シャドーイング練習: I Tested The Viral Reverse Engineering Repo - 動画で英語スピーキングを学ぶ

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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.

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

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So what kind of file is it?

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What libraries it uses?

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Where does it start?

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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.

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

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

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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.

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Now this is basically the entire app that just packaged together

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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

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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.

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Now, here's exactly what happened.

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Well, first we set everything up, right?

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Then I gave both models the exact same prompt.

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So I said, use RIA to figure out how

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

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This is what I included in the prompt.

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

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aggregations tab, and then the indexes tab.

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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.

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

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

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Haiku 5 .5 costs $0 .85.

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With Opus, it took 42 minutes.

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With Haiku, it took about 15 minutes.

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And you can see that they look pretty similar here.

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

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You can see it's a little bit simpler.

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

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

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but you get the idea, right?

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We're able to build the entire thing.

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

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

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I can look at the index.

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I can connect to different databases.

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

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

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And it works properly.

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

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

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

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I mean, this is super cool.

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

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And it really shows

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

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

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

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

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

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I can remember when code was sacred

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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

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and rebuild this really technically complex app that you made.

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

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go through, run all of the compiled code.

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

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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

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

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

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

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

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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.

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That's my opinion.

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

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

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このレッスンの語彙とスピーキングのポイント

このスピーキングレッスンは、動画「I Tested The Viral Reverse Engineering Repo」を教材にしています。繰り返し出てくる語は次のとおりです：code, tool, model, app, reverse。この動画には、シャドーイング用の文が182文、単語が2437語あります。音声の長さは11:23です。話す速さは速く、1分あたり約214語です。音のつながりや弱く発音される音が多くなります。単語の85%は英語の頻出3,000語に含まれます。残りは練習の前に確認しておきましょう。

この動画の重要語彙

動画に出てくる覚えておきたい単語15語を、発音と意味つきで紹介します。

単語	発音	意味
rebuild 動詞	/ɹiːˈbɪld/	建て直す, 再建する
compass 名詞	/ˈkʌmpəs/	方位磁石, 羅針盤
clone 名詞	/kloʊn/	クローン
server 名詞	/ˈsɝvɚ/	サーバー
database 名詞	/ˈdeɪtəˌbeɪs/	データベース
install 動詞	/ɪnˈstɔl/	導入する, インストールする
cloud 名詞	/ˈklaʊ̯d/	雲, 雲霞
package 名詞	/ˈpæk.ɪd͡ʒ/	包み, パケット
desktop 名詞	/ˈdɛsktɒp/	机の上
compare 動詞	/kəmˈpɛɚ/	比べる, 比較する
solve 動詞	/sɒlv/	解決する
connect 動詞	/kəˈnɛkt/	繋げる
edit 動詞	/ˈɛdɪt/	編集する
judgment 名詞	/ˈd͡ʒʌd͡ʒ.mənt/	判決, 裁き
module 名詞	/ˈmɑd͡ʒul/	モジュール

動画に出てくる句動詞

単語	意味
show up 動詞	現れる
wipe out 動詞	一掃する
write down 動詞	書き留める, 記す

繰り返し練習したい文

動画に出てくる短く完結した文です。日常会話でそのまま使えます。

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.

この動画の文法

話し手がよく使っている文型を、動画の実際の表現とともに紹介します。

文型	動画での表現
「used to」 used to + 動詞 — 今はもう違う過去の習慣や状態	used to be
受動態 be + 過去分詞 — 誰がするかより、何が起きるかに焦点を当てる	is built · was already built · is linked
関係詞節 who / which + 節 — 人や物について情報を加える	Compass, which is · group, which is · tokens, which makes
現在完了形 have/has + 過去分詞 — 過去の出来事が今も関係している	have been · I've seen

注意したい発音

話し手はdon't, I'm, you'reなど、短縮形や弱形を38回使っています。聞こえたとおりの短い形で発音しましょう。

「sh」と「zh」の音: screenshot /ˈskriːn.ʃɑt/, aggregation /æɡɹəˈɡeɪʃən/, dashboard /ˈdæʃˌboɹd/
長い単語（アクセントの位置に注意）: legitimate /lɪˈd͡ʒɪt.ɪ.mət/, technically /ˈtɛk.nɪˌkəl.i/, decompiler /diːkəmˈpaɪlə(ɹ)/, aggregation /æɡɹəˈɡeɪʃən/, executable /ˈɛksəkjutəbl/

日本語話者が苦手な音：

/r/ と /l/ の区別 — /l/ は舌先を歯茎につけ、/r/ はどこにもつけない: rebuild /ɹiːˈbɪld/, variable [ˈvæɹ.i.ə.bl̩], viral /ˈvaɪɹəl/, decompiler /diːkəmˈpaɪlə(ɹ)/
/v/ — /b/ にならないように、上の歯を下唇に当てる: reverse /ɹɪˈvɝs/, server /ˈsɝvɚ/, solve /sɒlv/, protective /pɹəˈtɛk.tɪv/, dive /daɪv/
/f/ — 「フ」ではなく、上の歯と下唇で出す: filter /ˈfɪltɚ/, halfway /ˌhæfˈweɪ/, folder /ˈfoʊldɚ/, fallback /ˈfɔːl.bæk/

この動画での練習方法

まず声を出さずに動画を最後まで聞き、知らない単語をメモします。
まず0.75倍速で一文ずつシャドーイングし、慣れてきたら通常の速度に戻します。
自分の声を録音して元の音声と比べます。rebuild, compass, cloneなどの単語に特に注意しましょう。

シャドーイングとは？英語上達に効果的な理由

シャドーイング（Shadowing）は、もともとプロの通訳者養成プログラムで開発された言語学習法で、多言語習得者として知られるDr. Alexander Arguelles によって広く普及されました。方法はシンプルですが非常に効果的：ネイティブスピーカーの英語を聞きながら、1〜2秒の遅延で声に出してすぐに繰り返す——まるで「影（shadow）」のように話者を追いかけます。文法ドリルや受動的なリスニングと異なり、シャドーイングは脳と口の筋肉が同時にリアルタイムで英語を処理・再現することを強制します。研究により、発音精度、抑揚、リズム、連音、リスニング力、そして会話の流暢さが大幅に向上することが確認されています。IELTSスピーキング対策や自然な英語コミュニケーションを目指す方に特におすすめです。

シャドーイングのやり方: ステップ別の完全ガイドを読む →

シャドーイング練習: I Tested The Viral Reverse Engineering Repo - 動画で英語スピーキングを学ぶ

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