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我花了三天，找到了张雪峰的数据来源

GokuScraper悟空爬虫 — Sun, 21 Jun 2026 23:39:55 +0000

我花了三天，找到了张雪峰的数据来源

故事是这样的。

前阵子帮亲戚家孩子看志愿，查了一堆网站，越查越觉得不对。

每个跟高考志愿有关的网站，都遮遮掩掩的。它们给你看一些数据，但从来不说这些数据打哪来的。我问的是最原始的那个来源，就是某个大学某个专业在某个省到底录了多少分，这个数据你从哪拿到的。

没人说。

这让我想起了企查查那个事。企查查你知道吧，做企业信息查询的，后来做成了上市公司。它的数据来源其实是公开的，就是全国企业信用信息公示系统，国家官网上放着的。它做的事情就是把官方数据爬下来，整理好，让你搜起来更方便。就这么一个生意，做出了一个上市公司。

后来相关部门出了规定，要求这些企业信息查询平台必须标注数据来源。所以你上企查查看一家公司，底下会有一行小字，写着「信息来源，国家企业信用信息公示系统」。

我就在想，这个逻辑放在高考志愿数据上，是不是也一样。

那些民营的高考志愿网站，他们手上那些数据，肯定也有来源对吧。他们不可能自己编一个录取分数出来。如果有来源，那这个来源应该也是某个公开的地方，只是我们普通人不知道在哪。

那我顺着这个思路去找，是不是就能找到。

然后就开始了。

我先查了新疆的教育考试院官方网站。为什么选新疆呢，因为它是比较偏远的省份，如果这种省份的数据都能在官网上查到，那说明我的方向是对的。如果连官网都没有，那这个事就更有意思了。

进去之后，找了半天，只有投档分数线。

什么叫投档分数线呢，就是每个学校在新疆录取的最后一个考生的分数。但是那个最后一名读的什么专业，查不到。你想知道某个大学某个专业录取了多少分，对不起，没有。

我又去翻了他们的官方微信公众号，里里外外翻了一遍。

也没有。

网上，就是没有这些数据。

那这些数据到底在哪呢。

我接着找。后来在一个角落里发现，原来这些数据，全在一套书里。

这套书叫「新疆招生与考试」，分成好几卷，其中有一卷叫「本科院校专业录取数据卷」，专门就是你要的东西。

对，纸质书。

出版社把这些数据印在书里，只卖纸质版。

网上你想找电子版，门都没有。

倒是有一些私人在抖音上卖电子版，几十块一份。但我翻遍了所有官方渠道，官方从来没有发布过电子版，一次都没有。

很明显，他们就是把这套数据当成信息差在做，根本没觉得这东西应该是免费公开的。

我又顺手查了江苏。江苏也有类似的东西，叫「江苏省普通高校招生计划专刊」或者「招生录取资料汇编」。一样，没有免费公开。

我当时看到新疆这个价格的时候，心想，150块四本，还行吧。但转念一想不对。这只是新疆一个省的数据。全国有多少个省呢，26个省市自治区（不算港澳台）。如果你想查全国的数据，你得把这26个省市的这种书，全部买回来。

一本大概几十到一百多。全部买下来，几千块钱吧。

这笔钱不多吧。但问题是，你得知道这些书的存在，你得知道去哪买，你得一本一本去收集。

普通家庭谁会干这个事。

但张雪峰他们就会。

我猜他们的做法是这样的。26个省市，每一个省的那几本书，全都买回来。一本一本翻，把所有的录取数据手动录入或者扫描成电子版，汇总成一个全国数据库。

这是个笨办法。但在现在这个系统下，就是唯一的办法。

因为官方压根就没有把电子数据公开过。

你想想看。你在网上看到的所有高考志愿相关的商业网站，他们手上的数据，大概率也是这么来的。要么自己买书录入，要么从别人那买数据。没有别的路。

这就造成了一个非常荒诞的局面。

在2025年，在AI时代，几百万考生的命运所系的核心数据，被锁在一本本纸质书里。你想看，你得买书。你想看全国的数据，你得买26个省的书。你想查起来方便，你得找张雪峰他们。

