शैडोइंग अभ्यास: I Tested The Viral Reverse Engineering Repo - वीडियो के साथ अंग्रेजी बोलना सीखें

अभ्यास मोड चुनें

हर मोड पूरी स्क्रीन का अभ्यास कक्ष खोलता है।

182 वाक्य

यदि वाक्य बहुत छोटे या लंबे हों, तो समायोजित करने के लिए Edit पर क्लिक करें।

I want you to look at these two apps on my screen.

Now, one of them is a real production application called MongoDB Compass, which is built by MongoDB, is open source and is pretty popular.

Now, another one was a clone of this that I built with AI in 30 minutes and cost me $13.

Now, I built this clone using reverse engineering anything, which I'm going to talk about in this video, but right now, honestly, try to guess.

You can see one app on the side here.

I can click into different database fields, right?

I can see all the different entries, same one over here.

And to be honest with you, even though I know the answer, it's almost impossible for me to tell.

So can you figure out which one is which?

Well, on the right -hand side, we have the clone that I built with AI.

And on the left -hand side, we have the actual MongoDB Compass application.

Now, again, this took me 30 minutes to clone and just $13 in AI credits using a very popular package.

Now we're plug -in called Reverse Engineering Anything, which I want to get into.

So this is the GitHub repo for Reverse Engineer Anything.

Now it's been going viral over the past few days here.

It has over 344 ,000 stars, and basically it does what it says.

It allows you to reverse engineer anything using AI.

And I'm going to explain exactly how it works, but to install it is literally as easy as just copying this GitHub URL,

going to your agent and telling it, Hey, I want you to install reverse engineering anything, add all of the tools to cloud code,

and then it will add the tools and you can start using it to make clones like I just showed you.

So before we go into the details, let me start with how re or reverse engineer anything actually works.

Now when you install an app, you just get the finished product, you don't get the source code.

Now reverse engineering is the process of working out how that finished product works and the tools for doing that, things like decompilers and debuggers,

have been around for years now.

Now they're just really slow and they're pretty difficult to use by hand if you don't know what you're doing.

Now what re does is it sits in the middle.

So on one side, you have your coding agents like CloudCode, Codex, Cursor, whatever.

On the other side, you have these real reverse engineering tools like Gidara, EDA, Frida, GDB, whatever.

I'm probably mispronouncing them because I don't even know how to use them, but I don't have to because I now can use this tool.

Now, basically, you can add them into CloudCode using things like MCP servers, and then we can run all of these on your own computer,

do everything locally, decompile, understand the application, and give you back answers.

And every answer comes back with evidence.

So what it found, where in the app it found it, which tool found it, and what's still unknown.

So the agent doesn't need to guess.

It can actually rebuild something by looking at the exact way that it was already built.

Now, this is really interesting because you don't just need to do this on web apps.

You can do this on really any type of application, even desktop apps that typically are pretty difficult to reverse engineer.

So what can the agent actually do with re?

Well, it gets 207 different tools.

Now, the biggest group, which is 79 of them, is for compiled programs.

So things like reading decompiled code, searching for text, and finding who calls what.

Then there are tools for Android apps, websites, and Electron apps.

There's also live debugging with Frida and GDB, and a whole group for tracking evidence and checking that a rebuild actually matches the original.

Now, one tip if you want to try this here is

that those 207 tool descriptions can add up to around 270 ,000 tokens with every single message.

Now in Claude code, you're actually able to set an enabled tool search to true.

I'm going to show you how to do this in one second.

And that will mean the tools only load when the agent actually needs them.

Now for me that cut each message to about 28 ,000 tokens, which makes a massive difference when you're running this at scale.

Let me quickly show you.

So I'm just going to tell Claude code, this set enabled tool search to true for your tool calls.

And now this way it will start searching through the tools

and figuring out

which one it needs to use rather than looking at all of them every single time it calls an AI model.

So you can see it was able to update this year in our settings.

Now, what I want to do is show you how I ran this,

because again, this is the cloned application that we got here using re which is just absolutely crazy.

And I want to dive more into the mechanics of how this actually works.

So you understand where it will be useful for you.

Okay.

So to run everything that you're about to see,

I actually use today's sponsor open router now it's a really good fit for this because reruns can get extremely expensive.

Now the agent actually loops over and over again

and I've seen people spend you know hundreds of dollars on a single rebuilt product.

So what I wanted to do was compare an expensive model with a cheap one

which I'm going to show you the results of in a minute

and to do that I used open router

which gives me one API key and one endpoint for covering over 400 different models.

