跟讀練習: How a CPU Works in 100 Seconds // Apple Silicon M1 vs Intel i9 - 通過影片學習英語口語

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The central processing unit, or CPU, is like the engine in your car, or the brain in your skull, and is like a really fancy calculator used to run the applications on your computer.

When you write software in a language like JavaScript or Python, you're actually writing a set of instructions that will be executed as machine code by a CPU,

which itself is a carefully crafted piece of metal and silicon that contains billions of tiny transistors, or on-off switches that represent ones and zeros.

To perform mathematical calculations, a CPU will combine multiple transistors together to form logic gates.

For example, AND will take two binary inputs and validate that both are true to produce a true output.

It only takes a few basic logic gates to solve highly complex computational problems.

Modern chips contain billions of transistors, and they can be flipped on and off billions of times per second.

The state of the CPU is synchronized by an oscillator known as the clock generator.

In general, the more times the clock can pulse per second, the faster the CPU can compute, and is normally measured in gigahertz.

Gamers will sometimes overclock their CPUs to gain more performance at the cost of higher temperatures and a lower life expectancy.

Now, in order to run applications, it interacts with the system memory or RAM in a series of four steps known as the machine or instruction cycle.

Step one is the fetch phase.

Think of a software program as a set of instructions stored in the RAM.

The CPU has registers to temporarily store the address and memory that it wants to interact with.

The program counter starts at zero and copies that address to the memory address register.

Then the control unit sends out a signal to copy the data from that address to the instruction register, at which point it needs to figure out how to use this instruction in the decode phase.

The control unit parses the actual bits in the instruction.

Most importantly, the opt code contains the instruction, like add or subtract, and the operand is the address and memory to perform that operation on.

The final execute stage takes the decoded information and passes it as electrical signals to the relevant parts of the CPU.

The Arithmetic Logic Unit, or ALU, can perform math on the data and then store the results in RAM to change the state of the program.

The cycle is repeated billions of times per second, and modern chips utilize multiple CPU cores to run multiple computations in parallel.

That's how a CPU works under the hood, but there's a lot more you should know about processor architectures if you're a developer.

And that's why I've invited Alex from the most underrated tech

channel on YouTube to break down some more advanced concepts like Apple Silicon

and how to run performance benchmarking on your own machine.

Hi everybody, my name is Alex

and I'm here to take you beyond 100 seconds in your quest to learn about how processor architectures differ

and also how those differences affect our productivity as developers.

I'll show you what the Apple Silicon chip and a refrigerator have in common, and then I'll share some of my own experiences with using developer tools

and build execution on the first generation Apple Silicon chip called the M1 chip, as compared to the Intel chip.

First and foremost, I just want to say

that my personal productivity as a developer depends less on processors inside my computer

and more on how much coffee I had and whether I'm I'm trying to work after a big meal.

But aside from human factors, let's dig deeper into why the new Apple Silicon machines have been really kicking up a storm and changing the industry, software development included.

And spoiler alert, the M1 machines have been beating the Intel machines in pretty much every build test I've thrown at it.

Except a couple.

But it's not all roses, so let's talk about some downsides later too.

So why is it so darn fast?

The first point I want to discuss is the physicality differences

of the new Apple Silicon way versus the old Intel and AMD way.

This is what Apple Silicon and your refrigerator at home have in common.

Alright, imagine for a minute that you want to make a turkey with cheese sandwich.

You go over to the refrigerator and in one place you have the turkey breast, the cheese, the mayonnaise, the mustard.

All those ingredients are right there in one place and you don't have to run around the house to gather them.

You don't have to drive to lots of stores to pick them up.

And this saves you lots of time and energy.

This kind of efficiency can be found in the new Apple Silicon chips because they aren't just a new processor.

Apple Silicon is a collection of many chips that are housed inside one silicon container.

And this type of system is known as a system on chip or SOC.

And it's essentially an entire computer on one chip.

The main CPU, the GPU, the I.O controller, the ML engine, they're all co-located.

So when the task is to make an electronic sandwich, so to speak, or in other words, to do some work that involves all these different components,

a system on chip is going to be a lot more efficient in terms of energy usage, using only a tiny bit of power, and at the same time, it's going to be faster than,

say, a typical machine that has all the components separate.

Intel based machines have a CPU that's a single chip and memory is located somewhere else on the motherboard.

The I.O is somewhere else.

The individual components might be even more powerful than the ones available at the moment on the latest Apple silicon based machines.

But that comes at a cost since these powerful components are like supermarkets that carry different sandwich ingredients.

As a result, when you want a sandwich on an Intel machine, You'll have to drive all over town because one store will have the turkey another store will have the cheese

Yet another store will have the mayonnaise each store might be really good at their own process

and have the best ingredients

But each store also takes a lot of power to run losing out on the efficiency

And since all you really want is just to make a simple sandwich You're wasting a ton of time

and energy picking up the ingredients from all the different stores Of course, some might say the drawback with the system-on-chip design is that,

at least for now, with the current selection of Apple Macs, you won't be able to upgrade or change any of the components.

