跟讀練習: Nobody is hiring vibe coders. Sorry - 通過影片學習英語口語
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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.
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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.
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Every single one of them that I got far enough with in the process went to the technical interview.
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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.
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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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本課的詞彙與口語要點
這節 B2 級別的口語課以影片“Nobody is hiring vibe coders. Sorry”為素材。 影片中反覆出現的詞有：interview, code, companies, developer。 這段影片共有 160 個句子、2257 個單詞可供跟讀。 講話部分時長為 12:25。 說話人語速較快，每分鐘約 182 個詞，會出現較多連讀和弱讀。 83% 的單詞屬於英語最常用的 3,000 詞，其餘的詞建議在練習前先學一下。
影片中的重點詞彙
影片中最難的 15 個單詞，附發音和釋義：
| 單詞 | 發音 | 釋義 |
|---|---|---|
| stack 名詞 | /stæk/ | 堆 |
| scratch 動詞 | /skɹæt͡ʃ/ | 抓, 劃 |
| autopilot 名詞 | /ˈɔtoʊˌpaɪlət/ | 自動駕駛儀 /自動駕駛儀, 自動駕駛 /自動駕駛 |
| debug 動詞 | /diːˈbʌɡ/ | 除錯 /除錯, 除錯 /除錯 |
| cache 名詞 | /kæʃ/ | 快取 /快取, 快取 |
| obsolete 形容詞 | /ˈɑbsəlit/ | 淘汰, 過時 /過時 |
| adapt 動詞 | /əˈdæpt/ | 適應 /適應 |
| implement 動詞 | /ˈɪmpləmənt/ | 實施 /實施, 執行 /執行 |
| compiler 名詞 | /kəmˈpaɪlɚ/ | 編者 |
| fluency 名詞 | /ˈfluːənsi/ | 流暢 /流暢 |
| invert 動詞 | /ɪnˈvɜɹt/ | 反轉, 倒轉 |
| memorize 動詞 | /ˈmɛm.əˌɹaɪ̯z/ | 記住 /記住, 熟記 /熟記 |
| misread 動詞 | /mɪsˈɹiːd/ | 讀錯 /讀錯 |
| misunderstand 動詞 | /mɪs.ʌn.də(ɹ)ˈstænd/ | 誤會 /誤會, 誤解 /誤解 |
| pendulum 名詞 | /ˈpɛnd͡ʒələm/ | 鐘擺 /鐘擺, 擺 /擺 |
影片中出現的短語動詞
| 單詞 | 釋義 |
|---|---|
| go through 動詞 | 通過 /通過 |
| follow up 動詞 | 跟進 /跟進 |
| point out 動詞 | 指, 指出 |
值得反覆跟讀的句子
影片中簡短而完整的句子，可以直接用在日常對話裡：
- 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.
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 working · you've been following · have been complaining |
| 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 | you've probably seen · have learned · have already tried |
| 被動語態 be + 過去分詞 — 強調發生了什麼，而不是誰做的 | are actually conducted · is split · should be allowed |
| 定語從句 who / which + 從句 — 補充說明人或事物 | people who know · layer, which is |
需要注意的發音
說話人用了 57 次縮略和弱讀形式，例如 we're, you're, they're。請按聽到的簡短形式來說。
- “th” 音: algorithmic /ˌælɡəˈɹɪðmɪk/, anthropic /ænˈθɹɒp.ɪk/, walkthrough /ˈwɔːk.θɹuː/, thrive /θɹajv/, underneath /ˌʌndɚˈniθ/
- “sh” 和 “zh” 音: cache /kæʃ/, memorization /ˈmem(ə)ɹaɪˌzeɪʃn̩/, preposition /ˌpɹɛpəˈzɪʃən/, situational /sɪtjuːˈeɪʃənəl/, hash /ˈhæʃ/
- 長單詞——注意重音位置: developer /dɪˈvɛləpɚ/, autopilot /ˈɔtoʊˌpaɪlət/, algorithmic /ˌælɡəˈɹɪðmɪk/, memorization /ˈmem(ə)ɹaɪˌzeɪʃn̩/, misunderstand /mɪs.ʌn.də(ɹ)ˈstænd/
中文母語者容易讀錯的音：
- /v/ — 不要讀成 /w/，上齒輕觸下唇: developer /dɪˈvɛləpɚ/, invert /ɪnˈvɜɹt/, interviewer /ˈɪntɚvjuɚ/, thrive /θɹajv/, evolve /ɪˈvɑlv/
- 詞尾子音 — 要讀清楚，後面不要加母音: stack /stæk/, prompt /pɹɑmpt/, scratch /skɹæt͡ʃ/, autopilot /ˈɔtoʊˌpaɪlət/, debug /diːˈbʌɡ/
- /r/ 和 /l/ 的區別: algorithmic /ˌælɡəˈɹɪðmɪk/, irresponsible /ˌɪɹ.əˈspɑn.sə.bəl/, grill /ɡɹɪl/, leverage /ˈlɛv.(ə.)ɹɪd͡ʒ/, irrelevant /ɪˈɹɛləvənt/
如何用這段影片練習
- 先完整聽一遍影片，不要開口，記下不認識的單詞。
- 先用 0.75 倍速逐句跟讀，熟練之後再回到正常速度。
- 錄下自己的聲音並與原聲對比，特別注意 stack, scratch, autopilot 這類單詞。
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。