Now I simply just pointed cloud code at open router by changing two simple environment variables.

And then after that, I was able to switch to different models and see all of the pricing directly in my dashboard.

So you can see that every request actually shows this exact cost here in the activity page.

And that mattered a lot for this video because Claude Code's own estimate really overstates the cost.

And in this example, it overstated it by up to four

and a half times what it actually cost me to run this.

So every number that you see in this video comes directly from OpenRouter.

And even if a model gets rate limited halfway through a long run,

I can have a fallback model and move over to something else that my program doesn't completely stop.

If you want to try Rhea for yourself, OpenRouter is linked in the description.

I use it for pretty much anything where I need an API key.

And I have a ton of usage.

It's a great platform and really the best way to use AI models.

You also can call image, video, voice, everything from one single API key.

All right, so once you've got Rhea installed inside of CloudCode, you can simply just ask it to use Rhea

and rebuild pretty much any app that's on your computer where they can access from the web.

Now, if you go into your terminal -based environment to use CloudCode, you'll be able to see the MCP server.

So if you type

Cloud here, just go, yes, I trust this folder, and then type slash MCP,

you'll see the Rhea MCP server shows up here with 207 different tools.

Now, it also has some prompts and some other stuff inside of here, and basically it's ran through this kind of NPM package.

However, if you try to look at the MCP server from the desktop application, after you add it, you probably won't see it.

That's fine.

It's still there, but it will just appear when you start asking it to do something.

So if I say, Hey, rebuild the OBS application using reverse engineer, anything.

Then if I press enter here, it should find that connector.

So you see Rhea pops up right here and it will start building that out.

Now I won't wait for all of this to finish because I already built MongoDB Atlas,

but basically that's as easy as it is to start doing this.

So now I want to discuss what Rhea is actually doing behind the scenes, specifically when it's looking at compiled programs.

So things like a Windows executable file like OBS.

100

So first what the agent will do is open the binary and try to get an overview of the program.

101

So what kind of file is it?

102

What libraries it uses?

103

Where does it start?

104

Then it needs a starting point.

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So it's going to search for text that you can actually see in the application.

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It's like a button, a label, an error message, and it's going to jump to the code that uses that reference.

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Then it reads that function.

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So the decompiler is going to turn machine code back into C -like pseudocode, which the model will actually be able to read.

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Then it's going to follow all of the calls.

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So what calls this function?

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What calls that function?

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Where is the data flowing throughout the application?

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Now, anything that it can't prove, it's going to write down as an unknown instead of just guessing.

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And at the end of it, it's going to compare the rebuilt code against the original to check if they behave the same way.

115

Now for compiled programs, you will need a decompiler installed.

116

However, it can automatically install it for you if you just put it in bypass permissions mode.

117

Now Compass, the one that I did here, is actually a JavaScript app.

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So it skips the decompiler because the code ships as JavaScript instead of plain machine code.

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So for MongoDB Compass, which Claude was able to remake, I pointed Rhea at a single 49 megabyte file.

120

Now this is basically the entire app that just packaged together

121

and in under three minutes it was able to map the entire thing.

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So it had 530 different files, over 3 ,500 code modules

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and over 13 ,000 findings including how the app's windows actually talked to the back end

124

and how all of the communication occurred.

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So here's what these models actually built and I say models because I ran this with two models,

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Opus 5 .5 and Haiku 5 .5 to see what actually was the difference with an expensive, and a cheap model.

127

Now, here's exactly what happened.

128

Well, first we set everything up, right?

129

Then I gave both models the exact same prompt.

130

So I said, use RIA to figure out how

131

Compass works, how it's styled, and then rebuild it as a desktop application with these eight features.

132

This is what I included in the prompt.

133

So I wanted the ability to connect a database sidebar, browsing documents with filters and pages, inserting, editing, and deleting, schema the tab,

134

aggregations tab, and then the indexes tab.

135

And I wanted it to look exactly the same as Compass, as you guys already saw.

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I also gave it a small test database that it could load up here that I already had built with MongoDB.

137

So here's the results that I actually got that I had Claude's screenshot.

138

So you can see Opus 5 .5 costs $13.

139

Haiku 5 .5 costs $0 .85.

140

With Opus, it took 42 minutes.

141

With Haiku, it took about 15 minutes.

142

And you can see that they look pretty similar here.

143

Now Haiku has some visual differences compared to the real compass version.

144

You can see it's a little bit simpler.

145

The icons aren't quite there, but generally it gave me pretty much the exact same stuff.