一层一层，全他妈是信息差。

我有时候觉得，这其实就是信息在被刻意地折叠。

有一本我很喜欢的书，叫「北京折叠」。那篇小说里，北京被分成三个物理空间，不同阶层的人生活在不同的空间里。高考志愿数据也一样。

第一层，是各省的纸质书。你花几十块钱买一本，能看到本省的数据。

第二层，是跨省的数据。你想看外省的，你得再买另一本书。一个普通家庭，谁会为了填个志愿去买二十几本书。

第三层，是整合后的数据。有人把26个省的书记录汇总成数据库，你就可以在这个数据库里一次性查到全国的数据。但你要么付钱给他们，要么自己花几千块买书再花几百个小时录入。

然后这些信息差，最终变成了咨询费、会员费、课程费。

再说说张雪峰。

很多人把他当神，也有很多人觉得他贩卖焦虑。我自己的看法是，他确实帮很多人打破了信息差。但真正荒谬的是，这些信息差本来就不应该存在。

打个不太恰当的比方。这就像有人把一扇本该敞开的门锁上了，然后把钥匙卖给你。你确实通过他进了门，但你应该问的是，这扇门为什么是锁着的。

这个事最让我觉得无力的地方在哪呢。就是它不是一个技术问题。如果是一个技术问题，总有办法解决。但它是一个利益问题。各省的教育考试院出版社靠着这些书在赚钱，一年好几个亿的市场，他们没有任何动力去改变。

而那些做高考志愿服务的商业公司，他们也不希望这些数据免费公开。因为数据公开了，他们的信息差生意就没得做了。

出版社不愿意。商业公司不愿意。那谁愿意呢。

好像没人愿意。

唯一希望这些数据公开的，可能就是那些马上要填志愿的家庭。几百万个家庭，几百万个焦虑的家长和考生。他们才是这个系统里最弱势的一群人。他们甚至不知道这些数据是被刻意隐藏的，他们只会觉得自己信息不够，觉得自己没本事，然后花钱去买那些本该免费的信息。

而张雪峰的第一步信息差，就是在这儿来的。不说别的，就最基础的那些录取数据本身，大部分人就已经够不着了。你连起跑线在哪都看不到，你怎么跟人家跑。

这就让我想到了企查查那个故事。企业信息以前也是不透明的。你得找关系、托人才能查到一家公司到底怎么样。后来国家把企业信用信息公示系统做起来了，所有数据都公开了。企查查它们做的事情就变成了「帮你更方便地查」，而不是「帮你查到本来看不到的数据」。

这才是健康的模式。数据是公开的，商业公司做的是体验和效率。而不是反过来，数据是不公开的，商业公司做的是「帮你去搞本来看不到的数据」。

高考志愿的数据什么时候能走到那一步呢。

我不知道。

但我还是始终相信，这些墙在汹涌向前的洪流之下，必然会倒塌。

新的时代，一定会到来的。

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！

Is it "Defending Open Source" or "Cyberbullying a High Schooler"? — A Look at the Downfall of an Indie Developer with 140K Followers

GokuScraper悟空爬虫 — Wed, 10 Jun 2026 19:27:38 +0000

Is it "Defending Open Source" or "Cyberbullying a High Schooler"? — A Look at the Downfall of an Indie Developer with 140K Followers

Foreword:

On June 10, 2026, a controversy erupted in the open-source community. A well-known independent developer with 140,000 followers accused a high school student of "plagiarizing" his open-source project and publicly expressed his dissatisfaction.

The incident quickly escalated, with supporters flooding the latter's project comment section condemning the "plagiarism." But as details surfaced, things were far from a simple act of "defending open source."

