化

使用描述性的 alt 文本

![Next.js 15 项目初始化命令行界面截图](/images/posts/nextjs-blog-setup/init-command.png)

文件名优化

好的文件名示例：

• nextjs-15-features-comparison.jpg
• typescript-config-example.png
• tailwind-css-responsive-design.gif

避免的文件名：

• IMG_001.jpg
• 截屏2025-01-15.png
• 图片1.jpeg

7. 实用工具推荐

图片压缩工具

• 在线工具：TinyPNG、Squoosh
• 命令行工具：imagemin、mozjpeg
• 图像编辑器：Photoshop、GIMP

图片格式转换

# 使用 ffmpeg 转换格式
ffmpeg -i input.png -quality 80 output.webp

# 批量处理
for file in *.png; do
  ffmpeg -i "$file" "${file%.png}.webp"
done

8. 故障排除

图片不显示的常见原因

1. 路径错误 - 检查文件路径是否正确
2. 文件不存在 - 确认图片文件已上传
3. 权限问题 - 确保文件有读取权限
4. 缓存问题 - 清除浏览器缓存

调试技巧

<!-- 添加 onerror 事件处理 -->
<img src="/images/posts/example/demo.jpg" 
     alt="演示图片" 
     onerror="this.style.display='none'" />

总结

在博客文章中添加图片需要注意：

1. 组织结构 - 按文章分类存放图片
2. 命名规范 - 使用描述性的英文文件名
3. 优化压缩 - 控制文件大小和质量
4. 响应式设计 - 确保在不同设备上正常显示
5. SEO 优化 - 使用合适的 alt 文本

