Shadowing Practice: I've Changed My Opinion On DEVELOPERS Vibe Coding - Learn English Speaking with Video
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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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About This Lesson
In this lesson, you will focus on enhancing your English speaking skills by practicing with a video that discusses "Vibe Coding" and its implications in software development. By engaging with the content, you will learn to articulate your thoughts clearly and effectively while expanding your vocabulary in the context of technology and coding. This exercise is particularly beneficial for improving your pronunciation, intonation, and overall fluency. Get ready to implement the shadowing technique to replicate the speaker's tone and rhythm, which will significantly aid in your English speaking practice.
Key Vocabulary & Phrases
- Vibe Coding: A casual or intuitive approach to coding without a deep understanding of the underlying principles.
- Architectural decisions: Key choices made while designing software that influence its structure and functionality.
- AI models: Artificial intelligence systems designed to generate or assist in coding tasks.
- Hallucination: In the context of AI, an inaccurate or nonsensical output generated by the model.
- Spec files: Documents that outline the functionality and specifications of a software project.
- Context management: The practice of maintaining relevant information to guide AI models towards desired outcomes.
- Daily workflow: The routine tasks or processes a person engages in regularly.
Practice Tips
To maximize your learning from this video, consider using the shadow speak method, where you mimic the speaker's voice, pace, and pronunciation. This specific video has a moderate tempo, ideal for practicing your English pronunciation. Follow these steps for effective engagement:
- Listen Carefully: Play the video at a comfortable speed. Pay close attention to how the speaker articulates each word, particularly technical terms related to coding.
- Repeat Aloud: Use the shadowing technique to repeat phrases immediately after you hear them. This helps reinforce memory and improves your intonation.
- Focus on Key Phrases: Repeat the key vocabulary listed above. Try using them in sentences to deepen your understanding and skills.
- Record Yourself: After practicing, record your own voice as you speak. Compare it to the original to identify areas for improvement.
- Engage with the Content: Discuss the main themes with a partner or in a group setting. This will further enhance your English speaking practice, making you more comfortable with complex vocabulary and concepts.
By following these steps, you will be able to integrate new vocabulary and improve your fluency, significantly contributing to your journey in mastering English.
דקדוק בסרטון הזה
המבנים שהדובר משתמש בהם הכי הרבה, עם המילים המדויקות מהסרטון:
| מבנה | בסרטון |
|---|---|
| הווה מושלם (Present perfect) have/has + past participle — פעולה שקרתה בעבר ועדיין יש לה חשיבות או השפעה עכשיו | I've been · I've said · have changed |
| משפטי זיקה (Relative clauses) who / which + clause — מידע נוסף על אדם או דבר | syntax, which is · ones who can |
| סביל (Passive voice) be + past participle — הדגש הוא על מה שקורה, לא על מי שעושה את הפעולה | was generated · you've been burned |
מהי טכניקת השאדואינג?
שאדואינג היא טכניקת למידת שפה מבוססת מדע שפותחה במקור להכשרת מתורגמנים מקצועיים ופופולרית על ידי הפוליגלוט ד"ר אלכסנדר ארגואלס. השיטה פשוטה אך עוצמתית: אתה מקשיב לאודיו באנגלית ילידית וחוזר עליו בקול רם מיד — כמו צל העוקב אחרי הדובר עם עיכוב של 1–2 שניות בלבד. בניגוד להאזנה פסיבית או תרגילי דקדוק, שאדואינג מכריח את המוח והשרירים בפה לעבד ולשחזר דפוסי דיבור אמיתיים בו זמנית. מחקרים מראים שזה משפר משמעותית את דיוק ההגייה, האינטונציה, הקצב, הדיבור המחובר, הבנת השמיעה ושטף הדיבור — מה שהופך אותו לאחת השיטות היעילות ביותר להכנה לIELTS Speaking ולתקשורת באנגלית בעולם האמיתי.