쉐도잉 연습: The hottest programming skill in 2026 - 영상으로 영어 말하기 배우기
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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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이 수업에 대하여
이번 비디오에서 다룰 핵심 개념은 시스템 사고입니다. 시스템 사고는 각 구성 요소가 시간에 따라 어떻게 상호작용하는지를 이해하는 기술로, 프로그래밍과 소프트웨어 공학 분야에서 필수적인 스킬입니다. 유튜브 영어 공부를 통해 시스템 사고에 대해 깊이 있게 배우며, 이를 다양한 상황에 적용할 수 있는 능력을 기르게 될 것입니다. 또한 이 수업에서는 실제 예제와 함께 시스템 사고의 중요성을 강조하여, 효과적인 의사소통과 문제 해결 능력을 함양할 것입니다.
주요 어휘 및 구문
- 시스템 사고 (Systems Thinking) - 각 부분이 어떻게 상호작용하는지를 이해하는 사고 방식입니다.
- 프로그래밍 (Programming) - 컴퓨터 소프트웨어를 작성하는 기술입니다.
- 소프트웨어 공학 (Software Engineering) - 소프트웨어 시스템을 설계 및 개발하는 과정입니다.
- 프라임 패티 (Krabby Patty) - 특정 예를 통해 시스템 사고를 설명하는 요소입니다.
- 기계 학습 엔지니어 (Machine Learning Engineer) - 데이터 분석 및 알고리즘 구축에 필요한 역할입니다.
- API (Application Programming Interface) - 시스템 간의 상호작용을 지원하는 프로그래밍 인터페이스입니다.
- 사기 탐지 알고리즘 (Fraud Detection Algorithm) - 거래를 분석하여 이상 행위를 식별하는 시스템입니다.
- 데이터 엔지니어 (Data Engineer) - 데이터를 수집하고 처리하는 역할을 맡는 엔지니어입니다.
연습 팁
비디오에서의 내용은 다소 빠르기 때문에, 처음에는 shadow speak 기법을 활용해 보세요. 비디오의 원어민 발음을 따라하면서 각 문장과 단어의 발음을 정확히 익히는 것이 중요합니다.
특히, shadowspeaks를 통해 문장을 반복하세요. 처음에 어려운 부분은 느리게 재생하여 발음을 확인하고, 점점 자연스러운 속도로 연습하는 것이 좋습니다. 또한, 토론 및 사전 지식을 활용하여 배운 내용을 실제 상황에 적용해 보는 것이 유익합니다. 이러한 방법은 shadowing site에서 제공하는 다양한 자료와 함께하면 더욱 효과적입니다.
마지막으로, 비디오의 주제를 유념하며 각 내용을 소화해 보세요. 시스템 사고는 복잡한 개념일 수 있지만, 다양한 예제와 실습을 통해 점차 익혀 나가면 됩니다. 이 연습을 통해 유튜브 영어 공부의 효과를 극대화해 보세요!
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| 문형 | 영상 속 표현 |
|---|---|
| 수동태 be + 과거분사 — 누가 하는지보다 무슨 일이 일어나는지에 초점 | can be applied · was meant · isn't used |
| 현재완료 have/has + 과거분사 — 과거의 일이 지금도 관련이 있을 때 | I've sold · have always been · you've been reduced |
쉐도잉이란? 영어 실력을 빠르게 키우는 과학적 방법
쉐도잉(Shadowing)은 원래 전문 통역사 훈련을 위해 개발된 언어 학습 기법으로, 다언어 학자인 Dr. Alexander Arguelles에 의해 대중화된 방법입니다. 핵심 원리는 간단하지만 매우 강력합니다: 원어민의 영어를 들으면서 1~2초의 짧은 지연으로 즉시 소리 내어 따라 말하는 것——마치 '그림자(shadow)'처럼 화자를 따라가는 것입니다. 문법 공부나 수동적인 청취와 달리, 쉐도잉은 뇌와 입 근육이 동시에 실시간으로 영어를 처리하고 재현하도록 훈련합니다. 연구에 따르면 이 방법은 발음 정확도, 억양, 리듬, 연음, 청취력, 말하기 유창성을 크게 향상시킵니다. IELTS 스피킹 준비와 자연스러운 영어 소통을 원하는 분들에게 특히 효과적입니다.