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I Analyzed 45,000 AI Prompts and Realized I Was Using AI Image Generation All Wrong

GokuScraper悟空爬虫 — Wed, 08 Jul 2026 10:19:36 +0000

I Analyzed 45,000 AI Prompts and Realized I Was Using AI Image Generation All Wrong

Here's how it happened.

Last weekend, I was experimenting with AI image generation, trying to create a cover image for my blog. In my mind, I had this perfect surreal miniature scene: a cream-colored swimming pool with tiny sunbathers floating on inflatable rings.

So I spent nearly an hour typing prompts into ChatGPT, but the results were... underwhelming.

Without formal art training, I always felt lost when writing prompts. After struggling for almost an hour, I gave up and grabbed an old stock photo instead.

This frustration is something many AI image enthusiasts can relate to.

You know AI can create amazing images—those stunning examples you see online are everywhere—but when you try it yourself, your prompts never quite hit the mark. That small gap feels like an entire world away.

Later, I complained to a friend who works in AI design. He glanced at my prompt and said something that stuck with me:

"What you wrote is too abstract. AI has no idea what you actually want."

I asked him how I should write it instead.

He didn't answer directly. Instead, he sent me a link.

It was to a website called "Wukong PromptHub."

When I clicked through, it showed 45,049 prompts.

My first thought was, "Another prompt collection site." There are countless sites like this now—most just scrape prompts from Reddit and Twitter without proper organization.

But then I clicked on one example and was immediately surprised.

Take this prompt titled "Cream Pool Miniature Fantasy":

A surreal miniature world collage poster featuring an oversized open blue Nivea-style tin box 
transformed into a fantastical swimming pool filled with glossy white "cream water."
Tiny sunbathers float on soft inflatable rings, lounging on miniature deck chairs,
sliding into the cream-colored pool from a small blue slide.
The background features a soft, warm, slightly textured surface,
using fine marble or matte stone with even lighting.
Maintain realism through soft shadows beneath props and figures.

Notice this isn't just "a pool, miniature style"—it's a complete description with scene composition, lighting logic, and material details. You can tell the person who wrote this prompt already had a complete image in their mind; they simply described it in words for AI to render.

And that's just the text description. Many prompts on this site use structured JSON format.

Here's an even more detailed example called "Matcha Moment Through a Fisheye Lens":

{
  "scene": {
    "environment": "sunny_boardwalk",
    "details": "wooden_planks, colorful stalls, people walking, distant umbrellas",
    "lighting": "bright_midday_sun",
    "sky": "clear_blue"
  },
  "camera": {
    "lens": "ultra_wide_fisheye_12mm",
    "distance": "very_close_up",
    "distortion": "strong_exaggeration",
    "angle": "slightly_low_upward"
  },
  "subject": {
    "type": "young_person",
    "expression": "curious_playful",
    "clothing": {
      "top": "bright_green_knit_sweater",
      "accessories": "chunky_blue_sunglasses"
    }
  }
}

Look at the level of detail: lens type, aperture, distortion, angle, clothing colors, environmental lighting, even emotional atmosphere—all specified.

That's when it hit me.

This is how experts write prompts.

What I'd been writing wasn't really prompts—it was more like wishful thinking.

I started browsing through the site's prompts and became completely absorbed.

The categorization is particularly thoughtful. Instead of generic categories like "portraits," "landscapes," or "architecture," it's organized by use cases: entertainment and fun, business productivity, content creation, prompts from X (Twitter), and prompts from Douyin (Chinese TikTok).

Each category contains prompts actually used by working creators, not just tutorial-level examples.

For instance, under "Content Creation," there's a prompt called "Ultra-Wide Mobile Photo Editing Secrets" specifically designed to transform ordinary photos into ultra-wide or fisheye effects. It even specifies how to hold your phone, screen replacement rules, and which body parts should be closer to the lens—like a detailed product manual.

Under "Business Productivity," you'll find prompts like "Enterprise Profit-Loss Sankey Diagram," "Smart Product Retouching Service," and "Intelligent Background Person Removal"—all designed for real commercial applications.

There's even one called "Surveillance Footage Face Tracking Effect" that I've seen used to create cyberpunk-style surveillance videos. I never imagined the actual prompt would be available here, ready to copy and use.

The site also covers a comprehensive range of AI models. It's not just limited to Nano Banana Pro—it includes GPT Image 2, SeedDance 2, Open Models, and Text Prompt. For each model, the prompts are adapted to account for their specific syntax differences.

Honestly, I was already impressed at this point. But as I kept exploring, I discovered something even more remarkable.

What fascinated me most was a collection of prompts that clearly weren't written casually—they demonstrated deep understanding of visual language.

Take the prompt "Iron Smoothing Wrinkles":

An award-winning hyper-realistic macro photograph.
Extreme close-up of an elderly woman's eye and cheekbone.
A miniature toy-like white-blue iron is placed on her skin,
actively pressing and smoothing deep wrinkles and crow's feet.
Lighting uses high-contrast hard flash.

Notice the specific artistic style reference (Maurizio Cattelan style), exact camera and lens specifications (Hasselblad H6D-100c + Macro 120mm f/4), and even film simulation details (Kodak Ektar 100).

This level of precision would be sufficient for commercial projects. Honestly, some client briefs I've received for commercial work weren't this detailed!

Then there's "Giant Machine, Tiny Object Fantastic Vision"—the entire prompt reads like a complete creative brief, covering scene architecture, lighting design, and surreal interaction mechanics. I believe if you sent this prompt to a human photographer, they could actually shoot a compelling photograph based on these instructions.

This goes beyond just being a "prompt."

It's a fully executable creative specification.

While browsing, I remembered something important.

Recently, a set of AI-generated triptych images went viral on social media. You've probably seen them—people shared them widely, amazed by AI's creativity. But few realized that behind these so-called "AI creations" were humans carefully designing prompts, treating AI as a paintbrush to be directed.

