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The Car Light Modifier and the Printer Renter Start Learning AI

GokuScraper悟空爬虫 — Wed, 03 Jun 2026 10:22:11 +0000

The Car Light Modifier and the Printer Renter Start Learning AI

Let me tell you a funny story.

Even though it’s a small thing, I think it’s worth writing down. Because it’s so visceral—so direct that it slaps you right in the face and forces you to see what’s actually happening in the AI era.

Here's the deal.

I have a friend named Hao. He sells a product on Taobao. Not a physical item, but an installation tutorial for Codex. It costs just a couple of bucks—the kind of cheap where you literally can't get ripped off.

The day before yesterday, he sent me two screenshots from his seller dashboard.

Just two screens. No order notes, no inquiries, no "Hey, are you there?" Just two purchase records, silent as can be.

But right there in the buyer ID section, their store names were visible.

One was called "Car Light Modification". The other, "Printer Rentals".

I was stunned.

Wait a second, let me add some context.

You might think, it’s just a couple of bucks for a tutorial, basically a random click—what does that prove?

Alright, let me give you another number. A Codex subscription costs around fifty bucks a month.

See? This isn't just "buying it to take a look." This is a decision to actually pull decent money out of their own monthly profits.

Okay, keeping that number in mind, look at those two names again.

I don't know what comes to your mind when you see them. But my first mental image was an auto parts market. The kind of shop where, the moment you walk in, you're hit with the smell of engine oil mixed with rubber. Shelves stacked with projector lenses, ballasts, and angel eyes; wall racks lined with headlight assemblies. A mechanic, still bearing the black grease marks from tightening bolts, sitting behind an old PC with a yellowing monitor bezel on the counter.

He wasn't browsing Taobao to kill time. He was probably trying to build something. For instance, an automatic configurator for custom lighting setups. Punch in the car model and year, and it auto-matches the lens model, wattage, whether it needs a decoder—and bam, the quote is generated.

He does not know what a neural network is. Or a transformer. And he doesn't need to.

He just knows that if he has this thing, his quote drops 10 minutes faster than the shop next door. The customer is standing right at the counter waiting, and sometimes, those 10 minutes are the difference between closing a sale and losing it.

Now look at "Printer Rentals".

This one is even wilder.

Think about what a guy renting out printers does every day. He’s either delivering machines, fixing machines, or wrestling with toner cartridges and ink. He probably holds the contact info of a thousand corporate clients in his hands. Which company's contract expires next month? Whose toner needs replacing? Which machine has completely jammed up and needs swapping out? All of this is either in his head or scribbled in a beaten-up, dog-eared notebook.

Why the hell is he buying a Codex tutorial?

He’s not trying to write poetry or make PowerPoint decks. He probably wants to write a script to auto-track consumable lifespans, send automatic renewal reminders to clients, and generate contract renewal plans. He wants to liberate himself from that torn-up notebook.

Look, this is the most fascinating part of the whole damn thing.

Every day, we watch the news about AI. We see LLM parameters multiplying, read about top-tier conferences publishing endless papers, and hear about another unicorn raising billions in VC money. We have this illusion that this revolution is happening inside bright corporate high-rises, on whiteboards in the meeting rooms of tech communes, or in coffee shops where venture capitalists wave Term Sheets around.

But it really isn't.

The real revolution is happening in Taobao seller dashboards.

It’s happening in transaction logs worth a couple of bucks. It’s happening in quietly deducted monthly subscriptions. It’s happening in the most unglamorous, blue-collar industries that you will never see on a tech blog.

If you want to judge whether a tech revolution is real or fake, shallow or profoundly deep, don't look at the spotlight on center stage. Look at this. Look at whose hands those cheap tutorials are finally ending up in. Look at who is silently paying that monthly subscription.

Does this look like hype?

Yeah. It fucking does.

An auto light modifier and a printer rental guy both running to learn AI programming. You're telling me this isn't a hype bubble? You're telling me they aren't just easy marks? Call it that publicly, and you'll get a flood of comments: "Idiot tax," "Getting fleeced," "Every random is trying to jump on the bandwagon."

But.

Think one layer deeper.

What is going through the mind of that auto light shop owner? Is he thinking, "I'm going to launch an AI startup"? Is he thinking, "I'm going to disrupt the industry"?

Bullshit.

He’s thinking: Can I use this thing to pump out quotes faster, make better setups, and snatch one more deal away from the shop next door?

And the printer rental guy? He’s thinking: Can I use this to manage my 1,000 clients so I get auto-reminded when their contracts are up, before my competitors poach them?

You call that hype?

That is an incredibly cold-blooded survival instinct. It is survival-driven micro-innovation.

This isn’t a bubble blown up by VC cash burns. This is driven by bottom-tier, raw survival competition. These guys aren't tech evangelists or AI purists. They’re just small business owners hustling on their own tiny turf, fighting like hell to live just a little bit better.

They heard about this thing, and heard it might be useful. So they spent a few dollars to buy it and try it out.

Actually, later I realized an even harsher truth. These guys most likely had no idea that just installing Codex isn’t enough. There’s still a monthly subscription fee attached.

When they bought the tutorial, they probably genuinely thought a couple of bucks was all it took.

But take it one step further—what does that mean? It means their survival instinct was so strong that they rushed in before even getting the full picture. They didn't even know if it was a trap or a path, but they caught a whiff of "maybe useful," and they already planted their foot in the door.

You call them suckers?

That is sheer, raw vitality.

They are using whatever means necessary, grabbing the cheapest, most accessible AI tools they can find to solve the hyper-specific pain points within their own tiny commercial kingdoms.

This is the most terrifying kind of market penetration.

It doesn't make noise, it doesn't raise funds, it doesn't hold keynotes. But it is real.

From "Buying a Machine" to "Equipping an Upgrade"

Okay, let's talk about the big picture here.

You ask me whether this is the Industrial Revolution or just AI Hype.

My answer is that it’s more profound than the Industrial Revolution. Because it's doing something the Industrial Revolution never did: inverting power.

Think about the steam engine era.

If you wanted to open a textile mill, you had to buy a steam engine first. That thing weighed dozens of tons. You needed a dedicated factory floor for it, boilermen, and mechanics. That machine was the boss, and you had to serve it. Tech was centralized, and power was distributed from a single top-down shaft. The hundreds of workers in your factory—including you—were all ultimately accessories to that machine.

Buying that machine didn't give you power. It turned you into a part of its system.

Now look at the guy who bought the tutorial.

He bought a tutorial for a few cents.

He isn't buying "part of a system." He is using AI to arm himself into a more powerful, independent system.

The core of "installing Codex" isn't about installing software. It's a blue-collar sole proprietor installing an intellectual upgrade for himself.

Before, he could only rely on his hands and his brain. Now he has something else—a tool that can write code, calculate data, and build spreadsheets for him. One single guy suddenly possesses a fraction of the soft power that only massive corporations used to be able to afford. He doesn't need to hire programmers. He doesn't need to buy an ERP system. His little, grease-stained, toner-covered shop is suddenly digitally armed.

What is this?

This isn’t just technological progress, my friend. This is a transfer of power.

In the past, scale was the moat. Big corporations had the money to buy systems, employ tech teams, and use information asymmetry to crush small mom-and-pop shops.

Now, a car light modifier and a printer renter can spend a couple of dollars and potentially level that playing field—just a little bit.

Even if it’s just a little bit.

That is a revolution.

The Industrial Revolution of the Ordinary Person

So back to the original question. Is this an Industrial Revolution, or AI Hype?

I say, it’s a silent revolution disguised as "hype."

The elites are still sitting around debating the ethical threats of AI, arguing over whether it’s just another bubble. But the grassroots market doesn't care about that bullshit. They are voting with their wallets. Those few bucks, and those monthly subscription fees, are the most authentic, burning-hot ballots cast in our era.

They don't need to know how the tech works under the hood. They only need to know if it can help them make another hundred bucks.

This isn't even an Industrial Revolution anymore. It’s more like a Renaissance.

What did the Renaissance do? It liberated humans from the authority of God and placed humans at the center of the world.

What is the democratization of AI doing today? It is liberating ordinary people from "technological authority" and "capital scale." It allows a guy who fixes car lights and a guy who rents printers to become masters of tech, not just consumers, and not just flesh and bone on an assembly line.

So, yes.

This is the Industrial Revolution.

But not the kind started by Watt and Boulton in grand halls and later written into high school history textbooks. No.

This revolution is being collectively ignited by countless nameless "John Does" fixing lights and renting printers. It happens the moment they spend a couple of dollars on Taobao, sitting in their dingy, grease-and-toner-scented shops, and click "Install".

This is the Industrial Revolution of ordinary people.

It’s a Renaissance that smells like motor oil.

The Fire-Sellers

Finally, back to Hao.

That day, after he showed me the screenshots, he stared at those two orders in his dashboard for a long time.

I asked him, "Aren't you afraid people will call you a scammer? You sell this tutorial for a few bucks, and when they buy it, they realize they still need to shell out for a monthly subscription. What if they turn around and call you a fraud?"

He said, "I'm selling an installation tutorial. For a few bucks. The title says 'Installation Tutorial', the description says 'Installation Tutorial'. I am just clearly explaining how to install the thing. Whether they subscribe after installing it is between them and Codex. That has nothing to do with me. My tutorial is worth exactly what I charge for it, and I know that."

Then I asked him, "So what are you really selling for those few bucks? Just a few installation steps? You can Google that stuff."

He answered it himself.

"No, I'm selling an ember. A spark."

"A thought of 'What if this actually works?' A possibility of 'I can try this too.' The last bit of stubbornness that says, 'Fuck this, I'm not getting left behind by this era.'"

Those most grounded, most stubborn small business owners are taking this spark and using it to light up their own little slice of the world.

Most likely, they will fail. They might fail to install it, realize they can't afford the subscription, or tinker with it for half a day only to find it doesn't help them at all.

But that doesn't matter.

What matters is that they showed up.

They lifted their heads from their own little worlds, took a look outside at what was happening, and made a decision: I need to try this too.

We are lucky.

We are lucky to be pitchfork sellers and observers in the most granular corners of this era.

These silent backend orders are the most authentic, raw footnotes of our time.

Thank you all for reading! If you found this interesting, please go ahead and like, subscribe, and share!

To catch my articles as soon as they drop, don't forget to star ⭐ the account, so you won't lose track of us.

Alright, that's it for today.

Win or lose, life is grand. See you next time!

I Roasted My Friend's X (Twitter) with This Open-Source Tool and Got Blocked...

GokuScraper悟空爬虫 — Mon, 01 Jun 2026 10:06:32 +0000

I Roasted My Friend's X (Twitter) with This Open-Source Tool and Got Blocked...

Recently, that Wordware Twitter personality analysis tool blew up. Everyone was lining up to try it, and their servers were absolutely crushed. I took a look, and I had to admit—it's incredibly fun. You plug in an X (Twitter) handle, and the AI fires back a personalized personality "diagnosis" packed with savage, painfully accurate roasts.

But the pain points are obvious: It's slow as molasses, and it's not open-source.

Enter today's star: X-POSE, an open-source, free Twitter personality analyzer you can run in your browser with one click. Powered by the DeepSeek V4 Pro model, it scrapes your (or your friend's) tweets and generates a 15-dimension report card. It even lets you download high-res screenshots to instantly drop into your group chats.

I tested it on myself, and the AI roasted me so hard I legitimately considered deleting my account.

Just How Idiot-Proof Is This Tool?

In a word: Brainless.

You don't even need to download any code. Just pull up the web app (link at the end), type in a Twitter username, and the tool handles the rest automatically:

Grabs the profile picture, bio, and recent tweets (bypassing anti-bot protections without you lifting a finger).
Feeds all that juicy context straight to DeepSeek using a custom "savage roast" prompt.
In a few dozen seconds, it renders 15 report cards: About You, The Roast, Strengths, Weaknesses, Love Life, Wealth, Health, Career... There's even a "What people secretly think of you" section.

The best part? You literally only do one thing: Enter a Twitter ID. It's a true one-click cyber-fortune-teller.

Why Should You Bookmark This (Even If You Don't Use It Right Now)?

Because this thing is built for viral social sharing.

Think about it:

Take a screenshot of your (or your buddy's) savage report, post it on Twitter or Insta, and watch your comments blow up.
The downloaded PNGs are high-quality enough to use as your phone wallpaper for some self-deprecating humor.
It has a built-in "Share to X" button with pre-written copy, ready to post.

Plus, it's fully open-source. That means as long as the dev doesn't delete the repo, you can spin up your own private instance anytime, drop in your own API key, and play with it however you want. Bookmark it now, and be the first in your friend group to break out this absolute weapon.

Let's Be Real: What Are the Limitations?

I'm not going to blindly hype it up. Here are a few real caveats:

Scraping can be hit or miss. Twitter's anti-bot measures act up randomly. You might run into failures and have to retry. The creator uses a cloaked browser to bypass it as much as possible, but it's not a 100% guarantee.
The analysis relies on tweet quality. If you run it on a burner account that never tweets, the AI has nothing to work with, and the report will be pretty generic.
The AI has absolutely no chill. DeepSeek can sometimes cross the line from "funny" to "mean." It's incredibly entertaining, but please don't take it too seriously or use it to start actual beef.
The web app limits concurrent users. To save on server costs, the dev capped the number of people who can analyze accounts at the same time. During peak hours, you might have to wait a little bit.

But honestly, given that it's free and hilarious, these are minor nitpicks.

How to Get Started

The Lazy Way (Recommended)

Just hit the web link at the bottom, enter a Twitter username (supports @username, full URL, or just the handle), click analyze, and wait for the magic to happen.

The Hacker Way (Deploy it yourself)

Clone the repo: git clone https://github.com/gokuscraper/x-pose.git
Install dependencies: pip install -r requirements.txt
Install Chromium: playwright install chromium
Drop your SiliconFlow API key into .streamlit/secrets.toml
Run it: streamlit run streamlit_app.py

Takes exactly 5 minutes. If you have a Python environment, you're good to go.

A Few Pro Tips

Test it on a burner or a celeb first. Don't jump straight to roasting your crush. If the report is too savage, feelings will be hurt.
Check for sensitive info. The reports pull snippets from active tweets. Make sure you're not doxxing yourself before you share the screenshots.
Switch between English and Chinese. There's a language toggle in the sidebar, so you can roast your international friends too.
If it fails, just try again. 99% of the time, the second attempt works perfectly. It's usually just network hiccups.
Remember, it's just for fun. No matter how smart the AI is, it's just doing probability math on text. It's not a real psychological evaluation—just laugh it off!

Summary & Links

TL;DR: X-POSE is currently the most out-of-the-box, open-source "Twitter personality analysis + roast" tool out there. Period. Play with it online, deploy it locally, share the screenshots, switch languages, and enjoy DeepSeek's razor-sharp tongue.

Live Demo: xpose7.streamlit.app

GitHub Repo: https://github.com/gokuscraper/x-pose

If you find it funny, go star the repo and let more people know about this hidden gem.

Warning: Proceed with caution. Make sure your ego can handle getting completely roasted by an AI before playing.

云展网电子画册下载？这个开源工具，可能是目前最适合普通人的方案

GokuScraper悟空爬虫 — Fri, 29 May 2026 10:54:59 +0000

云展网电子画册下载？这个开源工具，可能是目前最适合普通人的方案

云展网上的画册、杂志、企业宣传册确实做得漂亮，翻页效果丝滑。但问题来了——你想把某本电子书保存下来离线看，或者做个资料归档，发现官方压根没给下载按钮。

别急，今天彪哥就给大家安利一个开源小工具：悟空云展网下载器。完全免费、不用登录、不用学代码，粘贴链接就能把整本书变成 PDF，是真的“有手就行”。

一、为什么要下载？

离线阅读：飞机上、地铁里没网的时候，随时翻看重要资料。

内容存档：有些电子书可能过段时间就下架了，提前备份更安心。

二次利用：比如做报告时需要引用其中几页，有了 PDF 就能直接截图或标注。

以前要干这事儿，要么得一张张截图拼成 PDF，要么就得折腾浏览器插件和抓包工具，普通人根本搞不定。现在有了这个工具，难度直接降到“会粘贴网址就行”。

二、这个工具有多“傻瓜”？

彪哥用了这么多下载工具，这个的体验能排前三。

免登录、免配置 不问你账号密码，不让你去注册，也没有 “请先关注公众号获取密码” 的套路。工具本身是开源的，放心用。
一键出活 在网页界面输入云展网链接，点“开始执行任务”，下面就会开始跑进度日志：全程肉眼可见，不用自己操作任何一步。
下载后顺手分析 还能直接告诉你：这个 PDF 一共多少页、文件多大，省得你再去右键属性。

说句实话，这套流程对普通用户来说，已经做到极致友好了。

三、为什么我建议你哪怕现在不用，也得收藏一下？

因为这种需求往往来得特别突然。

比如领导突然要一份三年前的电子内刊，或者你正好看到一个很棒的摄影画册想存下来当参考。到时候再去找工具，要么满屏广告，要么要付费，要么早失效了。

这个项目放在 GitHub 上，MIT 协议开源，在线体验地址也一直挂着（https://yunzhan.streamlit.app/），哪天需要了，打开就能用。收藏不吃亏，用上一次就回本。

四、实话实说：它有哪些局限性？

彪哥一向不爱吹得天花乱坠，这个工具也有几个明显的限制，要提前跟大家说清楚：

只能下载公开书籍 如果云展网的电子书需要登录、或者设置成了私密，那就下载不了。工具没有破解账号权限的能力。
分析功能还比较简单 现在只能看总页数、文件名和文件大小，更高级的分析（比如页面分辨率分布、色彩统计）要等作者后续更新。
不是官方工具 这跟云展网官方没有任何关系，纯粹是第三方开发者做来方便大家用的。所以哪天平台改版了，有可能暂时失效，需要等维护更新。

五、具体怎么上手？

就三步。

第一步：下载项目
去 GitHub 仓库 gokuscraper/yunzhan365-scraper 把代码拉下来，或者直接点右上角的 “Code” → “Download ZIP”。

第二步：安装依赖
打开命令行，进入项目目录，运行：

pip install streamlit pillow

确保你的电脑已经装了 Node.js 和 Python 3.10+。

第三步：启动界面

streamlit run streamlit_app.py

浏览器会自动打开 http://localhost:8501，看到页面后粘贴链接，开始下载就行。

如果你连装环境都嫌麻烦，可以直接用作者提供的在线版： https://yunzhan.streamlit.app/

六、彪哥的一些私人建议

先试试在线版：不用装任何东西，纯粹体验一下流程。能成功下载一本再决定要不要部署到本地。

选书有讲究：优先下载自己真正想存档的，或者很快会失效的内容，别把工具当“屯书”用，理性使用。

尊重版权：下载下来的 PDF 你自己看、学习、研究都没问题，但不要二次分发或者商业用途，毕竟内容版权还是人家的。

遇到报错别慌：八成是 Node.js 没装好，或者链接不对。去项目的 “常见问题” 里看一眼，基本都能解决。

七、总结与项目地址

悟空云展网下载器 就是一个让普通人也能轻松把云展网公开电子书存成 PDF 的小工具。零门槛、可视化、干净利落。

GitHub 仓库：https://github.com/gokuscraper/yunzhan365-scraper
在线体验：https://yunzhan.streamlit.app/

最后再啰嗦一句：工具虽好，请别滥用。记住它的定位——帮助你看得更方便、存得更合理，而不是拿去干坏事。

感谢各位朋友捧场！要是觉得内容有有点意思，别客气，点赞、在看、转发，直接安排上！

想以后第一时间看着咱的文章，别忘了点个星标⭐，别到时候找不着了。

行了，今儿就到这儿。

论成败，人生豪迈，我们下期再见！

Why Can't the Chinese Internet Nurture a "Generous Hugging Face"?

