쉐도잉 연습: The hottest programming skill in 2026 - YouTube로 영어 말하기 배우기

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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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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!

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.

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