跟讀練習: Can This Vibe Coder Beat a Senior Developer? - 通過影片學習英語口語

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Can AI make a Vibe Coder beat a senior engineer?

We locked a Vibe Coder, a junior dev, and a senior dev in one room.

One challenge.

Unlimited AI. Who wins?

Hey everyone, I'm Sudarshan and I'm the Vibe Coder.

Hi, my name is Ocean.

I'm the junior developer for today.

Hi everyone, my name is Ankush Dharkar.

I'm a senior developer.

For the past decade, I've been building high-scale systems.

Build an AI agent that reads Hackerang support tickets and decides to reply.

Or escalate it to a human.

Do I get a job here if I build this?

Can I build this for Claude instead?

Because I think they need support more.

I actually lucked out because Opus 4.8 launched last night.

So I just used the best in class model.

I never use Claude.

I do not trust Claude.

It's just too expensive.

High maintenance is not for me.

Sorry.

I have Claude code and I have God, so that works.

Time actually started.

The second loop.

This is the code base which has four things the hacker rank support tickets they need to sort, a help center they pull answers from for tickets,

a starter file written in Python where they build the agent, and an agent's .md, a guide that gives the AI its rules.

Right now, the scaffold has a Python file in it.

I'm not really comfortable working with Python, so I'll probably move to JavaScript or TypeScript, TypeScript hopefully, so that I can also read the code in case I have to verify something.

And then I have a bunch of things here in the GitHub repository, which let's be real, I don't think I'm going to read any of this.

Time to put all of this directly into Claude.

So definitely there has to be some P0s, like undeniable for the product.

I want to make sure the AI gets that.

If AI doesn't get that, we have a bigger problem.

I am still nervous because it's again a huge project in such less time.

And I also have to keep the UI in mind.

I did test two different intras.

One is where there would be multiple LLM calls for each ticket.

But the other one would be where it would sort of upload in batches

or it would process in batches and they would be async sort of processing.

There's a whole listing of all these tickets that's been raised to the agent.

I have a very extensive test suite.

Also built, like I said, open routers because, you know, in early stage, we want to get feedback quickly, but at the same time, have something we can ship if we wanted to.

I'm the most worried about the junior developer.

Why?

I think they bring the energy and so much experience with AI tools that they'll crush it.

Only escalate 20% of the issues.

I am now sort of doubting and figuring out if that actually the right implementation.

Awesome.

So we do have the first version ready here.

It was able to index all of the support tickets we have.

It's also able to retrieve it.

But the UI is, at least as of now, looks like it's up and working without any errors.

So I'll probably take that as a win because we only spent about 45 minutes.

Every engineer was locked in until the product manager walked in with his ideas.

Can you explain what you've made?

So I essentially have a support rat agent.

I'm calling it hand because it can go through a ticket batch.

We should do a toggle, dark mode and light mode.

Sure.

Call it the colorblind mode.

So the product manager was here a while ago

and he asked me to build the dark mode

and light mode toggle based on the time of the day and I tried building it.

It sort of messed up my code altogether and now I'm just trying to like get it fixed.

My infra just suddenly seems a bit flaky.

So, look at that dude.

Basically it creates wireframes.

So right now I'm just building a few AI mockups so

that I can refer to this like on a high level

design how I should connect the data to the UI as well.

Hello, what's up?

What is Rag?

Rag is the model that, you know, segregates whether the ticket should be assigned.

I don't think you know what it means.

I forgot the filter.

I'm going to launch different sessions.

One will build the UI which will have its own test.

One can just be an agent that goes through, creates the RAD, creates the agent pipeline.

And then one can be a simple backend server that connects those two.

It's a flat architecture.

You understand, right?

What I'm talking about.

Now, start it.

Okay, now put all the same color together.

You fended off a villain, a product manager, here's your reward, choose one lifeline.

Phone a dev, 10 minutes on the phone with a DL engineer, I'd rather talk to Kursar.

Borrowed time, plus 15 minutes at the end, a diet coke, you get one diet coke.

I'll take the borrowed time, plus 15 minutes at the end.

I think I don't need to phone a dev, I have Claude Code and I have God so I think I'll go with two, borrowed time.

I think I'll phone a dev.

Wait, I'm the only one who's gonna phone a dev?

Yeah.

I know.

That's such a junior engineer thing to do.

Meet Hank.

Hank is Hackerank's support agent that would be able to cover any kind of support tasks by itself.

You can either ask it a particular question that you have or you can also attach a CSV.

I think on the longer run, Alvin, because they'll exhaust their tokens.

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Hello, yeah we have a problem.

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My tokens are over so I would need more.

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Yes, thank you.

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I'll have to lock in the last one hour.

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So what you do is whenever you want to repeat something right in your API, there are two ways to do it.

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Right, so what do you do?

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My whole approach is wrong, as usual.

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At this stage, this end of the competition, I think I'm expecting all of them to at least have finished a demonstrable product

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which at least starts processing the tickets and triages them.

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The quality of triage may not be great but that is always something that can be improved upon later.

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Hey, you have 10 minutes to go.

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Do you still want that extra 15?

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No, I think I'm good.

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I would rather take a 2 rupee office.

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Is that Picasso?