GokuScraper悟空爬虫 — Thu, 21 May 2026 11:33:07 +0000

Why Can't the Chinese Internet Nurture a "Generous Hugging Face"?

If you are an AI developer, Hugging Face is probably your go-to place for "freebies."

Llama, Gemma, Qwen... you can download model weight files that are tens or hundreds of gigabytes in size with a single click, completely unrestricted.

Want to upload your fine-tuned models? Go ahead, it's totally free for public repositories, and even private ones have a free tier.

Want to showcase a live demo? Spaces is free to use, and they even throw in some computing power. Developers happily take advantage of all this, while Hugging Face gives it away effortlessly.

Some people even use it as a cloud drive or CDN. The experience feels a lot like GitHub—store your code freely, long live the open-source spirit!!!

But if you turn your attention to China, the picture looks completely different.

ModelScope, WiseModel, OpenI... these platforms are certainly working hard to build open-source ecosystems, but you’ll notice a subtle difference: download speeds are strictly calculated and controlled, uploading files requires a stricter review process, and various "anti-freeloader" mechanisms lurk in the background, ready to throttle you if you aren't careful.

The overall vibe can be summed up in a few words: strictly managed and meticulously calculated.

This raises a puzzling question. They are all AI model hosting platforms, both catering to developers looking for free resources—so why are their postures so different?

Is Hugging Face just "dumb and rich," or do domestic platforms lack the "bigger picture"? How exactly is the underlying math of these costs calculated?

The answer lies in something seemingly inconspicuous but actually incredibly heavy: public network bandwidth.

In this article, we’ll cut through this angle and peel back the layers: who is really paying for Hugging Face’s "generosity"? Why is public network bandwidth in China absurdly expensive? How does the price gap in residential broadband spawn the PCDN gray-market arbitrage? And why did telecom carriers in 2026 crack down so hard on "freeloaders"?

Hugging Face’s “Generosity” Is Paid For By Tech Giants

At first glance, Hugging Face seems like the ultimate charity in the AI world.

Developers can upload models freely, often moving weight files in the tens or hundreds of gigabytes, with unlimited and unthrottled downloads.

By 2026, the number of public models on HF had surpassed 2.5 million. This level of generosity would make even GitHub call it "big brother"—after all, how big is a code repository? The weight file of a mainstream 70B model in 2026 is equivalent in size to hundreds of thousands of code repos.

So, in the minds of many developers, Hugging Face is like a living Bodhisattva with more money than sense.

But if you believe that a unicorn with a secondary market valuation approaching $9 billion is surviving purely out of the goodness of its heart by "running on love," you're looking at the business world through rose-colored glasses.

Peeling back this layer of free offerings, HF’s strategy for monetizing B2B operations in 2026 was already highly mature:

First, the Enterprise Hub:

Pfizer, Bloomberg, and even Apple and Tesla... these tech giants won't put their core models on a public platform. They need private deployments, extremely strict permission management, and SLA guarantees. Pay up, and HF sets it all up flawlessly.

Second, Compute Reselling and Inference Endpoints:

By 2026, model deployment is where the real money is. HF rents cloud-based GPUs on an hourly basis, letting you turn models into production-ready APIs with a single click. Just like that, it became the world’s largest middleman for AI compute.

Third, Extending from Software to Hardware:

In 2025, HF acquired the French robotics startup Pollen Robotics. Today’s HF doesn't just let you download code; it lets you download action datasets for robots. It has started selling its own open-source hardware, aiming to plant a flag in the physical world.

You see, HF isn't avoiding taking money. It just points its "free" offerings at end-user developers and reserves its "fees" for enterprise clients with budgets. This is a classic "build the ecosystem first, harvest the B-side later" playbook.

But this playbook isn't enough to explain how it can "burn" cash so lavishly. Its real trump card lies in its list of strategic financial backers.

Although its Series D round in 2023 capped out at $4.5 billion, entering 2026, a "luxury syndicate of backers" comprising Google, Amazon, NVIDIA, Salesforce, Intel, AMD, and Qualcomm continues to inject cash. This is not ordinary financial investment; it is collective strategic life support:

For NVIDIA: HF is the place where developers globally download models and run inference. More models running means more demand for GPUs—the money they invest in HF is essentially buying an "entry ticket" for their CUDA ecosystem.

For Cloud Giants (AWS/GCP/Azure): HF's Spaces and Inference Endpoints run on AWS and GCP. Bandwidth? Compute? Provided directly at "internal rates" or even "resource credits," reducing costs to almost zero. If you don't use HF, you might just use their cloud directly anyway. Giving it to HF buys a good reputation for "supporting the open-source ecosystem" and boosts developer retention.

This is the underlying logic behind HF’s "generosity": The money it loses is the "military budget" in the global war for AI supremacy.

In this logical chain, HF plays the role not of a "bandwidth buyer," but of an "internet tollgate."

When developers globally make it a default habit to push code and models to HF, it controls the Strait of Hormuz of the AI world. Whoever wants developer attention and habits must give HF money, resources, and help it burn cash.

So, Hugging Face isn't squandering money. It’s using strategic losses to bet on becoming the foundational standard of AI infrastructure. Once this ecosystem is built, the data value, network effects, and switching costs will each serve as an ironclad economic moat.

It’s burning cash to build an empire.

With the Same Playbook, Why Can't Domestic Platforms Keep Up?

Understanding HF’s underlying logic, looking back at domestic platforms may leave you even more confused.

ModelScope, WiseModel, OpenI... don't they want to emulate HF? Don’t they want to wave a wand and let developers upload and download freely to secure the ecosystem first?

It's not that they don't want to; the math just doesn't add up.

The starting point of the contradiction is a cognitive bias that is hard for an average person to notice—we think "broadband is cheap." 1000M fiber optic internet at home costs a few dozen bucks a year, and downloading a movie takes seconds.

Based on this standard, how expensive could platform bandwidth really be?

Sorry, but the stuff the platform uses and the "broadband" in your home are two entirely different commodities.

The "Dual Pricing" of Public Network Bandwidth

What you have installed at home is called "residential broadband." It has two hidden attributes you might not know: First is shared overselling—100 households in a building share one main outlet, and the carrier bets that you won't all be downloading at top speed simultaneously. Second is upload throttling—1000M refers to downstream traffic. Upload speeds are usually capped around 30M to 50M. Furthermore, your contract clearly states, "For residential use only, commercial use is prohibited."

What AI model hosting platforms need, however, is IDC (Internet Data Center) bandwidth. What does that require? It must be dedicated, symmetrical, and full-duplex. If someone downloads a model, you have to upload it. Upload and download speeds must be identical. And it's not for one household; it's for thousands or tens of thousands of simultaneous users. This bandwidth also needs to be BGP multi-line—ensuring fast speeds regardless of whether the visitor is using China Telecom, China Unicom, or China Mobile.

The price? Residential 1000M costs a few dozen bucks a year. IDC’s dedicated 1000M BGP bandwidth costs tens of thousands or even over a hundred thousand RMB a year. A thousand-fold price difference.

It's the same bottle of water. Tap water boiled at home and mineral water sold at KTV are both H₂O, but their cost and pricing logic aren't on the same spreadsheet.

Sky-High "Toll Fees"

If it were just expensive, that would be one thing. But what really drives domestic BGP bandwidth to astronomical prices is the "siloed" layout of the top three national carriers.

China Telecom, China Unicom, and China Mobile each built their own network. In many areas, these three networks do not interconnect freely—or rather, they can connect, but they charge a "toll fee," technically known as a peer-to-peer settlement.

Suppose you are a platform and only bought bandwidth from China Telecom to save money. When Unicom and Mobile users come to download models, the data packets have to cross from the Telecom network to the other two. Every time it crosses, the carriers settle the cost. If it crosses too much, the user doesn't experience speed, but lag—packet loss, latency, crawling along at a few KB.

When the user experience falls apart, platforms have no choice but to bite the bullet and pay for BGP. BGP is more expensive because it is effectively renting right-of-way from all three carriers simultaneously. Data can use anyone's network, efficiently finding the optimal path, and all settlement costs are included. It's expensive not because of the tech, but because of the coordination, settlement, and invisible "toll fees."

The "Cross-Subsidy" Calculation

Okay, at this point you might ask: Why can residential broadband be squashed so cheap while data center bandwidth can't drop its prices?

This gets to the deeper operational logic of China's telecom industry: Cross-subsidization.

In China, broadband is not just a commodity; it has the attributes of a quasi-public good. Carriers have a strict mandate for "universal service"—even a village sitting at 4,000 meters above sea level needs fiber optics and 4G coverage. From a purely economic standpoint, such projects wouldn’t break even in a hundred years.

Who covers those massive losses? Since prices for residential broadband are compressed to rock bottom, they have to make it up somewhere else. Enterprise users, especially those buying IDC and BGP bandwidth, become the highly anticipated "cash cows." Carriers take the infrastructure costs they lose on the consumer side and tack them onto the price of business products, using the "whales" to subsidize the "retail investors."

Therefore, the bandwidth domestic AI platforms purchase doesn't just reflect its own value; it inadvertently bears part of the societal cost of universal service. HF in the US or Europe can use peering to exchange traffic at extremely low costs, but domestic platforms must pay hard cash for every megabyte, while also helping amortize the fiber-optic bill for distant villages.

Compliance Costs

Finally, there is an extra expense unique to the domestic market: Content moderation.

When HF hosts a model, copyright and open-source licenses are the developer’s responsibility. But in China, platforms must take responsibility for the safety of uploaded content. Every model file and every online Space demo requires a backend running sensitive word filters, image safety scans, and illegal content blocking. The larger the file, the more compute and time these scans consume.

Think about it: how many GPU hours does it take to just open and check a 70B parameter model that is hundreds of gigabytes in size? Foreign platforms largely dodge these moderation costs or bear less responsibility, but for domestic platforms, it’s a non-negotiable hard expense.

So It's Actuarial Precision, Not Being Cheap

Laying out these four layers makes the ledger clear.

Hugging Face’s bandwidth and compute costs are directly wiped out as "ecosystem credits" by their deep-pocketed backers. Domestic platforms face a thousand-fold premium on IDC bandwidth, interconnect settlements from three carriers, universal service costs hidden in the bill, and the unavoidable expense of compliance audits.

When every megabyte of traffic comes with a price tag of real gold and silver, you can’t afford not to calculate with precision.

It's not an issue of having "no vision." It's just that the people running the numbers are genuinely burning their own cash.

The Temptation of the Gray Area: When People Use Residential Broadband for Commercial Jobs

Earlier we mentioned the thousand-fold price gap between residential and commercial broadband.

To the average person, this gap is just an exasperated sigh about "how expensive enterprise internet is." But to another group of people, it's a crack shining with gold—as long as you can "package" residential traffic as a commercial good, the difference is pure profit.

Thus, a massive gray-market industry quietly sprung up.

Residential broadband may have plenty of flaws—upload throttling, dynamic IPs, deeply nested NATs—but they are all trumped by one word: Cheap. When you can buy a whole year of 1000M residential broadband for $70, while an enterprise pays tens of thousands for the exact same speed commercially, clever folks will naturally wonder: is there a way to pool thousands of cheap pipes into a commercial torrent that can be sold for profit?

The answer is yes. This business is called PCDN (Peer-to-Peer Content Delivery Network).

How Does PCDN Work? — The Case of OneCloud and JD Cloud Wireless Router

The name PCDN is all too familiar to hardware-obsessed tech geeks. Whether it’s OneCloud or JD Cloud Wireless Router, they are essentially running the exact same hustle.

The game is incredibly simple: You buy a set-top box and plug it in at home, or install a client on your NAS or router. It sits there quietly in the background, consuming a fraction of your upload bandwidth and a bit of your idle hard drive space. In exchange, you get a "power subsidy" of a few dimes to a few bucks a day.

That’s the user’s perspective. What are the manufacturers doing behind the scenes?

They collect hundreds of thousands or even millions of these "boxes" nationwide, weaving them into a "distributed bandwidth network" covering almost every neighborhood in every city. Then, they knock on the doors of video streaming sites and hand over a quote: Hey, aren't you trying to serve HD video to users on iQIYI, Bilibili, and Douyu? Aren’t you paying carriers tens or hundreds of millions in commercial CDN bandwidth fees every year? Look, I'll use my "residential network" to handle your delivery for a third of the price.

Looking at that quote, it’s hard for a streaming site not to be tempted. After all, when a user plays a video, they pull the data from a nearby local node, and the experience feels nearly identical. But the site can save millions of actual dollars a year. Why wouldn't they do it?

And so, the profit loop is closed: Users get electricity subsidies, PCDN vendors pocket the arbitrage, and streaming sites save their budgets.

The Only Loser: The Telecom Carriers

Now, look at this picture from the top floor of a telecom carrier’s office.

You’ve worked tirelessly to lay fiber optics and build infrastructure, fulfilling "universal service" commitments in remote villages even if it means losing money. Your business model was supposed to be simple: use affordable residential broadband to cover the masses, and use pricey commercial broadband to make the profits back from enterprise clients.

Instead, a group of people exploited a loophole in this pricing system. They took your cheap residential water pipes, hooked them up to your high-priced commercial reservoir, and siphoned off the toll fees you were supposed to collect from enterprise clients.

From the carrier’s perspective, what is this called? This isn't technical innovation; it's commercial arbitrage. In plain English, they're fleecing the landlord.

The foundation of the PCDN business isn't advanced tech; it is the fact that the price of civilian broadband in China has been suppressed by the state to levels far below market value. It’s a business model built on price control arbitrage—essentially the exact same logic as using subsidized industrial electricity to mine Bitcoin.

Residential broadband agreements explicitly state "For residential use only, commercial use is prohibited," but for a long time, carriers looked the other way. After all, they were trying to grab market share, installation numbers were key KPIs, and it wasn't wise to pursue these issues too aggressively.

But now, the AI era has arrived. Large model files are routinely tens of gigabytes, and multimodal training data is dealing in astronomical numbers. Traffic consumption is orders of magnitude larger than in the video era. If carriers don't start hauling in the nets now, it won’t just be video traffic leaking into PCDN. Even the distribution of large models could get devoured by this "army of ants" residential network.

It’s no longer a matter of a few bucks for an electricity bill. It’s threatening the carriers' fundamental revenue structure.

Therefore, the rules had to tighten. The gray areas had to be illuminated. And this time, carriers were genuinely preparing for a fatal crackdown.

How Does Bandwidth Cost Shape Our Internet?

In the previous sections, we unpacked the economics, gray-market arbitrage, and regulatory tug-of-war riding on a broadband pipe. But the story so far is missing one final puzzle piece—how do all these things added together actually shape the internet we are currently experiencing?

Hugging Face becoming the global "default repo" for AI hinges on one easily overlooked prerequisite: its bandwidth costs were strategically erased by tech giants.

Domestic platforms don't have that prerequisite. When every gigabyte of a model download has to be tallied as hard costs, a purely free, unlimited model is fundamentally impossible from day one. It’s not an issue of "not wanting to copy HF"; the ledger is right there, and there's no way around it.

Therefore, China's "Hugging Face equivalents" are destined to take a path that commercializes earlier and hits the ground sooner. While they are still in the phase of building an ecosystem, they are forced to consider: Should this download button be throttled? Should we charge a small distribution fee for this large model file? Where do we draw the line between public and private repositories so that we can attract developers without bankrupting ourselves?

The tighter this ledger is balanced, the narrower the space for free and open access becomes.

Taking it a step further, high bandwidth costs could become an invisible barrier blocking domestic large models from going global. When domestic developers want to publish their fine-tuned Qwen or DeepSeek models to international markets, who pays the cross-border bandwidth bill? If hosted on a domestic platform, the download speeds will make foreign users want to smash their keyboards. If uploaded to HF, the data and models are handed over to someone else's infrastructure. This dilemma is essentially a structural scar etched into the industry framework by bandwidth costs.

The current domestic model—using low residential prices to fulfill universal service obligations, and using high commercial prices to recoup costs—was formed during a specific historical period. It efficiently solved the mission of "getting 1.4 billion people online." But as the AI era arrives, as every developer wants to distribute tens of gigabytes of models, and as PCDN undermines the pricing system with its "ant colony" approach, the cracks in this model are becoming increasingly obvious.

Raising prices blindly merely patches up immediate leaks. The long-term problem is that high bandwidth costs raise the barrier of entry for the entire ecosystem. The development, distribution, and iteration of AI all require massive traffic exchanges. If the cost of exchange is too high, the speed of progress slows down.

Is there a solution? The peering culture abroad serves as a point of reference—carriers, and large enterprises working with carriers, swapping traffic for free to lower the flow costs of the entire network. But this requires competition, it requires more players entering the market, and it requires breaking down the invisible walls between the silos.

That’s not easy. But only tackling the tough issues will decide the foundation of the internet for the next decade.

When we talk about bandwidth, we are actually talking about the infrastructure fairness of this era—who gets to build it, who can afford to use it, and who gets shut out. The answer to that is vastly more important than the price of a strand of fiber optic cable.

Traffic Isn't Free; Someone Is Just Picking Up the Check

Late at night, you click the download button on a multi-gigabyte model. The progress bar starts moving, and you turn around to pour yourself a glass of water. When you return, the model is resting quietly on your hard drive.

Those tens of gigabytes of data traversed Pacific subsea cables, were exchanged for free at some IX (Internet Exchange), sprinted across the backbone networks of AWS or Google Cloud, and finally arrived at your home router. You didn't spend a single cent in the entire process.

In that fleeting moment, you wouldn't think about what stands behind those bytes of data: Google's strategic investments, NVIDIA's ecosystem layout, interconnect settlements among the top three Chinese carriers, infrastructure subsidies for remote mountain villages, and some PCDN gamer who just got their internet cut off for anomalous upload traffic.