This article has no intention of picking sides, but only attempts to answer three questions: Legally speaking, did Burrow infringe copyright? Behaviorally, who was actually bullying whom? And what deep-seated issues in the open-source community did this storm expose?

An Issue, a Post, and Two Unequal Opponents

The two protagonists:

The author of Mole: A KOL (Key Opinion Leader) in the tech space on X (formerly Twitter) with 140,000 followers. He developed a Mac cleaning tool called Mole. The command-line version (CLI) is open-sourced under the MIT license, and there is also a paid desktop version (Mole Mac).

The author of Burrow: A high school student. He independently wrote code under the MIT license to create a free tool called Burrow, which has a UI and features similar to Mole's desktop version, and published it on GitHub.

The trigger for the controversy was: Burrow used the wording "a free alternative to the paid Mole Mac" in its project description.

Upon discovering this, the author of Mole did two things:

He opened an Issue in Burrow's GitHub repository, pointing out issues with the promotional wording and the similar UI.

Almost simultaneously, he posted a link to this Issue on X, publicly calling it out and stating that it was "quite chilling." He mentioned wanting to close the source code.

It was this second action that fundamentally changed the nature of the incident.

Legally Speaking, Did Burrow Actually Infringe?

This is a question that must be clarified first. The answer is: On the core issue, no.

Mole's CLI version uses the MIT open-source license. This license explicitly grants anyone the right to freely use, copy, modify, merge, publish, distribute, sublicense, and even sell copies of the software.

Choosing the MIT license is choosing a legally binding contract. It means that while the author enjoys the propagation benefits brought by open source, they must also accept that anyone can build a functionally similar tool, including a free one, based on this code.

So, is the similarity of UI and interaction considered plagiarism?

This is the most hotly debated part. A concept needs to be introduced: copyright law protects specific "expressions" (like code, icons), but does not protect "ideas," "functions," and "methods of operation."

The UI layout and interaction logic of a software are categorized as "methods of operation" or "ideas" in most judicial practices. If the first person to design a "pull-to-refresh" mechanism could permanently monopolize this operation, the entire internet's innovation would stagnate.

Therefore, as long as Burrow's front-end code was written line by line (even if written by AI) and didn't directly copy Mole's images, icons, or CSS files, then even if it looks similar, legally it belongs to "a lawful independent implementation," not an infringement.

Here's an analogy that might help: No one can monopolize the color "green."

If someone invented "green" and then claimed that anyone using green in the future was plagiarizing, it would clearly be absurd. UI layouts and interaction logic are treated as "ideas" and "methods of operation" under the law and are not protected by copyright. Only specific code, icons, and images are protected "expressions."

The Only Line Burrow Crossed

In its promotional copy, Burrow used Mole's commercial brand name mole.fit and the phrase "free alternative." This touched the boundaries of trademark rights and unfair competition. Less than 12 hours after the Mole author raised the Issue, the Burrow author updated the README and removed the related expressions.

At this point, the sole flaw in legal compliance was resolved.

The Real Bullying: Using 140,000 Followers to Crusade Against a High Schooler

If the incident had been limited to communication within the Issue, it would have been just a normal, even encouraging, course correction within the community.

But that post on X changed everything.

An account with 140,000 followers publicly posting a link to a specific project, accompanied by heavily emotionally biased words like "quite chilling," effectively acts as a clear signal directing followers' attention and pressure.

The power disparity here is extreme:

On one side is an industry KOL with 140,000 followers.
On the other is an up-and-coming high school developer with a GitHub project that had barely 400 stars.

Following this, a massive amount of aggressive and accusatory comments rapidly appeared in Burrow's Issue section and related discussions. Regardless of the Mole author's subjective intent, his actions objectively steered an unevenly scaled internet hate campaign against a minor independent developer.

The Mole author stated in the post that he "might make the CLI version closed-source in the future." This sentence exposes the essence of the storm: it wasn't a pure defense of the open-source spirit, but rather a stress response when the free Burrow posed a potential threat to his paid desktop version.