You get what's on the menu, and that's it.

But that's not news to most people that are familiar with the Apple ecosystem.

Once you accept that idea, you might even see the benefits of having a more efficient design where all the components are on one chip,

and they outweigh the cons and still provide better performance than the alternative.

All right, that's enough Let's talk about food.

So how does this all affect real world development workflows?

So I've been doing a bunch of developer focused tests on the latest Apple Silicon machines over on my channel,

as well as comparing them to other machines like the Intel Macs and PC machines too.

And in general, the new design has been showing really great promise for my own workflows as a developer, coffee and sandwiches excluded.

Now there are lots of technology stacks that developers use, of course, and I'm trying builds in a few of them.

But now let me share some of the results I've seen with Node and JavaScript tests.

And after that, I'll also discuss the tech stacks

that have the biggest gains on the new machines

and also the stacks that have the biggest losses right now at the moment.

I started off trying out some existing JavaScript tests that are in the browser and then in Node.

The browser test consisted of running Speedometer, which is a browser benchmark that measures the responsiveness of web applications.

It uses demo web apps to simulate their actions such as adding to-do items.

You visit the app in a browser of choice.

I tried Chrome and Safari for this.

And you execute the automated test

that runs through a collection of applications built with some of the more popular UI frameworks like Angular, React, Ember, even Vanilla.js and jQuery.

And a whole bunch more.

And then this test reports the results as a single number when it's done.

This number represents the runs per minute.

And the higher the better.

When it was finished, I found that it had significantly more iterations on the M1.

Safari had the best results, even going off the scale, and Chrome did pretty well too.

I also ran some JavaScript benchmarks in a node environment and found a pretty CPU-intensive algorithm called

Fancook Redux that's implemented in JavaScript and created for Benchmarks Game, a website that collects algorithms and tests in different languages.

And while my 16-inch MacBook Pro with the Intel Core i9 processor did beat the MacBook Air with the M1 chip, it really didn't do so by a lot.

And when you consider the price differences between the machines, and the fact that the M1 stayed cool throughout the test, and that the battery hardly even took a hit on the M1,

you might be thinking whether the extra few seconds saved

while running this benchmark on the Intel i9 is really worth the money.

So running benchmarks is often very telling, but it doesn't necessarily line up with real-world scenarios.

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So that's why I also like to conduct my own tests, whether using my own projects or other open-source projects that are out there and that commenters send to me.

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And here's what I found.

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I ran a build of the official NativeScript plugins repository, which is a project based on NX workspaces.

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If you're not familiar with NX workspaces, it allows you to scale large JavaScript and other tech stack projects, and you can see the NX quick start right here on this channel.

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In my test, the build that took about three minutes on each machine only differed in build time by tens of seconds,

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with the M1 MacBook Air beating out the Intel MacBook Pro two out of three times.

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So let's talk about what dev stacks benefit the most from the new Apple Silicon chips at this time.

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For JavaScript developers, the benefits are already visible.

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However, if you're building mobile apps for iOS or compiling C++ code, then this is where you'll see a 40 to 50% improvement in build times.

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I ran a few Xcode builds and Swift builds, and I did some C++ algorithms and built OpenCV and WebKit.

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In all those tests, the M1 came out on top.

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So what DevStacks benefit the least?

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So far in my own testing, any builds that have to do with running natively built software

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and building using native tooling has absolutely destroyed Intel in speed and battery performance,

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native being compiled for the Apple Silicon architecture.

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Even when running some software via Apple's Rosetta,

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that's the translation layer that allows you to run Intel and AMD-based x64 and x86 programs on the new Apple hardware,

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even some of those software packages ran better than on Intel.

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And that's just amazing.

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But there are workflows that aren't ready yet, in my opinion.

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If you are an Android developer, Android Studio and official Android emulators work on Apple Silicon,

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but they're currently using Rosetta for translations.

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And while Rosetta is generally pretty good at running x86 targeted code on ARM chips, it's not enough for the CPU-hungry Android workflows,

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and I found the results to be not very usable at the moment.

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The latest version of .NET, which is version 5 right now, isn't fully supported to run on ARM.

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Simple console applications ran just fine for me, but testing any web workflows like ASP.NET Core doesn't work at all yet.

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Now, it's expected that by the end of this year, .NET 6 will come out.

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It's in alpha right now, and we'll have full support for ARM.

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Until then, if you need to develop .NET apps, I suggest using a PC with Windows.

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Oh, and if you think you can use a virtual Windows machine, I've tested this as well.