Hugging Face's "free access" is the war chest of tech giants fighting for ecosystem dominance, burning bright and decisive. Domestic platforms' "limitations" are born of survival rationality under high cost pressures and strict compliance demands, calculating every penny against their will. The rise and fall of PCDN represents a gray-market weed that sprouted in the thousand-fold price crack between "residential" and "commercial" broadband, only to be uprooted.

These three things seem disconnected, but they all point to the same truth: Not a single byte on the internet is truly free. It is merely being paid for in a place you cannot see.

Sometimes the person paying the bill is a strategic investor; sometimes it's an enterprise paying commercial bandwidth fees; sometimes it's the infrastructure budget for a far-flung village; sometimes it's crossfire catching a tech enthusiast; and sometimes it's an average person paying a monthly broadband bill, never suspecting they belong to the crowd doing the subsidizing.

Understanding bandwidth isn't about understanding the transmission speed of a fiber optic cable. It is about understanding how pricing, game theory, subsidies, arbitrage, and regulation all crash together simultaneously on a single wire.

And that, perhaps, is half of what makes up the internet.

Thank you all for reading! If you found this interesting, please like, share, and hit the 'in-look' button without hesitation!

To see my articles as soon as they drop in the future, don’t forget to add a star ⭐, so you won't lose track of the page.

Alright, that's it for today.

Whether in victory or defeat, life is a bold adventure! We'll see you in the next one!

When 'I Can't Code' Becomes a Badge: Beware the AI Marketing Bubble

GokuScraper悟空爬虫 — Tue, 12 May 2026 08:38:44 +0000

When 'I Can't Code' Becomes a Badge: Beware the AI Marketing Bubble

On short-video platforms, a creator who calls himself an "independent developer who can't code" goes by the name Hushu. In his profile bio, he highlights two eye-catching claims: Kitten Fill Light (No. 1 on the App Store paid chart) and Nuwa.skill (8K+ stars on GitHub). Those two labels have earned him plenty of traffic and a strong trust halo.

But if you take a closer look at both projects, their actual weight may be far lighter than they first appear. This article is not meant as a personal attack; it is simply a fact-based review of two publicly verifiable claims.

1. Kitten Fill Light

1.1 Fact-checking the claim of being No. 1 on the paid chart

On short-video platforms, Hushu's bio still says "Kitten Fill Light (No. 1 on the App Store paid chart)." As of May 12, 2026, that line is still there, with no date and no further explanation.

So does that claim hold up?

If you open the App Store paid overall chart and scan through the top 100 apps, you won't find an app called Kitten Fill Light. In fact, the paid overall chart has long been dominated by products from major commercial companies. A $1 utility app making it into that list would already be pretty unusual.

Digging further, we find that the app is currently ranked No. 23 on the paid chart in the Photography & Video category.

That reveals the real meaning behind the phrase "No. 1 on the paid chart": it was never the No. 1 app on the overall paid chart. It was a peak ranking in a specific subcategory—the paid chart for the Photography & Video section. And even there, it has now slipped to No. 23.

The conclusion is straightforward: the slogan "App Store paid chart No. 1" leaves out three critical details—no time reference (is it still No. 1 now?), no scope (overall chart or subcategory?), and no current status (is it still at the top today?). By blurring those key details and packaging a temporary subcategory achievement as a permanent badge, the claim becomes misleading, whether intentionally or not.

1.2 Product barriers and replaceability

One of Hushu's core labels is "an independent developer who can't code." It sounds like an underdog story: someone without technical skills built an app that made it to the paid chart. But before getting impressed, it is worth asking what kind of product this actually is.

If you search for "Kitten Fill Light" in the App Store, the results page shows nearly 10 apps with the same or very similar names. Open them up and you'll find that the functionality is almost identical: they use the screen as a light source to simulate a fill light.

That raises a real question: if the market can quickly replicate an app nearly 10 times over, where exactly is the moat?

The answer is: there basically isn't one. The app's underlying logic is simple. It is essentially just controlling screen brightness and color temperature. The code footprint is small, the development cycle is short, and there is no meaningful technical barrier. Put more bluntly: if a developer wants to package and launch a similar product in an afternoon, there is almost nothing stopping them.

Which leads to another question: if someone who claims he "can't code" can still build something like this in such a low-barrier category, does that prove technical ability—or does it say something else? Maybe skill at understanding traffic channels, or an instinct for what certain users actually want?

That is worth thinking about.

1.3 Product reality and longevity

The life of an app ultimately comes down to what the numbers say.

Kitten Fill Light costs $1. According to its App Store page, it currently has 952 ratings with an average score of 4.7. That's not bad, but in the context of paid apps, the rating count is still modest.

What matters more is the timing of those reviews. A large share of them came in 2025, and once 2026 began, new reviews almost disappeared. Judging from the review content and user avatars, the audience is highly concentrated in one specific circle: female users on Xiaohongshu. That means the app's growth has depended mainly on a one-time traffic spillover from a single platform, with little evidence of sustained acquisition from multiple channels.

On top of that, the app's last update was three months ago. Combine that with almost no new reviews in the past six months and no visible user growth, and the picture is clear: this product has entered a decline phase. It is no longer being actively iterated, and it has not established stable growth in the market. It looks more like the byproduct of a short-term marketing event.

So we end up with a product that is already fading, barely updated, and yet still carries a bio line that says "App Store paid chart No. 1" with no date and no scope. Once an achievement is stripped of its limits, its time context, and its current status, and repeatedly used as personal branding, its persuasive power drops sharply.

2. Nuwa.skill

2.1 Community hype

If you open the GitHub repository for Nuwa.skill, the star count in the top-right corner shows 18.7K. That number is real. In the open-source world, it is a very respectable figure.

But here we need to clarify one concept: what exactly does a GitHub star count mean?

In the ideal case, stars reflect how much the developer community values a project. But in the real world of internet distribution, stars usually reflect attention, not necessarily technical depth. A project can get a lot of stars because it rides a hot trend, has a catchy title, or is marketed well, even if the code quality and technical substance are limited. That has been repeatedly proven during the recent AI open-source boom—high-star, low-quality projects are not rare.

So 18.7K stars may be real, but that does not automatically mean the project is technically strong. The real question is what exactly supports those ten-thousand-plus stars.

2.2 The core question: where is the dataset for the "distillation"?

One of Nuwa.skill's main selling points is that it can "distill" the style of public figures like Elon Musk and Donald Trump, then imitate their language patterns in conversation.

Let's be clear about a basic technical principle: in machine learning, "distillation" usually means using the outputs of a large model (the teacher) as training signals for a smaller model (the student), so the smaller model picks up similar capabilities. More broadly, it can also mean training a model on a specific person's language data so it learns to imitate that person's speaking style.

Either way, there is one unavoidable prerequisite: you need data.

If you want a model to learn how Elon Musk talks, what is the first thing you need? You need real speech data from Elon Musk. Where did that data come from? Was it collected by the project itself, or taken from an open dataset? How large is it? How was it cleaned? These are the foundational questions any style-distillation project must answer. A dataset is the prerequisite for reproducibility, and reproducibility is the baseline for technical integrity.

But if you look through the Nuwa.skill repository and resource list, there is no prominent explanation of the dataset. The project says it uses "six parallel agents" to collect data, but it does not clearly explain the source, scale, deduplication method, or compliance handling.

There is also an important technical reality here: large-scale scraping from X (formerly Twitter) is not easy. Since Elon Musk bought the platform, access controls have tightened significantly. Without logging in, even basic browsing and search are heavily restricted; after logging in, there are still rate limits and anti-scraping defenses. A reliable scraping setup requires account pools, proxy rotation, request throttling, and a full engineering stack around it. In essence, this is a competition of resources—not something you can solve just by slapping the word "agent" on it.

So if a project cannot clearly explain where its data comes from, then from a technical standpoint, its "distillation" result cannot really be verified.

A more reasonable inference is that this project is not true model distillation at all, but more likely a wrapper around advanced prompt engineering. The system prompt may preload the target person's common phrasing and stance, allowing the model to mimic that style in conversation. In technical terms, that is fundamentally different from distillation.

2.3 The whole AI bubble in one picture

Step back for a moment, and that 18.7K star count may be more interesting than the project details themselves.

Why would a project that struggles under serious technical scrutiny still attract such massive attention? It reflects one troubling side of the current AI wave: once the "AI" prefix is put on a pedestal, ordinary users develop wildly unrealistic expectations about what it can do.

In that atmosphere, words like "distillation," "agent," and "style imitation" sound magical to non-technical people. Project rigor, data transparency, and reproducibility—things that should be basic consensus in a technical community—get buried under a collective frenzy for novelty.

Nuwa.skill's huge star count is a monument to that collective mood. What it proves is not that this distillation technique is especially solid or innovative. It proves how big the AI bubble is right now, and how wide the information gap is between ordinary users and technical reality.

That is probably more worth thinking about than the project itself.

Conclusion: let technology be judged as technology, and marketing be judged as marketing

At the end, it is worth restating the point of this article: this is not an attack on any one person, but a verifiable fact-check of a public-facing technical persona.

Hushu, as an independent developer, clearly has a strong instinct for marketing and a sharp eye for traffic. In today's content environment, that is unquestionably an advantage. He identified two highly contagious narrative hooks—"I can't code" and "AI." Combined, they create a very attractive story: a person without a technical background uses AI tools to build a paid chart-topper and an open-source project with tens of thousands of followers.

But a story is a story. Facts are facts.

After checking each claim one by one, the so-called "App Store paid chart No. 1" turns out to be a time-limited achievement in a specific subcategory. Presenting it as a timeless, scope-free title is essentially using information asymmetry to crown oneself.

The so-called "Nuwa.skill 10K-star project" does have real GitHub stars, but a project that cannot clearly explain where its dataset comes from cannot have its technical substance independently verified. It looks more like a sophisticated prompt-engineering system dressed up with fashionable terms like "distillation" and "agent." Its real success lies in traffic mastery, not in solid technical contribution.

An expired subcategory No. 1 and a 10K-star project with an unclear technical foundation—those two cards alone do not support the image of a technical guru. What they do prove is that this developer is good at getting seen, not necessarily at creating.

In the current environment, where the AI information gap is still huge, cases like this are not rare. They remind everyone who cares about technology to stay skeptical and verify carefully: let technical achievements be judged as technical achievements, and let marketing capability be judged as marketing capability.

Only by keeping those two separate can we preserve clear judgment in an era full of hype.

I Curated Over 2,000 Seedance 2 Prompts into a Free Website and Open-Source Dataset for You to Use

GokuScraper悟空爬虫 — Sun, 10 May 2026 12:12:52 +0000

I Curated Over 2,000 Seedance 2 Prompts into a Free Website and Open-Source Dataset for You to Use

When I was making AI videos myself, finding good Seedance 2 prompts was a huge pain.

I scoured X (formerly Twitter), TikTok, and Discord, only to find endless screenshots that I couldn't even copy and paste. Most of the so-called "prompt collections" online were either hidden behind paywalls or just dry walls of text with no original videos, no categorization, and no structure. They were practically useless if you wanted to dig deeper.

So, I decided to organize a list myself. But the more I gathered, the bigger the project became. Eventually, I decided to just build a website and an open-source dataset.

The URL is prompthub.gokuscraper.com. It's ready to use right out of the box—no registration or login required.

Currently, the supported models include Seedance 2, Midjourney V6, Flux, GPT Image 2, and Nano Banana Pro. It essentially covers all the mainstream AI image and video generation tools.

The prompts are categorized by use case: Trending, Today's Updates, Entertainment/Memes, Business/Productivity, and Content Creation. There are also source-based categories like "From X (Twitter)" and "From TikTok" to help people with different needs filter quickly.

Every prompt comes with a video preview—it's not just a plain text list, so you can see the results at a glance. It also supports searching by title, tags, and content, so you can easily find specific styles. Just click the "Copy" button, and you can grab the entire prompt without the hassle of manual highlighting.

There's also a lightning-fast "Generate Image" ⚡ button that takes you straight to the corresponding platform. Scroll down, and it automatically loads more. It feels like scrolling through an endless feed, and before you know it, you've gathered a ton of inspiration.

But the website is just a shell. The real effort went into the dataset behind it.

If I just wanted to build a site to display prompts, I wouldn't have gone to all this trouble. From the very beginning, I believed this data shouldn't just sit on a webpage—it needed to be truly open data.

The dataset is called seedance-2-prompts-datasets, hosted on Hugging Face. The total size is 12GB, containing over 2,110 Seedance 2.0 generated videos (mp4) and cover images (jpg).

The core of it is a metadata.jsonl file, where every prompt has been structurally processed. Titles, tags, English/Chinese translations, video file mappings, resolutions, durations, and safety ratings are all neatly labeled and standardized. Here’s an example of a data entry:

{
  "id": "SD2_00133",
  "category": "Entertainment",
  "raw_p": "Environment: A colossal glacial canyon under pale blue twilight...",
  "media": {
    "v": "seedance-2/videos/SD2_00133.mp4",
    "c": "seedance-2/covers/SD2_00133.jpg"
  },
  "spec": { "width": 1280, "height": 720, "ratio": 1.78, "duration": 15.12 },
  "i18n": {
    "zh": { "t": "冰谷虎蛇战", "p": "环境：一座巨大的冰川峡谷...", "tags": ["冰川峡谷", "冰虎", "霜蛇"] },
    "en": { "t": "Glacial Tiger vs Frost Serpent", "p": "Environment: A colossal...", "tags": ["ice canyon", "cinematic"] }
  }
}

For developers, you can load the entire dataset with just one line of code:

import pandas as pd
df = pd.read_json("https://huggingface.co/datasets/GokuScraper/seedance-2-prompts-datasets/raw/main/metadata.jsonl", lines=True)

It’s perfect for secondary uses like research, tool development, or model training. The entire dataset is under the CC BY 4.0 license, meaning commercial use is totally fine—just give attribution.

Why bother making it structured data?

In the AI era, prompts are essentially a new "productivity language." But the current reality is that good prompts are scattered everywhere—in screenshots, tweets, and video comment sections. They are fragmented; you can find them, but you can't easily use them.

What I want to do is simple: collect those scattered, high-quality prompts and turn them into data that machines can read, humans can search, and developers can use directly. It’s not just a "display"—it’s a computable, redistributable data asset.

This project and website are just the first step.

Of course, it's far from perfect right now.

To be honest, building it is one thing, but making it great is another. There are still many things about this project and website that I’m not entirely satisfied with. I'll list them out frankly:

Regarding the website:

A total of 2,110 prompts is far from enough for something meant to be a "Hub".
Model coverage is still incomplete. Right now, Seedance 2 is the main focus, and the volume for other models is visibly lacking.
Categorization could be much more granular. Some tags are a bit too broad right now.
The mobile experience hasn’t been specifically optimized, so it’s not the most comfortable to browse on a phone.
There’s no user system yet. Features like favoriting, liking, and personalized recommendations haven't been built.

Regarding the dataset:

Structured organization currently only covers Seedance 2. High-quality prompts from other models haven’t been integrated yet.
Data sources lean heavily on X (Twitter) and TikTok; content from other platforms is sparse.
Updates currently rely mostly on manual work. I'm still slowly building pipelines for automated scraping and cleaning.
The quality of the Chinese translation is mixed, and some parts need proofreading and rework.
The tagging system isn't detailed enough. Ideally, you should be able to filter by dimensions like camera shot types, lighting styles, and motion types, but that’s not possible yet.

These are the tough nuts I need to crack moving forward. There’s no shame in listing them—hiding the flaws misses the point.

But the direction is clear.

Right now, this data is just a starting point.

In the short term, I want to expand model coverage. Prompts for Midjourney, GPT Image-2, and other models need the same kind of structured organization. I’m building automated update pipelines so I don't have to manually scrape data every time, allowing the dataset to grow sustainably.

In the medium term, I hope to see more creators join in and contribute the great prompts they’ve refined. I want this Hub to be more than just me dumping stuff in. The ideal scenario is that people find it useful and naturally decide to share their own hidden gem prompts, growing the data pool for everyone.

If I'm lucky, this project might go even further—becoming a genuine public infrastructure for prompt data. Not a private asset, no paywalls to unlock things, just a clean, continuously updated, open-source data resource that anyone can use. It’s an ambitious thought, but it's a direction worth pursuing.

How to Access and Download

🌐 Try it online: https://prompthub.gokuscraper.com/

🤗 Download the full dataset: https://huggingface.co/datasets/GokuScraper/seedance-2-prompts-datasets

⭐ Synced updates on GitHub, stars and issues are welcome!

Wrapping Up

I spent a lot of time on this project and website, but it’s still far from perfect.

If you use it and have any thoughts, complaints, or suggestions, please let me know. I built this for people to use, and your feedback will directly guide the improvements in the next version.

Thanks for checking this out! If you found this interesting, please don't hesitate to toss a like, share it, or spread the word!

If you want to see my future articles as soon as they drop, don't forget to star ⭐ my page, so you don't lose track of it later.

Alright, that's all for today.

Even Sam Altman Would Want to Buy From Them: The Hubris of Grassroots AI Proxy Bosses Billing With Their 'Entire Net Worth'

GokuScraper悟空爬虫 — Sat, 09 May 2026 06:16:32 +0000

Even Sam Altman Would Want to Buy From Them: The Hubris of Grassroots AI Proxy Bosses Billing With Their 'Entire Net Worth'

In our investigation into the supply chain of AI proxy services, one phenomenon caught our attention: some ridiculously cheap, grassroots proxies are actually claiming they can "accept corporate bank transfers and issue official invoices."

This is, undeniably, a contradiction. According to common sense, this type of business—which relies on gray-market sources like stolen credit cards, exploits, and vulnerabilities to sell API access at 20%-30% of the official price—should be hiding behind anonymous payment methods. However, the real samples we obtained present a completely different picture:

Upon verification, the operator of this studio is the site owner himself. He uses his real name, registered a sole proprietorship (个体户 in China), and opened a real corporate bank account.

This behavior itself is a puzzle worth deconstructing: Why would the operator of a business whose supply source is inherently shady proactively expose his real identity, business registration, and bank accounts to the sunlight? Does he not understand the law, or does he think he understands it too well? Is he truly fearless, or has he just not done the math?

This sample gives us the best entry point to observe the survival state of grassroots proxy services. It allows us to bypass industry rumors and start directly from pieces of living evidence left in the registry and banking systems, to see exactly what logic these end-of-the-line players are using and what kind of game they are playing.

1. The Industry Panorama: The Three-Tier Architecture of AI Proxies

Before diving deep into the "real name, real ID" sole proprietor sample, it's necessary to establish the entire coordinate system of the AI proxy industry. Based on the nature of the supply, operating entities, and compliance levels, the players in the current market can generally be divided into three tiers.

