Luyện nói tiếng Anh bằng Shadowing qua video: Nobody is hiring vibe coders. Sorry
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Coding interviews are dead.
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You don't need to learn how to code anymore.
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At least that's what your social media feed keeps telling you.
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But here's what the data actually says.
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Since 2023, technical interviews have nearly doubled in the US and they're becoming more challenging than ever, not easier.
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So what's going on here?
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Because getting this wrong either way can cost you.
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Either you keep grinding LeetCode style interviews and prep for an interview that no longer exists,
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or you misread the industry and walk away from a career that is very much still hiring.
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So let's ignore what people are saying and look at what companies actually do whenever their money is on the line,
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because the facts tell a completely different story from what you're hearing.
vì những sự thật đang kể một câu chuyện hoàn toàn khác với những gì bạn đang nghe.
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So a few months ago, I was actually going through my own interview process and I interviewed with 11 different companies.
Vài tháng trước, tôi cũng đang trong quá trình phỏng vấn xin việc và tôi đã phỏng vấn với 11 công ty khác nhau.
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Every single one of them that I got far enough with in the process went to the technical interview.
Tất cả những ứng viên mà tôi đã đi đủ xa trong quy trình tuyển dụng đều được tham gia vòng phỏng vấn kỹ thuật.
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These were a mix of coding interview challenges, system design questions, and anything that was related to whatever that company's specific niche was.
Đó là sự kết hợp của các bài toán phỏng vấn lập trình, câu hỏi thiết kế hệ thống và mọi thứ liên quan đến lĩnh vực chuyên môn cụ thể của công ty đó.
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So if it was database, I got a lot of database questions and so on.
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Now, two of these were actually the classic solve Sudoku problem.
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If you've ever went through that technical interview process, you might have seen this.
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This is just where you have to solve a Sudoku board.
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essentially just make sure that the board is either valid or not.
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Now, maybe I just got unlucky and I picked the 11 most old school companies in the industry, but that's not what the data actually shows.
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According to CoderPad, this is the platform where a lot of these companies actually conduct these interviews.
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Technical assessments are up 48% globally since mid-2023,
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and 43% of those companies still conduct the classic algorithmic assessments.
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Now, the rest didn't drop the technical bar they moved it to system design interview questions pair programming
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and more of real world debugging situations
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but okay maybe these are just the companies
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that are slow to adapt enterprises
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that are still stuck in 2019 interviewing like the ai wave never happened i guess
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that could be a fair objection but the issue here is
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that the strictest interviews are actually conducted by the companies building these AI tools. For example,
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OpenAI's most commonly reported interview question is implementing an LRU cache from scratch live as you explain your reasoning through the process.
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An LRU cache is just a hash map plus a linked list.
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The exact obsolete fundamentals your feed keeps telling you are now irrelevant.
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Anthropic, on the other hand, publishes its official policy for candidates and for live interviews, it says on it, no AI assistance unless we indicate otherwise.
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We're curious to see how you think and how you reason through problems in real time.
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And Meta went the other way entirely.
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They built a round where you're actually encouraged to use AI.
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And the accounts coming out of these say that this round is harder than the classic one.
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The companies that are closest to the models, the people who know exactly what AI can and cannot do, are the ones testing the hardest.
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Why do you think that is?
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Well, before I answer that, since this video doesn't have a sponsor, I want to show you something that Brad and I have been working on.
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If you've been following this channel for some time, you've probably seen our 20-hour long video courses, whether that's on Udemy or our website.
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And that's how hundreds of thousands of you have learned.
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And we're still very proud of those courses and we'll always continue to make those.
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But we realized we need a different approach for those of you that like to be hands-on.
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And that's why we launched Start.dev.
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It's an interactive learning platform where lessons are bite-sized with an in-browser editor.
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So you can start writing code today.
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So if you're looking to learn about the tools and languages that are most in demand in the AI era, like Python and TypeScript, or you wanna learn about web development,
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I highly recommend you check it out.
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Each course has an AI version of its instructor.
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So for example, you get AI Brad in each lesson with you.
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He knows what you're working on.
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He sees your code and he's designed to give you leading answers, not some long-winded chat GPT responses that knock you out of your flow.
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And yes, there's an AI version of me too.
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And no, you can't make me say weird stuff people have already tried.
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There's thousands of students on the platform already and absolutely loving it, so it's free to get started, but if you want 20% off premium access,
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use the coupon code down in the video description.
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Okay, so why do AI companies test the hardest?