遵循这些最佳实践，您的博客图片将具有更好的性能和用户体验。

1. 提升认知的基础：学习与思考

(1) 广泛而深入的学习

• 为什么： 广泛学习拓宽你的视野，不同领域的知识交叉会让你看问题更深刻。

• 怎么做：

• 读经典书籍：覆盖哲学、心理学、历史、科学等领域。

• 学习一门深层次的专业知识，成为某个领域的专家。

• 跟随顶级思维者学习，比如阅读行业领袖、哲学家、经济学家的作品（如查理·芒格、尤瓦尔·赫拉利等）。

• 利用碎片化学习：在通勤或休息时听播客、音频课程（如《得到》、Coursera）。

(2) 深入思考与提问

• 为什么： 认知差异来源于你对事物的独立思考和深入理解，而不仅仅是知识的积累。

• 怎么做：

• 比如某个社会现象背后的经济原因，个人情绪背后的心理机制等。

• 问自己：“为什么是这样？还有其他可能性吗？”。

• 追问“本质是什么？”。

• 学会提问：

• 反复思考问题的假设和前提：

• 写作与总结：写下来可以逼迫你理清思路，并从混乱的想法中找到重点。

2. 提升认知的深度：批判性思维与框架构建

(1) 训练批判性思维

• 为什么： 批判性思维让你能够跳出现有的“认知框架”，避免盲从，找到隐藏的真相。

• 怎么做：

• 质疑信息来源：不轻信，而是寻找多方证据支持。

• 分析逻辑漏洞：判断一个观点是否站得住脚，是否存在矛盾。

• 对自己保持怀疑：假设自己可能犯错，不断检验自己的观点。

• 练习“反向思考”：从反面验证一个观点是否成立。

(2) 学会构建认知框架

• 为什么： 框架是帮助你理解复杂问题、快速分析和行动的工具。

• 怎么做：

• 学习经典模型：比如“第一性原理”（从本质问题出发）、SWOT 分析、二八原则、系统思维等。

• 建立自己的思考模板：对常见问题总结出处理方法，如决策时优先分析“可控因素”和“不可控因素”。

• 不断迭代你的框架：通过学习新知识或遇到新问题，改进自己的思维工具。

3. 提升认知的广度：接触不同视角和领域

(1) 跨领域学习

• 为什么： 不同领域的知识会形成交叉，带来创新性和独特的视角。

• 怎么做：

• 涉足与你本专业无关的领域，比如工程师学习心理学，设计师学习经济学。

• 关注领域之间的连接点，比如科技与人文、经济与环境的关系。

(2) 借助他人的智慧

• 为什么： 别人的经验和视角可以帮助你突破自己的思维局限。

• 怎么做：

• 与多元背景的人交流：跟不同行业、文化、年龄层的人探讨问题，获取新的视角。

• 阅读传记：学习那些改变世界的人是如何思考的（如史蒂夫·乔布斯、埃隆·马斯克）。

• 参与社群或论坛：加入专业圈子或讨论组，倾听高手的分享。

4. 提升认知的独特性：打破思维惯性

(1) 挑战常识与惯性思维

• 为什么： 常识背后往往隐藏着未被质疑的假设，挑战它才能找到认知差异。

• 怎么做：

• 质疑习以为常的规则：为什么事情一定要这样做？是否有更优的解决方案？

• 学会打破传统路径：尝试在问题解决上“绕开障碍”或者“重新定义问题”。

(2) 探索少有人走的路

• 为什么： 认知差异的来源之一是看到了别人没有看到的机会。

• 怎么做：

• 关注新兴领域：比如 AI、区块链、元宇宙、生物科技等，提前进入快速增长的赛道。

• 学习冷门但潜力巨大的技能或知识。

• 从细分领域切入：找到一个未被关注的小众问题或市场。

5. 提升认知的应用：实践与反馈

(1) 在实践中检验认知

• 为什么： 实践是检验认知的唯一标准，只有通过行动，才能知道自己的认知是否正确。

• 怎么做：

• 将你的思考付诸行动，例如尝试创业、做实验、设计小项目等。

• 通过结果回溯认知是否正确：如果失败了，分析为什么错；如果成功了，总结为什么有效。

(2) 借助反馈不断修正

• 为什么： 他人和环境的反馈可以暴露你的盲点，帮助你不断优化认知。

• 怎么做：

• 主动寻求反馈：请教比你更有经验的人，听取他们的建议。

• 接受批评：即使是负面的反馈，也能帮你改进。

• 学会反思：每天抽出时间回顾自己今天的思考和行动，寻找改进空间。

6. 长期主义：持续积累认知优势

认知差异是长期积累的结果，而不是一蹴而就。坚持以下原则，才能真正形成认知上的优势：

(1) 聚焦核心领域

• 在你感兴趣或擅长的领域深耕，成为这个领域的专家。

• 即使在学习其他领域，也以你的核心领域为基础，寻找关联点。

(2) 持续迭代

• 保持“动态认知”：世界在不断变化，过去有效的认知可能失效，持续学习是关键。

• 定期更新自己的思维模型，根据新的信息修正旧的看法。

(3) 保持开放心态

• 不断尝试新事物，不固守已有认知。

• 接受与自己不同的观点，避免陷入“认知舒适区”。

总结

获得认知差异的核心在于 比别人多看一步、多想一层、多做一点：

• 学习与思考：深入掌握知识，同时不断提问和反思。

• 批判与框架：训练逻辑和批判性思维，建立自己的认知模型。

• 广度与深度：跨领域学习，吸收多样视角，同时深耕一个领域。

• 实践与反馈：通过行动检验认知，并不断修正。

认知差异的本质： 它是看问题的角度、思维的深度和行动的积累的综合结果。只要坚持不懈地学习、思考和实践，任何人都可以逐步形成自己的认知优势，从而在生活和事业中脱颖而出。

在这篇文章中，我将详细介绍如何使用 Next.js 15、TypeScript 和 Tailwind CSS 从零开始搭建一个功能完整的现代化博客系统。

为什么选择 Next.js？

Next.js 是一个基于 React 的全栈框架，具有以下优势：

• 静态网站生成 (SSG) - 更好的性能和 SEO
• 服务端渲染 (SSR) - 首屏加载速度快
• 文件系统路由 - 简化路由配置
• 内置优化 - 图片优化、代码分割等
• TypeScript 支持 - 开箱即用的类型安全

项目初始化

首先创建一个新的 Next.js 项目：

npx create-next-app@latest my-blog --typescript --tailwind --eslint --app
cd my-blog

安装必要依赖

安装处理 Markdown 文件所需的依赖：

npm install gray-matter remark remark-html date-fns @tailwindcss/typography
npm install -D @types/node