Tier 1: Grassroots Proxies. These are the core targets of our investigation. They are extremely small, usually consisting of a single person or a loose grouping of a few, with no decent office space and no formal employees. They have only one core competency: extreme low prices. The supply comes from upstream gray/black market channels, and financially, they have zero compliant procurement costs. Consequently, they can sell at 20% to 30% of the official prices—a discount that logically self-destructs: if this price were truly sustainable legitimately, major companies like OpenAI should be buying from them instead. Their existence is precisely the lowest-level noise signal in the entire supply chain.

Tier 2: Domestic "White Glove" Companies. These players have formal operating entities in China, but their compliance is not built on their own supply. Instead, it’s achieved through a "shell" structure—setting up a compliant front entity overseas, which makes formal purchases from vendors like OpenAI, and then resells them back into the country. The cost of this operation is that every layer of the compliance chain eats up profit margins, so their selling prices are mostly retail prices with very low discount rates. Essentially, they are earning a service fee for providing a compliance bridge, rather than performing informational arbitrage.

Tier 3: Legitimate Overseas Enterprises. The operating entities and core principals of these players are located overseas, subject to local laws, and the entire business chain operates within a compliance framework from start to finish. They don't need "shells" or "white gloves" and exist in a completely different legal and commercial coordinate system from domestic grassroots proxies and white glove companies. Their pricing is relatively flexible, but that is the product of a different set of rules and is beyond the scope of this article.

What this article will dissect next is a highly representative cross-section of the first-tier grassroots proxies: those who, despite having illegal supplies and deformed pricing, dare to operate as sole proprietors under their real names. Their existence provides us with a rare, verifiable window into the edge ecology of this industry.

2. "Sole Proprietorships" and Invoicing

Why do these grassroots site owners uniformly choose "Sole Proprietorship" (个体工商户) as their business identity? The answer lies in one word: Invoicing.

For a solo developer with no partners and no registered capital, legally there are only two ways to issue a formal invoice to a client: either go to the tax bureau as an individual to have them issue it on your behalf—which is cumbersome, has tight limits, and looks unprofessional—or register a market entity and use your own entity to issue invoices. Among all entity types, a sole proprietorship has the lowest barrier to entry, the fastest process, and the lowest cost. It doesn't require paid-in registered capital, partners, or a commercial office address. Within a few days, you can get a business license, complete tax registration, and obtain invoices.

In other words, a sole proprietorship is the only shortcut for an individual developer to gain "formal invoicing rights."

Once they have this identity, a series of functional upgrades follows: they can open corporate bank accounts to bypass various limits on personal collections; they can collect corporate payments, meeting the rigid requirements of clients who need to go through enterprise reimbursement processes; and they can issue VAT invoices, packaging a transaction that should be "shady" into a legitimate business dealing.

More importantly, this identity comes with a psychological camouflage. "Real-name registration, corporate collection, capable of issuing invoices"—when these three signals are combined, in the client's subconscious, they automatically translate to "legitimate, traceable, won't run away." For a site whose prices are suspiciously low, this aura of trust is an almost zero-cost customer acquisition tool.

But this is exactly where the problem lies: this "perfect shell" only solves the issue of form, it cannot cover up the substance. The sole proprietor identity gives him the right to issue invoices, but it doesn't give him a legal source of supply; it gives him the qualification to open a corporate account, but it chains him to unlimited joint liability. The identity is legal, the business is illegal. This crack between appearance and reality is the sum of this character's tragedy.

3. When the Most "Compliant" Facade Meets the Most "Illegal" Supply

Now, we arrive at the core tension of this specimen.

Looking from the outside, this studio possesses almost all the formal elements: traceable business registration, complete tax records, and a genuine corporate bank account. After an enterprise client completes the corporate payment process and receives a VAT invoice, no abnormal alerts will trigger in their financial system. On the surface, this studio is indistinguishable from the legitimate businesses operating on the street corner.

But flip it over, and the situation is entirely different. Its supply—the underlying resource supporting that extreme "20%-30% of official price" discount—financially does not exist. It's not that the margins are thin; there are simply zero compliant procurement costs. He doesn't need to pay OpenAI bills, doesn't need to ask for input invoices from any formal distributors, and doesn't need to record a single cent of traceable expense in the ledger. Every dollar he sells corresponds on the books to almost a dollar of pure profit. This is no longer an issue of operating efficiency; this is a financial illusion born of an illegal supply.

Put these two sides together, and you get an absurd picture: a micro-enterprise that looks flawless in the commerce and tax systems, while its true business core is a gray operation whose costs cannot be explained. And that invoicing capability, which the owner views as a "bonus feature," is precisely the deadliest finishing touch in this picture.

The invoice is the nerve ending of the entire tax system. The moment he issues an invoice, he is actively declaring income to the tax bureau. A clear data point is left in the system: on such-and-such date, this studio sold an API service for X amount. This data automatically feeds into his tax declarations. The income side is precisely recorded, but what about the cost side? Zero. Not a single input invoice. A severe mismatch between input and output—in the tax system, this signal doesn't require manual auditing to be discovered. As a basic risk control metric, the algorithm can flag it in red directly.

By issuing this invoice, he is essentially signing a confession.

What is an invoice, legally? It is black-and-white proof of a business action. It bears his invoice seal, his studio's tax ID, and spells out exactly what he sold. If law enforcement needs to secure evidence, these very receipts are the most direct physical proof. No advanced technical reconnaissance is needed, no complex digital forensics. The records pulled from the tax bureau and the bank are enough to piece together a complete flow of funds and paperwork. He has used the most standard commercial documents to leave the most standard evidence of his business model.

Bigger trouble awaits at the bank. A sole proprietorship account registered in Zhengzhou but opened in Shanghai, frequently receiving scattered payments from businesses and individuals all over the country, and then periodically transferring large sums out. This pattern of capital flow—large volumes in and out, fast money movement, cross-regional accounts—is almost a textbook profile for "abnormal transactions" in Anti-Money Laundering (AML) systems. As he starts invoicing, the frequency and scale of funds moving in and out will rise, meaning he actively feeds more analyzable signals into this system. It's only a matter of time before the account is flagged by risk control, restricted from non-teller transactions, or frozen entirely.

This is the most ironic part of this whole tale: He chose to package himself using the most standard, mainstream commercial practices—registering a sole proprietorship, opening corporate accounts, issuing invoices. Taken individually, each action is legal and compliant, and might even be seen as a sign of "business acumen." But it is precisely these actions that push him into the triple crosshairs of tax audits, legal prosecution, and financial risk control. He used standard methods to dig himself a precision trap.

4. The Structural Mismatch of Risk

The previous three sections dissected the logic of his identity, the black hole of his supply, and the invoicing trap. But all of these lead to one ultimate question: Who bears the risk?

This question separates these grassroots proxy owners from actual gray/black market masterminds.

In a normal illicit supply chain, risk is compartmentalized in layers. The upstream suppliers hide behind anonymous networks and encrypted communications. The intermediate financial channels turn money around using purchased shell identities or companies. The downstream cash-out ends are similarly layered in camouflage. There are firewalls between every level. If any one node is busted, it's hard for the fire to spread to other tiers.

But this sole proprietor sample from Zhengzhou presents an entirely inverted configuration.

He has placed his true identity—the operator name on the business license, his real name tied to his WeChat, the ID card used to open the bank account—directly on the outermost layer of the whole business. It's not a bought shell, not a borrowed name; it is him, personally. From the moment a client wires money to the corporate account for any transaction, a complete chain of evidence is generated: who the payee is, who the operator is, what the ID number is—all of it searchable, traceable, and fully retrievable in under three minutes.

Why would he do this? A reasonable explanation is that he confused two concepts.

He mistook a "Sole Proprietorship" for a "Limited Liability Company" (LLC). In most people's simple intuition, "registering a company" equals "personal assets are protected." If the company goes down, the company takes the hit, and it has nothing to do with the individual. But an LLC is called "limited liability" because it is a legally independent corporate person, responsible for its debts with its own assets. Shareholders only bear losses up to their subscribed capital. If the company goes bankrupt, the fire stops at the desks and chairs in the company's name.

A sole proprietorship is not like that. Under Chinese law, sole proprietorships lack independent corporate personhood. According to Article 56 of the Civil Code of the PRC, the debts of a sole proprietorship operated by an individual shall be borne by their personal property. Translated into plain English: The studio's debt is the site owner's personal debt. It is not limited to the startup capital he put into the studio; it is limited by every piece of personal property under his name.

What does this mean? When a tax audit comes down, discovers a massive input-output mismatch, and demands back taxes and fines, law enforcement can directly freeze his personal bank deposits, Alipay balances, and WeChat wallets. If the debt remains unpaid after these online accounts are drained, next up are real estate and vehicles under his name. After surviving these rounds, there is one final blow: being blacklisted as a "Dishonest Judgement Debtor" (失信被执行人). No high-speed trains, no flights, no loans, and he won't even be able to get a credit card.

This recovery path is not theoretical deduction; it is the most standard operational procedure in China’s civil enforcement process.

Even more worth asking is: By shouldering all this risk, whose business is he taking the fall for? The upstream suppliers hiding in the shadows bear zero legal responsibility and won't share a dime of the fines. This site owner uses his real identity, his entire net worth, and his personal credit score to act as the ultimate risk absorber at the tail end of the supply chain. The upstream makes a guaranteed profit, while the downstream walks on thin ice. This is the practical meaning of the legal concept of "unlimited joint liability" when it crashes into the grassroots proxy business.

This is the most mismatched relationship in the entire story: A side hustle that might only gross a few tens of thousands of RMB a year takes on legal risks completely disproportionate to its revenue—risks severe enough to obliterate a person's entire financial foundation. He isn't running a business. He is gambling. Gambling that he will never be noticed, that the risk control algorithm's threshold will always be slower than he is, and that his little puddle of "deemed taxation" will never run dry.

5. Unmanageable Content Compliance and the "Illegal Business Operations" Red Line

If the input-output mismatch is a slow-burning fuse, then content compliance is an immediate detonator.

In pursuit of extreme cost control, these grassroots proxies rarely possess—and refuse to spend money on—sensitive word filtering and content safety auditing systems (such as text moderation APIs). When their clients use these cheap API endpoints to input or generate politically sensitive, explicit, or terror-related illegal content through foreign large models, regulators will trace the data flows and financial trails upstream. The source will lock directly onto this real-name registered, completely exposed sole proprietor.

When that time comes, he won't be facing back taxes and fines. He will be directly crossing the criminal red lines of the "Cybersecurity Law" and the crime of "Illegal Business Operations" (非法经营罪).

Conclusion: A Gray Market Specimen "Naked in the Sunlight"

The account sample in our investigation—a real-name sole proprietorship, a cross-provincial corporate bank account, a gray business daring enough to issue invoices—ultimately patches together not a tightly organized black-market network diagram, but a staggering portrait of an individual.

He is not a hacker hiding in the dark. He uses zero technical means to conceal his identity. Quite the opposite, he plasters his real identity, business registration, and bank accounts right on the outermost layer, running a business consisting entirely of unmentionable supply chains in a manner akin to streaking naked. The sun shines on him not because he is innocent, but because he walked directly into the sunlight himself.

He attempts to use a compliant toolbox to carry illegal goods. Sole proprietorships, corporate accounts, VAT invoices—these commercial infrastructures meant for legitimate market entities have, in his hands, morphed into a hyper-realistic shell. This shell indeed fools his clients, and perhaps even fools himself for a while. But it cannot fool the input-output comparisons of the tax system, the fund flow analysis of anti-money laundering models, and certainly not the penetrating power contained in the short few dozen words of Article 56 of the Civil Code.

And the result of being penetrated is that he uses his entire savings, real estate, credit history, and future to shoulder unlimited joint liability for an arbitrage game that cost him zero to play.

This is the truest scene at the tail end of the current AI gray market arbitrage chain: it is more grassroots, more amateur, and more fragile than the outside world imagines. It is composed not of omnipotent crime syndicates, but of ordinary people daring enough to set up a stall on the edge of a cliff under their real names. Every single operational step they take leaves a trace; every invoice they issue acts as a footnote for the day of reckoning; every corporate account is standard evidence handed directly to law enforcement.

They are not doing evil in the dark. They are standing in the sun, assuming standard postures, and digging the deepest of graves for themselves. From any angle, this is arguably the most primitive, the most fragile, and the most pitiful existence in this supply chain.

Thank you for reading, friends! If you found this content interesting, please don't hesitate to like, share, and subscribe!

If you want to be the first to catch our articles in the future, don't forget to star ⭐ our account so you don't lose track of us.

That's all for today.

Through triumph and defeat, life is a grand adventure. See you next time!

Deconstructing the Fatal Bug of the “One-Person Company”: How to Write the Ultimate Legal Disaster Recovery Code with a 1% Family Share

GokuScraper悟空爬虫 — Fri, 08 May 2026 14:47:41 +0000

Deconstructing the Fatal Bug of the "One-Person Company": How to Write the Ultimate Legal Disaster Recovery Code with a 1% Family Share

1. Introduction

Open X (formerly Twitter) or Xiaohongshu (RED), and you'll see the myth of the "One-Person Company" has become rampant.

"No employees, no office, rely entirely on AI to write code, and make a million a year all by yourself." If you have even the slightest yearning for freelancing, this pitch will eventually find its way into your feed. They package this state as the ultimate form of an indie hacker: a "one-person business" in the physical sense, and a "One-Person Limited Liability Company" in the legal sense. It sounds both freeing and secure, allowing you to wear the legendary bulletproof vest of "limited liability."

As a result, masses of developers and freelancers, full of excitement, rush to the local commerce bureau to register a "One-Person Limited Liability Company" (一人有限责任公司). The moment they get their business license, many think they've finally put on the protective talisman of modern commercial civilization, sealing all risks strictly within their registered capital.

But Brother Biao is here to tell you a harsh truth: Operationally, you can be a lone wolf, but in terms of legal structure, going solo is like putting your entire net worth on the roulette table.

That bulletproof vest you think exists won't even stop a single bullet in court.

2. What Exactly is a "One-Person Company"?

The shortcomings of sole proprietorships (个体户) are well known: unlimited joint liability. You earn hard money, but you carry the risk of bankruptcy. Creditors can easily pierce that paper-thin veil and put your bank cards, cars, and house on the table for liquidation. Precisely because of this, those influencers have room to sell their "solutions."

Their selling point sounds perfectly logical: Just register a "One-Person Limited Liability Company." They claim this provides limited liability, meaning no matter how big the risk is, it only burns up to your registered capital. You hold 100% of the equity, you don't have to split money with anyone, and this is the dignity an indie hacker deserves.

The logic is beautifully flawless. But the problem is, this narrative ignores a legal clause specifically designed for you under Chinese Company Law.

The current "Company Law" stipulates that if the shareholder of a One-Person Limited Liability Company cannot prove that the company's property is completely independent of their personal property, they must bear unlimited joint liability for all the company's debts.

Read these words carefully: If you cannot prove it, you are jointly liable.

This is where all the scams blow up. In an ordinary LLC with two or three shareholders, if a creditor wants to pierce the corporate veil to go after your personal assets, they must pull your bank statements and dig up evidence of commingling personal and business funds. This is called "he who asserts must prove," and the burden of proof is extremely high. Most people simply can't break through.

But on the battlefield of a One-Person Company, the rules are inverted. The law assumes by default that you and your company are mixed together, and then places the entire burden of proving your innocence squarely on your shoulders. You need to spend your own money and time to produce flawless annual financial reports signed by third-party auditing firms, just to prove to the judge: I have never, in my life, spent a single dime inappropriately from this company's account.

What is the reality?

In reality, very few solo developers and freelancers can produce such a report, let alone a continuous, complete set of auditing materials covering the entire existence of the company.

Once you can't produce it, the verdict only has one outcome: that paper-thin limited liability instantly melts down.

Looking back at this deal now: you paid higher registration costs than a sole proprietor, hundreds of bucks a month in accounting fees, and adopted a complex financial process, all in exchange not for safety, but for a paper armor that can't withstand a single decent attack in court. A sole proprietorship is transparent about its unlimited liability; a One-Person Company first paints you a picture of limited liability, and then waits for you to fall into the trap during the burden-of-proof phase.

In terms of legal risk, it is essentially just a more expensive, more troublesome "pair of crotchless pants."

3. The Disaster Recovery Architecture: The "99% + 1%" Defense System

Now that the problem is disassembled, here is the solution.

People who truly understand risk management never bet their entire net worth on a line of code without exception handling. They bury an extremely lightweight, redundant node in the system. It stays completely silent normally but activates instantly when disaster strikes. In a legal architecture, this redundant node is called the "1% nominal share."

The architecture is simple and intuitive:

The execution is straightforward. You hold 99% of the shares, and you bring in your parents or a trusted relative/friend to hold the remaining 1%. With just this one step, in the corporate registry and the court's database, your company's nature instantly switches from a "One-Person LLC" to a standard "Limited Liability Company" (普通有限责任公司).

This 1% equity shift triggers a fundamental reversal of the legal rules. Remember what we discussed in the last section? If a One-Person Company gets sued, you must pay for audits to prove to the court you never commingled funds—this is a reversal of the burden of proof. But now, the rules of engagement change. If creditors want to pierce the corporate veil and go after your personal assets, sorry, the law reverts to a default track: the burden of proof lies with the plaintiff. The opposing party has to dig up your bank records and find traces of commingling themselves. In judicial practice, this represents a hellish difficulty level, and most plaintiffs simply can't make it work.

Your limited liability firewall is only truly electrified the moment this 1% equity transfer is completed.

Of course, this 1% nominal defense assumes your actual financial pipelines don't have "low-level bugs." If you use your corporate account every day to buy groceries or pay personal rent, even with that 0.1% family share, the court can still shoot right through you using the "Substantive One-Person Company" logic. The 1% is your legal shield, but "strict separation of personal and business finances" remains the underlying operational logic of your daily routines.

By the way, under the new 5-year paid-in capital rules in China, indie hackers should try to keep their registered capital as "lightweight" as possible (e.g., 10,000 or 50,000 RMB). This way, the actual paid-in amount for that 1% is only a few bucks, completely insulating your family member from any potential joint liability risks.

Many people's first reaction to this is anxiety: If I give my parents shares, do I have to pay them a salary and pay their social security? Do I have to give them a bonus at the end of the year?

The answer is no. This is ownership, not an employment relationship. They are purely shareholders, not employees. The law does not require any company to pay salaries or social security to shareholders; that obligation exists solely in an employment relationship. Their only theoretical source of income is "dividends," and whether to distribute dividends or not is entirely governed by your single vote as the 99% absolute majority shareholder. If you don't sign off, that 1% is purely a static configuration item that generates zero financial cost.