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Well, it comes down to what everyone got wrong about interviews in the first place.
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The people
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that bought this AI killed coding interviews narrative have a fundamental
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misunderstanding of what the technical interview was even there for and really what the job of software developer actually requires.
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The thinking goes that if AI writes the code, now we're somehow out of the loop.
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As if writing the code was our job all along.
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Well, the interview was never about the code either, and honestly, I'm a little bit shocked that we even have to say this, but here we are.
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It wasn't about the syntax.
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In fact, lots of interviewers let you choose the language
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that you test in because that was never the part that actually matter to begin with.
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What they're grading you on is your judgment behind every solution.
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Can you think through a problem?
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Why this decision over that decision?
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And what are the trade-offs and alternatives?
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Where does this problem break at scale?
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Now, a large chunk of the interviews
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that I've taken were usually some kind of system design where I'm implementing some kind of different pattern
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or working through a larger idea.
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And most of the interviews I've taken weren't even coding interviews at all.
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I would say probably less than 50% of them were.
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And most of them were actually just some kind of scratch pad or a situational walkthrough on a whiteboard.
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And on the other side where I've interviewed a lot of candidates in my days, the best interviews I've ever conducted were not the candidates that gave me the answer,
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but the ones that had a great conversation with me through their process, walked me through all their solutions and ideas.
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And the way they reasoned told me
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that they would really be able to add value to my team and allowed me to see really what's under the hood, not that they just memorize something.
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There's a funny thing happening in Meta's AI assisted interview round where candidates keep claiming
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that somehow the models are dumber during the interview than they were the day before when they were practicing.
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Same models, same prompts, and all of a sudden it's writing buggy code and it's not pointing out obvious mistakes.
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Now, it's not confirmed by Meta that they purposely make their models dumber, but honestly, it wouldn't surprise me.
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It's a great way to watch where a candidate catches obvious issues and where they just glaze over them.
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So imagine thinking that AI somehow ended all of this and nobody was going to ask you to explain what you built.
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Exposing someone has never been easier.
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A few follow-up questions and it's over.
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Now, I'm not gonna tell you that nothing has changed because things are changing, just not in the direction that a lot of us think.
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Developers have been complaining about interviews since long before AI.
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They've been claiming that things are too abstract, too much memorization and nothing like the real job.
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And for many years, really nothing has moved but because of AI, it's actually forcing companies to really evaluate this process and change things up.
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Companies are starting to swap single function puzzles for more real world simulations.
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They're challenging candidates on multifile code bases and adding more realistic debugging scenarios and working inside of existing code bases
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instead of greenfield toy projects.
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Let's invert this binary tree and more.
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Here's our code, make it better.
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That's much closer to the actual day-to-day engineering work while still leaning on the deep-rooted concepts that you needed to know before.
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Now, this is really the first change in years that I would call progress when it comes to the interview process.
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But here's where the industry is split.
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Nobody agrees on how much AI should be allowed.
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Roughly a third of the companies still ban it entirely, half allow it, and the rest decide on a case-by-case basis.
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And where it is allowed, I've seen companies ask you for your prompt history, grill you deeply on the take-home projects that they give you,
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and then have you extend it live on a call.
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So no matter where a company lands on this, whether it's partial AI, full AI, or somewhere in between, there's still no version where knowing less is a winning move.
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The pattern of interviews has been evolving over the past 70 years.
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Think of it as a stack.
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At the bottom, the mechanical stuff.
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Typing out working code.
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In the middle, we have implementation, picking an approach and making it work.
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And at the top, we have the judgment.
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This is the trade-offs, catching what's wrong and defending our decisions.
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Producing working code used to prove the layers above it.
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You couldn't get something working without understanding the layers underneath it.
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Every era of tooling has always deleted the bottom of the stack and just moved the test upwards.
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For example, compilers deleted assembly, frameworks deleted boilerplate, and nobody is interviewing you on memory management for a React job.
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AI is deleting yet another layer, which is producing the actual code, so the test is just moving up another layer.
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Now, as AI adoption grows, we're gonna see more companies adjust their interviews to include it and focus less on producing the actual code.
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But just because we may be writing less code by hand doesn't mean
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that the bar dropped the bar just moved up the stack.
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And this is the part that really gets me upset whenever I see this rhetoric online.
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AI bros are telling beginners they don't need to learn how to code, that knowledge is obsolete, they just need to prompt harder.
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That's like telling a pilot that they can skip flight school because we have autopilot.
