跟读练习: We will all be generalists. - 通过视频学习英语口语

In the past, usually those developers who have deeply specialized in just one thing have been the ones paid the most.

So someone who was maybe into native Android performance or someone who specialized on Bluetooth low energy.

But now that output and research have gotten so much cheaper and faster with AI, there's ultimately more time for people to get better at more various things.

So how should we actually position ourselves in future?

First of all, what even makes someone an expert in a field?

Well for me at least it's someone who doesn't just have

the plain knowledge about a certain thing like Bluetooth low energy

but has implemented that in various different environments and gained real world experience that way.

So a BLE expert doesn't just know

which code has to be written in order to connect to a device for example

but they also know things like on which manufacturers there are which kind of quirks, what are edge cases to think of, which use case isn't it good for and why.

So I'm talking about those things you usually can't learn by simply watching a video or so.

Those things where you build something and then find out some environments or devices, it just doesn't work that well on.

Or you need some specific implementations to work around certain things.

And doing so not just for that one type of app, but for various.

So this kind of experience is what someone needs for me to at least consider them an expert.

And of course, if there's now a company having Bluetooth low energy problems, then they will be much better off having someone who spent a lot of time on that, to implement

that in various different environments than just taking let's call it

a general purpose android developer just like you would also prefer

going to a knee surgeon for a knee surgery rather than like a general practitioner

so can ai do

that can ai really implement such super specialized domain knowledge about

again let's take the example bluetooth low energy no ai in the end is trained on publicly available data

and such expert knowledge is of course hard to find anywhere in a structured format.

AI can build the happy path pretty well

but struggles at the edge cases especially when it's about such niche things like Bluetooth.

So AI cannot solely substitute the knowledge and the experience a real specialized expert has.

But still I think the level of expertise we as developers will have will become broader in future.

The thing is this while AI can't give you the specialized experience it makes gaining that significantly faster and easier.

You in the end still have to make this experience yourself

but this process of making it is just a lot faster

because first of all you can just get to a first working prototype a lot faster

so you can get this prototype on lots of devices faster

which makes you collect real world data on that prototype faster

and based on this real world data you can then again iterate on that prototype faster.

You can improve it fast.

You can again collect new real world data faster.

It just all got a lot faster nowadays to brainstorm how you could approach a problem

or a fix and all of that is what AI enables.

So what this ultimately means is, while you still have to do the same things to become really good at one niche thing, this process overall has strongly accelerated,

which means you have overall more time available to spend on other things, or in other words, more time to become an expert in other niches. And

that is why I do think the focus is shifting away

from being super duper great at one thing towards being super duper great at many things.

In times of AI, you're not special anymore, knowing a programming language

or knowing how to make a rest request that's simply not enough anymore to get a job

but something that any ai nowadays does very well in a minute

so our focus has to shift to a higher level and

that is knowing which tool to pick for which job

and this will be relevant across domains your main thing may be native android development

but just spending time on this one isolated domain is likely also not going to cut it anymore in future

but you will need surrounding knowledge so knowing okay

when would it be better to build the app non -native

for example how does the api design need to look like

to fit to the app what should your ci pipeline consist

of what is really the goal of the business behind the

app how we can improve this business purpose where does a more complex solution pay off

and where is it overkill maybe some kind of back -end

system design being able to design this whole thing not just the android app

but also the system that it connects to

so the bar to switch to related field

that is fundamentally different in implementation details

but very comparable on a higher level has just gotten

so much lower for example

if before this era of ai you are a native android developer

and you would feel well in this domain building android applications

in kotlin then you wouldn't just be able to open xcode

and start building an ios app some years ago

but now now it's possible you technically don't have to know

how to make a network call in swift anymore you don't

have to know how to set up a database table on iOS.

The AI does that for you

and as long as you know what a good network call in general looks like

and what a good database schema looks like you can also understand most of the code generated.

Just like you probably couldn't write a for loop in every

single programming language out there you would probably be able to understand a for loop in any language when seeing it.

It would likely be more difficult to build a stable backend as a mobile device

because it's a very different set of system design challenges

but an iOS app for example That comes with the same system design challenges we also have on Android.

Just the OS restrictions may be a little bit different here.

And I think this is really a big and important thing.

We as possibly native Android developers have to realize

that the exact look of low -level implementation details is becoming less and less important.

It won't matter

so much anymore how exactly a function is implemented as long as it does the job and is properly integrated, of course, into your architecture.

And as long as it fulfills the performance requirements you may have.

But whether a for loop is implemented or whether a while loop is used, how some variables are named inside, how it exactly works, that's not going to matter much anymore.

I really think we will ultimately all be software architects.

So we have to know how we build the house in a way

that we can put more and more floors on it, how we build it so it's not cold in winter.

But we don't have to know in detail anymore how we really put bricks on top of each other.

And that, by the way, is also a big question for me.

So what kind of content do I want to make in future?

And I think to some degree, I will always be the Android guy here.

And that's how most of you see me.

And it's also really something I love doing.

I love Android.

I love mobile development.

I love Kotlin.

But I also really see the value in gaining knowledge across domains.

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Because it just has gotten so much faster, so much easier for us, mobile developers to maybe build an iOS app, a native one, or a website, for example, which is also just a client -based app.

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And the ideas behind that are the same.

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We have different frameworks to build the UI, different libraries, but in the end, it's a client application, it's making network calls, has maybe real -time communication, maybe has some kind of local storage.

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Yes, it runs in a browser, that makes it a bit different, but all in all, it's very comparable.

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So I'd actually love to cover a few topics here and there outside of the Android ecosystem, but really have no idea if that's something you would also want to do and which direction you find interesting.

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For example, I always found backend interesting.

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I found web development in general interesting, but also native iOS.

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I kind of found everything interesting, but I also to some degree need to pick something.

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So let me propose the following.

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If you feel like, hey, that would be super cool to learn topic XYZ from Philip, please take a look in this video's comments if someone else already proposed that.

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If that is the case, you simply give them an upvote so I can really see what most of you want to see.

111

If there is no such comment yet, then you just add a new comment so other people can upvote that.

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That would really help.

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This doesn't mean in any way that I will stop Android also.

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Please don't think that.

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I will keep doing primarily Android videos or mobile development content.

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But I feel like I also need to adapt a little bit.

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Because I'm not sure if in future it's enough to just be an Android guy.

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So please let me know below what you would like to learn.

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Thanks so much.

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See you back in the next video.

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Have an amazing rest of your week.

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Bye bye.

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We'll see you next time.

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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。

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