To close the loop: have that friend or relative who holds the 1% share also serve as the company's "Supervisor" (监事 - a statutory role in Chinese corporate governance). The law states a director cannot also serve as a supervisor. So, you serve as the Legal Representative, Executive Director, and General Manager, while the other shareholder takes on the role of Supervisor. Just two people perfectly close the loop of the statutory corporate governance structure, without needing to drag a third person into it.

4. The Complete Compliance Action Guide for Indie Hackers

With the architecture explained, here is the deployment manual.

Putting this plan into daily operation is nowhere near as complex as you might think. Just follow these three steps.

1. The Registration Phase: Don't Choose the Wrong Company Type

You cannot afford to make a mistake on step one. When registering, directly choose a standard "Limited Liability Company." Do not check the box for "One-Person Limited Liability Company." Set up the shareholder structure exactly as outlined above: you take 99%, your nominal proxy takes 1%, and they act as the Supervisor. The entire process is identical to registering a regular company; you just need to submit one extra ID copy.

The essence of this step is choosing the right underlying architecture at the time of system deployment. If you choose wrong, no patches will stick later.

2. Daily Operations and Accounting: Spend a Little to Save a Lot

Once the company is up and running, find a local proxy accounting firm. Do not skimp on this small expense. It solves two core problems for you.

First, cost pooling. Your server fees, AI API subscriptions, cloud bills, and hardware purchases (like laptops and phones) should all go through the corporate account. The accounting firm will compliantly process these as company R&D expenses, lowering your taxable profit on the books.

Second, salary design. Pay yourself a low salary, keeping the amount right around the individual income tax threshold, and stack it with special additional deductions (like mortgage, rent, and child education). This way, the money you transfer from the company account to your personal card each month incurs basically zero personal income tax. At the same time, your social security is normally paid by your company with no gaps. The combined cost is far lower than if you were to pay it yourself as a freelancer.

You'll find that this accounting framework is completely indistinguishable from any regular micro-enterprise. Even if you get audited, it doesn't matter—your costs are real, your salary is reasonable, and everything holds up to scrutiny.

3. Physical Isolation of Financial Links: Segregated Account Management

This is the biggest headache for many indie developers doing overseas business, but the principle is actually very simple.

If you don't immediately need to convert the USD you receive through channels like Stripe or Wise into RMB, just leave it in your offshore accounts. You can put it into low-risk USD money market funds (which currently yield around 5% risk-free annually) and use it directly to pay for your offshore servers and various SaaS subscriptions. The entire financial pipeline runs offshore, meaning it doesn't trigger any currency exchange processes and stays completely off the domestic tax system's radar.

Alternatively, handle your procurement and subscriptions for R&D tools directly offshore. This not only physically isolates your business operations but also legally avoids the compliance friction and double taxation costs of frequent currency exchanges. When you need to spend money domestically, you can repatriate it through compliant B2B channels, keeping your books crystal clear and unassailable.

Physically isolate the two pipelines; keep domestic and offshore separate. You no longer have to stress over whether and how to report taxes for every single payment received; the structure itself has already drawn the boundaries for you.

5. Conclusion

In the world of coding, no qualified system architect would bet their life and fortune on a piece of logic with zero exception handling. We write try...except, we deploy redundancies, and we bury monitoring nodes on critical paths. Because we know better than anyone: if the system doesn't crash, it's not because you're lucky, but because you caught every possible exception.

The commercial world operates on the exact same logic.

Those influencers online teaching you to register a One-Person Company are essentially urging you to deploy a production system with zero disaster recovery mechanisms. It might look like it's running smoothly, but the moment you face a moderately sized lawsuit, the entire system will crash through its illusion of limited liability, taking your personal assets down with it.

This 1% equity design is the most crucial line of exception-handling code you will ever write for your life's system. It stays completely silent normally, requires no maintenance, and consumes no resources. But when disaster truly strikes, it will be the first to activate, firmly erecting the firewall of burden-of-proof between you and your creditors.

Physically, you can absolutely be a highly efficient, free, and maverick one-person enterprise. But in terms of legal architecture, never register a naked "One-Person Company" that throws you onto the gambling table.

True freedom is never the bravery of streaking naked. True freedom is dancing in the air with a safety net below.

Thank you for reading, friends! If you found this content interesting, please don't hesitate to like, share, and subscribe!

If you want to be the first to catch our articles in the future, don't forget to star ⭐ our account so you don't lose track of us.

That's all for today.

Through triumph and defeat, life is a grand adventure. See you next time!

I Built a Feishu-to-Markdown Tool, and Overworked Office Workers Kept It Alive for Free

GokuScraper悟空爬虫 — Fri, 08 May 2026 06:42:32 +0000

I Built a Feishu-to-Markdown Tool, and Overworked Office Workers Kept It Alive for Free

1. The Pain Points of Feishu (Lark)

Feishu (known globally as Lark) is genuinely great for writing, but exporting your work is a nightmare. The official export feature is buried deep in the menus, and the formatting often gets completely messed up. If you want to feed your docs into Obsidian, Notion, or GitHub, you have to spend ages tweaking them manually.

What’s even more annoying are the public documents shared by others. The web version restricts copying and won’t let you download at all. If you want to save it locally for later reading, you're just out of luck. What are you supposed to do, transcribe it by hand?

So, I built a web app called the Goku Feishu-to-Markdown Exporter. You just paste a link, and it downloads a Markdown file directly, automatically grabbing the images from the document and zipping them all up.

No extensions to install, no login required. Just open the page and use it.

To be honest, I didn't write the core conversion code. I found an MIT-licensed open-source project on GitHub, wrapped it in a UI using Streamlit, and threw it up on the cloud. Rather than asking folks to clone the repo and set it up themselves, it made more sense to just provide an out-of-the-box online tool. And of course, my wrapper code is open source too.

2. Surprisingly, People Kept Showing Up

When I tossed this site onto the cloud, I knew about a specific catch.

Streamlit Cloud has a strict policy: if your app goes 24 hours without traffic, it gets aggressively put to sleep. The next person to open it has to sit there like an idiot for a minute or two while the container spins back up.

Hey, you get what you pay for when you're on the free tier.

So, I didn't have high expectations at first. I figured the site would spend most of its life napping. If someone used it, lucky them; if not, it would just lie dormant.

Then, after it had been running for a while, I peeked at the backend analytics and froze.

It hadn't gone to sleep. Not once.

It was online and stable every single day. Instant load times whenever I checked, as if the 24-hour sleep policy didn't even exist.

But wait—I definitely didn’t write a keep-alive script, nor did I pay for a premium tier. Who the hell was constantly hammering away at this scrappy little site that doesn’t even have a login page?

3. They Were All Real People

When I opened the backend visitor logs, I had to laugh.

Anonymous Altocumulus, Levitating Danish... strings of random aliases drifting through the server like a bunch of cyber ghosts.

I didn’t come up with those names. To protect privacy, Streamlit dynamically assigns these quirky pseudonyms to unauthenticated visitors. If you check the backend without user accounts, all you see is this parade of "anonymous pastries" and "unidentified weather phenomena" popping in and out.

At this point, you might wonder: Was it just search engine crawlers artificially bumping up the traffic?

Impossible.

Streamlit uses a Single Page Application (SPA) architecture. The UI is fully rendered via JavaScript, and communication runs entirely over persistent WebSocket connections. Your run-of-the-mill crawler—like a Python requests script, a curl command, or any basic scraper—will only ever see an empty HTML skeleton. They physically can't establish a WebSocket session.

In Streamlit's analytics, no session means no visit. Crawlers can't even get through the front door, let alone get assigned a name.

So, this bizarre list of "Danish pastries" and "Altocumulus clouds" in my backend? Every single one of them was a real, live human being.

The ones creeping in at 2 or 3 AM were programmers still wrestling with bugs, desperately trying to back up some technical specs from Feishu. The ones showing up at 6 or 7 AM were product managers rolling out of bed to organize their knowledge bases, converting a competitor's public docs into Markdown for their own notes.

The mismatched time zones combined with the heartbreaking reality of standard around-the-clock crunch culture meant that every few hours, guaranteed, an actual human body was opening the site, pasting a link, and hitting download.

They probably had no idea Streamlit has a 24-hour hibernation policy. They didn't know they were acting as "keep-alive" pings. They just needed the tool, used it, and left.

But every single click reset that 24-hour countdown.

No scripts, no black magic. Just an army of exhausted office workers, driven by pure necessity, manually keeping my server awake.

4. It Accidentally Became a Cyber Confessional

This tiny site has an incredibly stable Daily Active User (DAU) count of around 10 people. The number is so small that I couldn’t be bothered to build an actual user system. No database, no auth, no isolation. As long as it works, we’re good.

But that led to a totally unexpected side effect.

Because I never bothered to clear the input field state, whenever the next person opened the page, they’d see the leftover Feishu document link pasted by the previous user.

Sometimes I’d go to the site and see a completely foreign link sitting in the text box. I'd click it—sometimes it was a public manifesto from an influencer, sometimes an open document from a startup team, or maybe just someone’s public reading notes.

On this cold, free-tier server, that residual link became a strange sort of beacon. No chatboxes, no avatars, no social features—but when you saw it, you knew you weren't the only one grinding over documents in the middle of the night.

A tiny, leftover bug had turned into a digital safe haven.

Occasionally, I'd see that someone had pasted a link to a private, permission-locked document. Obviously, that extraction would fail. My tool can only grab public docs; anything behind a login wall is off-limits.

But they pasted it anyway.

Which means they really tried. Maybe out of sheer desperation, or just holding onto a "what if it actually works" kind of hope. Seeing links like that felt both funny and a little sad—just another poor soul driven mad by Feishu's export restrictions.

5. Turn Software into SaaS, and You Win

Honestly, there are plenty of open-source Feishu exporters out there. A quick GitHub search brings up several.

The problem is, almost all of them require you to jump through hoops. You either have to clone the repo, set up a Python environment, and run CLI commands, or you have to download some executable and configure a bunch of settings. Realistically, very few people are going to endure that workflow. Most users see the words "Please install dependencies" and immediately close the tab.

All I did was one thing: skip all those steps.

No downloads, no configuration, no installs. Open the page, paste the link, hit download, done. I turned a piece of software directly into a SaaS.

I didn't write the core code, and the UI is as bare-bones as it gets. But it has one massive advantage: if you show it to someone with zero technical background, they instantly know how to use it.

Put simply: keep the complexity for yourself, and give the simplicity to the user.

If you can find the pain point that forces someone to suffer for 10 minutes every day, and whittle that down to 10 seconds, you’ve built something that will survive on its own without a dime of marketing.

Wrapping Up

Looking back, the reason this sketchy little site didn’t get killed by Streamlit’s 24-hour sleep policy wasn’t because I summoned any keep-alive dark magic. It was simply because of one thing: it’s stupidly easy to use.

Finding pain points can be complex, or it can be simple. You just have to locate the dirtiest, most tedious chore that people spend 10 minutes a day complaining about, hack together an MVP as fast as humanly possible, throw it online, and make the experience zero-friction. You don't have to manage the rest.

Real demand will keep it alive for you.

Those overworked tech folks will use their own physical presence to stomp your server awake in the middle of the night. That pack of Anonymous Altocumulus and Levitating Danish had no idea they were doing a good deed, but every time they needed the tool, clicked in, and exported a file, they gave the container another 24-hour lease on life.

The server doesn’t sleep, because the users' bare necessity pumps oxygen into it every single day.

Thanks for tuning in, folks! If you found this interesting, don't hesitate to like, share, and spread the word!

To catch my posts as soon as they drop, don't forget to star/bookmark this page so you don't lose it.

Alright, that's all for today.

Deep Decryption of OpenAI's Anti-Gray Market Registration: "Outsourcing" Risk Control and "Deterring" via Costs

GokuScraper悟空爬虫 — Thu, 07 May 2026 11:28:57 +0000

Deep Decryption of OpenAI's Anti-Gray Market Registration: "Outsourcing" Risk Control and "Deterring" via Costs

In the past few years, OpenAI's payment gateway was essentially an ATM for the gray and black markets. From the mass opening of first-month free trials on Japan’s PayPal, to the replay of Apple App Store receipts for "one ticket, multiple charges," and the forced unlocking of trials via Frida hooking on Google Play—every single pathway had actors engaging in large-scale arbitrage. However, by mid-2026, these loopholes were entirely sealed, bringing the era of "zero-dollar purchases" on the payment side to an end.

But the demand did not disappear; the battlefield simply shifted. The main forces of the black market retreated from the payment side to the more foundational registration side: one strategy involves mass-registering free accounts to build a pool, accumulating small gains into a large one; another focuses on exploiting various trial events to shear free Plus quotas.

In response to this shift, OpenAI's risk control strategy completed a quiet paradigm shift. It is no longer obsessed with "identifying whether you are a machine or a real person," but instead focuses on increasing the cost of registration through risk control. It calculates a cold economic equation: when the money, time, and effort you spend registering an account exceed the price for which it can be sold, you will naturally give up.

It is from this precise angle that we launched a routine packet-capture audit of OpenAI's registration process. In the critical initialization request https://ab.chatgpt.com/v1/initialize, we captured a JSON configuration of over 3000 lines. This dynamic instruction set, relying on the Statsig feature management platform, constitutes the command center of OpenAI's global risk control.

Based on our deconstruction of this configuration, we identified the three pillars of this defense system: an email domain blacklist—cutting off resource supply at the source; differentiated verification channels—using WhatsApp to implement a dimensional strike against traditional SMS reception platforms; and multi-dimensional environmental fingerprinting with full behavior recording—leaving the traces of automated registration scripts nowhere to hide. The three are centrally orchestrated by the dynamic rollout engine driven by Statsig, empowering OpenAI with the ability to tighten or relax global strategies with a single click in the backend, without needing to release any code.

This article will break down this anti-black market defense matrix in the registration phase layer by layer.

Chapter One: Cutting It Off at the Source - The Strictest "Email Blacklist" Strategy on the Web

The first line of defense in traditional risk control is typically set at the "verification" stage—CAPTCHA, SMS verification codes, email confirmation links. But OpenAI's approach is much more direct: it moves the defense line forward to the "registration eligibility" itself. Before a user even reaches the verification step, the system has already made a judgment through a simple field—whether the email domain you are using is on the blacklist.

In the JSON returned by the ab.chatgpt.com/v1/initialize interface, under the configuration ID 739871931, there hides a list named disabled_domains, containing 156 email domains. This list is distributed by Statsig, meaning it can be added to or deleted from in the backend at any time, without a frontend release. It is the first gate of the entire registration process—those who hit it will not even see the verification code page.

These 156 domains were not randomly blacklisted. After breaking them down, we found that OpenAI's strike logic covers four clear dimensions, every cut severing the black market's resource lifeline.

Dimension One: A Blanket Ban on Privacy-Encrypted Emails

The most prominent category on the blacklist is email service providers that focus on privacy protection and end-to-end encryption: proton.me, protonmail.com, tutanota.com, etc., are all listed.

These types of emails are the "favorites" of black marketers hoarding accounts. They do not require a phone number to register, support unlimited aliases, and are insensitive to the registration IP, making them naturally suitable for batch automated operations. Black market teams often have tens of thousands of ProtonMail or Tutanota accounts backlogged, ready to be linked with registration bots. OpenAI's strategy is exceptionally simple: since the core selling point of such emails is "anonymity" and "difficult to trace," then to me, they equate to "untrustworthy." A blanket ban instantly turns the massive stock hoarded by the black market into garbage.

Dimension Two: Regional Email Blocking

A large number of local email service providers from specific countries appear on the list, presenting a clear geofencing logic:

Mainland China: qq.com, 163.com, 126.com, etc.

South Korea: naver.com

Japan: yahoo.co.jp

Russia: mail.ru, yandex.ru

Poland: wp.pl, op.pl

This is most likely not a random selection, but rather based on attribution analysis of registration data. OpenAI has clearly calculated the "low-quality registration rate" for email domains globally. When the proportion of spam accounts among registration requests from a country's local email exceeds the tolerance threshold, the entire domain is blacklisted. This means that the path for the black market to detour through niche local emails to bypass risk control is also blocked.

It is worth noting that the ban on qq.com and 163.com has an ancillary effect: it shows that OpenAI has no intention of leaving a loophole for users in mainland China through the traditional email registration path. To some extent, this is also part of forward-looking compliance risk management.

Dimension Three: "Mandatory SSO" for Big Tech Emails

The most surprising entries on the blacklist are gmail.com, hotmail.com, outlook.com, and icloud.com—the major tech emails with the highest global coverage.

This does not mean OpenAI is rejecting Gmail or Outlook users, but it wants you to enter in a different way—you must click the "Continue with Google/Microsoft/Apple" button and go through OAuth authorization login, rather than directly entering an email and custom password into the registration form.

The logic behind this is clear: POSTing an email address directly to a registration endpoint is too easy to simulate; automated scripts can do it in milliseconds. But going through OAuth means OpenAI can obtain structured credit data for this account from Google or Microsoft—how long the account has been registered, whether it’s bound to a phone number, whether its behavior in the Google ecosystem is normal. The cost of simulating a plain email is close to zero, but the cost of simulating an SSO login state from a major tech company with complete credit backing is orders of magnitude higher. This essentially transfers the cost of identity verification to Google and Microsoft.

Dimension Four: Phishing Prevention for Internal Corporate and High-Net-Worth Industries

Some special entries also appear on the blacklist: openai.com, mail.openai.com—these are OpenAI's own corporate email domains. Blacklisting their own emails might seem strange, but it is standard intranet security practice. It prevents internal employees from mixing company emails in the public registration process, and it also eliminates the possibility of attackers impersonating an OpenAI identity to carry out phishing.

In addition, domains of well-known consulting and financial companies such as bcg.com (Boston Consulting Group), bain.com (Bain), citadel.com (Citadel Securities), and moodys.com (Moody's) also appear on the list. Once the emails of these high-value enterprises are maliciously registered, they can be used for social engineering attacks—such as impersonating a BCG consultant to send a phishing request to OpenAI, or using a Citadel domain to register an account for internal or external fraud. Blocking such domains in advance is taking precautions.

From these four major dimensions, it can be seen that OpenAI's email blacklist has gone far beyond the crude traditional practice of "blacklisting a few disposable emails." It is a dynamically updated, multi-dimensional, precisely targeted resource blocking system. Its core philosophy is singular: to make the black market’s resource pool largely invalid right at the registration eligibility gate.

But this is only the first line of defense. Even if the black market acquires clean emails that are not on the blacklist, an even more thorny obstacle awaits them—the "dimensional strike on channels" during the identity verification phase.

Chapter Two: Dimensional Strike in Identity Verification - The Channel Game Between SMS and WhatsApp

The email blacklist is a block at the resource level. But for those black market actors lucky enough to use an email not on the blacklist, they immediately hit the second line of defense—identity verification. In this link, OpenAI did not choose to clash head-on with the endless stream of SMS receiving platforms, but instead used a beautiful "channel replacement," shifting the battlefield to a location of its own choosing.