This isn't incomprehensible, but wrapping "commercial anxiety" in "open-source morality," and resorting to asymmetrical public pressure for the latter, deviates from the track of normal commercial competition.

Whitewashing After the Fact: The Elegant "Rational Moderates"

What's most infuriating isn't just the cyberbullying itself, but the whitewashing performance put on by a group of tech experts afterward.

A representative figure is Xuanwo. He is a member of the Apache Software Foundation and a top expert in the open-source circle.

A top expert, in the face of naked cyberbullying, says absolutely nothing about "bullying the small," but instead talks extensively about how "both sides are great" and "worth learning from." He forcibly whitewashes a public shaming orchestrated by a 140K KOL into "healthy interaction in the open-source community."

This isn't stupidity; it's malice. He knows better than anyone where the law and right/wrong stand, yet he still chooses to use his professional authority to endorse the unethical behavior of an insider.

Small-Peasant Mentality Won't Make You Rich

This turmoil reflects a deep-seated cultural dilemma in the Chinese open-source community: Many practitioners work in the most cutting-edge tech industry, but their business ethics and concepts of competition still carry strong pre-modern colors.

To sum it up in one sentence: "Deep down, many independent developers still think like peasants."

This is not a derogatory remark, but a metaphor worth pondering. The logic of an old peasant goes like this:

I worked hard to clear a piece of land (wrote code).
I grew crops (made a product).
I built a fence (chose the MIT license, but secretly hoped others "wouldn't take it too seriously").
If someone next to my land uses my methods to grow the exact same crops and gives them away for free, then they are "dishonorable," "stealing my craft," and "smashing my rice bowl."

This set of logic is rooted in the "favors" and "rules of propriety" of an agrarian society, rather than the "contracts" and "competition" of a modern commercial society.

But the foundational rule of open source is a legal contract, not a charity of "tossing someone a crumb." When a developer clicks the "MIT" option, they are not setting up a private plot protected by village regulations, but releasing the code into a public domain governed by modern rule of law and market rules.

You can continuously innovate technically to build new moats; you can pursue excellence in service to win users' willingness to pay.

But you cannot, after enjoying the dividends of open-source distribution, try to use moral condemnation and fan-driven public pressure to build back the very fence you legally and voluntarily gave up.

What Kind of Open-Source Ecosystem Do We Really Need?

The biggest warning from this incident isn't that a certain project was "borrowed," but the terrible signal it sends to all subsequent innovators:

If you abide by open-source licenses and create a legal alternative, you might be torn to shreds by those with a larger voice under the guise of "chilling them" or being "immoral."

When "morality" becomes an attack weapon that can be invoked at will—without bearing any legal responsibility—its suppression of innovation will be far worse than code plagiarism.

We call for a more mature open-source environment:

Leave the law to the law. If there is suspicion of infringement, please file a DMCA or a lawsuit.
Leave business to business. If you fear competition, choose your open-source licenses carefully, or deeply cultivate irreplaceable technical barriers.
Leave morality to morality. Using asymmetrical public opinion power against an individual with no actual illegal behavior is inherently immoral.

The essence of open source is openness and collaboration based on contracts, not charity and dependence based on personal favors. Only when more people learn to respect contracts and face competition head-on can China's open-source soil truly modernize.

Don't let the rustic plain morality become the final straw that crushes the next generation of innovators.

Thank you all for your support! If you found this interesting, please don't hesitate to like, watch, and share!

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

That's all for today.

I Asked AI to Roast Linus Torvalds' GitHub, and It Was Absolutely Brutal

GokuScraper悟空爬虫 — Mon, 08 Jun 2026 04:52:42 +0000

I Asked AI to Roast Linus Torvalds' GitHub, and It Was Absolutely Brutal

"Your GitHub bio is blank—you couldn't even be bothered to write a single sentence, but your 300,000 followers have already filled it in for you."