依赖说明

• gray-matter - 解析 Markdown 文件的 frontmatter
• remark - Markdown 处理器
• remark-html - 将 Markdown 转换为 HTML
• date-fns - 日期格式化工具
• @tailwindcss/typography - 美化文章内容的样式

项目结构设计

my-blog/
├── src/
│   ├── app/
│   │   ├── page.tsx          # 首页
│   │   ├── posts/[id]/       # 文章详情页
│   │   ├── tags/             # 标签页面
│   │   ├── search/           # 搜索页面
│   │   └── about/            # 关于页面
│   ├── components/           # 组件
│   │   └── Navigation.tsx    # 导航组件
│   └── lib/
│       ├── posts.ts          # 文章处理逻辑
│       └── utils.ts          # 工具函数
├── posts/                    # Markdown 文章
└── public/                   # 静态资源

核心功能实现

1. 文章数据处理

创建 src/lib/posts.ts 文件：

import fs from 'fs'
import path from 'path'
import matter from 'gray-matter'
import { remark } from 'remark'
import html from 'remark-html'

const postsDirectory = path.join(process.cwd(), 'posts')

export interface PostData {
  id: string
  title: string
  date: string
  excerpt?: string
  tags?: string[]
  content: string
}

export function getSortedPostsData(): PostData[] {
  const fileNames = fs.readdirSync(postsDirectory)
  const allPostsData = fileNames
    .filter(fileName => fileName.endsWith('.md'))
    .map((fileName) => {
      const id = fileName.replace(/\.md$/, '')
      const fullPath = path.join(postsDirectory, fileName)
      const fileContents = fs.readFileSync(fullPath, 'utf8')
      const matterResult = matter(fileContents)

      return {
        id,
        content: matterResult.content,
        ...(matterResult.data as {
          title: string
          date: string
          excerpt?: string
          tags?: string[]
        }),
      }
    })

  return allPostsData.sort((a, b) => (a.date < b.date ? 1 : -1))
}

export async function getPostData(id: string): Promise<PostData> {
  const fullPath = path.join(postsDirectory, `${id}.md`)
  const fileContents = fs.readFileSync(fullPath, 'utf8')
  const matterResult = matter(fileContents)

  const processedContent = await remark()
    .use(html)
    .process(matterResult.content)
  const contentHtml = processedContent.toString()

  return {
    id,
    content: contentHtml,
    ...(matterResult.data as {
      title: string
      date: string
      excerpt?: string
      tags?: string[]
    }),
  }
}

2. 导航组件

创建 src/components/Navigation.tsx：

'use client'

import Link from 'next/link'
import { useState } from 'react'
import { useRouter } from 'next/navigation'

export default function Navigation() {
  const [searchQuery, setSearchQuery] = useState('')
  const router = useRouter()

  const handleSearch = (e: React.FormEvent) => {
    e.preventDefault()
    if (searchQuery.trim()) {
      router.push(`/search?q=${encodeURIComponent(searchQuery.trim())}`)
    }
  }

  return (
    <nav className="bg-white/80 backdrop-blur-md border-b border-gray-200 sticky top-0 z-50">
      <div className="max-w-6xl mx-auto px-4 sm:px-6 lg:px-8">
        <div className="flex justify-between items-center h-16">
          {/* Logo */}
          <Link href="/" className="flex items-center space-x-2">
            <div className="w-8 h-8 bg-gradient-to-br from-blue-500 to-purple-600 rounded-lg flex items-center justify-center">
              <span className="text-white font-bold text-sm">展</span>
            </div>
            <span className="font-bold text-xl text-gray-900">技术博客</span>
          </Link>

          {/* Navigation Links */}
          <div className="hidden md:flex items-center space-x-8">
            <Link href="/" className="text-gray-700 hover:text-blue-600 transition-colors duration-200">
              首页
            </Link>
            <Link href="/tags" className="text-gray-700 hover:text-blue-600 transition-colors duration-200">
              标签
            </Link>
            <Link href="/about" className="text-gray-700 hover:text-blue-600 transition-colors duration-200">
              关于
            </Link>
          </div>