In the JSON returned by the initialization interface, two arrays are hidden under the configuration ID 2516824722: sms and whatsapp, each containing a list of country/region codes. This seemingly ordinary list actually dictates which verification channel global users must go through during registration. And the allocation logic behind these two lists is the essence of OpenAI's suppression of the black market during the identity verification phase.

2.1 Core Logic: "Outsourcing" Risk Control to Meta

To understand this strategy, one must first look at how the traditional black market passes SMS verification. Virtual SIM card receiving platforms—these were once the core components of the black market's infrastructure. One machine packed with dozens of cheap SIM cards receives verification codes and automatically sends them back via API, with costs as low as a few cents per message. Faced with this infrastructure, simple SMS verification is practically useless.

But OpenAI's choice is: don't give them a chance to send an SMS.

In the configuration, countries/regions covering over 90% of the global population are forcibly assigned to go through the WhatsApp verification channel. The critical point of this decision is that, to have an account that can normally receive WhatsApp verification messages, you must meet the following conditions:

A real phone number (registering for WhatsApp with a virtual number easily triggers a ban).
The number must have successfully registered for WhatsApp and not been flagged by Meta's risk control system.
A stable network environment, as frequently switching IPs to log into WhatsApp is also high-risk behavior.

Each of these three conditions exponentially increases the costs for the black market. Traditional SMS receiving platforms are completely useless against WhatsApp verification, because these virtual numbers have never even registered for WhatsApp. Even if they had, they would be quickly identified and banned by Meta's anti-spam system.

Looking deeper, OpenAI's move is equivalent to outsourcing a thorny technical problem—determining whether there is actually a real person behind this phone number—to Meta. WhatsApp's parent company, Meta, has the world's largest social graph and top-tier anti-spam account detection systems. A phone number's historical behavior in the WhatsApp ecosystem, its group activity, its recorded reports, and its registration duration—all these dimensions collectively form a credit evaluation system far more complex than "can it receive a verification code."

For the black market to break through OpenAI's verification, it must first pass Meta's gate. And Meta's anti-spam capabilities are forged through over a decade of time and countless millions of attacks. This move of "borrowing a knife to kill another" is clean and decisive.

2.2 The Cost and Efficiency Game of SMS vs. WhatsApp

Of course, the SMS channel has not been completely shut down. The configuration still retains 12 countries/regions that can use SMS verification, including the US, Canada, Japan, South Korea, France, etc. The logic behind this is also an economic calculation, just with the algorithm reversed.

These exempted regions share a common feature: the cost of acquiring a phone number is extremely high, and telecommunications regulations are strict. In the US and Japan, the real-name authentication threshold for a SIM card itself constitutes a natural barrier. The cost of mass-registering in these markets, for the phone number alone, already exceeds the selling price of a free account. Since it's not viable for them to bot it, there's no need to defend it too tightly.

Conversely, in those countries forced to use WhatsApp—India, Indonesia, Brazil, Nigeria, etc.—international SMS in these regions is not only expensive, but delivery rates are also a concern. If OpenAI opened SMS verification to these regions, it would not only bear high SMS costs but also face the risk of the black market using cheap local SIM cards to mass-register accounts.

By going with the WhatsApp Business API, OpenAI pays Meta a lower fee and is completely untethered from the network quality of local operators. It's stable, cheap, and seamlessly uses Meta's risk control to block the black market—it is a win-win business.

2.3 The Deep Logic of the "Celestial Dragons" Country/Region Exemptions

So what is so special about the 12 countries/regions granted the privilege of using SMS? Breaking them down, they can be divided into three categories:

Category One: Five Eyes Alliance and Developed Economies—US (US), Canada (CA), Japan (JP), South Korea (KR), France (FR). Users in these markets have high value and a strong willingness to pay, and both the level of real-name registration and the acquisition cost of phone numbers are high. The comprehensive cost of botting accounts here far exceeds the returns, so OpenAI can safely give the green light.

Category Two: Geopolitics and Special Traffic Zones—Taiwan, China (TW), Thailand (TH). These two locations are relatively high-quality traffic pools for OpenAI in Asia, with relatively clean payment chains and low credit card fraud rates. The supporting payment risk control capabilities are strong enough, so there is no need for an extra lock at the SMS verification stage.

Category Three: A Few Small Places You'd Spend a While Looking for on a Map—Falkland Islands (FK), Niue (NU), Timor-Leste (TL), Vanuatu (VU), San Marino (SM). At first glance, this seems baffling, but the logic is simple: these places have extremely small populations, and large-scale black market SMS reception resources simply don't exist there. Since nobody is botting there, maintaining the status quo is the lowest-cost option.

From this list, it is clear that OpenAI's allocation of verification channels has no consideration of "principled fairness," only cold cost-benefit calculations. Which regions can use SMS, and which must use WhatsApp—every decision is built on a precise estimation of the abundance of local black market resources and the cost of identity falsification.

From the black market's perspective, this configuration means: unless you have a way to mass-acquire "clean," active WhatsApp accounts, the registration gateway in most parts of the world is closed to you. And the cost of mass-farming WhatsApp accounts is already beyond what traditional SMS platforms can cover.

This is the true meaning of a "dimensional strike." It's not about blocking your road; it's about forcing you onto a battlefield where building the road is too expensive.

But OpenAI does not stop there. Even if the black market handles a clean email not on the blacklist and procures a WhatsApp account that can pass verification, there is a third line of defense waiting in the backend—an "environmental fingerprint and behavior monitoring" system far beyond the imagination of traditional risk control.

Chapter Three: The Inescapable "Digital Portrait" - Full Behavior Recording and Environmental Fingerprints

The email blacklist filtered out the mass-registration resource pool, and WhatsApp verification blocked the pathways of cheap SMS receiving platforms. Up to this point, out of ten accounts the black market could push to the registration stage, maybe one survives. But OpenAI's defense is not over—even if you luckily make it here with a clean email and an active WhatsApp account, a monitoring net you can't even perceive is waiting for you in the backend.

The core of this monitoring net is not traditional CAPTCHAs, but full-dimensional collection and comparison of your "digital portrait." Every minute operational detail that you wouldn't even notice yourself is being recorded, quantified, and scored.

3.1 Human-Machine Behavior Analysis

At the very end of the initialization configuration, there is a field that looks innocuous: session_recording_rate: 1. This is the most spine-chilling value in the entire JSON.

It means that for every user who reaches the registration page, regardless of whether they ultimately register successfully, their current session is being recorded. The recording mentioned here is not screen recording, but a technology called human-machine behavior analysis—the system is collecting your mouse movement trajectory, click frequency, the millisecond interval between two keystrokes, page scrolling speed, and even the time the cursor hovers over a certain input box.

These behavioral data mean nothing on their own, but put together, they piece together a highly recognizable "personality portrait." When a real human fills out a form, the mouse trajectory has micro-jitters and random pauses, the typing rhythm alternates between fast and slow, and there are hesitant cursor movements when switching between different fields. However, a script—whether it's a browser extension or a Playwright-driven automated registration bot—has fixed behavioral characteristics: the mouse trajectory is either absolute geometric straight lines or mechanically generated curves; keystroke intervals are uniform, and the operational rhythm between page transitions is fixed.

Traditional CAPTCHAs can't see these differences. But human-machine behavior analysis can.

This behavioral analysis is not a real-time interception sieve, but a post-event chain of evidence. Your operations are recorded entirely and sent to the backend behavioral analysis engine for scrutiny. Even if you pass all verification steps on the frontend and successfully acquire an account, if you are judged as "non-human operation" during behavioral analysis, you will still trigger a ban later. The black market will never know which mouse movement was "too straight" that got them flagged.

3.2 Environmental Contradiction Detection: The "Deduction Items" in Risk Control Models

If behavioral analysis looks at "how you operate," environmental fingerprint detection looks at "what you are operating with." In the derived_fields and evaluated_keys fields of the configuration, the system records device profiles far more detailed than typical User-Agents.

Here lies an easily misunderstood yet exceptionally important risk control concept: environmental contradiction detection is not about "determining that you are a bad actor," but about "adding points" or "deducting points" from the current session.

Take geography and language environment as an example: when a request IP is geo-located to the US, but the browser language preference is zh-HK (Hong Kong Chinese), this will trigger a "deduction item" in the risk control engine. Of course, there is perfectly reasonable explanation for this combination—a Hong Kong immigrant living in San Francisco using their accustomed Chinese system, which is very normal. But what the risk control model looks at is not "whether it's possible," but "how probable this combination is in black market samples." In the scenario of bot mass-registrations, proxy IPs concentrated in US datacenters while the browser language exposes the script developer's Chinese environment is precisely a very typical configuration anomaly. Therefore, it won't be directly judged as "you are black market," but instead recorded as a risk weight, waiting to be compounded with other signals.

There are many dimensions like this:

Does the browser version you reported match the real version exposed by the JavaScript underlying navigator object? If the User-Agent was changed via script but underlying properties were left untouched, a hard mismatch appears here, serving as a heavily weighted deduction.

What is your browser window size? 1920x1080 is completely normal, but the default window size for headless browsers commonly used by mass registration bots is either very narrow and short, or vice versa—running desktop browser fingerprints within an obvious mobile viewport is equally a warning sign.

How many registration requests has your DeviceId been associated with in a short period? If the same device fingerprint appears in registration sessions across multiple different IPs within minutes, the device reuse logic of the black market is fully exposed. This item's weight is relatively higher because a normal person would almost never use the same device to complete multiple registrations from different IPs in a short timeframe.

Take any of these metrics individually, and every one has a reasonable exception. So, to prevent false positives, it does not do black-and-white, single-point interception; instead, it lets every suspicious signal cumulatively weigh in the backend. Only when the total score crosses a threshold does it trigger an action. This makes it very hard for the black market to reverse engineer exactly which step exposed them, and also ensures that normal users don't get accidentally harmed by an isolated "coincidence."

At this point, OpenAI's three lines of defense during the registration phase are clearly visible: the email blacklist cuts off the resource supply at the source, WhatsApp verification applies channel suppression during the identity verification phase, and environmental fingerprints and behavior recording weave an omnidirectional monitoring net in the backend. But the strength of these three layers is not set in stone. What truly makes this system come alive, and keeps attackers forever struggling to catch up, is the final trump card—the dynamic gray-release engine driven by Statsig.

Chapter Four: The Dynamic Defense Command Center - "Gray-Release Risk Control" Driven by Statsig

The email blacklists, WhatsApp verification, and environmental fingerprint monitoring described in the previous three chapters already look very tight. But if this system were hardcoded—the domain list hardcoded in the frontend, the verification channel configuration packed into a release—then the black market would only need to spend time reverse-engineering it once to find all the rule boundaries, and then bypass them one by one like solving a puzzle.

OpenAI's true trump card is making these rules alive. They are not static walls, but valves that can be tightened or loosened at will.

The core of this dynamic capability is the Statsig feature management platform. In the JSON returned by ab.chatgpt.com/v1/initialize, besides specific blacklists and configuration items, there are also a large number of boolean switches starting with gate__, mutable parameters carried by dynamic_configs, and functional entry points named with an enable_ prefix. These fields can be modified in real-time in the backend and synchronized to the initialization requests of all global users within milliseconds. What the black market is facing is a defense matrix that can evolve itself.

4.1 From Hardcoding to Valve-Style Adjustments: The Value of feature_gates

In the captured feature_gates list, there is a seemingly ordinary switch: gate__authapi_add_phone_enforce_sms_only_country_codes: false. At present, its value is false, meaning this rule is dormant, and nothing happens.

But once it is set to true, the situation completely changes.

Chapter Two of this article detailed how OpenAI suppresses SMS-receiving platforms by forcing most countries through WhatsApp verification. But if one day, black market teams in a certain region overcome this barrier—say, by relying on large-scale account-farming factories to produce enough cheap, active WhatsApp accounts, causing garbage registrations in that area to rebound—OpenAI doesn't need to rewrite any verification logic at all. It simply locates this switch in the Statsig backend, flips it to true, and all registration requests from that region are immediately forced back onto the SMS verification path. The WhatsApp accounts the black market painfully farmed are instantly rendered useless.

This is the truly terrifying part of this switch: it's not a lock, but a lock core that can be swapped out at any time. The black market spends weeks or even months pouring resources into breaking a verification path, and OpenAI uses a single click to make those investments practically worthless. By the time the black market readjusts its scripts to accommodate SMS verification, OpenAI can flip the switch back to false. The initiative of the attack-and-defense tempo is completely out of the hands of the black market.

4.2 Cold Start Contingencies for Real-Time Leak Database Validation

Another field captured in the config is even more intriguing: enable_signup_leaked_credential_check: false.

This switch is also currently off. But its very existence is a signal: OpenAI has already integrated the ability to compare against global known data leak databases (commonly known as "social engineering databases") at the code level. Once enabled, the system will check the current email against historical internet data leak events at the first step of registration—if the email account and password have ever been made public in a breach, it will be flagged as high-risk at OpenAI's registration entrance.

When the black market conducts large-scale account sweeping, they frequently use these real emails extracted from leaked databases. They look no different from normal emails, but the user is long no longer the original owner. OpenAI keeping this switch is like burying a cold-start landmine at its own door—normally it doesn't bother anyone, but once a wave of account sweeping attacks appears in a certain region, activating this field empowers them to intercept the attack within the very first second of registration.

4.3 The Labyrinthization of Registration Paths

In addition to specific defensive rules, the dynamic configuration also contains a series of switches designed to disrupt the flow of automated scripts. Their logic is not to intercept, but to make the bots' preset paths invalid.

enable_dynamic_redirect_for_existing_username_on_signup_screen: true is one of them. When a username is already occupied, normal flow would statically redirect to a specific prompt page. But if this switch is enabled, the system can randomly change the redirect target based on the current session's risk score—sometimes it's the email verification page, sometimes it demands more supplemental info, and sometimes it redirects straight to SSO. Automated scripts rely on fixed URL redirection logic; when the path becomes a labyrinth, the scripts freeze at unpredictable forks.

Partnered with it is the enable_redirect_to_social_for_existing_email series of switches. When the system detects the current email belongs to a high-risk category, it dynamically forces the registration flow from "fill out a form to register" to "please log in with Google/Microsoft." This means the exact same email will be directed to entirely different registration paths depending on its risk assessment. If a black market script only accommodates one path, the other instantly becomes a blind spot.

4.4 The Ultimate Advantage of the Attack-Defense Time Difference

The collection of all these dynamic capabilities ultimately translates into a tactical advantage that the black market can hardly overcome: time.

For the black market to study a set of risk control rules, crack the logic, develop adaptive scripts, test, and deploy at scale—this is a cycle measured in days, if not weeks. Yet, for OpenAI to shift its defensive focus—tweaking the value of a switch in the Statsig backend—takes only a few seconds, and this change is synchronized globally via CDN to all initialization requests in milliseconds. The time difference between the two is a weapon in itself.

More importantly, this time difference means OpenAI doesn't need to chase a 100% interception rate. Traditional risk control thinking requires catching every single attacker; missing one is failure. But in the logic of dynamic defense, missing some doesn't matter—because as the comprehensive cost of registration is continuously pushed higher, the black market finds the balance of input and output starting to tilt. If spending 30 to register an account yields a sale price of only 25, nobody is willing to continue this business after just three transactions.

The defensive philosophy exhibited across the entire configuration boils down to this single sentence: Do not chase a 100% interception rate, only aim to dynamically adjust the attacker's ROI. When the cost of registration exceeds the profit of selling the account, the black market will naturally exit the field without requiring you to seal every hole yourself. And what Statsig grants OpenAI is the power to tighten this cost valve at any moment.

Conclusion: Demanding Security from Cost - Lessons for Other Big Model Providers

Having broken down this system, looking back at the entire defense matrix reveals a clear thread.

The email blacklist is not verifying if you're a real human, but invalidating the black market's resource pool at scale. Mandatory WhatsApp verification is not adding an extra lock, but pushing costs upstream towards the account-farming supply chain. Environmental fingerprints and behavioral recordings are not outright blocking registration requests, but real-time scoring every session so that interception decisions are always delayed until sufficient evidence is compounded. The gray-release engine via Statsig allows all these strategies to be combined, toggled, and tightened or loosened based on specific regions or attack patterns at any moment.

From "identifying bots" to "raising registration costs", this is not just the defensive evolution of a single company, OpenAI, but a paradigm shift occurring across the entire internet risk control domain. For large domestic model providers, this system offers several transferable perspectives:

Firstly, rather than endlessly making additions at single points (more complex CAPTCHAs, trickier slider difficulties), consider shifting the frontline to the resource supply end—the email domain blacklist is a low-cost, high-efficiency paradigm.

Secondly, utilizing existing super-app ecosystems (WeChat, Alipay, etc.) to construct multi-layered verification channels is fundamentally the same logic as OpenAI borrowing Meta's WhatsApp network: outsource identity verification to platforms that have accumulated massive strata of user credit data.

Thirdly, the dynamization of risk control rules should not just be a slogan. Whether you possess infrastructure like Statsig, which allows strategies to take global effect in seconds, dictates who holds the initiative in the tug-of-war between attack and defense.

The future of black market offense and defense will no longer hinge on who can build thicker walls, but on which party can more finely manipulate the attacker's economic ledger. When an invisible cost tag is placed behind every gray market registration account, the defender has already won beyond the scope of rules.

Appendix: Blacklist of 156 email domains attached at the end of the article.

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In-depth Investigation of API Transit Stations: From Black Gray Products to White Gloves, Where is the Future of Domestic AI?

GokuScraper悟空爬虫 — Wed, 06 May 2026 06:14:55 +0000

In-depth Investigation of API Transit Stations: From Black Gray Products to White Gloves, Where is the Future of Domestic AI?

Every day, millions of API requests are sent from the servers of Chinese developers, entrepreneurs, and even top AI companies. They bypass blockades, pass through hidden third-party transit nodes, and eventually reach the servers of OpenAI, Anthropic, and Google.

These nodes are called "transit stations" by insiders.

They are ubiquitous, yet few know who is behind them. This article will uncover the industry chain of this gray area.

Introduction

If you search for "free codex" on Bilibili, you will find a surprisingly detailed tutorial.

A content creator shared how to use self-built emails to mass-register OpenAI CodeX accounts, combined with receiving code platforms to mass-register payment platform accounts. After the new account is bound to a payment method, it automatically gets a one-month free trial. With this process, he can mass-register a large number of premium accounts, getting a whole month for free.

This initially just looks like a "technical sharing", right?

But he revealed one detail—the GitHub project homepage.

