Практика Shadowing: I've Changed My Opinion On DEVELOPERS Vibe Coding - Изучайте разговорный английский с YouTube
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Hey guys, so I've been pretty vocal about Vibe Coding over the past year or so,
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Hey guys, so I've been pretty vocal about Vibe Coding over the past year or so,
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and most of what I've said has been pretty skeptical.
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Watching people ship apps without understanding a single line of what was generated,
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watching beginners get stuck the moment something broke,
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and not to mention all the influencers pushing the idea
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that you don't really have to know anything about software development to create successful apps and sasses,
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which I'll never agree with.
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However, I have changed my tune a little bit on Vibe Coding in general.
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All right, so before we go any further,
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let's define what Vibe Coding actually is,
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because there's really a spectrum when it comes to coding with AI.
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In fact, in my Coding with AI course,
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I talk about five levels ranging from one-shot prompts with platforms like Lovable to just using autocomplete and VS Code.
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And I think the sweet spot is right in the middle where you're letting the agent write the code,
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but you're making the architectural decisions,
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testing, writing spec files, etc. And that's what I teach in my AI course,
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which I'll have the link for in the description if you're interested.
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But to me, that's not vibe coding.
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Vibe coding is where you're barely looking at the code at all.
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And as the name implies,
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you're kind of going off the vibes,
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not the actual syntax, which is something that I've been totally against in the past.
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However, over the past couple months or so,
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I've been going all in with AI.
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I've created a home lab with eight machines that's managed by my open claw Travis.
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I have agents talking to each other,
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assigning tasks and been creating all kinds of projects,
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mostly things that I can use in my daily workflow.
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So I've gotten a bit,
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I guess, more liberal with just letting AI cook where before I would monitor and spec out every little feature.
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The main reason that I've been able to kind of change my opinion on this is because of the models.
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Opus 4.7 with Claude Code and GPT 5.5 with Codex
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and even OpenClaw with GPT 5.5 are all amazing
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if you understand how to direct these models to get the results that you want.
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So I really don't think that we have to babysit the code as much as we did with,
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for instance, GPT 5.3.
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And I'm seeing much less hallucination.
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And for the most part,
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it does what I want on the first try.
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Now, I know that some of that is because I've learned how to communicate with these models,
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how to manage my context and memory,
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how to map out my documentation and spec files.
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So it doesn't mean that just anyone can can pick up any of these models and build a successful SaaS.
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Which brings me to my main point,
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I don't think vibe coding is okay under any circumstance if you don't have a foundation in software development and architecture.
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So for instance, my mother,
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who basically just only knows how to use Facebook,
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will never be able to create a successful SaaS,
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no matter how amazing the model gets,
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unless she decides to learn software development,
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which I know she's not.
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And that's one thing I won't budge on and
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if I do I want you guys to hold me to it
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and I've seen this play out a hundred times you know
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somebody with no real dev background uses an AI tool to build something in fact my accountant just did this
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and told me about it
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and had this exact experience they get version one in an afternoon
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and they're thrilled version two is hotter version three breaks something in version one
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and they don't know why and they ask the model to fix it
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and the model makes it worse in a way that they can't see.
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And now there are three layers deep in fixes that don't address the actual problem.
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And the code base has architectural choices that nobody actually made on purpose.
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And they can't even describe the bug clearly enough for the model to help.
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So this is not a tooling problem.
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It's a knowledge problem.
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The user can't form the right mental model of what's happening because they never built the prerequisite intuition.
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And no matter how good that model is,
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it can only meet you where you are.
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So even if it's smart enough to fix what's wrong,
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you don't know where to tell it to look.
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And even if it knows where to look and finds it on its own,
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you can't understand the solution so that it doesn't happen again.
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And that's only part of it, right?
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There's a lot of technical skill that you need outside of just actual coding syntax.
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Imagine vibe coding if you didn't understand Git or version control or didn't even know what it was.
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If you didn't know how database tables work,
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If you don't know how to set an environment variable,
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you know, you don't even have the vocabulary to Vibecode.
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And the agent is, it's going to be asking you stuff and asking you to make decisions that you don't understand.
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And I haven't even mentioned deployment, maintenance, scaling.
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That's all stuff that, that you need to know.
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So with that said, you don't have to be a 20 year veteran software developer to use AI.
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I think that people get frustrated with us sometimes because we say learn the fundamentals,
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build a foundation, but what does that actually mean?
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So foundation, at least in terms of a web development context,
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it means understanding how the web works,
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requests, responses, status codes, what happens between the browser and the server.
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It means understanding data, how to model it,
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how relationships work, what an index is it means
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that you've written enough code by hand that you can read code and see it and not just skim past it.
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And it means that you've been burned by enough bugs to recognize bad patterns.
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You don't need a CS degree.
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You don't need to memorize sorting algorithms or be a genius,
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but you need to have built things from scratch,
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debug them, broken them, fix them there's no shortcut to to
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that pattern recognition and the model can't give
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that to you the model can only really amplify what's already there
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so with that said what should beginners do i wouldn't say stop using these tools these ai tools
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that ship has sailed and honestly fighting it is is the wrong move in my opinion
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but change what you're using them for use them to learn faster not skip learning.
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AI is actually a really incredible tool for learning how to code and I don't feel like that's talked about enough.
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In fact, me and my team are building on working on building a platform that merges AI with coding lessons.
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But you can have the model explain the code that it generates,
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type it out yourself, you know,
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break it on purpose and try to figure out why it broke.
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And the developers who are going to do well over the
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next few years are not the ones who can prompt the best.
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They're the ones who can prompt well and read code and design systems and debug under pressure.
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The AI handles the typing.
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You still have to handle the thinking.
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And this is mainly towards beginners.
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Back to vibe coding, once you get past that learning stage, vibe away.
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However, don't make it the only way that you create
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because you're going to forget a lot and depend on it too much.
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So I'm not against vibe coding anymore.
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I just think that it should be a tool,
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not your entire workflow for every project.
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В этом уроке вы будете развивать свои навыки разговорного английского языка, практикуя тени с видео о программировании. Вы узнаете о концепции "Vibe Coding" и получите представление о том, как искусственный интеллект влияет на разработку программного обеспечения. Это видео позволит вам не только расширить словарный запас, но и улучшить произношение английского, что является важным аспектом изучения языка.
Ключевая лексика и фразы
- Vibe Coding – концепция кодирования, основанная на интуитивном подходе, а не на строгом следовании синтаксису.
- искусственный интеллект (AI) – технологии, которые способны выполнять задачи, требующие умственных усилий, такие как создание кода.
- архитектурные решения – важные выборы, касающиеся структуры и дизайна программного обеспечения.
- первый опыт – начальный этап работы с новыми технологиями или концепциями.
- документация – письменные материалы, которые объясняют, как использовать или разрабатывать определенное программное обеспечение.
- модели – алгоритмы, которые можно обучать для получения желаемых результатов, такие как GPT.
- ужасные иллюзии – ошибки, происходящие в процессе работы с искусственным интеллектом, которые могут привести к неправильным выводам.
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