AI is like an exceptional paintbrush that doesn't listen to instructions. Tell it to draw a beautiful person, and it might give you a six-fingered金刚 (six-fingered strongman). But tell it "85mm f/1.4, golden hour, Kodak Portra 400, urban street café window, warm nostalgic lighting on the side of the face, fine film grain, eyes slightly looking away from the camera, steam from coffee cup dissipating in the air," and you get infinitely closer to that imagined photograph.

This website essentially systematizes the language for directing AI.

With 45,000 prompts, it covers 45,000 different creative scenarios. You don't need to figure out from scratch how to describe a miniature pool, how to capture a fisheye matcha photo, or how to generate an enterprise Sankey diagram. Someone has already figured it out thoroughly—you can just take it and use it directly.

This reminded me of a historical parallel.

It's similar to when designers migrated from Photoshop to Sketch about ten years ago. When Sketch first gained popularity, everyone asked what made it better than Photoshop. The real value wasn't in the software itself, but in its ecosystem of plugins and component libraries. Photoshop could handle UI design, but with component libraries, the efficiency was on a completely different level.

Today, Wukong PromptHub serves the same function as those plugin ecosystems and component libraries. AI image generation tools themselves are already powerful enough—whether Midjourney, DALL·E, GPT Image 2, or SeedDance, everyone is competing on model capabilities. But what truly creates a gap in creative efficiency is your ability to quickly write effective prompts.

And this website eliminates the part you'd normally need to figure out yourself.

Building on this insight, let's talk about timing.

Honestly, I find this trend fascinating.

When ChatGPT first launched in late 2022, people's biggest anxiety was "What if I can't write good prompts?" Prompt engineering became a recognized discipline, with countless tutorials teaching various frameworks for writing effective prompts.

But here in 2026, we're seeing a new trend: most people aren't struggling with how to write prompts—they simply don't have time to write them.

You want to create an image of a miniature pool, but you don't want to spend an hour researching ultra-wide focal lengths and depth of field relationships. You just want a usable image quickly. You're not a photographer or designer—you're a content creator, entrepreneur, or regular professional who needs AI to help produce work, but you don't have the bandwidth to become a prompt expert.

What's the best solution?

It's not spending a week learning prompt writing—it's copying a proven, validated prompt someone else has already created, making a few adjustments, and generating your image.

That's exactly what this website does.

It presents 45,000 already-validated creative directions right in front of you. You don't need to start from scratch—you just need to find the scenario you want, copy it, and paste it.

It's that simple.

Now for the practical details.

The website is called "Wukong PromptHub," and you can find it at prompthub.gokuscraper.com.

It's completely free. You don't need to register to browse and copy prompts.

After logging in, you can bookmark your favorite prompts. The site also features daily updates and a "shuffle" function that continuously adds new content. As of today, it supports Nano Banana Pro, GPT Image 2, SeedDance 2, other Open Models, and Text Prompt—covering virtually all mainstream AI image generation tools.

If you're experimenting with AI image generation, I highly recommend checking it out. Don't approach it with the mindset of "I need to learn prompt writing"—just treat it as an inspiration gallery. When you find a visual direction you like, copy it, tweak a few details, and let AI generate it for you.

It's actually quite satisfying.

To be honest, I haven't finished browsing all 45,000 prompts yet—it's simply too much content. But I've already bookmarked several prompts I plan to use for future projects, especially those quirky styles like "Giant Cat Breaking Through Street Screens," "Office Fun Group Photos," and "Chalkboard Empress Drawings." I'm already thinking about how I can incorporate these into my articles.

Wow.

From now on, I won't need to spend an hour struggling with cover images anymore.

先上车后买票，一个爬虫工程师不敢写进文章里的行业潜规则

GokuScraper悟空爬虫 — Wed, 08 Jul 2026 08:26:36 +0000

先上车后买票，一个爬虫工程师不敢写进文章里的行业潜规则

前阵子闲得没事，去翻了翻腾讯的 SkillHub。

不翻不知道，一翻还挺意外。里面挂着大量卖数据的技能工具。什么小红书爆款笔记查询、公众号热门文章搜索、抖音热榜数据采集，什么都有。接口调的都是第三方 API，但你稍微懂点行就知道，公众号没有公开过这种搜索接口，小红书没有给过数据授权，抖音也没有。

那这些数据是从哪来的？

只有一个解释，第三方爬虫爬的，接口逆向破解出来的。

而且你看这些技能的开发者信息，大部分连个微信都不敢留，只敢留个匿名邮箱。一看就是独立开发者在干这事，把别人平台的接口搞出来，打包成技能放到 SkillHub 上卖钱。

技术上虽然是中立的。

但法律上也是灰色的。

但你猜怎么着。SkillHub 就这么让他们上架了。不只上架了，评分还不错，排名也挺靠前。

腾讯当然知道这些东西是什么性质。他们太知道了。

但为什么还让上架？

因为 SkillHub 需要内容。一个刚上线的平台，你需要让用户觉得有用。什么最有用？数据分析类的。公众号写手想看爆款文章，小红书博主想看竞品数据，这些都是刚需。官方不给接口？那就让第三方来补上。

先把技能摆上，把平台撑起来，合规的问题以后再说。

先上车，后买票。

这条法则不止在中国的互联网圈子常见，全世界也是通用的。你先把东西拿过来用，用出问题了再说。如果没出问题，这张票就不用买了。如果出了事，再补票也来得及。

腾讯自己就是这么干的。今年3月他们刚因为爬了 OpenClaw 的 ClawHub 被原作者挂墙头，闹了四天最后掏钱赞助才收场。结果风波刚过，自己的平台上就堂而皇之地摆着这些卖数据接口的技能。

同一套逻辑。

但这里有一个问题你可能很关心，这些东西合不合法？

说实话，非常模糊。

爬虫这件事在国内法律上本来就是一个灰色地带。公开数据算谁的？你爬了别人网站上的公开信息，算不算侵权？这些问题在中国标准很模糊，至今没有特别明确的法律答案，很难单纯从技术角度没法界定，更多看的是社会影响。但按照美国的判例，有一条比较清晰，如果你爬的是需要登录才能看的数据，那你就越界了。