Insiders call this thing a "registration machine", an automated project for mass-registering CodeX. The project left a QQ group number. I searched it up, and it’s already full and expanding into the fifth group. This indicates that the first four groups were filled long ago. Based on 500-2000 people per group, a conservative estimate is at least 2,500 people, up to tens of thousands, are circulating around this project.

Even more "spectacular" is the website he made for the project.

Upon opening the site, advertisements rush to your face. On the left is a promotion for a certain transit station; on the right are clearly priced ready-made ChatGPT premium accounts—these were mass-registered using the method mentioned above.

The advertisement in the middle is the most interesting. The transit station set up a lottery to attract traffic and active community members.

10 Plus accounts. The official price is $20 each, with a total value of 1,400 RMB.

With a cost of 1,400 RMB, they bought thousands of clicks and precise developer users. This is far more cost-effective than running Baidu Ads.

This is the gateway to an industry chain. And the transit station in that advertisement is the first vine we need to trace down.

1. The First Vine - A Personal Site with "All-in-One Bucket" Architecture

The first target is hiding in the Bilibili tutorial's advertisement spot.

Its technical foundation can be described as "crude". The website is hosted on a sub-domain with a .top suffix—a "junk" domain that costs a few bucks a year, representing a complete grassroots approach. Yet, its official site looks decent because it directly uses the template from the open-source project new-api. This project has 30K stars on GitHub, offering a clean interface and comprehensive features.

This genuinely reflects the current ecosystem of transit stations: The technical threshold has been leveled. With open-source wheels, anyone who solves the "supply source" and knows a bit of operations can open their doors to customers. Their real "technical content" has shifted elsewhere—where to mass-register accounts? Where to exploit more free quotas? Where to find cheaper upstream channels? And where to pull in more users? This is their fundamental basis for survival.

Even the site's login method hints at its supply sources: besides the standard GitHub login, there's a portal for a "fleece-hunting station" next to it.

What truly exposes its core operations is its supplier list.

This list is chaotic yet rich. It includes official sources like OpenAI, Google, and Anthropic, cloud vendor channels like Azure and AWS, and even peer transit stations, as well as suppliers dedicated to "reverse engineering". So-called "reverse engineering" means hacking the protocols to forcibly "extract" free or restricted AI web interfaces and package them as APIs for sale.

The sole purpose of this "all-in-one bucket" procurement strategy is: Trading chaos for extremely high availability. If a certain upstream channel gets blocked, the station master can instantly switch to a backup link, leaving the users completely unaware.

Looking again at the "available token grouping" on the left, there hide the code words for supply channels:

Azure Claude / AWS: Represents relatively stable enterprise-class cloud channels.

Anti-gravity model / Ali reverse: This is pure underground tech work, cracking web version interfaces.

CCMAX-Unlimited: Implies some kind of high-concurrency, unthrottled special supply.

I also noticed an unexpected name in the groups: Xiaomi. A while ago, Xiaomi held an event where new registered accounts were gifted a certain quota. It seems someone has already exploited these quotas in bulk and is reselling them here.

By now, the business model of this site is very clear:

Sourcing: Exploiting cloud vendor free credits, registering trial accounts, acquiring cheap API Keys, and even reverse-engineering web interfaces.
Whitewashing: Accessing the new-api system to uniform all interface formats of different sources into the standard OpenAI format.
Distribution: Selling to those developers who need a vast amount of AI calls but don't want to maintain hundreds of accounts themselves.

It solves a real pain point: Chinese developers cannot smoothly use overseas AI services due to networks, payments, account restrictions, and high-price barriers.

But it simultaneously sends a signal: Things here are cheap, but not necessarily stable.

2. The Second Transit Station

In the supplier list of the first transit station, one was marked as the most crucial upstream. Its name lay in the first row.

This is the second vine we need to trace.

Upon opening the site, the style changes dramatically. This is a top-tier .ai domain. Amidst the current AI boom, such domains cost usually in the six figures. The page is no longer a crude open-source template but a meticulously designed "enterprise official website" that highlights enterprise-class services. A company name hangs at the bottom, looking respectable and proper.

This is an entity wanting to run B2B (Business-to-Business) business.

Since there is a company, let's do a background check.

The official website notes a Hong Kong company at the bottom, which indeed can be found; the entity exists. But digging deeper, things go wrong. I unearthed the founder's information—a Chinese entrepreneur, whose company is actually located in Xiamen, categorized as a micro-enterprise.

Checking Qichacha, another Singaporean company pops up.

Thus, I gathered its complete corporate entity list:

Entity Level	Company Name	Registry Location	Core Functions	Legal Liabilities	Actual Status
Top Holding Entity	xxx. LTD.	Singapore	Global holding, overseas collection, App Store/Google Play listing, international compliance	Assumes major international legal responsibilities	Active, current official overseas entity
Domestic Registered Entity	Xiamen xxx Company	Xiamen, China	Domestic domain filing, ICP qualification, domestic corporate collection	Assumes domestic internet service legal responsibilities	Active, pure shell company (0 insured people)
Historical Legacy Entity	xxxx Limited	Hong Kong, China	Early cross-border collections, overseas business signing	Basically holds no legal liability	Dormant, no public business activities

The founder’s background makes this "white glove" positioning even more self-consistent.

He previously ran a cross-border proxy business. Anyone who has done overseas business knows that massive API calls must be combined with proxy IPs, otherwise, the accounts are instantly blocked. Proxies are used to bypass risk control.

Transit stations, fundamentally, are also bypassing risk control—bypassing overseas vendors' account reviews, payment limits, and network blockades against Chinese users. If domestic companies could legitimately buy directly, who would buy from him?

Thus, stepping from cross-border proxy into API transit is not a cross-industry move at all, but a natural upstream and downstream extension of a business chain. Previously selling ladders, now selling water, the users haven't changed, and the demands never wavered.

But an eerie price contradiction arises that I cannot avoid.

Returning to the first transit station's supplier list. It explicitly noted this transit station as the upstream supplier and offered outrageous prices—Opu 4.5, Sonnet 4.5, and Gemini 3 Pro Image Preview were marked down to an 80% discount compared to official prices.

But on this transit station's own site, these three models were blatantly marked at original price.

The exact same supply source: retail original price, wholesale 80% off, a five-fold price difference. What does this mean? Either it possesses some secret channels where costs are close to zero, or what it sells in both channels are fundamentally different things.

Of course, it might be possible that the original webmaster priced it wrongly, and the data got outdated.

However, a boss starting from proxies, entering an industry gray area conceptually built to "bypass risk control"—how official are his so-called official channels exactly? This question could likely only be answered by himself.

The site also links to a company GitHub page, which pretends to be engaging in tech community building.

Clicking through, it’s entirely filled with AI-generated traction projects with meager star counts. Fundamentally, these are SEO and promotional materials, zeroing in contributions to the open-source community. Hanging out a GitHub page is nothing short of rounding out their "enterprise official site" persona better.

Evaluating the scale, this is a typical small-to-medium AI tool company, with 50 to 100 employees, pulling in tens of millions of revenue. It doesn't do fundamental research, rarely trains models, and avoids open source. It only conducts one role: constructing the most hidden pathway possible between overseas AIs and Chinese users.

And this road leads not only to average developers but also to names familiar to us all.

On February 24, 2026, Anthropic published an industry-shocking report, explicitly naming three top Chinese AI labs: DeepSeek, Moonshot (Kimi), and MiniMax. The report provided definitive data—these labs utilized around 24,000 fraudulent accounts generating over 16 million interactions.

What were they doing? In industry slang, this is called a "distillation attack"—crazy API calls to Claude, extracting out its logical reasoning, thought chains, and Agent capacities bit by bit, and feeding them to their proprietary models.

And these prominent AI enterprise factions would never use their official IPs to perform these tasks. If an official account gets banned, it's a severe compliance accident sufficient to trigger international lawsuits. But if an agent's account gets banned, they can shrug: "I just bought the services, I don't know how they handled it."

This embodies the "professional value" of transit stations. For tech giants, transit stations provide more than just sock puppets; they provide complete "adversarial engineering capabilities": when accounts are banned, new ones pop up instantly. Massive attack traffic is layered inside normal user requests, much like swarming ants, making the platform’s AI audit system fail to distinguish them.

Because the matter exploded, the US government pushed the PAIP Act in April 2026. The controls aren’t merely about "banning accounts" anymore; they are now focused on targeted sanctions against proxy service providers furnishing infrastructures for distillation attacks.

Looking backward, those convoluted offshore architectures, the seemingly excessive compliance designs, were likely not just guarding against OpenAI, but against these truly devastating legal consequences.

3. The Third Transit Station

This is also a supplier of the first transit station; though marginally lower in status than the second, it remains crucial.

This one uses generic Chinese pinyin allied with .ai for domains—a tier-two Top-Level Domain that costs around 100,000 RMB. Compared to the first station, it's fairly decent; compared to the second, it still lacks quite a bit of substance.

Its after-sales pipeline involves the standard gray-production trio: QQ groups, Telegram, and emails.

But what substantially shocked me was that it imitated vast model enterprises, providing Resource Packs and Coding Plans. This is not merely selling APIs; it's practically marketing "Developer Meals". Functionally, its product layout has increasingly drawn towards the formalized subscription models from certified manufacturers, trying to ascertain a steadier user relationship.

The platform embodies strict positioning: tilting towards developers, sometimes enterprises. Lacking the whole string of offshore company architectures and grand website layouts akin to the second, it understands superiorly than the first station what developers demand—beyond merely lowering prices, a bundled, predictive stability matters more. It occupies a role hovering midway between the grassroots forces and official corps.

4. Horizontal Comparison - A Chart Exposing the Segregated Realms

Three Transit Stations: Grassroots, White Gloves, Strikers, individually exemplifying three different ecological sectors stretching traversing this industrial chain. Aligning them reveals astonishing dissonances.

Dimension	Transit Station A	Transit Station B	Transit Station C
Domain Strategy	Secondary + .top	Top-Level + .ai	Secondary TLD + .ai
Domain Cost	Below 10 RMB	Above 200k	Above 100k
Website Operation Period	Under 6 Months	Over 2 Years	Over 10 Months
Tech Base	Open Source `new-api`	Self-developed	Self-developed
Supplier Structure	Bulk signup + Extracted + Peers	Official + Unknowns	Official + Unknowns
Ratio to Official GPT5.4	36x Cheaper	Original (Debatable)	5% Cheaper
Model Count	115	200+	200+
Promotional Tracks	QQ Groups + GitHub Ads	Self-Media + Rebate invites	Self-Media + Rebate invites
After-sales	QQ Groups	Email + Live chat	Email + Live chat
Target User Base	C-End freeloaders	B-End Local Enterprises	C-End Developers
Company Backdrop	Individual	HK + Singapore	Unknown

Browsing through the chart, the disconnects across these pivotal parameters appear piercingly glaring:

Domain translates to Class. One operates a sub-ten-bucks sub-domain, one drops two-hundred grands on a Top-Level .ai, and the final balances with a one-hundred-thousand-range tag. The domains inherently signal the ambition scopes and commitment inputs of their handlers respectively. The first chases brisk profits, the second nurtures long-term stakes, and the third strives upward to elevate itself.

The Contrasts behind Pricing. GPT-5.4 portrays models that sell 36 times cheaper, normal-priced, or 5% cheaper. Yet "Original price" masks the underlying crevices we discovered prior: If they wholesale identical inventories at 80% discounted metrics downriver, does "original" remain pristine, or are they exclusively displaying this frontally for you? Anything 36 times cheaper obviously houses tainted issues—they directly brandish their discounts transparently. The one tagging with identical metrics, however, harbors deeper abysses beneath it.

Fragmenting the Consumer Strata. C-End freeloaders, B-End regional enterprises, and C-End domestic developers. The tiers differ across three spending budgets and their comprehension of "stability." The first pool shifts when accounts vaporize instantly upon bans. The second's loss evokes commercial penalties and project debacles—necessitating corporate frontals, legal frames to masquerade into pure-hearted SaaS platforms.

An Unavoidable Void. Station C writes "unknown" towards official firm backings. A hub running upward of ten months clutching over two hundred models built atop proprietary schemas seldom boils down to mere indie ventures. Neglecting naked operations akin to A, discarding multi-shore framings analogous to B, it picks complete silence. Often, such deliberate taciturnity signals another painstakingly concealed shell game.

Three Transit Stations epitomize triad segments atop this commercial sequence: Grassroots Plunderers, Enigmatic Enterprise Gloves, and Aspirational Aggressors towards Official ranks. They aren’t combatants amongst themselves—A serves downriver of B—co-existing on shared trails absorbing disparate profit streams up along the path.

5. Beneath the Surface — E-commerce Platforms

If the aforementioned hubs dealt fundamentally with "Developer Business," the token exchanges on Taobao define the very bottom of this hierarchy.

Searching "CodeX" on Taobao uncovers overwhelming search results boasting meticulous operational phases. Keyword unifications are profound—"Domestic direct links", "Stabilized applications", "Issuance of Invoices", "Corporate transaction acceptance"—they target squarely upon local demographic weaknesses.

Their starting tags range dirt-cheaply: Ten bucks for two hundred calls spanning twenty-four-hour windows. Visually unmatchable, yet gazing deeper exposes calculating algorithms disjointed from authentic platform measures. Legitimate realms measure across 'tokens', not 'calls' nor 'queries.' Exploiting technological ignorance, these vendors specifically pamper layman mentalities.

Worsening matters, their currency is labeled as "USD". Obviously, this fails reality checks—a basic conversion of yuan into real-dollar quotas spells huge losses for them otherwise. These Taobao corners function on similar transit site backends pushed over to e-commerce shells. You are paying physical currencies into proprietary metrics, manipulated natively on customized scaling values handled implicitly behind doors! Sellers flex immense dictation adjusting rules universally over margins and refunds without clear-cut validations!

Going by the sellers' claims, 26 RMB can buy CodeX's "$50 USD" quota. This conversion ratio looks tempting, but keeping the previous transit stations in mind, it is essentially taking cheap inventory exploited upstream, cloaking it in a custom pricing module, and reselling it to laymen who know nothing about the industry. Multipliers can be adjusted, pricing units can be custom-defined, and refund policies are utterly up to the seller. Even those transit stations with their own websites never promised unbreakable prices. Once it reaches the Taobao level, transparency plummets to zero.

Browsing the comment section, the demands are highly concentrated. Someone asks if an invoice can be issued—yes, and it supports corporate payments. Someone asks if it can be used without a VPN—yes, via a domestic direct connection. These are gray demands that official channels simply cannot fulfill but exist massively in reality. Invoices and direct connections have become the core competitive advantages for Taobao token sellers.

But the deadliest issue within this entire chain is Trust.

You fundamentally have no idea what model is being called behind the screen. How can an ordinary person verify this? There are indeed people in the market who have created "model verification tools" to help users check whether the returned results genuinely come from the purported models. However, this verification faces a dead end: it’s impossible to stare at it 24/7. Sellers might use the real model during the day and switch to an inferior one late at night—no one would know. A user who bought "CodeX quotas" for 26 RMB might find the results generated at 2 A.M. completely switched to a cheap model trying to cut corners.

With no supervision and zero guarantees, everything relies solely on the seller's conscience. And unfortunately, in an industry chain starting at 10 bucks, "conscience" is probably the most worthless commodity.

However, in this ocean, it’s not only the small fish swimming around.

6. Entry of the Sharks: Fu Sheng and Justin Sun Eye the Same Cake

While grassroots webmasters were quietly raking in cash via the gray market, genuine sharks eventually caught the scent of blood.

This time, petty players stayed home. One is the CEO of a listed company, and the other is the controversial king of the crypto world. Almost concurrently, they reached into the same waters.

Cheetah Mobile: Fu Sheng “We Are Not a Transit Station”

Fu Sheng, Chairman and CEO of Cheetah Mobile, helm of a listed enterprise with over a million followers on Douyin/TikTok—the exact guy who drunkenly yelled at Zhou Hongyi in a group chat not long ago.

Here he comes, presenting a transit station attempting to mask itself otherwise.

He refuses to admit they are transit stations. But let's establish a strict reality check: the definition of a transit station is not delineated by whether they dabble in dark-gray markets or not. When there is something wedged between you and the primary AI model enterprise, dictating requests away from direct official connectivity—you function strictly as an intermediary station, period. The term "Transit Station" naturally stinks within common reputation, making evasion understandable, yet undeniable size and facades don't whitewash definitions.

Since it’s driven by a listed entity, reviewing UI visuals or framework structures becomes trivial. Let’s target directly at models and pricings.

ChatGPT-5.4 reflects an 85% markdown tag equivalently—15% cheaper than official paths. The model range runs limited, fielding roughly 37 options—clinging tightly to restrained layouts compared to the rampant 200+ selections littering general proxy sites, acting instead like a legit SaaS structural selection. Another tagging reveals the core strategy: DeepSeek-V4 sits at merely a quarter against official metrics! Naturally, this slashing defies sustainable realities; they are violently hemorrhaging investment pockets aggressively securing user mindsets to claim the early traffic domain.

Fu Sheng swoops in throwing cards embedded in compliance standards and branding. Ironically, the platform fails to manifest a direct corporate owner attached; nobody knows accurately which entity signs off, bills, or invoices the revenues drawn! How does a corporate-run pipeline maintain blurring this key feature? It beckons potential enterprise users into cautious second thoughts!

Justin Sun: A One-Letter Top-Tier Declaration

Justin Sun joined the game too.

He omitted preliminaries entirely and outright grabbed a prime top-tier AI domain—featuring solely a single letter! Such scales cost effortlessly within millions. Domain mirrors declarations: one letter broadcasts boldly, stating "I have arrived, I am serious, and I rule at the top-tier."

On the domain's landing, yet again—nobody can directly tell which company holds ground. Their pricing game twists remarkably distinctive: GPT-5.4 maintains absolute 1-to-1 official equivalence!

Not exactly cheap.

Coupled with an ultra-expensive domain alongside strict full-price structuring, Justin Sun plainly steers away from the price wars altogether! His trajectory signals an entirely different road: wielding an ultra-tier domain generating immense branding leverage, consolidating legitimized settlement channels targeting legitimately wealthy corporate clientele exclusively! Scarcity and raw trust represent his true weaponizations over pricing numbers!

Sharks have arrived, will the waters run clearer?

Fu Sheng slices 15% off official marks, Justin Sun peddles raw official metrics. One blasts venture capital aggressively securing tractions, the other constructs branding off colossal domains. These respectful sharks might visually contest with indie proxy nodes—in truth, they lock horns ferociously among themselves atop entirely separate evolutionary chains!

Shark invasions hardly guarantee the obliteration of gray markets. Sharks engulf mid-to-high level shoals consisting of enterprises insisting on invoices, contracts, and stable frameworks. The low-level small fry demographic remains continually lingering around the £10 Taobao proxy realms feeding on cheap, 200-call-rate scraps.

