跟读练习: The hottest programming skill in 2026 - 通过YouTube学习英语口语
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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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本課程介紹
在這堂課中，您將了解系統思維的重要性，以及它在程式設計領域中的應用。透過探索不同行業內的系統思維，您將學會如何將這種思考模式應用於日常的問題解決中。此外，這裡的練習將幫助您提高英語口語能力，特別是透過言語模仿的技巧。
關鍵詞彙與短語
- 系統思維 (systems thinking)
- 供應鏈管理 (supply chain management)
- 伺服器後端工程師 (backend engineers)
- 機器學習工程師 (machine learning engineers)
- 規模化 (scale)
- 演算法 (algorithm)
- 數據工程師 (data engineers)
- 技術面試問題 (system design interview questions)
練習技巧
進行影子跟讀（shadow speech）時，您可以透過觀看這個視頻來提高的技巧包括：
- 在視頻播放時，跟隨講者的語速，嘗試即時模仿他們的語調和口音。這對於提升您在雅思口語練習中的表現尤其有幫助。
- 注意講者如何組織語言，理解他們如何將複雜概念轉化為易於理解的表述。聆聽每一句話，並在說出前反覆思考。
- 在翻譯或理解不清的地方，可以重播放特定片段，重複聽取，幫助您更確切地掌握每個詞彙的用法。
- 將重點詞彙和短語馬上使用進入您的口語練習中，例如在日常交流中提及「系統思維」或「機器學習」，來提高您的英語發音。
透過這樣的練習，即使是看YouTube學英語，也能提升您的口說能力，使您在討論複雜概念如系統思維時更加自信！
什么是跟读法？
跟读法 (Shadowing) 是一种有科学依据的语言学习技巧，最初开发用于专业口译员的培训，并由多语言者Alexander Arguelles博士普及。这个方法简单而强大：您在听英语母语原声的同时立即大声重复——就像是一个延迟1-2秒紧跟说话者的影子。与被动听力或语法练习不同，跟读法强迫您的大脑和口腔肌肉同时处理并模仿真实的讲话模式。研究表明它能显着提高发音准确性，语调，节奏，连读，听力理解和口语流利度——使其成为雅思口语备考和真实英语交流最有效的方法之一。