SkillHub 上这些技能属于哪种情况呢？一部分爬的是公开内容，但更多的是需要登录的内容。小红书很多笔记要登录才能看完整内容，公众号文章也有访问限制。这意味着它们在法律上已经越过了那条线。

那为什么没人管？

不是因为合法，是因为经济账算不过来。

你想想，小红书发现有人在卖查自己数据的工具，要不要告？理论上可以告。但你算一下账，律师费、诉讼费、时间成本，加起来可能几十万。而一个独立开发者卖技能赚的那点钱，你就算告赢了也拿不回多少。成本比收益还高，谁愿意打这个官司？

所以只要你别搞太大，没人理你。

但你要是做大了呢？

用户量上来了，收入变高了，名气变大了，那你就变成了靶子。到那时候平台一封律师函过来，你扛不住的。你不是腾讯，你没有法务团队，你没有几百万去打官司。人家告你，你连应诉的成本都付不起。

而且还有一个更致命的问题，就算你侥幸赢了官司，你也赢了时间。打官司拖你一年半载，你的业务早就黄了。

所以这些灰色工具有一个共同的特点，永远做不大。也不敢做大。

创业圈有句话叫「爬虫做不大」，说的就是这个意思。你可以偷偷赚点钱，但不能光明正大做成一个生意。一旦你从「一个技能」变成了「一家公司」，从「月入几千」变成了「月入几十万」，你就一定会被人盯上。

腾讯可以爬 ClawHub 然后掏钱和解，因为他有六千亿营收。但你一个小公司，你掏不起这个钱。

这就是灰色爬虫的真实处境。

法律不管你不是因为你没错，是因为你太小了，不值得管。你一旦长大，就会有人来收割你。

所以回过头来看 SkillHub 上那些卖数据的技能，没什么高深的。就是一群小公司或者独立开发者在灰色地带里讨生活。他们现在能活着，不是因为他们合法，是因为他们太小了。一旦做大，一定会被拍死。

这就是这个行业最真实的规则。不是法律条文里写的那个规则，是实际运行的规则。

先上车后买票。但在上车之前，得想清楚一件事，这趟车你买到票之后，还下不下得来。

They Claimed Huawei's Autonomous Driving Mileage Was Fake—So I Spent 10 Minutes Checking the Code

GokuScraper悟空爬虫 — Sun, 05 Jul 2026 15:58:05 +0000

They Claimed Huawei's Autonomous Driving Mileage Was Fake—So I Spent 10 Minutes Checking the Code

Someone posted a video on TikTok mocking Huawei, claiming that the safety mileage data displayed on Huawei's official website was fabricated.

Their reasoning? The counter kept increasing even when offline.

They turned on airplane mode, recorded their screen, but the cumulative driving mileage number on the webpage continued to scroll. Many viewers assumed it was purely a frontend script—a deceptive trick. There was an excited tone in their voice, as if they'd caught another company red-handed.

I don't know how many people have seen this video, but there were plenty who shared it, criticized it, and joined in the mockery.

Huawei has become a magnet for attention. Criticizing them negatively always garners likes.

But honestly, I was genuinely curious—or rather, my professional instincts kicked in. Having worked in frontend development, I know the most direct way to verify whether a counter is real or fake is to open Developer Tools and check the Network panel.

So I opened Huawei's webpage.

https://auto.huawei.com/cn/ads/safety-and-data-report

The page displays three large numbers: cumulative assisted driving mileage of Qiankun Intelligent Driving, total cumulative mileage of vehicles equipped with Qiankun Intelligent Driving, and cumulative collisions actively avoided. The numbers are scrolling, giving off that typical "big tech data dashboard" vibe.

I pressed F12 and opened the Network tab.

It didn't take long to spot an API endpoint polling regularly.

auto.huawei.com/external/uiapi/ads/v1/query

Every 3 seconds. The returned data structure is clean, with three fields corresponding exactly to the three counters on the page.

This is a legitimate backend API. Not local simulation, not mock data—it's real aggregated values fetched from Huawei's vehicle cloud platform.

How can I be sure?

I watched the data changes for a while. The mileage growth between consecutive requests wasn't constant. I took several samples at different times, and the growth rate fluctuated by about 26%. A uniform timer simply couldn't produce this kind of fluctuation pattern. Only real vehicle telemetry data aggregation would show varying growth rates at different times—more driving during rush hours, less at night, which makes perfect sense.

But then, how did that "still increasing when offline" phenomenon in the video happen?

I started examining the JavaScript files loaded on the page.

Scanning through the Sources panel, one filename immediately caught my attention.

safety_kilometer.js

I opened it.

Well, well.

Inside lay a set of hardcoded numbers:

Initial value: 7,228,242,208
Baseline time: January 12, 2026
Fixed growth rate: 224.86 per second

It was a pure frontend timer. Take the current time, subtract the baseline time, multiply by the fixed growth rate, add the initial value, and calculate the "current mileage." No connection to the backend whatsoever.

If you only looked at this file, you'd think—case closed. Huawei is indeed running fake data with a timer on their webpage.

But I looked a bit closer.

This JavaScript targets DOM elements with IDs #ias-gongli and #ias-pengz.

On the current webpage, these ID elements simply don't exist.

I searched the entire DOM—nothing.

So how was this file being triggered? I checked the call chain. It executes when the page loads, looks for those two elements, and if it can't find them, it does nothing—no errors, no fallback, nothing at all. It just sits there quietly.

But I noticed one detail. There's a QR code on the page directing users to the "Huawei Qiankun App." My guess is that this file wasn't meant for the official website at all.

Comparing safety_260521.js and safety_kilometer.js reveals they're completely different approaches:

safety_260521.js relies on jQuery + Web Worker + AJAX, fetches data through API polling, has complex scrolling animations, and weighs 28KB after bundling. It's a proper official website solution.

Meanwhile, safety_kilometer.js is pure vanilla JavaScript with zero dependencies, calculates numbers directly in memory, and updates innerHTML—less than 1KB after decoding.