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Autopilot is great, but it's also not the reason why anybody gets to sit in that seat.
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Nobody is hiring vibe coders.
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And the market is telling you this with data.
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74% of developers are saying they're struggling to land a job, while 78% of tech leaders say they can't find qualified people.
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Now, both are true at once.
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The jobs exist, but qualified people are getting harder to find.
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We're about to experience the biggest demand for developers probably ever, and yet the public messaging is manufacturing the biggest shortage of qualified developers.
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Now, on the other side, we have the real engineers don't touch AI crowd.
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Well, I'm going to say something that will upset a few of you, but outside of some very specific use cases, I would not hire a developer that refuses to leverage AI tooling.
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And there's also common consensus out there amongst hiring managers that agree with me.
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The data is somewhere out there.
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I didn't pull it up for this video, but look it up.
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This is a common belief here.
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And at this point, it's really just getting irresponsible not to use AI and the pushback against it, which I can understand there's been a whole pendulum
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that swung the wrong way it's getting to the point where
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even the resistance against it is now a problem now i want to give you a quick example here
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so back in 2017 i hired my first developer to work on this project
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that i built out it scaled up
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and it was time to add some team members now this guy was good
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but he refused to move out of the php stack
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and would always complain about the frameworks that i was using his claim was
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that real developers should be able to write their own web server from scratch and that frameworks like Django and React, well, those were just for noobs.
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Imagine holding that belief today.
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I think we're past the AI is just a tool phase and really need to adapt here.
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You're not only gonna be graded on your ability in the future, you're also gonna be graded on your AI fluency as well.
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Now, I don't make the rules.
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That's just where the market is at right now and it's probably where we're going in the future.
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So to close this video out, no coding interviews aren't dead.
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There are more of them now than ever, and they're testing the same thing they've always tested.
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Whether you know what you're doing or not, AI didn't change that.
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So keep the fundamentals, get good with the tools, and understand everything you build.
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Because the developers that are doing both right now aren't scared.
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They're thriving.
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Từ vựng và ghi chú luyện nói cho bài học này
Bài luyện nói trình độ B2 này dựa trên video “Nobody is hiring vibe coders. Sorry”. Những từ được nhắc lại nhiều nhất trong bài: interview, code, companies, developer. Video này có 160 câu và 2257 từ để luyện shadowing. Phần lời nói dài 12:25. Người nói nói nhanh, khoảng 182 từ mỗi phút, nên sẽ có nhiều chỗ nối âm và nuốt âm. 83% số từ nằm trong 3.000 từ tiếng Anh thông dụng nhất; phần còn lại nên xem trước khi luyện.
Từ vựng quan trọng trong video
15 từ khó nhất trong video, kèm phiên âm và nghĩa:
| Từ | Phiên âm | Nghĩa |
|---|---|---|
| developer danh từ | /dɪˈvɛləpɚ/ | nhà phát triển |
| stack danh từ | /stæk/ | chồng |
| scratch động từ | /skɹæt͡ʃ/ | gãi |
| debug động từ | /diːˈbʌɡ/ | gỡ lỗi |
| cache danh từ | /kæʃ/ | bộ nhớ đệm, vùng nhớ đệm |
| obsolete tính từ | /ˈɑbsəlit/ | lỗi thời, mai một |
| implement động từ | /ˈɪmpləmənt/ | thực hiện, chấp hành |
| compiler danh từ | /kəmˈpaɪlɚ/ | biên dịch viên |
| fluency danh từ | /ˈfluːənsi/ | sự thành thạo |
| memorize động từ | /ˈmɛm.əˌɹaɪ̯z/ | ghi nhớ, ghi chép |
| pendulum danh từ | /ˈpɛnd͡ʒələm/ | quả lắc, con lắc |
| preposition danh từ | /ˌpɹɛpəˈzɪʃən/ | giới từ |
| whiteboard danh từ | /ˈwaɪtbɔɹd/ | bảng trắng |
| beginner danh từ | /bəˈɡɪnɚ/ | người bắt đầu, người mới học |
| syntax danh từ | /ˈsɪn.tæks/ | cú pháp |
Những câu đáng luyện lại
Các câu ngắn, trọn ý trong video mà bạn có thể dùng lại khi giao tiếp hằng ngày:
- You don't need to learn how to code anymore.
- Why do you think that is?
- He knows what you're working on.
- Can you think through a problem?
- Let's invert this binary tree and more.