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Parallels is the only vendor that currently supports the M1 chip to create a virtual Windows environment,

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but the issue is that the Windows guest operating system for ARM is still quite immature and needs a lot of work.

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Also, Visual Studio 2019, the latest Visual Studio offering from Microsoft, is unfortunately not compatible with ARM at all,

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and in my test is not stable, even using the built-in Windows translation of x64 software to run on ARM hardware.

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For game developers that use Unity, I'm pleased to say that it works surprisingly well on Apple Silicon via Rosetta.

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However, it's not as performant as running natively on x86.

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But by the time you watch this video, Unity might have a version natively compatible with M1, which I know they're working hard to get out as soon as possible.

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Overall, Apple Silicon has really put a boost into many workflows for developers and other professions, and eventually even gaming.

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And with the M1 tests, we're seeing the improvements with just their entry-level machines.

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The upcoming second and third generation Apple Silicon machines will be even more performant and I can't wait to see that.

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I think it's really going to help us as developers move to the next level

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and I think it's just going to lift up the entire industry.

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To follow the updates on the new Apple Silicon chips as they relate to us, developers, as well as seeing me conduct these tests that I've been discussing here,

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consider subscribing to my channel as well.

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The link is down below.

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I'm going to wrap it up there.

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Thanks to Fireship for hosting me to share this amazing new world that we're about to Enter as developers.

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為何這個影片是絕佳的口語練習素材

這個講解CPU工作原理的影片語速適中，邏輯清晰，涵蓋科技類詞彙與日常表達的結合，非常適合雅思口語練習。其內容既有"中央處理器就像汽車引擎"這樣的比喻句，也有"指令週期分為四步"的說明性語句，能幫助學習者熟悉不同場景下的英語表達。同時，影片中對專業術語的解釋通俗易懂，比如將"系統級晶片（SOC）"比作"冰箱裡的三明治食材"，這種生動的描述方式有助於提升口語表達的豐富性，是英語影子跟讀的優質素材。

拆解自然表達的關鍵句子

"The central processing unit, or CPU, is like the engine in your car, or the brain in your skull." 這句話使用了兩個比喻，"like"的重複讓表達更自然，避免單調。影子跟讀時需注意語調的起伏，突出比喻的形象性。
"Gamers will sometimes overclock their CPUs to gain more performance at the cost of higher temperatures and a lower life expectancy." 句中"at the cost of"連線利弊，邏輯嚴密。練習時要注意"overclock"等專業詞彙的發音，以及整體語速的把控。
"This kind of efficiency can be found in the new Apple Silicon chips because they aren't just a new processor." 因果關係清晰，"because"前的停頓的自然。通過shadowspeak練習，能更好地掌握英語中因果句的節奏。

簡單有效的影子跟讀練習流程

首先，選擇影片中1-2分鐘的片段，播放一句後暫停，立即跟讀並錄音。重點關注連讀（如"a set of"）、弱讀（如"the"）和語調變化。完成後對比錄音與原音，糾正發音和節奏。重複練習3-5次後，嘗試無暫停跟讀，提升反應速度。這種shadow speak方法能有效鍛鍊口語的流利度和語感，尤其適合科技類話題的表達訓練。堅持每天15分鐘，可顯著提升英語口語的自然度。

影片中的語法

說話人最常用的結構，並附上影片中的原話：

結構	影片中的用法
現在完成進行時 have/has been + -ing — 以前開始、現在仍在繼續的動作	have been beating · I've been doing · has been showing
被動語態 be + 過去分詞 — 強調發生了什麼，而不是誰做的	will be executed · can be flipped · is synchronized
現在完成時 have/has + 過去分詞 — 過去發生但與現在仍有關聯的事	I've invited · have been · I've thrown
定語從句 who / which + 從句 — 補充說明人或事物	Speedometer, which is · repository, which is · NET, which is

什麼是跟讀法？

跟讀法 (Shadowing) 是一種有科學依據的語言學習技巧，最初開發用於專業口譯員的培訓，並由多語言者Alexander Arguelles博士普及。這個方法簡單而強大：您在聽英語母語原聲的同時立即大聲重複——就像是一個延遲1-2秒緊跟說話者的影子。與被動聽力或語法練習不同，跟讀法強迫您的大腦和口腔肌肉同時處理並模模擬實的講話模式。研究表明它能顯著提高發音準確性，語調，節奏，連讀，聽力理解和口語流利度——使其成為雅思口語備考和真實英語交流最有效的方法之一。

影子跟讀法: 閱讀完整分步指南 →

跟讀練習: How a CPU Works in 100 Seconds // Apple Silicon M1 vs Intel i9 - 通過影片學習英語口語

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為何這個影片是絕佳的口語練習素材

拆解自然表達的關鍵句子

簡單有效的影子跟讀練習流程

影片中的語法

什麼是跟讀法？