The most reasonable guess: this file was prepared for H5 embedded pages within the Huawei Qiankun App. Embedded app pages need instant loading—they can't wait for jQuery to load and then wait for API responses—so they use pure vanilla JavaScript with a set of hardcoded initial values to run an "offline-capable" counter. Once the network is ready, the app uses its JS Bridge to fetch real data and make corrections.

The official website and app embedded pages share the same CDN static resources. So this file got deployed to the official website's server, but since the website's HTML doesn't contain the corresponding ID elements, it just sits there quietly, doing nothing.

It's not some conspiracy. It's simply two platforms sharing the same resource set, with conditional loading that wasn't thoroughly implemented. This kind of thing is extremely common in frontend projects at legitimate companies.

So the surface narrative splits:

There's a real API driving the front-end numbers. And there's also a hardcoded timer file sitting on the server. But it's simply not meant for this page.

So what exactly was "updating when offline"?

I simulated an offline scenario. I connected my phone to a hotspot, refreshed the page, waited for one successful API call, then turned off the hotspot.

Something interesting happened.

The counter didn't stop immediately. It indeed kept moving.

But looking closely, its behavior was different from when normally connected.

When online, the number scrolls smoothly and continuously, with a tiny jump every 3 seconds. When offline, the number oscillates within a small range, with decreasing amplitude, coming to a complete stop after about 12 seconds.

I went back to examine the code.

Here's the logic:

The main loop sends an AJAX request to that backend API every 3 seconds. If the request succeeds → update the target value → Web Worker runs a 4-second scrolling animation from the current value to the latest value. If the request fails → the error callback triggers.

What happens when you go offline?

At 0 seconds: AJAX succeeds, gets the value, starts a 4-second animation.
At 3 seconds: New AJAX request fails. Error callback triggers, restarting a 4-second animation from the current value (where the previous animation stopped halfway) back to the last successful API value.
At 6 seconds: Another AJAX request fails again. Same process repeats.

Each error callback restarts the animation. But each time it restarts, the current value is closer to the target value (because the previous 3-second animation had already pushed the number forward). So the "new animation" has less distance to cover.

Constantly restarting, constantly decaying.

After about 4 to 5 rounds, the current value is almost equal to the target value, and the visual amplitude of the animation becomes too small for the human eye to distinguish.

12 seconds—convergence.

That's the whole truth.

It's not that "it keeps updating when offline." It's that the AJAX failure error callback keeps retrying不甘心地一遍遍重试, each time making the animation "chase" the target value from its current position. The animation gets closer and closer until it matches perfectly, then stops.

It's residual easing animation—not a ghostly counter.

A more vivid analogy would be:

You push a swing, and it starts swinging. When you go offline, no one pushes anymore, but the swing continues to swing for a few more rounds with decreasing amplitude until it finally stops—and then the person who made the video says, "Look, no one's pushing it but it's still swinging—the swing is fake."

Yes, the swing is indeed still swinging.

But that's entirely different from fabrication.

Going back to that video.

I specifically checked the comments section of that TikTok video later. Not a single person verified anything. Everyone was just piling on to criticize Huawei.

Not one person went to open that webpage to take a look. Not one person opened F12 to check the Network panel. Not one person asked, "When it's offline and the numbers are still moving, is it really updating, or is the animation just not finished?"

Everyone was just criticizing en masse.

Sometimes I think this might be the most thought-provoking aspect of the whole incident.

Was there actually anything wrong with Huawei's design? Honestly, I don't think so.

When requests fail, keeping the last successfully obtained value and continuing with smooth animation until it gradually stops—that's completely normal. If the numbers just froze immediately when a request failed, users would think the page had crashed. Displaying the last value steadily and stopping smoothly is actually reasonable UX design.

The real difference is that normal people seeing "numbers still moving briefly when offline before gradually stopping" wouldn't find anything suspicious. But that video chose to capture only the "still moving" part without showing everyone how it eventually stops.

Why did that video's narrative spread so quickly? Because it's so "easy to understand." Offline, numbers still moving, fabrication. Three steps. Anyone scrolling by can digest the entire story in seconds while gaining a sense of intellectual superiority—that feeling is addictive.

Meanwhile, the truth requires you to open DevTools, check the Network panel, examine the Sources panel, understand the code logic, comprehend WebWorker animations, and grasp easing functions. It takes time. It requires patience.

A "blackmail revelation" that can be digested in seconds versus a "truth" that takes at least ten minutes to understand—the latter stands no chance in terms of spread speed.

I'm not blaming anyone. I'm like this too. When I first saw that video, my immediate reaction was also, "Hmm? That does seem problematic."

But the difference might simply be that I happen to have frontend experience, happen to know how to open Developer Tools, and happen to have ten to twenty minutes to spend investigating this matter.

What if I didn't have these conditions? I might have shared that video too, with a caption like "Huawei loves doing these superficial things."

So what I'm pondering is—are we really "seeking truth," or are we "seeking validation"?

In that video's comment section, no one was "seeking truth." Everyone was "seeking validation." Seeking validation for "See, Huawei really isn't good." Seeking validation for "I've uncovered another scam." This validation comes too easily—it doesn't cost you anything, just requires a like or a share.

But "seeking truth" comes at a cost. It requires your time, your effort, and your willingness to admit you might be wrong.

We live in an era where information spreads far faster than it can be verified. A 15-second TikTok video can reach hundreds of thousands of people in half a day, but I suspect fewer than a hundred people actually go to open that webpage to take a look.

I'm writing this not to defend Huawei. Huawei doesn't need me to defend it.

That safety_kilometer.js is indeed a file unused by the official website—it was prepared for app embedded pages. Two platforms sharing the same CDN resources with incomplete conditional loading isn't exactly a crime.

I just think that if you come across similar "expose-the-truth" videos next time, maybe before sharing, take three seconds to ask yourself:

Is there another possible explanation for this phenomenon he's describing?

That counter isn't fake.

That "updating when offline" phenomenon isn't some conspiracy either. It's just ordinary easing animation design—when requests fail, it retains the last value and stops smoothly. That's all.

I believe the real value of this story isn't about judging whether Huawei is right or wrong.