I didn't say that.

That was a "diagnosis report" generated by a little toy I built recently, specifically roasting Linus Torvalds' GitHub account.

It went even harder:
"Your naming taste is questionable."

"You're wasting GitHub's resources."

"You are the paragon of open-source extravagance."

The victim? Linus Torvalds. The father of Linux and Git.

The roaster? A fun AI app I built.

What the Heck is This?

I call this app Github-Roast.

It’s pretty simple:

You enter any GitHub username, it scrapes their public info via GitHub API, hands it to DeepSeek along with a rather savage prompt, and spits out a 13-dimension "toxic assessment report"—plus an account valuation based on stars, followers, original repos, etc.

It’s completely free, requires no sign-up, and you can just use it right in your browser.

Try it out here: groast.streamlit.app

Feeling bold, I decided to test it out on the final boss.

I typed in torvalds and hit start.

When the results popped up, I stared at the screen and laughed for a solid ten minutes.

Valuation: $600k USD. Too Much or Too Little?

AI threw out a number first.

4.3 million RMB (roughly $600k USD).

My first thought: Valuing Linus at just $600k seems a little cheap.

After all, almost every server on Earth runs Linux, and half of the global internet infrastructure calls him daddy.

But thinking about it realistically, the AI is calculating this based entirely on public GitHub metrics—stars, followers, original repos, forks, and a formula.

Linus only has 12 repos on his GitHub, and most of them are "weekend soldering projects" or side quests. His actual world-changing project, the Linux kernel, is basically just a mirror on this account. All the real collaboration happens on the mailing list, completely out of sight on GitHub.

So, this valuation makes sense if you treat him like:

An influencer with 300,000 followers who has only posted 12 videos.

Seen that way, maybe $600k is a fair quote.

The Roast: Every Line is a Critical Hit

The valuation was just an appetizer.

The real meat is the AI's roasting section.

Here are a few gems I saved while cracking up:

"Of your 3 forks, two explicitly state 'do not use,' and the third says 'for sync only'—dude, even you know people shouldn't touch your code without supervision."

"Your GitHub bio is blank—you couldn't even be bothered to write a single sentence, but your 300k followers have already filled it in for you."

"As someone holding the lifeline of global developers in your hands, you’ve only ever posted 1 Gist? You are the paragon of open-source extravagance."

That line, "even you know people shouldn't touch your code," was a dagger wrapped in barbed wire.

I half expected the AI to be smirking as it generated that.

Tech Stack & Weaknesses: The Godfather's Other Side

After the harsh jokes, the AI laid into his actual coding practices.

For his tech stack, the AI absolutely nailed it:

"The Godfather of C: From the Linux kernel to your EMACS hacks, every project is pure C. Solid as a rock."

"Embedded Hardware Tinkerer: Using KiCad to design guitar pedals and magnetic inductive scroll wheels? Your analog circuit skills rival professional engineers."

That has got to be the highest praise for a top-tier programmer in his fifties—

Not "he wrote a bunch of papers," but "he solders circuits in his garage on weekends and uploads the PCB files."

But when it got to the weaknesses, the gloves came off:

"Extreme Lack of Documentation: Most project descriptions are one liners. READMEs are either blank or phoned-in. Completely unfriendly to normal users."

"Questionable Naming Taste: 'stupid memory latency tester'? True to life, perhaps, but hardly professional."

When I saw "stupid memory latency tester," I actually thought the AI was holding back.

If you put that on your resume today, HR would toss it in the trash in the first round.

And then the AI dropped this piece of soul-crushing advice:

"You should hire someone just to write your READMEs."

Collaboration Style: Dictator Confirmed

The AI's exact words:

"You are the ultimate solo player."

"On GitHub, you act more like a lone-wolf hosting personal backups."

"Your collaboration style is 'I do everything, you do it my way'—an efficient dictatorship."

He follows exactly 0 people on GitHub.

