シャドーイング練習: The hottest programming skill in 2026 - YouTubeで英語スピーキングを学ぶ

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Systems thinking.
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Systems thinking.
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That's it.
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You can end the video right now.
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Every programming era had its own hot skill,
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whether you were writing Java in the 90s,
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PHP in the 2000s, Ruby in 2010,
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and in 2026, aside from token maxing, it's systems thinking.
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But what exactly is systems thinking?
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It's understanding how each part of a system interacts over time.
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Think of it like this.
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You work at the Krusty Krab,
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you can make a banger Krabby Patty,
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but that's only one feature of the Krusty Krab.
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How do they scale to a thousand Krabby Patties per hour.
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What happens when the fryer breaks?
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Or worse, Patrick is your waiter.
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Systems thinking has its roots in biology,
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and it can be applied to many different fields.
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Supply chain management, market dynamics,
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traffic, and of course, software engineering.
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Nowadays, anytime we hear systems and programming,
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we immediately think of system design interview questions.
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But that's only a part of it.
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Systems thinking and software engineering have an old relationship,
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and it's only getting more prominent as time passes.
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And I have some charts to back it A16Z published this graph showing software jobs are going up.
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I personally hate these graphs.
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Not because I'm Mr. Dario,
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I was meant to replace you 24 months ago, agentic pilled.
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But I hate seeing these graphs because it doesn't clarify what kind of software jobs are going up.
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Do you really think the demand for Pascal developers is going up?
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Really, bro?
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Thankfully, there's always a programmer with some spare time and a banger blog that did the research for me.
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This graph shows off the number of job postings by each job title.
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Look at the top three.
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They all develop carpal tunnel, but also systems.
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Look at machine learning engineers.
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They use tools like PyTorch, Kafka, Kubernetes.
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They use these tools to build stuff like fraud detection algorithms,
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which can analyze millions of transactions so your credit card isn't used in Belarus to buy an iPhone.
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The fraud detection algorithm is a system.
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Similarly, data engineers and backend engineers,
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when they're not gluing APIs together, they're building systems.
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The ability to think in systems is so hot right now.
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Anthropic has a section on their careers page just for infrastructure software engineers.
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Even the latest jobs coming out of the GPT wrapper factories.
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They're about making the use of LLMs more efficient, which is a system.
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Hopefully I've sold you on the importance of systems thinking,
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so now you can go learn about it.
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Learning systems is harder than anything else in programming,
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since you can't just replicate the scale of millions of users.
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Except, there is one way.
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Playing Factorio.
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I'm not even kidding.
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Factorio is a game where you learn how to build systems.
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For those that are unaware,
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the premise of the game is that you crash land on a planet,
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you need to build a rocket to escape.
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To build that rocket, you need to get resources.
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To get those resources, you need to build systems and get really good at building them.
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Factorio is the most underrated way to learn about large-scale systems and actually get some experience,
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because it teaches you about impact,
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bottlenecks, and how to continuously improve a large-scale system.
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There's this amazing video by Tony Zhu that goes over the relationship between Factorio and software engineering.
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Another unconventional way of learning about systems thinking is by watching Kevin Fang and reading the Cloudflare outage postmortems.
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If you want a more conventional method,
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you could always read this excellent book called Thinking in Systems by Donella Meadows.
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Now if you're absolutely insistent on building a project,
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you can look up public repositories of massive datasets and use that to stress test your project,
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whether it's by benchmarking how quickly you read and write to the database,
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or seeing if your front-end code can actually handle that much data.
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Fundamentally, systems have always been a part of software engineering,
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and it is the strongest skill you can learn right now,
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whether you're into back-end, front-end, or even game dev.
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Understanding how something as granular as your code fits into the bigger picture will always be valuable.
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And hey, at least you have an excuse to get good at Factorio now.
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You'll also get more time to play Factorio by using the sponsor of today's video,
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Convex, the backend platform that keeps your app in sync.
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In modern programming, you're not really just programming anymore.
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You're spending half your time coordinating between your frontend,
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backend, database, and whatever other shenanigans you have going on.
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I'm not judging, but essentially you've been reduced to the dude in front of the orchestra with the pointy stick.
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Not anymore though, Convex finally lets you build again.
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Instead of gluing services together,
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your backend is just TypeScript functions.
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You write functions to read and write data,
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and Convex handles a database,
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APIs, and syncing between everything automatically.
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And look at this sync bro, so smooth, so crispy.
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And is that real-time updates between two users?
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Convex fits right into the TypeScript ecosystem you're already using,
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and you can also use it it with a few friendly faces.
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No more raw dogging squeal,
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no more glue engineering, no more cash invalidation,
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and no more shadowboxing with your app to keep it in sync.
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You owe it to yourself to go to bigboxsweet.dev slash convex.

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このレッスンについて

このレッスンでは、「システム思考」の重要性について学び、その概念を実際にどのように応用できるかを探ります。システム思考は、異なる要素がどのように相互作用するかを理解する能力であり、ソフトウェアエンジニアリングやその他の分野で大変重要です。視聴者は、英語での理解力を高めるとともに、システム思考を用いた英会話の実践を行います。この動画を通じて、英語のリスニング能力や発音を向上させるためのYouTubeで英語学習を行いましょう。

重要な語彙とフレーズ

  • システム思考 (systems thinking) - 要素間の相互作用を理解する能力
  • プログラミング (programming) - ソフトウェアを作成するための技術
  • フライヤー (fryer) - 食材を揚げるための機械
  • 機械学習エンジニア (machine learning engineers) - データ分析のための技術者
  • データエンジニア (data engineers) - データ処理と管理の専門家
  • バックエンドエンジニア (backend engineers) - サーバー側の開発者
  • API (Application Programming Interface) - アプリケーション同士が接続するための手段
  • フraud検出アルゴリズム (fraud detection algorithms) - 不正利用を見つけるためのシステム

練習のためのヒント

システム思考に関するこの動画は、少し難易度が高いかもしれませんが、shadow speechのテクニックを使って、言葉のリズムやトーンを覚えましょう。まず、動画を何度か見て、内容を理解したら、次に音声に合わせて声を出してみてください。同時に発音やスピードを調整することで、より自然な英語が身につきます。また、shadowing siteを利用して練習するのも良い方法です。特に、IELTS スピーキング対策としても、こうした実践を行うと効果的です。初めは全てを完璧に行う必要はありません。少しずつ自信を持って発話できるようになりましょう。趣味の一環として、YouTubeで英語学習を活用し、色々な英語に触れてみることも大切です。

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。

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