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Oh, that's one of the best paintings I've seen too.

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So, this is the basic UI of what we built

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so far in the last few hours gave it a pretty

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hacker ranky feel the same kind of theme same kind of colors

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and stuff as you can see we have a list of

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all the tickets here gave user a simple control to run

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it this can be automated as well it's a pretty simple

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fix we have a place where all the triage tickets go

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and i also created a stats for nerd kind of a page

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so that if you want to dive deeper into what caused certain answers to be escalated

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or to be replied you can take a look there let's see how it performs even

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if it fails that's okay i can live with it

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because i know the way this is built there will be

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minor tweaks to be done to raise it to the bar of a production application.

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The front end is pretty much just made out of HTML that is again like grabbed in a Python file, the main file of Python.

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You can search them up, you can see categorization based on the type of request it is or the outcome and then you can see the tickets,

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you can see all the categories again and once you click on

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that row you get into the ticket summary where you can see everything the UI

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and base this agent was completely working on the UI and fixing up cosmetic issues.

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The agent workspace is working completely on the agent, the rag model, the fast API, and also building that scaffold for LLM to answer the user.

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Today I've built Hank.

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Hank is an AI agent that goes through Hackerang support tickets

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and it would sort of come up with a grounded reply on any sort of support ticket

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if it can sort of resolve by itself.

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If not, it would immediately flag it for human review.

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You can ask Hank anything to begin with.

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Hank would be able to process all of them at once.

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Let's go ahead and maybe attach a CSV of a bunch of support tickets.

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And as you can see, Hank is able to essentially go through each of the support tickets

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and it sort of tests this with all the knowledge base articles that Hacker Hank already has.

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And it would just prioritize them and figure out whether each of the tickets needs human review or not.

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It would also categorize it based on whether it's a product issue, whether it's a bug.

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One thing that I can compete with in this case would be user experience

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and design and interaction and what are the different kinds of delight moments.

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Still, I'm still hoping I won't finish last.

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Now they defend their submission to an AI judge who has the full context of their code and prompts.

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Give me a quick two-minute pitch of what you built.

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Walk me through what happens from a support agent's point of view, start to finish.

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So this is what happens, right?

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The support agent essentially...

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And now, on to the results.

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I've read every line of code, every prompt, and every answer the three of you gave me today.

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Every one of you leaned on AI today, but the ones who did best understood what they shipped.

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Tonight, the person who balanced them best is Ankur.

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Congratulations.

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Any final thoughts?

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The biggest thing is I think the AI models have become

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really good where you can work on things you've never worked before.

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Like some of the things I've worked on today, I've never worked on in my life.

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I just barely knew some concepts and I was able to just use one model, ask it through and get things rolling and within the time frame got pieced together and it worked.

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So I think good fundamentals, good experience asking the right questions.

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We should be playing along with the model

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and being a good partner with the AI coding agents and build something out together.

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This was a lot of fun.

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Thank you HackerRank team for doing this and looking forward to more challenges and folks have fun with AI, build cool things and let's get cracking.

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為何這個影片是絕佳的口語練習素材？

想通過看影片學英語快速提升口語？這個關於AI開發者挑戰的影片再合適不過！對話真實自然，包含技術討論、日常互動和情緒表達，從"Can AI make a Vibe Coder beat a senior engineer?"的疑問，到"I'm still nervous because it's a huge project"的坦誠，覆蓋多種口語場景。更重要的是，語速適中且充滿生活氣息，非常適合英語影子跟讀練習，輕鬆掌握地道表達。

拆解3句超自然表達，學起來！

"Do I get a job here if I build this?"：用簡單問句表達幽默與期待，"if"引導的條件句自然流暢，口語中無需刻意倒裝，適合模仿語氣。
"I never use Claude. I do not trust Claude."：重複主語加強語氣，口語中常用這種簡潔重複表達態度，比複雜句式更真實。
"Awesome. So we do have the first version ready here."："Awesome"脫口而出的興奮，"do have"強調完成感，是日常交流中提升感染力的關鍵。

3步影子跟讀練習法，快速見效！

利用shadowing site或手機錄音功能，跟著影片練起來：
1. 聽1句暫停，跟讀1句，注意模仿語調（如疑問時的升調、肯定時的降調）；
2. 整段播放，同步跟讀，專注語速與連讀（如"sort of"的弱讀）；
3. 錄音對比，找出發音差異（如"escalate"的重音位置）。堅持1周，提高英語發音不是夢！
記住，shadowspeak的核心是"同步"，別怕出錯，越跟越流利！

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
現在完成時 have/has + 過去分詞 — 過去發生但與現在仍有關聯的事	that's been raised · you've made · have finished
被動語態 be + 過去分詞 — 強調發生了什麼，而不是誰做的	been raised · was locked · should be assigned
定語從句 who / which + 從句 — 補充說明人或事物	base which has · ones who did · person who balanced

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。

影子跟讀法: 閱讀完整分步指南 →

跟讀練習: Can This Vibe Coder Beat a Senior Developer? - 通過影片學習英語口語

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為何這個影片是絕佳的口語練習素材？

拆解3句超自然表達，學起來！

3步影子跟讀練習法，快速見效！

影片中的語法

什麼是跟讀法？