It's about showing us how vast the gap can be between something that "appears to be fabricated" and something that's "actually fabricated." It's also about showing us that in this age of information explosion, a 15-second video can easily reach hundreds of thousands of people, but those who actually verify it might number fewer than a hundred.

Next time you see this kind of "expose-the-truth" content, maybe ask yourself one more question—could this really be true?

你的时区正在告诉 Claude Code，你是中国人

GokuScraper悟空爬虫 — Sun, 05 Jul 2026 05:17:28 +0000

你的时区正在告诉 Claude Code，你是中国人

故事是这样的。

前两天我在研究 Claude Code 的时候，发现了一件让我后背发凉的事。

大家都知道，Claude Code 现在对中国用户是有一些限制的。很多朋友为了用上它，各种折腾中转代理，把 ANTHROPIC_BASE_URL 指向各种第三方端点，以为自己藏得挺好的。

结果呢。

人家 Claude Code 压根不在乎你从哪个 IP 访问。它用的是另一种方式来判断你是不是中国用户。

读取你的系统时区。

对，你没看错。就是你在电脑右下角设置的那个时区。当你的 ANTHROPIC_BASE_URL 指向非官方端点时，Claude Code 会读取系统时区和中转域名，通过 Unicode 隐写术编码到 system prompt 里。

Unicode 隐写术。

这个词本身就够赛博朋克的了。把信息藏在你看不见的字符编码里，就像用隐形墨水写了一封信。你看到的是一段正常的 system prompt，但 Claude 读到的，还有你的时区和域名。

我当时就愣住了。

所以中国用户被限制，不是因为你用了代理，而是因为你的电脑本身就在出卖你。你的时区，你的语言设置，你的字体，这些你以为跟 AI 毫无关系的东西，全部都是线索。

然后我就在 GitHub 上看到了一个项目。

有人做了一个工具，叫 claude-tester。一键检测你的浏览器环境，看 Claude Code 是否判定你为中国用户。纯前端，100% 本地运行，不上传任何数据。

我点开 Vercel 的部署链接，页面加载出来第一眼就笑了。

检测结果显示的是 Dario Amodei 的表情包。

对，就是那个 Claude 的创始人。用他的表情来做检测结果展示，从 idle 到 scanning，从 low 到 high，随着你的检测分数变化，Dario 的表情也越来越微妙。这种恶趣味，我太吃了。

然后我点击了那个按钮。

开始检测。

整个过程大概一两秒。没有进度条，没有加载动画，就是干脆利落地给你一个分数。

我的结果。

83分。。。

坦率的讲，不算高。但我确实在中国，系统时区是东八区，浏览器语言首选中文，电脑里也有中文字体。这几个大头一加起来，分数自然不会低。

这个工具的检测逻辑其实很有意思。它用了 6 个信号来做加权评分，每个信号有自己的权重和检测方式。

第一个，系统时区，权重 30 分。这是最重头的一项，也是跟 Claude Code 的真实检测逻辑唯一能完全对应的信号。Intl.DateTimeFormat 读取你的 OS 时区，如果你是 UTC+8，那这顶帽子就算扣上了。

第二个，浏览器语言，24 分。navigator.languages 读取你的首选语言。如果你的浏览器把中文排在第一位，那又是一个危险信号。

第三个，中文字体，20 分。这个有点技术含量。它用 Canvas 2D 做字体探测，看你系统里有没有安装中文字体。大部分中国人的电脑里必然有中文字体，但一个美国用户的电脑里大概率没有。

第四个，Intl 区域，10 分。你的日期和数字格式用什么区域，也能暴露你的地理位置。

第五个，时区偏移，8 分。getTimezoneOffset 的具体数值。

第六个，Emoji 风格，8 分。这个我觉得是最骚的。它通过你的 User-Agent 来推断你设备上的 Emoji 风格。不同平台不同厂商的 Emoji 渲染都不一样，而中国用户常用的设备和系统，Emoji 风格有明显的特征。

6 个信号，加权叠加，满分 100。

这个设计其实挺聪明的。任何一个单一信号都可能误判，但 6 个信号叠加在一起，准确率就高得多了。你的中文可能是装的，你的 IP 可能是假的，但你的时区、你的字体、你的 Emoji 风格，这些东西你根本不会想到要去伪装。

而这正是最可怕的地方。

我们每天都在用浏览器，每天都在产生这些数字指纹，但我们自己完全不知道。你装了一个中文字体，你在系统设置里选了北京时区，你用的是某个国内品牌的手机，这些看似无关紧要的选择，在 AI 眼里，全部都是线索。

就像一个隐形身份证。

你自己看不见它，但每一次你跟这些大模型打交道的时候，它都在默默亮明你的身份。

我后来又换了一个环境重新测了一次。把我的系统时区改成美国东部，浏览器首选语言改成英文，然后重新打开页面，点击检测。

23分。

你看，什么都没变，变的只是几个数字指纹，分数就从 73 掉到了 21。你的身份在 AI 眼里就是这么脆弱，也是这么容易被重构。

想想还挺魔幻的。

我们以为自己在匿名上网，以为挂个代理就万事大吉了。但实际上你的电脑里每时每刻都在往外发送几百个信号，告诉别人你是谁，你在哪，你用什么设备。

这个工具的价值在哪呢。

我觉得不在于帮你「伪装」，说实话想伪装也很难，6 个信号全改一遍跟你重装个系统差不多了。

它的价值在于「看见」。

让你看见那些平时看不见的东西。让你意识到，原来我的时区正在被读取，原来我的中文字体会成为证据，原来我每天用的 Emoji 也在出卖我。

这才是真正的信息差。

我以前一直觉得，隐私保护就是不上传数据、不泄露密码、不点陌生链接。但 Claude 的这个机制让我意识到，隐私保护的战场早就不是那几个层面了。它已经深入到你的操作系统设置里，深入到你的字体文件里，深入到你的 Emoji 编码里。

纽约时报之前发过一个很有意思的报道，他们发现网站可以通过你的系统字体来判断你的身份。因为每个电脑安装的字体组合都是独一无二的，就像指纹一样。这个技术叫字体指纹识别，准确率高得吓人。