          {/* Search */}
          <form onSubmit={handleSearch} className="flex items-center">
            <input
              type="text"
              placeholder="搜索文章..."
              value={searchQuery}
              onChange={(e) => setSearchQuery(e.target.value)}
              className="hidden sm:block w-64 px-4 py-2 border border-gray-300 rounded-lg focus:ring-2 focus:ring-blue-500 focus:border-transparent outline-none"
            />
            <button
              type="submit"
              className="ml-2 p-2 text-gray-600 hover:text-blue-600 transition-colors duration-200"
            >
              <svg className="w-5 h-5" fill="none" stroke="currentColor" viewBox="0 0 24 24">
                <path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M21 21l-6-6m2-5a7 7 0 11-14 0 7 7 0 0114 0z" />
              </svg>
            </button>
          </form>
        </div>
      </div>
    </nav>
  )
}

高级功能

标签分类系统

实现标签页面，让用户可以按标签浏览文章：

// src/app/tags/page.tsx
export default function TagsPage() {
  const allPostsData = getSortedPostsData()
  
  const tagCounts = allPostsData.reduce((acc, post) => {
    if (post.tags) {
      post.tags.forEach(tag => {
        acc[tag] = (acc[tag] || 0) + 1
      })
    }
    return acc
  }, {} as Record<string, number>)

  // 渲染标签列表...
}

搜索功能

实现全文搜索功能：

// src/app/search/page.tsx
'use client'

export default function SearchPage() {
  const [searchResults, setSearchResults] = useState([])
  
  const performSearch = (searchTerm: string) => {
    const allPosts = getSortedPostsData()
    const results = allPosts.filter(post => {
      return post.title.toLowerCase().includes(searchTerm.toLowerCase()) ||
             post.content.toLowerCase().includes(searchTerm.toLowerCase()) ||
             post.tags?.some(tag => tag.toLowerCase().includes(searchTerm.toLowerCase()))
    })
    setSearchResults(results)
  }

  // 渲染搜索结果...
}

部署配置

静态导出配置

在 next.config.ts 中配置静态导出：

/** @type {import('next').NextConfig} */
const nextConfig = {
  output: 'export',
  trailingSlash: true,
  images: {
    unoptimized: true
  }
}

export default nextConfig

GitHub Actions 自动部署

创建 .github/workflows/deploy.yml：

name: Deploy to GitHub Pages

on:
  push:
    branches: [ main ]

jobs:
  build-and-deploy:
    runs-on: ubuntu-latest
    
    steps:
    - name: Checkout
      uses: actions/checkout@v4
      
    - name: Setup Node.js
      uses: actions/setup-node@v4
      with:
        node-version: '18'
        cache: 'npm'
        
    - name: Install dependencies
      run: npm ci
      
    - name: Build
      run: npm run build
      
    - name: Deploy to GitHub Pages
      uses: peaceiris/actions-gh-pages@v3
      with:
        github_token: ${{ secrets.GITHUB_TOKEN }}
        publish_dir: ./out

性能优化

1. 图片优化

• 使用 Next.js 的 Image 组件
• 配置适当的图片格式和尺寸

2. 代码分割

• 利用 Next.js 的自动代码分割
• 使用动态导入优化组件加载

3. SEO 优化

• 配置 metadata
• 添加结构化数据
• 生成 sitemap 和 robots.txt

总结

通过这个教程，我们成功搭建了一个功能完整的现代化博客系统，包括：

• ✅ 响应式设计
• ✅ Markdown 文章支持
• ✅ 标签分类系统
• ✅ 全文搜索功能
• ✅ SEO 优化
• ✅ 自动部署

这个博客系统不仅功能丰富，而且性能优秀，是学习 Next.js 和现代前端开发的绝佳实践项目。

下一步

你可以继续扩展以下功能：

• 评论系统
• 文章阅读统计
• 深色模式
• 多语言支持
• RSS 订阅

希望这篇文章对你有所帮助！如果有任何问题，欢迎在评论区讨论。

Welcome to the site. I'm Zhanbing Li, a frontend engineer who enjoys building products, learning in public, and turning ideas into durable systems.