That's not just playing hard to get; that's straight-up uninstalling the "social" from a social coding platform.

The AI crowned him the "Aloof God of 0 Followees," and honestly, I don't think he'd argue with that.

But his activity radar is fascinating—

The AI noticed he spends recent mornings pushing commits to GuitarPedal, while still maintaining the Linux kernel merge cadence. The ScrollWheel project was just created on June 2nd to mess with magnetic inductive sensors.

A guy pushing 60, simultaneously managing one of humanity's most important software projects AND building guitar pedal noise generators in his garage.

Calling him the programmer version of Tony Stark isn't too far off.

AI's Life Advice: Monetize the Noise

When it came to career advice, the AI actually showed a shred of respect.

"You are already at the pinnacle of your career; any advice feels redundant."

But it just couldn't resist sneaking in a jab:

"You really should hire someone to write your READMEs."

"Stop soldering for a second, write a README, and release a proper GuitarPedal kit to the public."

"Or, at the very least, package your noise generator into an expensive hobbyist desk toy."

That last line had me rolling.

Not only is the AI roasting him, it’s also pitching direct-to-consumer hardware startups.

His AudioNoise project has 4.3k stars, which means there really is an audience out there with a genuine, burning need for "ear-piercing random digital noise."

Linus, think about it. Launch an official Linus Torvalds Noise Generator on Kickstarter.

I'll even write the marketing copy: "Let your neighbors know you use Linux."

As for achievement badges, the AI awarded him these:

🏅 President of the 200k Star Club
🏅 The Dual-Founder (Git & Linux)
🏅 Hardcore Crossover Tinkerer (Software + Hardware)
🏅 Benevolent Dictator of Code
🏅 The Aloof God of 0 Followees

That last badge just perfectly captures the essence.

How Does It Work?

Back to the tool itself.

The core logic takes about one sentence to explain:

Fetch public data via GitHub REST API → Format it into structured info → Feed it to DeepSeek with a "savage prompt" to generate the report → Render the frontend with Streamlit → Export screenshots with html2canvas.

No dark magic here, but the prompt is tuned to be incredibly toxic, making the results way more entertaining than standard analytics tools.

The project is entirely open-source, so feel free to dig through the code if you're interested.

Your Turn

After roasting Linus, I felt a chill run down my spine.

I hesitated for about thirty seconds, then nervously looked up my own GitHub...

I will not be posting the results. It was way too humiliating.

But I want to see yours.

Here's the link. Play around with it:

👉 Live Demo: groast.streamlit.app

👉 Open Source Code: github.com/gokuscraper/github-roast

Enter your GitHub username, wait a minute, and the AI will hand you your personalized "toxic assessment."

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.

DEV Community: GokuScraper悟空爬虫

我花了三天，找到了张雪峰的数据来源

我花了三天，找到了张雪峰的数据来源

Is it "Defending Open Source" or "Cyberbullying a High Schooler"? — A Look at the Downfall of an Indie Developer with 140K Followers

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An Issue, a Post, and Two Unequal Opponents

Legally Speaking, Did Burrow Actually Infringe?

The Real Bullying: Using 140,000 Followers to Crusade Against a High Schooler

Whitewashing After the Fact: The Elegant "Rational Moderates"

Small-Peasant Mentality Won't Make You Rich

What Kind of Open-Source Ecosystem Do We Really Need?

I Asked AI to Roast Linus Torvalds' GitHub, and It Was Absolutely Brutal

I Asked AI to Roast Linus Torvalds' GitHub, and It Was Absolutely Brutal

What the Heck is This?

Valuation: $600k USD. Too Much or Too Little?

The Roast: Every Line is a Critical Hit

Tech Stack & Weaknesses: The Godfather's Other Side

Collaboration Style: Dictator Confirmed

AI's Life Advice: Monetize the Noise

How Does It Work?

Your Turn

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

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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"?

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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

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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

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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

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