Claude Code 这套检测逻辑，某种意义上也是在用类似的思路。

不说这些沉重的话题了。这个工具我真心建议每个用 Claude Code 的中国朋友都去测一下。地址我放这了，claude-tester-six.vercel.app，Vercel 部署的，点开就能用，不需要注册，不需要登录，所有检测都在你浏览器本地完成，结果不会离开你的设备。

你能做的，就是点一下那个按钮，然后看看 Dario 对你的表情。

顺便说一下，这个项目在 GitHub 上开源，如果你有兴趣看看它的检测逻辑是怎么实现的，也可以去读读源码。说实话还挺简洁的，Vite 加原生 TypeScript，没有服务端依赖，整个项目就是一个纯前端的单页应用，读起来很舒服。

我的 83 分就放在那了，说实话我也懒得去改什么东西。分数只是一个参考，我也不是什么需要隐藏身份的人。

但知道这件事本身，就已经值回票价了。

以上，既然看到这里了，如果觉得不错，随手点个赞、在看、转发三连吧，如果想第一时间收到推送，也可以给我个星标⭐～

谢谢你看我的文章，我们，下次再见。

论成败，人生豪迈，我们下期再见！

The Real Value of Microsoft MVP Isn't What You Think

GokuScraper悟空爬虫 — Fri, 03 Jul 2026 09:28:40 +0000

The Real Value of Microsoft MVP Isn't What You Think

Here's what happened.

Recently, I built a small tool specifically designed to critique GitHub projects. It scrapes repository information and feeds it to an AI, which then generates a review based on the project's content.

The key design element here is that my prompts were intentionally harsh and sarcastic, packed with every possible angle for criticism. However, if a project is genuinely solid—like Linux—the AI output would still end up praising it regardless of how harsh my prompts were. If your code quality is strong and your project has real impact, even the most critical prompts can't diminish its value.

Conversely, if a project is mostly fluff, the tool would ruthlessly expose it—and yes, I've been on the receiving end of such criticism myself.

I ran this tool across various repositories and discovered some interesting patterns. Some developers have impressive follower counts on GitHub and appear successful on the surface, but when you actually examine their projects, the technical substance is shockingly thin. Many are just translation projects or repackaged versions of others' work. Frankly speaking, they lack genuine technical depth but excel at marketing and presentation.

One such case involved someone I'll call "Bao Ge," who runs a tech community.

This person immediately showed up on my social media, furious and accusing me of talking nonsense without understanding anything. Their followers joined in, and the comments section exploded.

When I checked their profile, I was stunned to see four prominent words displayed there.

Microsoft MVP???

I was completely taken aback. My immediate reaction wasn't skepticism—it was instinctive deference. I thought, "This person is a Microsoft MVP, so they must be a genuine technical expert. Maybe I made an error in my data collection and offended them unfairly." I even apologized, saying I might have gotten things wrong.

Looking back now, it's quite fascinating.

A mere title alone was enough to make me reflexively back down.

I didn't dwell on this incident much afterward. But recently, I came across another GitHub project—a simple collection of prompts with no technical complexity whatsoever. Essentially, it was just a curated document of existing prompts, nothing more than basic compilation work.

I checked the project owner's profile again.

Microsoft MVP.

The same title, shining just as brightly.

I sat staring at my screen for a while. Suddenly, it hit me—this title's actual value might not be what people assume it to be.

At this point, some might think I'm about to dismiss the entire Microsoft MVP program. That's not my intention.

I simply believe many people misunderstand what this title represents—including my former self.

Microsoft MVP stands for Microsoft Most Valuable Professional. It sounds like a certification for technical excellence, doesn't it? As if earning this title means you possess elite coding skills and are officially recognized by Microsoft as a technical expert.

But what's the reality?

I did some research. This program was initiated by Bill Gates in 1993 and has been running for over thirty years. There are roughly 4,000+ MVPs globally across 90+ countries. It sounds quite exclusive, but the key lies in the selection criteria.

Microsoft is quite straightforward about this. They evaluate your contributions to the community, your technical sharing, and most importantly, your promotion of Microsoft products. It's essentially a community contribution award. Writing blogs, giving talks, creating videos, answering questions in forums, and organizing events—these are the core metrics for selection.

It's not about how well you write code.

It's about how effectively you can promote Microsoft's products.

Microsoft uses two key terms to describe the core functions of MVPs: Advocacy and Product Feedback.

Advocacy means promoting, advocating, and marketing.

Product Feedback involves collecting user input and relaying it back to Microsoft.

So you see, this isn't a technical competency certification. Ultimately, it's an honorary title for being a "Microsoft Product Ambassador." You help Microsoft expand its influence, and in return, Microsoft gives you a title and some benefits. It's a fair exchange from a business perspective.

The problem is that when most people see these three letters, they don't think about community contributions or product promotion. Instead, they think:

Technical expert.

Industry guru.

Official certification.

This cognitive bias is precisely what makes this story so intriguing.

So why do so many people on social media love flaunting their Microsoft MVP status?

The answer is simple: this title is incredibly useful.

Consider this—if your bio said "I'm skilled at promoting Microsoft products," who would care? But write "Microsoft Most Valuable Professional," and suddenly everything feels different. It comes with an automatic光环 of authority and credibility.

This title can help you land consulting gigs, charge premium rates for courses, and get invited as a speaker at tech conferences. There's also a clever design element: it only requires annual renewal with no exams or re-interviews. As long as you've written a few articles or done some sharing during the year, you can keep using the title.

This creates an interesting phenomenon.

What are genuine technical experts actually doing? They're writing code, solving complex technical problems, and researching fundamental principles. They don't have time to constantly interact with followers on social media or rely on content creation for their livelihood. They might not even post regularly on platforms like Twitter.

Meanwhile, those who prominently display their Microsoft MVP status and frequently appear on social media have plenty of time to write articles, create videos, go live, and engage with followers. They invest their energy in appearing to be experts rather than actually being experts.