146

Just again, like a little bit of small issues with kind of the spacing and the indexes, and it wasn't quite as clean,

147

but you get the idea, right?

148

We're able to build the entire thing.

149

And when we used Opus 5 .5, it gave us a near identical clone.

150

Again, just to open this up and show you, like I can go to aggregations, I can run queries, I can view specific schemas,

151

I can look at the index.

152

I can connect to different databases.

153

Like, like how crazy is that, that this actually just works.

154

I can directly even like edit a value here and change this and save it.

155

And it works properly.

156

And this is just all done with AI with literally two or three prompts.

157

So with all of that said, what does this actually mean?

158

Well, to be honest with you, I don't really know.

159

I mean, this is super cool.

160

It's obviously very interesting that we can do this.

161

And it really shows

162

that the value of software is no longer just in writing a bunch of code like it used to be.

163

It's really more in making something that's legitimately valuable that solves a problem and then people actually want to use.

164

This leads me to believe that more and more software will just de facto become open source.

165

And the companies are probably going to be less protective of trying to guard all of their secretive code bases.

166

Because really, there's just less value in doing that now than there used to be in the past.

167

I can remember when code was sacred

168

and you wouldn't want to share certain modules or files

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or what you're doing because you don't want anyone to get in on your secret

170

and rebuild this really technically complex app that you made.

171

Now we literally have an AI tool that can rebuild pretty much anything in a matter of few minutes and even decompile,

172

go through, run all of the compiled code.

173

It's just crazy where we're at now in 2026.

174

So I think it shapes what we use software for, what the value of software is and what we're going to look at going forward

175

but I don't believe this wipes out software engineers.

176

And to be honest, the more cool tools we get like this, the more interesting software we can build.

177

And again, in my opinion, the value of software is knowing what to build, not just building it itself,

178

having that judgment, building a nice UI, solving a legitimate problem.

179

Now, more than ever, the code is not that valuable, but being able to know what to code and having that judgment is becoming more valuable.

180

That's my opinion.

181

I don't know if you guys agree with me.

182

Let me know in the comments down below, and I will see you in another video.

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इस पाठ की शब्दावली और बोलने के सुझाव

यह स्पीकिंग पाठ वीडियो “I Tested The Viral Reverse Engineering Repo” पर आधारित है। सबसे ज़्यादा दोहराए गए शब्द ये हैं: code, tool, model, app, reverse। इस वीडियो में शैडोइंग के लिए 182 वाक्य और 2437 शब्द हैं। बोला गया हिस्सा 11:23 लंबा है। वक्ता तेज़ बोलता है, लगभग 214 शब्द प्रति मिनट, इसलिए जुड़ी हुई और कमज़ोर ध्वनियाँ ज़्यादा मिलेंगी। 85% शब्द अंग्रेज़ी के 3,000 सबसे आम शब्दों में से हैं; बाकी शब्द अभ्यास से पहले देख लेना अच्छा रहेगा।

इस वीडियो की मुख्य शब्दावली

वीडियो के 15 सीखने लायक शब्द, उच्चारण और अर्थ के साथ:

शब्द	उच्चारण	अर्थ
reverse संज्ञा	/ɹɪˈvɝs/	विलोमित, विपरीत
compass संज्ञा	/ˈkʌmpəs/	क़ुतुबनुमा, दिक्सूचक
server संज्ञा	/ˈsɝvɚ/	सर्वर
database संज्ञा	/ˈdeɪtəˌbeɪs/	डेटाबेस
cloud संज्ञा	/ˈklaʊ̯d/	मेघ, बादल
package संज्ञा	/ˈpæk.ɪd͡ʒ/	पैकेज, पैकेट
compare क्रिया	/kəmˈpɛɚ/	तौलना
solve क्रिया	/sɒlv/	सुलझाना, हल करना
edit क्रिया	/ˈɛdɪt/	संपादन करना
judgment संज्ञा	/ˈd͡ʒʌd͡ʒ.mənt/	मूल्यांकन
module संज्ञा	/ˈmɑd͡ʒul/	मॉड्यूल
occur क्रिया	/əˈkɜː/	बीतना
label संज्ञा	/ˈleɪ.bəl/	लेबल
plain संज्ञा	/pleɪn/	मैदान
estimate संज्ञा	/ˈɛs.tɪ.mət/	आकलन, अनुमान

दोहराने लायक वाक्य

वीडियो के छोटे, पूरे वाक्य जिन्हें आप रोज़ की बातचीत में इस्तेमाल कर सकते हैं:

You can see it's a little bit simpler.
We're able to build the entire thing.
It's obviously very interesting that we can do this.
I don't know if you guys agree with me.