What this blog covers

Here you will find writing about:

• Frontend engineering: JavaScript, TypeScript, React, Vue.js, Next.js, and related tooling
• Learning systems: how I study, practice, and improve through real projects
• Tooling and workflow: developer tools, productivity systems, and useful resources
• Long-term growth: reflections on building skills and doing better work over time

My stack

Most of my work currently revolves around:

Frontend frameworks

• React - the framework I use most often
• Next.js - for full-stack web products
• Vue.js - a flexible option for structured frontend work

Development tools

• TypeScript - JavaScript with stronger guarantees
• Tailwind CSS - utility-first styling for fast iteration
• Git - version control that keeps work traceable

Backend tools

• Node.js - the runtime behind many of my projects
• Express - a simple way to ship web services
• MongoDB - document storage when the use case fits

What the site is built for

This blog is built with Next.js 15 and keeps the experience intentionally simple:

1. Clean design for focused reading
2. Responsive layout that works across devices
3. Search to find posts quickly
4. Tag archives for topic-based browsing
5. Solid SEO defaults for discoverability

Contact

If you have feedback, questions, or just want to say hello:

• GitHub: Zhanbingli
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Published on January 1, 2025, as the first post on this site.

最近，我发现以前的同学保研了，心里有点羡慕，哈哈，还有点嫉妒，有复旦的，还有国防科大的，当然也有本校推免的。对于我这样，既不能考研，也不用担心工作的定向医学生确实会感到没啥前途可言，但好在，我找到了一种很好的方式来平衡自己的未来。

如果你是一位即将大学毕业的年轻人，目前不打算考研，也没保研，那这个专访内容值得你耐心看完。（温馨提示：内容有点多。）

【澎湃新闻对话复旦教授梁永安】

澎湃新闻：关于“提倡有条件的大学生毕业之后啃几年老”，可否介绍一下你说的“啃老”是什么含义？这样的建议有什么具体的语境吗？

梁永安：关于建议年轻人毕业后先啃老几年，这样的话我其实在多个场合提到过不止一次。所谓“啃老”并非指追求安逸、直接躺平、不劳而获。而是建议年轻人在大学刚刚毕业之后，先不着急直接投入工作。在找到理想工作之前，可以给自己两三年时间，慢下来探索自我、理解差异、认识世界，建立更宽广的人生观念，如此能帮助他们走得更远。

在我看来，一个年轻人如果按部就班地从毕业到就业，过程中会存在一定的“缺环”，也就是缺少自我探索阶段。没有走过世界，就没有能力理解世界的广泛和差异性，眼下虽然安稳，但后面会缺失竞争力和生长力。

其实这样的建议有一定的语境。按照社会人口统计，现在中国18到24岁年龄段的年轻人，受高等教育的比例是有史以来最高的。这批受过良好教育的年轻人，理应拥有更高质量、更具创造性的发展。

但目前的现实情况是，大学生面临的就业形势严峻，学历挤压现象较为严重，年轻人选择的质量开始下降，人生的可能性更加逼仄。甚至有不少人觉得毕业后无业会被判定为“失败”，为了逃避“无业焦虑”，有时候会仓促地选择一份工作就起步。但如果将就选择不喜欢的工作，年轻人很容易在工作一段时间后，出现职业倦怠。

我希望年轻人能够意识到，目前的工作选择空间只是被前一代人建构出来的，这个世界上除了既有的社会分工体系外，还有很多工作选择的“空白”。年轻人应该尽量避免受限于被规定好的狭窄劳动空间，而去探索更多未知的、富有活力的增长点。很多时候，思维的受限是“只缘身在此山中”，所以我觉得可以考虑借鉴国外“gap year（间隔年）”的工作性旅行方式，前往跨文化、跨文明的空间里体会生命应该怎样过，再回头看就可能形成新的自我关照。这不仅是对年轻人个体的意义，还有对整个社会发展的长远意义。

在进行跨文化体验时，有时需要外部资源支持一下，才能拥有前进探索的自由，这就形成了“啃老”一说。就像梵高依靠弟弟的支援才能自由地作画，肆意发掘天赋；巴尔扎克大学毕业之后不愿从商，家人虽不满意但也勉强同意给他三年的生活资金，他才得以坚持写作。此刻的“gap year”并非游手好闲，恰恰相反，是为了更远期的成长性而投资，甚至是对长期发展更有思考、更有规划的体现。