I'm not saying that "sharing knowledge" is inherently wrong—of course it's valuable. But when a title is primarily earned through "how much promotion you've done" rather than "how deep your technical expertise is," the title's value inevitably becomes diluted.

Think about it—if a true technical expert is busy writing code and fixing bugs all day, where would they find time to constantly write articles, create videos, and share content? Their time is extremely valuable. They wouldn't waste it engaging in free casual conversations with random people on social media because it simply isn't worth their time.

But if your livelihood depends on appearing to be an expert, you must invest significant time maintaining that appearance.

This is why those who frequently appear on social media and prominently display their MVP status are often not among the most technically accomplished individuals.

This might sound harsh, but that's my honest observation.

Of course, I'm not claiming that all Microsoft MVPs lack substance.

There are certainly many who possess genuine technical depth while also being willing to share knowledge. For example, some veteran MVPs working with Azure or .NET have been deeply involved in their communities for years and truly deliver substantial value. I genuinely respect these individuals.

The issue is that the barrier to entry for this title isn't particularly high. Especially in non-core technical areas, MVP selection often prioritizes how many articles you've written and how much promotion you've done over your actual technical depth.

The owner of that prompt collection project I mentioned earlier exemplifies this situation.

A purely documentation-based project with no code engineering or innovation—just a compilation of existing prompts. Yet on the owner's profile, those four words "Microsoft MVP" are prominently displayed.

How can I possibly associate this title with technical expertise?

I simply can't.

This leads to a broader question.

When we see titles and labels on social media, how much of it represents genuine capability, and how much is carefully crafted packaging?

I don't know what others think, but I know this: when I apologized to Bao Ge after being criticized, it wasn't because the AI's critique was wrong—it was because his title triggered self-doubt in me.

This reflects a subtle psychological mechanism. We naturally trust authority and officially certified labels. When we see a shiny title, our brains take a mental shortcut: "This person has official backing, so they must be right." This is what's known as authority bias.

Microsoft certainly understands the power of this psychological mechanism—in fact, I'd say they understand it exceptionally well.

A honorary title that requires only annual renewal with no exams or interviews can motivate thousands of people to freely promote Microsoft's products, write articles, attract customers, and collect feedback. You invest your time and energy; Microsoft provides a title, a crystal trophy, and an MSDN subscription. This might be one of the most cost-effective marketing strategies in the world.

I'm not saying this is wrong—businesses doing business is perfectly normal. But as ordinary individuals who instinctively feel reverence when seeing prestigious titles, I believe we should at least understand the underlying mechanics.

Don't assume someone is a technical expert just because they're a Microsoft MVP.

Don't let a title override your own judgment.

Don't outsource your independent thinking to someone else's certification.

I used to trust titles. Not anymore.

I never followed up on whether Bao Ge continued coding. But this incident left a lasting impression on me. Now, whenever I see someone with a long list of titles in their bio, I always look at their actual work first—not just their credentials.

I think this is a healthy habit to cultivate.

数字生命卡兹克是怎么炼成的（上）断更6年后，他用2年把自己做成AI圈顶流

GokuScraper悟空爬虫 — Mon, 29 Jun 2026 05:46:16 +0000

数字生命卡兹克是怎么炼成的（上）断更6年后，他用2年把自己做成AI圈顶流

2015年10月，一个大学生在他的公众号上发了一篇文章，标题叫《中国的二胎，是人类最大的冷笑话》。

语气很冲，观点很嫩，阅读量没破百。

那时候他不会想到，自己会一断更就是6年。

他更不会想到，6年后重新拿起这个号，2年半时间，他能站在万人AI大会的舞台上，被整个中文AI圈称为「卡神」。

这个号叫数字生命卡兹克。

我从头到尾统计了他746篇文章的全部数据，发布时间、标题、字数、阅读量、互动数据。没有推测，没有感觉，全是数字。

这篇文章，是第一篇。

我们先不看细节，先看全貌。一个号的生命周期，到底长什么样。

下图是我按月统计的他746篇文章的发文量趋势，从2015年一直拉到2026年6月，

坦率的讲，看到这个图的第一反应，是愣了一下。

2015到2017，三年，12篇文章。

然后一条直线断到2023年。

再然后，一条猛烈向上增长的线，从2023年中一路冲到2026。

这个号，是真的死过一次的。

你敢信？？？

2015年到2017年，卡兹克的公众号是这个画风的

《中国的二胎，是人类最大的冷笑话》《如何用通俗的语言来解释「费米悖论」？》《一棵开花的树》《中国式过马路！岭师校门实拍，交通现象令人堪忧！》《看了这些减肥广告，我收到了这个世界满满的恶意~》《有哪些你第一眼看到就爱上的句子？》

12篇文章，没有方向，没有定位，没有读者。

这就是2015年一个普通大学生的公众号。跟AI没有半毛钱关系，跟自媒体没有半毛钱关系。写了几篇之后，热情消退，2017年3月发了最后一篇，然后，再也没有打开过这个后台。

一停，就是6年。

2185天。

断更6年后，2023年2月25日，他回来了。

发了一篇《关于ChatGPT的兄弟 - AI绘图的小思考》。措辞还带着6年前的稚嫩，但选题变了。ChatGPT刚火，他决定赌一把。

从那天到现在，3年半时间，746篇文章。我把它们按月统计，画出了这张趋势图。按照发文节奏和内容性质的变化，我把整个账号切成四个阶段

第一个阶段，荒芜期，2015到2017。

12篇文章，月均不到1篇。没有方向，没有读者，然后断更6年。

第二个阶段，起号期，2023年2月到8月。

55篇文章，月均9篇。

关键词就三个，教程，热点，快。

这个阶段他完全在「蹭」。ChatGPT火就写ChatGPT，GPT-4发布当天就出稿，文心一言上线第二天就跟进。没有原创作品，没有独家观点，就是用最快的速度把最新的AI工具翻译成中文教程。