इस वीडियो का व्याकरण

वक्ता जिन संरचनाओं का सबसे अधिक प्रयोग करता है, वीडियो के असली शब्दों के साथ:

संरचना	वीडियो में
“used to” used to + क्रिया — बीती आदत या स्थिति जो अब नहीं है	used to be
Passive voice be + past participle — ज़ोर इस पर कि क्या होता है, न कि कौन करता है	is built · was already built · is linked
Relative clauses who / which + उपवाक्य — व्यक्ति या वस्तु के बारे में अतिरिक्त जानकारी	Compass, which is · group, which is · tokens, which makes
Present perfect have/has + past participle — बीता हुआ काम जिसका असर अभी भी है	have been · I've seen

ध्यान देने योग्य उच्चारण

वक्ता 38 बार संक्षिप्त रूपों का प्रयोग करता है, जैसे don't, I'm, you're। इन्हें वैसे ही छोटा बोलें जैसा आप सुनते हैं।

“sh” और “zh” की ध्वनियाँ: screenshot /ˈskriːn.ʃɑt/, aggregation /æɡɹəˈɡeɪʃən/, dashboard /ˈdæʃˌboɹd/
लंबे शब्द — बलाघात सही रखें: legitimate /lɪˈd͡ʒɪt.ɪ.mət/, technically /ˈtɛk.nɪˌkəl.i/, decompiler /diːkəmˈpaɪlə(ɹ)/, aggregation /æɡɹəˈɡeɪʃən/, executable /ˈɛksəkjutəbl/

हिंदी भाषियों के लिए मुश्किल ध्वनियाँ:

/v/ और /w/ का फ़र्क़ — /v/ में ऊपर के दाँत निचले होंठ को छूते हैं, /w/ में होंठ गोल होते हैं: reverse /ɹɪˈvɝs/, server /ˈsɝvɚ/, solve /sɒlv/, protective /pɹəˈtɛk.tɪv/, dive /daɪv/
शब्द की शुरुआत में s + व्यंजन — पहले “इ” न जोड़ें: skip /skɪp/, sponsor /ˈspɒn.səː/, screenshot /ˈskriːn.ʃɑt/, schema /ˈskiːmə/
/æ/ — “ऐ” से ज़्यादा खुला: package /ˈpæk.ɪd͡ʒ/, variable [ˈvæɹ.i.ə.bl̩], halfway /ˌhæfˈweɪ/, mechanic /mɪˈkænɪk/, bypass /ˈbaɪpæs/

इस वीडियो से अभ्यास कैसे करें

पहले बिना बोले पूरा वीडियो एक बार सुनें और जो शब्द नहीं आते उन्हें लिख लें।
0.75× गति से शुरू करें, एक-एक वाक्य की शैडोइंग करें, और सहज लगने पर सामान्य गति पर लौटें।
अपनी आवाज़ रिकॉर्ड करें और मूल से तुलना करें, ख़ासकर reverse, compass, server जैसे शब्दों पर ध्यान दें।

शैडोइंग तकनीक क्या है?

शैडोइंग (Shadowing) एक विज्ञान-समर्थित भाषा सीखने की तकनीक है जो मूल रूप से पेशेवर दुभाषिया प्रशिक्षण के लिए विकसित की गई थी। विधि सरल लेकिन शक्तिशाली है: आप मूल अंग्रेज़ी ऑडियो सुनते हैं और तुरंत इसे ज़ोर से दोहराते हैं — जैसे वक्ता की छाया 1-2 सेकंड की देरी से। शोध से पता चलता है कि यह उच्चारण सटीकता, स्वर, लय, जुड़ी हुई ध्वनियाँ, सुनने की समझ और बोलने की प्रवाहशीलता में काफ़ी सुधार करता है।

शैडोइंग तकनीक: पूरी चरण-दर-चरण गाइड पढ़ें →

शैडोइंग अभ्यास: I Tested The Viral Reverse Engineering Repo - वीडियो के साथ अंग्रेजी बोलना सीखें

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इस पाठ की शब्दावली और बोलने के सुझाव

इस वीडियो की मुख्य शब्दावली

दोहराने लायक वाक्य

इस वीडियो का व्याकरण

ध्यान देने योग्य उच्चारण

इस वीडियो से अभ्यास कैसे करें

शैडोइंग तकनीक क्या है?