Ngữ pháp trong video
Những cấu trúc người nói dùng nhiều nhất, kèm đúng cụm từ trong video:
| Cấu trúc | Trong video |
|---|---|
| Thì hiện tại hoàn thành tiếp diễn have/has been + -ing — việc bắt đầu từ trước và vẫn đang tiếp tục | have been working · you've been following · have been complaining |
| Thì hiện tại hoàn thành have/has + quá khứ phân từ — việc đã xảy ra nhưng còn liên quan đến hiện tại | you've probably seen · have learned · have already tried |
| Câu bị động be + quá khứ phân từ — nhấn vào việc xảy ra, không phải người làm | are still stuck · are actually conducted · is split |
| Mệnh đề quan hệ who / which + mệnh đề — thêm thông tin về người hoặc vật | people who know · layer, which is |
Phát âm cần chú ý
Người nói dùng 57 dạng rút gọn, ví dụ we're, you're, they're. Hãy nói theo dạng ngắn đúng như bạn nghe.
- Âm “th”: algorithmic /ˌælɡəˈɹɪðmɪk/, anthropic /ænˈθɹɒp.ɪk/, walkthrough /ˈwɔːk.θɹuː/, thrive /θɹajv/, underneath /ˌʌndɚˈniθ/
- Âm “sh” và “zh”: cache /kæʃ/, memorization /ˈmem(ə)ɹaɪˌzeɪʃn̩/, preposition /ˌpɹɛpəˈzɪʃən/, situational /sɪtjuːˈeɪʃənəl/, hash /ˈhæʃ/
- Từ dài — đặt trọng âm cho đúng: developer /dɪˈvɛləpɚ/, autopilot /ˈɔtoʊˌpaɪlət/, algorithmic /ˌælɡəˈɹɪðmɪk/, memorization /ˈmem(ə)ɹaɪˌzeɪʃn̩/, misunderstand /mɪs.ʌn.də(ɹ)ˈstænd/
Những âm người Việt thường gặp khó:
- Cụm phụ âm cuối — đọc đủ từng âm, đừng nuốt âm cuối: prompt /pɹɑmpt/, adapt /əˈdæpt/, implement /ˈɪmpləmənt/, invert /ɪnˈvɜɹt/, misunderstand /mɪs.ʌn.də(ɹ)ˈstænd/
- Âm /tʃ/, /dʒ/ và /ʒ/ — tiếng Việt không có, đừng đọc thành “ch” hay “gi”: scratch /skɹæt͡ʃ/, pendulum /ˈpɛnd͡ʒələm/, chunk /t͡ʃʌŋk/, objection /əbˈd͡ʒɛkʃən/, niche /nɪt͡ʃ/
- Âm /æ/ — mở miệng rộng hơn âm “e”: stack /stæk/, scratch /skɹæt͡ʃ/, cache /kæʃ/, adapt /əˈdæpt/, algorithmic /ˌælɡəˈɹɪðmɪk/
Cách luyện với video này
- Nghe hết video một lần, chưa cần nói, và ghi lại những từ bạn chưa biết.
- Bắt đầu ở tốc độ 0,75×, nói đuổi từng câu, rồi quay lại tốc độ bình thường khi đã quen.
- Ghi âm giọng mình rồi so với bản gốc, chú ý các từ như developer, stack, scratch.
Phương Pháp Shadowing Là Gì?
Shadowing là kỹ thuật học ngôn ngữ có cơ sở khoa học, ban đầu được phát triển cho chương trình đào tạo phiên dịch viên chuyên nghiệp và được phổ biến rộng rãi bởi nhà đa ngôn ngữ học Dr. Alexander Arguelles. Nguyên lý cốt lõi đơn giản nhưng cực kỳ hiệu quả: bạn nghe tiếng Anh của người bản xứ và lặp lại to ngay lập tức — như một "cái bóng" (shadow) đuổi theo người nói với độ trễ chỉ 1–2 giây. Khác với luyện ngữ pháp hay học từ vựng bị động, Shadowing buộc não bộ và cơ miệng phải đồng thời xử lý và tái tạo ngôn ngữ thực tế. Các nghiên cứu khoa học xác nhận phương pháp này cải thiện đáng kể phát âm, ngữ điệu, nhịp điệu, nối âm, kỹ năng nghe và độ lưu loát khi nói — đặc biệt hiệu quả cho người luyện IELTS Speaking và muốn giao tiếp tiếng Anh tự nhiên như người bản ngữ.