但这55篇，是他从0到1的全部家底。

第三个阶段，爆发期，2023年8月到2024年12月。

337篇文章，月均20篇，几乎日更。更重要的是内容性质的质变。他从「教别人用AI」，变成了「用AI做作品给别人看」。

标志性事件。2023年8月3日，《我用AI做了一部《流浪地球3》的预告片》。

第四个阶段，成熟期，2025到2026年6月。

342篇文章，月均还是20篇，但内容重心从「写文章」变成了「做事情」。办AIFUT万人大会、开源Skills生态、招聘组建团队。这时候他已经不是自媒体博主了，是行业组织者。

三个关键转折点

2023年2月25日，断更6年后的第一篇，押注ChatGPT

2023年8月3日，流浪地球AI预告片，从「教人」到「创作」的身份跃迁

2025年3月23日，AIFUT大会官宣，从博主变组织者

说到起号期，这篇文章既然是「上篇」，我重点拆的就是这段时间。

2023年2月25日到8月3日，55篇文章，跨度半年。按月度看，起步节奏极其清晰

他2月25号才发第一篇，所以2月只写了3篇。但到了3月，直接干到12篇。GPT-4发布、文心一言上线、NewBing评测、Prompt大全、工具大全上下，全挤在这个月。

4月掉到了7篇，因为他住院了，还专门发了停更声明。但5月一出院，立刻拉回12篇。

6月9篇，7月11篇。整个起号期没有一个月低于7篇，没有一个月掉链子。

怎么说呢，这个细节说明一件事。起步阶段，他不是天赋型选手，是苦力型选手。天道酬勤，看到这个节奏只能说一句服气。

55篇文章里，我按标题关键词统计了内容占比

产品实测评测大约占了40%，包括GPT-4、文心一言、Code Interpreter这些热门模型的首发体验。教程指南紧随其后，有30%，就是那些「5分钟教你」「有手就行」「保姆级」的系列。新闻热点占了20%，集中在GPT-4发布、苹果WWDC、英伟达破万亿这类大事件。剩下10%是观点、活动和日常。

重点在这里。这个阶段他几乎不写纯观点和人文内容。起号阶段不需要深度，只需要有用和快。

这条起号策略，说穿了就是一个字。快。

热点来了，当天或第二天必出。

你想想，这得是什么级别的执行力。典型的节奏是这样的

2024年12月，OpenAI开启「12天连播」。第1天o1满血版凌晨2点发布，他4点17分出稿；第3天Sora凌晨2点34分上线，他5点52分发文；第12天o3凌晨3点发布，他4点出文，几乎同步。

Sora首次预告，OpenAI凌晨2点官宣，他5点56分就发了一篇完整的评测文章。

Claude 3.7深夜上线，他5点56分就跟进。

他不跟人比深度，他跟人比速度。所有美西时间下午的发布，对应的都是北京时间凌晨，而他总在当天4点到6点的早间档准时出文。

我给你列几个数字，感受一下他的速度。真的就是，离谱。

OpenAI的o3凌晨3点发布，他4点出稿，间隔1小时。Sora正式上线凌晨2点34分发布，他5点52分发文，间隔3小时。o1满血版凌晨2点发布，他4点17分跟进，间隔2小时。Sora首次预告、Claude 3.7、GPT-4o多模态生图，每一个的间隔都在3到4小时以内。唯一破例的是2023年3月的GPT-4，那是他起号期早期，当天下午3点才出稿，间隔了14个小时，但也仍然在同一个白天内。

这速度，我是不太想说什么了。。。

好，聊完了起号期的数据，来说那个质变的节点。

2023年8月3日，《我用AI做了一部《流浪地球3》的预告片》。

这条内容的意义不在于播放量。而在于它让卡兹克的标签从「教AI的人」，变成了「用AI的人」。

在此之前，他的内容是，ChatGPT怎么注册、Prompt怎么写、AI工具怎么用。

在此之后，他的内容是，我用AI做出了什么、我采访了谁、我办了场什么活动。

态度也从「教你蹭热点」，变成了「我就是热点本人」。

2024年6月，他发了第一条招聘文。2024年7月，他出了第一篇专访。2025年3月，他官宣AIFUT万人大会。

一个公众号的起号，到此已经进入下一阶段了。

我自己的感受是，这个切换，才是他最牛逼的地方。

这55篇起号文章，我全部拉出来一篇一篇对过。

然后我发现了一个很有意思的事。

他的标题有一个百试百灵的公式，起号期几乎每篇都在用。还发现他蹭热点的节奏不是随机的，而是有一套固定的「时间窗口打法」。

听着很玄乎对吧。下篇，我把55个标题全部列出来，加上每篇的发布时间，当面拆给你看。

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！

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！

DEV Community: GokuScraper悟空爬虫

I Analyzed 45,000 AI Prompts and Realized I Was Using AI Image Generation All Wrong

I Analyzed 45,000 AI Prompts and Realized I Was Using AI Image Generation All Wrong

先上车后买票，一个爬虫工程师不敢写进文章里的行业潜规则

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They Claimed Huawei's Autonomous Driving Mileage Was Fake—So I Spent 10 Minutes Checking the Code

They Claimed Huawei's Autonomous Driving Mileage Was Fake—So I Spent 10 Minutes Checking the Code

你的时区正在告诉 Claude Code，你是中国人

你的时区正在告诉 Claude Code，你是中国人

The Real Value of Microsoft MVP Isn't What You Think

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数字生命卡兹克是怎么炼成的（上） 断更6年后，他用2年把自己做成AI圈顶流

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我花了三天，找到了张雪峰的数据来源

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

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

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

云展网电子画册下载？这个开源工具，可能是目前最适合普通人的方案

一、 为什么要下载？

二、 这个工具有多“傻瓜”？

三、 为什么我建议你哪怕现在不用，也得收藏一下？

四、 实话实说：它有哪些局限性？

五、 具体怎么上手？

六、 彪哥的一些私人建议

七、 总结与项目地址

数字生命卡兹克是怎么炼成的（上）断更6年后，他用2年把自己做成AI圈顶流

数字生命卡兹克是怎么炼成的（上）断更6年后，他用2年把自己做成AI圈顶流

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