澎湃新闻：“啃老”有怎样的适用边界？你强调“有条件”是指怎样的条件？

梁永安：“啃老”需要综合看家庭经济能力，以及青年人的未来规划意识、心理成熟度等。如果家庭的经济条件的确并不适合支持孩子自由探索，更迫切需要获得收入，那么“是否啃老”已经不只是经济问题，而涉及代际伦理问题、道德问题。这种情况下，我也会建议年轻人先找工作，在工作过程中开展自我探索。

澎湃新闻：你提到允许年轻人“啃老”和自由探索，不仅是对年轻人个体的意义，还有对整个社会发展的长远意义，如何理解？

梁永安：从更广阔的中国社会发展过程中来看，当前的时代有两重不可避免的趋势，即更加深度的城市化和中产化。

一是整个社会的城市化转型。从1978年城市人口只占18%，到2023年末常住人口城镇化率达66.16%，城市化将是必然趋势。尤其是当下的城市化发展且不再局限于大中型城市，而是向2800多个县城蔓延，可以看到县城的图书馆、歌剧院、音乐厅、博物馆也开始大规模普及，人们的精神需求普遍放大。同时县乡里大量原生的物质资源、非物质文化遗产，尚缺乏挖掘和转化。比如中西部文创资源非常丰富，大量富有民族性的文化资源、古老传统、图案器具等尚未被纵深发掘，其中潜藏很多的创造环境，年轻人只有在大地上先走过、理解过、观察到，才能有所发现。

二是中产化，即生活方式的转变。人的需求从衣食住行的需求向着更高的精神需求、文化需求、心理需求转变。社会文明的内在质量正发生变化。人们的思维方式、价值观、对现代契约、游戏规则的认知都要有变化，未来所需的人才模式也将不再一样。

现代社会的这两大转向里，蕴藏着青年人重新认识世界的契机、创造新生活的契机。如果青年只追求快速安稳、安身立命，过早将自己框住，丧失的可能是历史机遇。我认为不管是时代的资源，还是当代年轻人的学识、认知资源，都不应该被浪费。利用好天分与学识，把握创造性空间，是这一代人的历史使命，对社会、国家的未来发展也会有长远影响。

澎湃新闻：毕业先“啃老两三年”的说法，一定程度上，违背了求学就业、买房安身、结婚生子的常规“社会时钟”，也与中国传统的“孩子成年就自力更生养活自己、赡养父母”的代际模式有冲突性，如何让父母理解“啃老”这件事？

梁永安：其实何时求学、就业、买房安身、结婚生子的“社会时钟”，也来自既定的文化建构。我相信未来将是更具流动性的文化创造时代，人们的生活需求将越发差异化，不会再像过去那么标准化、板块化。因此在这样的环境中，更需要每个人发展文化个性，看到远方即将发生的变化。

两代人代际之间观念难以调和也是非常正常的事情。主要因为父母辈与年轻人的思维模式存在根本差异，如同农耕时代与航海时代的两种模式的碰撞。上一代父母是较为偏向农耕社会的文化心理，渴望平稳安定，很多父辈对子女的期待停留在“安身”上，希望让孩子在诸多不确定中找到坐标，早日获得稳定工作、买房结婚生活，风调雨顺过一生。从数据统计来看，中国首次购房者平均年龄仅为27岁，比很多发达国家的青年都早10年左右。但这样的快速安定，可能跟社会发展下一步对年轻人的期待和必然出现的新型人才模式错位。

我非常建议父母在有条件支持孩子的情况下，应该先谨慎考虑往哪个方向去给孩子投入：是为了此刻的安身，还是为孩子未来十年后的发展而投资？用于买房安身的这笔宝贵资金，是否还有更加能发挥价值的地方？

当代年轻人的思维模式更偏向航海时代的思考方式。他们实际上算是“现代中国成长的第一代人”，处在时代交替之中，年轻人需要出海探索未知，发掘创造力，实现自我的精神成长、建立对世界更广的理解。两代人的思维方式存在根源上的差异，但在代际维度上又贴得很紧密。因此难免会有不理解和摩擦。

我认为当代父母应该扮演的角色，是作为一个很好的港口，用温暖目光看着孩子扬帆远航，但要随时准备好迎接他路上可能遇到的不顺利和波折风浪，帮助他疗伤安歇再出发。父母和孩子最好能保持这样一种文化关系和情感关系便可以了。