The division of this industry's layout becomes terrifyingly transparent.

The Final Chapter: The Legal Red Line and the Endgame Forecast

Coming this far into the investigation, a fundamental question must be addressed: Why is it called a "transit station"?

The antonym for a transit station is a direct connection. Direct connection means dynamically buying services straight from giant model vendors directly sending device pings into international servers—while additionally functioning strictly within legit internet connectivity pipelines. However, this trajectory stays notoriously severed for immense swaths of domestic denizens.

Therefore, transit points execute one distinct directive: setting up proxy server points domestically. A user ping reaches this domestic point first to be routed externally toward foreign hubs afterwards!

What does this indicate?

This falls fundamentally under illegal circumvention tapping into global internets! Disregarding how attractively disguised they are, any foreign AI proxy presenting local interfaces crosses into statutory breach domains right from inception point architectures!

Descending strictly beyond this boundary line, legal frictions crash across three cascading planes:

First Threshold: Illegal Business Operations. Proxies fundamentally transact telecommunication value-added features illegitimately. Marketing information transit capacities to locals devoid of licensing credentials falls flat beneath telecom authorizations entirely.

Second Threshold: Infringement by Providing Tools Invading/Illegally Accessing Computer System Configurations. Services waving flags of "Interface reverse engineering" lean exclusively upon cracking network communication paths tapping into authorized architectures illegally! Facilitating such hacks and charging entry defines crystalline violations procedurally!

Third Threshold: Fraud alongside Data Security Violations. Large-scale exploiting via fraudulent subscriptions scraping trial cash breaches fundamental swindling! Exporting civilian data through contraband internet pipes illegally across international servers inherently ignites strict data-safety charges respectively! Triple thresholds represent independent criminal liabilities collectively.

However, courtlines hardly comprise the exclusive threats proxy operators suffer from. Defensive retaliation originating upstream simultaneously constricts heavily!

OpenAI and Anthropic witness rapid enhancements into their scanning mechanisms heavily weaponizing precise behavioral biometric 'fingerprinting' structures. Eras permitting trivial evasions via swapping IPs plus spamming newly scripted signups are closing swiftly; mouse winning-odds diminish exponentially! Simultaneously, streams of supply vanish continuously—Google, Microsoft, among cloud giants tighten limits enforcing stronger registration walls cutting gifting allocations. Ultimately, these historically cheap limitless inventory veins will drain completely.

Justice lines restrict externally whilst source avenues suffocate at their roots; wedged heavily between these two crushing parameters paints an unequivocally crystalized endgame path going forward!

Over eighty percent of indie runners alongside small group fractions will melt away silently amongst these collapsing pressures. Chat rooms disassemble, proxy links yield infinite error pages—erasing them as completely unproven occurrences altogether. Without proper legal backbones shielding them, dealing defensively is genuinely non-existent.

A rare fractional cut of robust syndicate frames won't die easily; instead forcing them backward into far deeper obscure crypt markets shedding away open marketing entirely transacting strictly internally beneath dense security parameters exclusively restricting entry altogether.

The evacuated consumer voids finally invite assimilation handled dynamically via Cheeta or Justin Sun-styled semi-legit platforms equipped adequately with vast corporate bankings covering immense legal buffers surviving offshore! These titans hardly enter intending toward exterminating shadows; instead charging resolutely simply capturing the abandoned user spoils left completely vulnerable beforehand!

Standing statically observing this whole theater—one peculiar question overshadows concerns over proxy-bustings vastly: Why exactly does this market flourish robustly anyway?

Thriving proxy hubs directly manifest twisted necessities stemming between the local delay traversing domestic AI breakthroughs matching alongside ravenously demanding consumer pools respectively! Millions of devs, businesses, coupled with startup nodes never truly wish upon violating laws voluntarily—they just crucially thirst for code strings functioning seamlessly generating operational solutions guaranteeing they avoid dropping dead entirely behind progressing era parameters!

Blocking proxy nodes stands as standard statutory jobs; yet strictly enforcing proper, mighty domestic AI parameters yielding capable power thresholds comparable across globally competitive fronts—enabling developers the sheer grace completely absolving "stealing fired promethean elements" exclusively guarantees this obscure abyss dissipating permanently off radar charts finally!

Before that very horizon crosses lines, millions upon millions of these exact API paths shall endlessly tunnel onward through identical shadows.

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OpenAI Shut the Door, and Relays Are Out for Blood: The "Tragedy of the Commons" in the Token Economy

GokuScraper悟空爬虫 — Mon, 04 May 2026 04:17:50 +0000

Right now, when you try to sign up for a new OpenAI Codex account, you are no longer greeted by that familiar email verification interface, but by a cold, hard wall – overseas phone number verification is now mandatory.

This is not just a UI interaction change; this is an iron door, and one that has been completely welded shut. Once upon a time, all kinds of "freeloading" secret manuals circulated through tech circles: account generators, account pools, seamless switching. That was the idyllic era of the Token economy, when developers were self-sufficient, sharing the thrill of bypassing restrictions on GitHub.

But now, the door is closed.

Why was it closed? Because OpenAI got sick of being leeched. This is no longer the "jungling" behavior of a handful of tech geeks; it has become an organized, large-scale industrial plunder. When tens of thousands of API Keys roll off the black-market assembly line like snowflakes, and when cloud computing resources are used like a free cash machine running day and night, the "Tragedy of the Commons" inevitably strikes. Resources are limited, but greed is infinite. When everyone pursues their own maximum benefit, the result is the pasture degrades, the gates are locked, and everyone is left outside.

I. Anatomy of an Industry: The Quadruple "Russian Doll" of the Token Economy

Behind this welded-shut door lies an already highly stratified and meticulously operated gray industry. It is like a Russian nesting doll; peel back one layer to reveal another, each layer feeding on the corpse of the one above it.

L1 – The Bottom Layer (Black Zone): Digital Ghosts That Make Something From Nothing
At the very bottom of the chain lies pure criminal activity. The main players here are credit card fraud and batch automation scripts. Black-market actors use credit card information stolen from all over the world to fraudulently purchase cloud services or directly register for API access. For them, this is the true "something for nothing" business. Card issuing, account registration, and token consumption are all done in one seamless motion. They are the fuel suppliers of the entire arbitrage game, and also the segment bearing the highest risk.

L2 – The Technical Layer (Gray Zone): A Frenzied Reverse-Engineering Race on the Web
This layer is closer to home, inhabited by many self-proclaimed "tech geeks" and developers. They don't directly call the paid API; instead, they conduct "packet sniffing" on the ChatGPT web interface through reverse engineering. They analyze request headers, simulate Session sessions, and forge browser fingerprints to forcibly convert the free or subscription-based chat capabilities of the web version into a programmable "Web2API." It sounds cool, but in essence, it's an act of bypassing the billing system and conducting a large-scale "account sharing" fraud.

L3 – The Circulation Layer (Arbitrage Traders): Exchange Rate Gaps and Information Asymmetry
This is the "smartest" layer. They do not produce illegal accounts; they are movers of profit. They exploit pricing differences between countries or buy accounts directly from upstream sources, acquiring genuine subscriptions or API quotas at extremely low discount prices in bulk, then break them apart and sell them retail. Some go even further, exploiting AI enterprise discount agreements or even educational offers obtained by some startups, fraudulently registering massive numbers of sub-accounts to resell. They are parasites living in the cracks of a global pricing system.

L4 – The Top Layer (White Zone): The Difficult Survival of Compliant Aggregation
At the very top are aggregation platforms aiming for legality and compliance. They try to solve the demand problem with business logic – direct official connections, invoices, SLA guarantees. They have the highest costs and thinnest margins, walking on the edge of the tech giants' ecosystems, struggling to act as a "compliant router" between developers and models. They are currently the most stable key, but one that could be snapped at any moment by an upstream policy change.

Of course, reality is far more chaotic than this layered breakdown. The vast majority of relay stations are actually a four-tier hybrid abomination. A little black-card volume pumped in at the bottom, a Web2API setup running in the middle to keep things afloat, and some cheap genuine accounts from Turkey to prop up the facade—if risk controls tighten, they scramble at the last minute to sign an enterprise discount agreement. The proportion of each layer is like a hotpot base recipe: outsiders will never know it, and the owners themselves will never volunteer it.

II. Infighting: When the "First Traitor" Attacks with Full Fury

This seemingly stable pyramid is full of internal mistrust. And the "Killeryou" incident was the knife that tore through this veil.

The sequence of events was simple: "Killeryou," a big player running multiple relay stations in the gray-market world, suddenly launched a full-frontal attack on OpenAI's official community. He openly posted, exposing in detail the cheating methods of his other gray-market peers, including how to batch-register accounts using forged identities and how to evade risk controls, in a tone fierce enough to sound like a righteous whistleblower.

However, he was not a righteous man; he was a player whose interests had been damaged. The truth soon came out: a mass rug-pull on the "black cards" supplied to him upstream led to the banning of three stores under his name, costing him a direct loss of $25,000. With his upstream supplier fleeing and his downstream customers coming for him, he chose to flip the table, cornered and desperate.

This is a textbook case of "thieves falling out." It reveals a brutal truth: when the stability of the gray market completely collapses, and the fraudulent chain it depends on breaks, infighting is the only outcome. There are no rules here, no contracts, only interests. When interests are harmed, the cost of betrayal approaches zero.

III. The Truth About Relays: No Contracts, Only the "Right of Interpretation"

In the black and gray industries, especially those on the internet, no one talks to you about contracts.

1. Top-up Equals "Donation": A Digital Illusion in Lawless Territory

The logic of a relay station is absurdly simple: you send money, which goes through several layers of untraceable money-laundering links. The boss receives the money, then adds a few digits to your ID in the backend database.

This is a game of "Bro, trust me." The Chinese yuan you top up shows on the web panel as a "balance," but that is essentially just a string of code that can be wiped out with one click, or evaporated completely by the "accidental" server shutdown. In the gray-market world, there is no such thing as a "User Agreement." The moment you press the top-up button, you are essentially making a directed donation in a lawless territory. If the boss decides tomorrow to switch to selling pork ribs somewhere else, or if the server gets taken down by the authorities, your balance won't even count as a worthless piece of scrap paper.

2. Multiplier: The Arbitrary Baton in the Boss's Hand

The so-called "multiplier" is essentially the "shadow tax" that relay stations use to harvest dynamically. If the multiplier is 2, then the purchasing power of your 1 Chinese yuan shrinks instantly to 50 cents.

Everyone knows the multiplier can change, but don't expect any "agreement protection." This thing is a dynamic sickle the boss uses to balance costs and harvest profit.

No Law, Only the Backend: The gray market is gray precisely because it is not subject to any regulation. If the boss feels costs are high today, or if upstream black cards get banned, he can just move his finger in the backend, adjust the multiplier from 1x to 10x, and your balance will collapse ten times faster in an instant.

The Logic of Violent Cost-Shifting: Exchange rates fluctuating? The boss wants a new computer? He doesn't need to issue an announcement, let alone ask for your consent. This absolute centralization of arbitrariness is the biggest pitfall of relay stations—your usage cost depends entirely on the boss's mood and greed that day.

3. The Ultimate Price: You're Not Just Paying, You're "Feeding Data"

This is the most insidious and real price in the entire chain. You think you've spent money to buy a service, but you are actually a self-funded miner, personally feeding your most core data assets into the relay station's hands.

Prompt as an Asset: Every single API request is as transparent as the emperor's new clothes in a relay station's backend. Your painstakingly optimized Prompt templates, your company's core business logic, even the code for your closed-source project—all of it lies "awaiting review" in someone else's database.

Self-Funded "Distillation Corpus": The boss, while taking your money, can conveniently export these high-quality conversation datasets. What can this data be used for? It's the most coveted "distillation corpus" for model training. While charging you a fee, he casually places you on the chopping block, turning you into free fertilizer for someone else's model evolution.

IV. The Endgame of Risk Control: KYC

This cold wave is not unique to OpenAI. The recent risk-control actions of its biggest competitor, Anthropic, foreshadow an even harsher future—mandatory real-name verification.

This is not a simple wave of bans, but a combination punch: account bans, a tightening of refund policies, and strong real-name authentication (KYC). Now, to register a stable Claude API account, you may need to submit a government ID (identity card/passport) and cooperate with a real-time selfie verification. This directly blocks countless domestic developers.

This has created a massive structural dilemma. The high wall of network latency, the barrier of international credit cards, and strict real-name requirements—these three mountains are the soil that has nurtured the living space of relay stations. But this parasitic service has never been stable—developers are sinking into a deep anxiety of "interrupted supply at any moment": the service you built based on a certain Key might wake you up to a screen filled only with 429 errors.

The so-called "right to access top-tier foreign models" is being stripped away layer by layer—it is not a privilege, but a temporary pass that can be revoked at any moment.

V. Epilogue: Those 29 "Crying Faces"

At the end of the story, let's turn our eyes to that relay station community channel that collapsed in an instant. Beneath the service shutdown announcement, 29 crying-face emojis lined up in a silent queue.

They are students, indie developers, and micro-entrepreneurs. They just wanted to test a demo at a lower cost, or build a niche AI application. They are not the initiators of this industry chain, but they are the most direct bearers of the cost every time the "Tragedy of the Commons" erupts. Supporting characters in the tide, the ones breathing their last bubbles as they drown.

But this time, their crying faces should not just mourn the death of a relay station. This screenshot should become a piece of evidence—evidence of the predicament we were in, parasitizing our creativity on someone else's ecosystem.

As OpenAI and Anthropic weld one door after another shut, we are finally pushed to the real question: Do we keep looking for the next crack in the wall? Or do we stop and forge the key ourselves?

This question brooks no further delay.

DEV Community: GokuScraper悟空爬虫

The Car Light Modifier and the Printer Renter Start Learning AI

The Car Light Modifier and the Printer Renter Start Learning AI

Does this look like hype?

From "Buying a Machine" to "Equipping an Upgrade"

The Industrial Revolution of the Ordinary Person

The Fire-Sellers

I Roasted My Friend's X (Twitter) with This Open-Source Tool and Got Blocked...

I Roasted My Friend's X (Twitter) with This Open-Source Tool and Got Blocked...

Just How Idiot-Proof Is This Tool?

Why Should You Bookmark This (Even If You Don't Use It Right Now)?

Let's Be Real: What Are the Limitations?

How to Get Started

A Few Pro Tips

Summary & Links

云展网电子画册下载？这个开源工具，可能是目前最适合普通人的方案

云展网电子画册下载？这个开源工具，可能是目前最适合普通人的方案

一、 为什么要下载？

二、 这个工具有多“傻瓜”？

三、 为什么我建议你哪怕现在不用，也得收藏一下？

四、 实话实说：它有哪些局限性？

五、 具体怎么上手？

六、 彪哥的一些私人建议

七、 总结与项目地址

Why Can't the Chinese Internet Nurture a "Generous Hugging Face"?

Why Can't the Chinese Internet Nurture a "Generous Hugging Face"?

Hugging Face’s “Generosity” Is Paid For By Tech Giants

With the Same Playbook, Why Can't Domestic Platforms Keep Up?

The Temptation of the Gray Area: When People Use Residential Broadband for Commercial Jobs

How Does Bandwidth Cost Shape Our Internet?

Traffic Isn't Free; Someone Is Just Picking Up the Check

When 'I Can't Code' Becomes a Badge: Beware the AI Marketing Bubble

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1. Kitten Fill Light

1.1 Fact-checking the claim of being No. 1 on the paid chart

1.2 Product barriers and replaceability

1.3 Product reality and longevity

2. Nuwa.skill

2.1 Community hype

2.2 The core question: where is the dataset for the "distillation"?

2.3 The whole AI bubble in one picture

Conclusion: let technology be judged as technology, and marketing be judged as marketing

I Curated Over 2,000 Seedance 2 Prompts into a Free Website and Open-Source Dataset for You to Use

I Curated Over 2,000 Seedance 2 Prompts into a Free Website and Open-Source Dataset for You to Use

How to Access and Download

Wrapping Up

Even Sam Altman Would Want to Buy From Them: The Hubris of Grassroots AI Proxy Bosses Billing With Their 'Entire Net Worth'

Even Sam Altman Would Want to Buy From Them: The Hubris of Grassroots AI Proxy Bosses Billing With Their 'Entire Net Worth'

1. The Industry Panorama: The Three-Tier Architecture of AI Proxies

2. "Sole Proprietorships" and Invoicing

3. When the Most "Compliant" Facade Meets the Most "Illegal" Supply

4. The Structural Mismatch of Risk

5. Unmanageable Content Compliance and the "Illegal Business Operations" Red Line

Conclusion: A Gray Market Specimen "Naked in the Sunlight"

Deconstructing the Fatal Bug of the “One-Person Company”: How to Write the Ultimate Legal Disaster Recovery Code with a 1% Family Share

Deconstructing the Fatal Bug of the "One-Person Company": How to Write the Ultimate Legal Disaster Recovery Code with a 1% Family Share

1. Introduction

2. What Exactly is a "One-Person Company"?

3. The Disaster Recovery Architecture: The "99% + 1%" Defense System

4. The Complete Compliance Action Guide for Indie Hackers

5. Conclusion

I Built a Feishu-to-Markdown Tool, and Overworked Office Workers Kept It Alive for Free

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1. The Pain Points of Feishu (Lark)

2. Surprisingly, People Kept Showing Up

3. They Were All Real People

4. It Accidentally Became a Cyber Confessional

5. Turn Software into SaaS, and You Win

Wrapping Up

Deep Decryption of OpenAI's Anti-Gray Market Registration: "Outsourcing" Risk Control and "Deterring" via Costs

Deep Decryption of OpenAI's Anti-Gray Market Registration: "Outsourcing" Risk Control and "Deterring" via Costs

Chapter One: Cutting It Off at the Source - The Strictest "Email Blacklist" Strategy on the Web

Chapter Two: Dimensional Strike in Identity Verification - The Channel Game Between SMS and WhatsApp

Chapter Three: The Inescapable "Digital Portrait" - Full Behavior Recording and Environmental Fingerprints

Chapter Four: The Dynamic Defense Command Center - "Gray-Release Risk Control" Driven by Statsig

Conclusion: Demanding Security from Cost - Lessons for Other Big Model Providers

In-depth Investigation of API Transit Stations: From Black Gray Products to White Gloves, Where is the Future of Domestic AI?

In-depth Investigation of API Transit Stations: From Black Gray Products to White Gloves, Where is the Future of Domestic AI?

Introduction

1. The First Vine - A Personal Site with "All-in-One Bucket" Architecture

一、为什么要下载？

二、这个工具有多“傻瓜”？

三、为什么我建议你哪怕现在不用，也得收藏一下？

四、实话实说：它有哪些局限性？

五、具体怎么上手？

六、彪哥的一些私人建议

七、总结与项目地址