澎湃新闻：有人认为“啃老”是逃避工作、延缓进入社会的借口，并指出工作也能帮助年轻人理解世界，无需“gap year”。对此你怎么看？

梁永安：不可否认工作对年轻人的成长是有意义的。工作关系不仅是科层制下的一个指令体系，工作过程中跟你相处在一起的群体也是富有差异化的共同体，我们可以在工作相处中体会到人性深处或者社会关系的复杂性，有利于形成对社会的观察能力和链接能力。此外，在工作中常常遇到很多难关，也会让年轻人发扬韧性、磨炼坚持力。这些品质是不管做什么事情都需要具备的。

我建议年轻人可以“啃老三年”的意思，并非拒斥工作，不是说“如果有一份理想的工作我偏不去，一定要去漫游”。而是不必那么着急地、迫于约定俗成而按部就班地走进工作环境。在进入工作岗位之前，可以给自己保留成长探索的时间和可能性，别轻易让自己步入将就的选择。

正如我之前也对年轻人提过建议：“一辈子只需要拿一半的时间去工作就行了，你活在这个世界上，一定要努力去过自己喜欢的生活。”

澎湃新闻：如果拥有“啃老”条件，你觉得年轻人如何利用好这段时间、做哪些事情，能够真正达到探索自己、理解世界的效果？对年轻人的自由探索有何建议吗？

梁永安：我想探索过程不单是要有知识与技术的磨炼，更重要的还是内在精神力量的成长。对于一个年轻人的成长要素来说，我认为最需要的是才华、专业、勇气。

一个人一辈子不要轻易放弃寻找自己的才华点。其实这个世界上人人都有才华，但很多人一辈子没有达到自己应该达到的才华点，只是缺乏了探索的机会。过去某些社会评价体系可能存在单向度的倾向，导致唯独标准化能力被认可，其他方面的才华得不到重视。但今天的社会是多元的，存在极为广阔的社会分工领域。年轻人更应该抓住机会，别放弃寻找自己的天赋与才华。一个人到底适合什么，必得经历探寻过程。“活到自己的天分上去”将会是一种很尽兴的人生。

第二是专业，任何行业都有运作逻辑和系统，投入任一行业都需要长期观察和磨炼基本功。比如深入了解一个地区、一种产业或者行业发展到什么水平。拿当下的城市化背景举例，年轻人完全可以潜下身去，到县乡探索，将原生的县乡遗产文化带到大城市中挪用为新元素；也可以将大城市的社会观察、文化理念等带到县乡，这其中会诞生很多有待打开的全新公益空间，年轻人可以尝试尽兴地参与社会、建立社群联系，将情感、精力释放到劳动实践中。而后会发现，任何专业发展到较高的程度，都是一门艺术，是具有创造性的。

第三是勇气。在探索体验的过程中，会发现世界上很多原以为跨不过去的东西其实都可以跨过。你可能也会发现很多人不赞成你，因为你走的路或者价值选择过于独特，甚至让一些人觉得被冒犯。但一个人面对自己独立的选择，要有坚持的勇气。你也将在不断的观看和探索中扩展心理容量，打破一元化的认知，不再只和自己喜欢的人在一起，学会接受不符合自己价值观的一切，理解不赞成我的人。年轻人要学会差异性生存，学会和很多不同的人在一起共生、创造更好的世界。而这首先就要经历很多不同，才能达到如此广阔的心理空间。

澎湃新闻：或许花两三年探索世界之后，并未获得大多数人眼中可变现的智识、可量化的“收获”。如何看待这段旅途可能的“无意义”“没结果”？

梁永安：即便没有得到太实际的回报，但是阴晴风雨都是所得。从过程哲学上来看，已经足够了。就如“被认识的生活才值得过”，获得的一切都已经在自己心中，追求的从来不是为别人可见的结果。哪怕游历一圈归来，仍住简陋的房子，也没有办法买车，但觉得心理上曾有波折，拥有了不一样的过程，看过世界和生命的颜色，未尝不是一种“满载而归”。

结论：当找工作很难，成本很高时，不妨试试“gap year”。（我自己添加的）